Author

Mike Siemasz

Published

4 September 2026

Estimated Read time

6Mins

Mike Siemasz, Director of Product Marketing at Rocket Software on viewing cyber modernisation as a continuous process aligned to the needs of the organisation

Mainframe systems remain central to the function of many of the financial world’s mission-critical systems. Each day, mainframes power 90 percent of all credit card transactions across the globe. These systems, some decades old, continue to function as pillars of the financial services industry’s most critical systems.

As guardians of sensitive information, financial institutions are prime targets for hackers. They are subject to many global and regional regulations, all the while building on systems put in place decades ago. As global regulations and new technologies rapidly evolve, the financial services sector is being increasingly pushed to consider its IT modernisation strategies. In fact, research reveals that modernisation has moved to the top of the C-suite agenda. Planned investment is projected to reach 25-30 percent of IT budgets over the next two years. 

A well-crafted IT modernisation strategy must balance security, compliance, innovation and legacy system realities. Failing to get this balance right can lead to crippling fines for non-compliance with regional regulations like DORA and NIS2, weakened cybersecurity resilience, and lasting reputational damage. 

The core of financial transactions: the mainframe 

Mainframes store a wealth of valuable data while setting the benchmark for uptime in high-stakes environments. They are a popular choice for financial institutions because they can handle transaction-heavy workloads at scale with precision and reliability. The data they store can also be a valuable resource for business intelligence and AI initiatives. By continuing to invest in mainframe technology, these organisations are therefore optimising their operational resilience and competitive decision-making processes. 

As the topic of IT infrastructure modernisation increasingly dominates boardroom conversations, assessing the role of the mainframe within broader infrastructure strategies has become unavoidable. For example, despite AI readiness being a top priority for organisations, only 25 percent of IT leaders currently feel confident that their infrastructure can support AI workloads. Businesses face key decisions regarding their IT modernisation strategies. These include choosing between modernising off the mainframe, modernising in place, or adopting a hybrid cloud model. 

Moving off the mainframe

Some organisations have chosen to move away from the mainframe entirely, opting for a full-scale re-platform. While this can be a reasonable choice for a small percentage of cases, it is considered the riskiest and most expensive way to modernise. This is because of the significant disruptions to daily operations. Institutions that move away from the mainframe without a sound strategy raise the risk levels of the modernisation process. This can be catastrophic in the financial services sector. 

The costs of misconfiguration in complex rewrite projects can be more than just directly financial. They also risk data leaks, loss of business due to temporary outages, and even regulatory fines if compliance is compromised. In fact, according to Forrester, rewrite projects can take up to six attempts before achieving results, with 90 percent failing the first time. These difficulties can stem from a lack of talent or skills, complex technology environments, and heightened security concerns.

Modernising in place for resilience

Discarding the mainframe entirely also overlooks its intrinsic value: decades of embedded business logic, compliance, resilience, and performance honed over time. Rather than eliminate this data, the more sustainable course is to evolve it – making it observable, testable, and adaptable. The goal is to retain what works and modernise what holds progress back.

Due to the sheer scale of the existing IT infrastructure and the associated risk factors of a re-platform, many financial organisations choose to modernise in place. This is an especially popular choice for institutions whose core processes are embedded in mainframe systems. By choosing this approach, they have a foundation in place whereby they can smoothly adopt next-generation technologies. 

This evolutionary approach allows organisations to combine the stability of the mainframe with modern innovation, while progressing at their own pace. In an industry that is constantly developing, mainframes hold and protect complex transactional rules and handle high-volume inputs and outputs. This kind of steady reliability is key for financial institutions dealing with massive volumes of sensitive information. And their customers who rely on continuous access to their assets. Institutions can therefore utilise this approach to respond to changing regulatory and customer demands. And without compromising the stability and integrity of the systems that enable those daily core transactions.

The hybrid cloud approach 

As financial institutions navigate the complexities of modernisation, cloud platforms present an appealing solution. This is due to the cloud’s ability to dynamically scale resources to meet fluctuating demand and effectively avoid the costs associated with maintaining peak capacity year-round. The size of the global market for cloud migration services is currently valued at $11.84 billion. It is expected to reach $42.92 in 2033, demonstrating strong market demand. 

The hybrid cloud can be particularly suitable for institutions that are choosing to keep their mainframe systems. And those also looking to innovate beyond the required levels prescribed by regulatory compliance standards. Moving to the hybrid cloud enables them to take advantage of advanced analytics and AI, while still benefiting from the security of the mainframe. For many institutions, the historical data and easily securable environment the mainframe provides are far too important to completely dispose of, while the cloud accelerates data processing considerably. Faster data processing means workflows can be optimised across the whole organisation. This enables institutions to manage market volatility with greater agility. 

However, a challenge that emerges when migrating processes to the cloud is the management of data across the different environments. Institutions should not underestimate the importance of data discovery as a means to understand the environment, manage risks proactively, improve compliance and enhance data security. 

Security and compliance

Regardless of the approach, modernisation is not without its challenges. Security and compliance remain top priorities, particularly when it comes to protecting sensitive financial data and personal information. With just 24 percent of IT leaders feeling extremely confident in their organisations’ ability to address security vulnerabilities over the next year, it comes as no surprise that this ranks as the biggest concern moving forward. For the financial services sector, however, compliance and security are non-negotiable. A systems breach can rapidly downgrade a financial institution’s reputation, cause long-term harm to customers and lead to crippling fines.

It therefore makes sense that the financial services sector is reluctant to part ways with mainframes which are widely recognised as the gold standard for highly securable environments, thanks to their centralised processing and storage architecture. This centralisation minimises attack surfaces, incorporates built-in encryption, and features granular access controls which aids organisations in boosting their cyber resilience and meet the requirements prescribed in industry regulations including NIS2 and the EU’s DORA act.

Ultimately, financial institutions find themselves at a critical crossroads. Mainframe modernisation isn’t merely a technical update; it represents a crucial balancing act of optimising what’s working and determining where to evolve in other directions, such as the cloud. The future lies in adopting a strategic and measured approach that incorporates various modernisation methods over time. Viewing modernisation as a continuous process aligned to the needs of the organisation empowers institutions to achieve greater efficiency and speed, without compromising the stability that has long been their hallmark.

Learn more at rocketsoftware.com

  • Cybersecurity in FinTech
  • Digital Payments

Author

Serge Bejjani

Published

Estimated Read time

3Mins

Serge Bejjani, Co-Founder & CEO of Shootday, on the importance for FinTechs of treating brand indentity as intellectual property

It’s a pervasive idea that reproducing the visual language of credible, established institutions lets new FinTech entrants borrow trust by association. It’s also wrong, and the consequences are real.

Familiarity isn’t reassuring anyone

FinTech customers have, over the last decade, run into all manner of low-trust players dressed in the same polished aesthetic. The reassuring visual cues that once signalled FinTech’s credibility have been hijacked by unscrupulous outfits as a cheap disguise.

Where generic branding might once have read as safely conventional, it now reads as deliberately evasive. Sameness has become the hallmark of a company with nothing much to say for itself, a shrug that leaves the customer with nothing to go on when deciding whether it’s worthy of their trust.

Trust in financial services isn’t built through aesthetic conformity but through demonstrable reliability and a coherent identity. That takes time, and for a weak FinTech, time only increases the odds that its shortcomings get exposed.

At a moment when customers have every reason to approach fintech with caution, messaging that whispers “this could be any FinTech” is a red flag. Customers who can’t tell one FinTech from another aren’t reassured by the homogeneity, it just strips them of any basis to form an opinion, good or bad, which is a poor foundation for a relationship built on money.

Similar by accident

No firm decides, one day, to become generic as a strategy. Product, growth and marketing teams independently optimised toward frictionless onboarding, familiar messaging and low-risk visuals. Because those choices tested well, cleared compliance faster and reassured investors used to recognisable patterns. Individuality got lost across a decade of small, rational decisions. What comes next will happen a great deal faster.

Copy and paste

Generative tools now produce marketing copy and design at a pace no internal FinTech team can match, and they’re doing it by training on a body of content that’s already remarkably homogenous.

Ask any AI model to design a “trustworthy landing page” for the sector and, in an afternoon, it will hand back the same blues, the same reassuring sans-serif type and the same promises of ease and control that already define the sector’s digital presence, because that’s what the training data mostly contains.

Every firm using these tools without creative oversight is baking the same clichés into its marketing at scale. The firms moving fastest to fold AI into their marketing stack may, in practice, be moving fastest toward irrelevance. In 18 months, those who let AI construct their identity may wake up to find they don’t really have one.

Trust your people

This isn’t a problem a better font or a wider colour palette will fix. As AI increasingly shapes what FinTech content looks like, the advantage shifts to firms with a genuinely distinctive identity, one AI can’t replicate.

Executive headshots and stock imagery still have their place. They fill a LinkedIn profile or an investor deck. But a templated one, the same backdrop and the same pose every other firm is using, does nothing to separate one FinTech from the next, and an AI-generated stand-in does even less. What actually differentiates is real footage from real moments: a founder in an actual client meeting, a team at an actual product launch, a conference stand that looks like the people running it rather than a template.

That means treating brand identity as intellectual property worth protecting, and giving marketing and product teams room to choose to look different, rather than defaulting to the safest option every time.

Serge Bejjani is co-founder and CEO of Shootday, a global photo and video production partner providing corporate event photography, event videography, and headshot services for businesses in 150+ cities worldwide.

  • Artificial Intelligence in FinTech
  • Embedded Finance
  • Fintech & Insurtech

Author

Wayne Scott

Published

2 September 2026

Estimated Read time

7Mins

Wayne Scott, Governance, Risk & Compliance Solutions Lead, Escode on meeting the challenge now regulation has pushed resilience up the agenda

When it comes to resilience, many firms are still making the same mistake: focusing heavily on cyber risk while overlooking supplier, service and concentration risks that are just as likely to disrupt critical services. At the same time, regulatory pressure has increased significantly.

Frameworks such as DORA and the UK’s operational resilience regime have pushed firms to think more seriously about their dependencies, their critical services and their ability to withstand disruption. The risks are not limited to cyber events. Supplier failure, service deterioration and concentration risk all have the potential to disrupt critical services. This applies to both FinTech providers and the financial institutions that rely on them.

That’s all positive, and most firms have adapted well. However, a more subtle misconception is emerging. Being cyber secure is often taken as a sign that an organisation is resilient. In practice, it rarely works like that.

Organisations everywhere have been required to document their processes by mapping important business services, identifying suppliers and putting policies in place. On paper, it often looks robust – but when you start to delve into the detail and test what happens if a key supplier fails, things can look very different.

That gap between what’s written down and what actually works is where the real risk sits.

The problem with ‘on paper’ resilience

Most resilience frameworks rely heavily on documentation and firms are rightly asked to demonstrate that they understand their dependencies. Their contingency plans are checked – as are their abilities to exit from critical suppliers if needed. The issue is that much of this is built on assumption, not evidence.

Supplier contracts often include continuity clauses. Due diligence processes gather assurances from vendors about how resilient they are. Internal teams record that an exit is ‘possible’. Possible it may be, but very few organisations actually test whether those things hold up under pressure. There is a significant difference between an exit being theoretically possible and a stressed exit that has been tested and proven to work under disruption.

If a supplier fails, it’s not enough to just have a clause sitting in a contract. You need to know whether you can access the underlying software, rebuild it, move it, replace it – and how long that will take. That’s a very different question. Regulated entities now need to focus on whether their plans actually work.

The hidden risk in third- and fourth-party dependencies

One of the biggest blind spots in FinTech is the dependency chain. Most firms have a reasonable or good understanding of their direct suppliers, but what’s much less visible are the suppliers behind those suppliers – platforms, libraries, cloud services and infrastructure that everything ultimately sits on. That’s where concentration risk starts to build.

Too many FinTech platforms rely on the same hyperscale cloud providers. Many also depend on a relatively small number of specialist software vendors. When everything works, that concentration drives up efficiency and drives down cost. But when something goes wrong, it creates a shared point of failure.

You only really discover that exposure when something goes wrong. At that point, it’s too late to map the dependencies or negotiate alternative arrangements. You’re dealing with the consequences of decisions that were made months or years earlier, and it’s those decisions that can start as operational contagion, but rapidly move to financial contagion.

That’s why regulators are increasingly focused on sub-outsourcing and fourth-party risk. It’s also why firms need to move beyond a surface-level view of their supply chain.

Why supplier failure is not a theoretical risk

Supplier failure is often treated as an edge case, but in practice it’s far more common than many organisations assume. Most organisations have already had to deal with a supplier failing at some point. Across both fintechs and established providers, disruption and service deterioration are regular features of the market, and as the number of software providers grows – particularly with the rise of AI-driven services – that risk only increases.

Supplier insolvency, service deterioration and withdrawal of support are all real scenarios that firms are dealing with today. When they happen, the impact is immediate. In many cases, there is little time to react once a failure occurs. Systems stop working as expected, updates aren’t delivered, and the organisation is left trying to work out how to maintain continuity.

The challenge is that many of the controls needed to respond to those situations have to be put in place in advance. You can’t build a stressed exit strategy in the middle of a failure. Just like you can’t negotiate access to critical software once the supplier has disappeared. And you can’t quickly untangle complex dependencies when everything is already under pressure.

Moving from assumption to evidence

Currently, what we’re seeing in the sector is a shift towards evidence. Historically, firms have relied on supplier statements, certifications and contractual commitments. But that’s no longer enough. Regulators are now starting to test whether services can actually be maintained and whether exits can be executed in practice. They’re also looking at whether contingency plans have been thought through in detail. For FinTechs and the financial institutions that rely on them, that changes the conversation quite quickly.

It’s no longer sufficient to say ‘we have a plan’. The question becomes: can you demonstrate that your most critical services will continue to operate if a key supplier fails?

That involves testing stressed exit scenarios, validating that software can be rebuilt, and ensuring that there are clear, enforceable rights over the assets needed to maintain a service. These are practical steps that determine whether a business can continue to function under stress.

The role of leadership in resilience

One of the more interesting shifts is where these conversations are happening. Resilience has traditionally sat with IT or risk teams, but regulatory expectations have now pushed accountability firmly into the boardroom. CFOs are becoming more aware of the financial impact of downtime and supplier failure, and CIOs are balancing the need for agility with the risk of long-term lock-in. But there is still a gap between where decisions are made and where accountability sits. In practice, many of those decisions are being made across IT, procurement and legal teams, often without clear oversight at board level. That creates a clear challenge for leadership: decisions on risk are being made across the organisation, but accountability still sits at the top.

Much of the focus remains on cyber risk, which is understandable. What’s often less clearly owned are the non-cyber risks, such as supplier insolvency, service degradation, concentration risk and the practical ability to exit or replace a vendor.

Delegating responsibility for these risks is tricky, as they don’t sit neatly in one function. They cut across technology, procurement, legal, finance and operations. They also sit within vendor management, business continuity and disaster recovery functions, with different teams making decisions on risk that ultimately sits with senior leadership. That makes them harder to manage, but also harder to ignore.

The organisations that are getting ahead of this are the ones treating resilience as a shared responsibility, rather than something that sits in a single team. They are also recognising escrow as a key control and embedding it into their control frameworks as a key control.

What FinTech leaders should be doing now

None of this means tearing up existing strategies or abandoning the cloud. It starts with understanding where key risks sit, including supplier failure, service deterioration and concentration risk.

That means mapping where critical services actually sit, including the layers beneath direct suppliers. It also means identifying where concentration risk exists and where no credible, tested exit is in place.

From there, it’s about putting in place the controls that allow you to respond if something goes wrong. That includes ensuring access to the underlying source code that runs critical services and validating that the underlying source code and supporting components can be rebuilt into a working system.

One way organisations are addressing this is by introducing independent controls such as escrow. This ensures that source code, dependencies and supporting materials are securely held, verified and accessible, allowing systems to be rebuilt and operated if a supplier can no longer support a service.

Closing the gap

Regulation has pushed resilience up the agenda, which is a good thing. But compliance is only part of the picture. The real test is what happens when something actually fails.

Suppliers that focus solely on meeting regulatory requirements risk missing that point. Those that examine whether their systems, suppliers and services can actually withstand disruption will be in a much stronger position, because when a critical supplier fails, the difference between compliance and resilience becomes very clear, very quickly.

Learn more at escode.com

  • Cybersecurity
  • Digital Payments

Author

Nick Heather

Published

1 September 2026

Estimated Read time

5Mins

Nick Heather, Head of Trading at ONE.io Why Stablecoins are the quiet infrastructure behind modern finance 

Stablecoins aren’t having a breakthrough moment so much as a practical one. After years of being tied to retail speculation and crypto market noise, they’re now showing up in places where they make day‑to‑day operations easier. Such as cross‑border payouts, supplier payments, and treasury transfers. Where traditional rails can be slow and sometimes unreliable. The shift is coming from businesses that need fast, cheap, always‑on settlement and struggle to get it from traditional rails.

What’s emerging is a more grounded phase of adoption. High‑velocity sectors – such as igaming and gambling sectors and digital exchanges and trading platforms – are using stablecoins. Why? Because they can solve real problems, and institutions are starting to pay attention for the same reason. Last November, US Treasury Secretary Scott Bessent said the stablecoin supply could reach $3 trillion by 2030.

As the infrastructure matures, with faster settlement, unified fiat‑to‑digital workflows, and clearer regulatory frameworks, stablecoins are shifting. They are moving from niche experiment to arguably a functional building block in modern financial operations. 

So, what are its detractors missing?

Speculation to real-world utility 

What detractors of stablecoins and other digital assets overlook is that their primary use case has already shifted from trading to real-world settlement. Including, tokenised assets and other blockchain‑based settlement instruments designed for institutional workflows. And that use case is growing exponentially. Stablecoins reportedly having an annual transaction volume of up to $35 trillion. Research from McKinsey shows a growing share of this activity reflects real payments. These include vendor payments, payroll, remittances and capital markets settlement, now reaching approximately $390 billion annually.

Adoption is being driven by businesses addressing the limitations of existing financial infrastructure, particularly in cross-border and time-sensitive environments. This is most evident in transaction-intensive sectors such as global e-commerce and remittances. Here, delays in settlement translate directly into potential operational friction and lost revenue.

While critics focus on volatility in the broader digital asset market, operators are prioritising stability, speed, and control in settlement. Stablecoins, by design, support this need, while other digital assets can enable more transparent and programmable financial workflows. The advantages are clear: near-instant settlement, reduced counterparty risk, and greater control over liquidity. By contrast, correspondent banking remains constrained by ‘business hour’ cut-off times and multi-day clearing cycles.

As a result, transaction-intensive sectors are leading this shift. They are using stablecoins to move capital efficiently, manage liquidity, and reduce reliance on intermediary banking layers.

Building institutional-grade infrastructure

What is also often missing in this debate is how quickly the infrastructure around stablecoins and other digital assets is maturing to meet institutional expectations. The market is moving towards a more unified financial architecture. One where fiat currencies and digital assets can coexist within the same operational environment. This makes it easier to move between them without the fragmentation that has historically slowed adoption.

At the same time, compliance and risk frameworks are becoming more robust. As usage grows, the supporting infrastructure is becoming more aligned with regulatory expectations. It is developing stronger controls, greater transparency, and clearer supervision. That matters because mainstream adoption will not be driven by speed alone, but by whether institutions can use these rails within credible governance and risk parameters. And we are already seeing a huge appetite with large traditional institutions, such as J.P. Morgan, Citigroup and Mastercard, suggesting the industry is addressing these issues wholesale. 

The emergence of ‘always-on’ payment rails reinforces that shift. Instant USD settlement and other blockchain-based payment models are showing that financial operations no longer need to be constrained by banking hours, cut-off times, or multi-day clearing cycles. For businesses operating across borders and time zones, this has clear operational value.

This is becoming especially relevant in sectors and markets where traditional banking access has become more limited. As some banks continue to de-risk more complex client segments and corridors, regulated digital-asset platforms are increasingly providing continuity, stability, and more flexible access to settlement infrastructure for businesses that have historically been underserved.

The next phase of adoption

The next phase of stablecoin and digital asset adoption is likely to be defined by integration rather than experimentation, as institutions begin to incorporate these instruments into treasury and payment workflows where they offer clear operational advantages.

As this adoption deepens, demand is moving towards infrastructure that can support higher-value, institutional-grade transactions, with the reliability, governance, and liquidity depth required for large-scale financial operations.

At the same time, the challenge is evolving from proving utility to enabling scale. Interoperability remains a key constraint, as fragmented ecosystems, disconnected liquidity pools, and inconsistent standards continue to limit seamless movement across networks. Addressing this will be critical to ensuring that the infrastructure can support growing transaction volumes without introducing new forms of friction.

Alongside this, the role of platforms is also becoming more defined, with institutions increasingly seeking partners that can support implementation, navigate compliance requirements, and provide operational expertise in complex or high-growth environments. This is particularly relevant where businesses require both access to digital asset infrastructure and the assurance that it can be deployed within appropriate risk and regulatory frameworks.

As a result, expectations of the market are becoming more exacting. With access alone no longer sufficient, and greater emphasis being placed on platforms that can combine infrastructure, compliance, and execution into a cohesive, institution-ready offering.

Gathering momentum

What this ultimately points to is a shift in how financial infrastructure is being defined.

Stablecoins and other digital assets are not emerging as a parallel system to traditional finance, but as a complementary layer that addresses long-standing inefficiencies in how capital moves. The question is no longer whether they have a role to play, but how quickly existing systems and institutions adapt to their presence.

For businesses operating in time-sensitive, cross-border, and increasingly complex environments, the advantages are already tangible. Faster settlement, greater liquidity control, and more flexible access to financial rails are no longer theoretical benefits, but operational requirements.

As the infrastructure continues to mature and institutional adoption deepens, stablecoins are likely to become less visible as a distinct innovation and more embedded as part of the underlying fabric of financial operations. In that sense, their role is not to disrupt finance, but to quietly help modernise it.

And it is precisely this shift, from visibility to utility, that many detractors continue to underestimate. Stablecoins become infrastructure when they stop being noticed, like cloud computing or payment processors.

  • Blockchain & Crypto
  • Neobanking

Author

James Lawrence & Paul Bermingham

Published

28 August 2026

Estimated Read time

6Mins

James Lawrence, Insurance Specialist at Netcall and Paul Bermingham, CEO at Ecliptic, on why in today’s market, the holy trinity of service, efficiency and compliance isn’t a luxury. It’s a competitive advantage

For years, the insurance industry has operated under a persistent myth: that you can deliver excellent customer service, achieve operational efficiency, or maintain rock-solid compliance – but not all three at once. This idea of an inevitable trade-off between competing priorities has shaped countless transformation strategies, often limiting progress and frustrating innovation.

It’s a myth rooted in the industry’s history. Insurance has long been a world of complexity – fragmented systems, legacy platforms, and regulatory demands that make even minor changes feel high-risk. As a result, when businesses talk about improving the customer experience, it often comes at the cost of back-office control. When they talk about driving efficiency, compliance is treated as a barrier. And when compliance leads the agenda, service and speed are typically sacrificed.

But that mindset no longer holds. Technology has evolved. The tools now exist not just to manage this tension, but to eliminate it. And for insurers willing to rethink how they approach transformation, the so-called “holy trinity” of service, efficiency, and governance is no longer out of reach – it’s entirely achievable.

The low-code shift

What’s changed is the rise of low-code platforms. These technologies allow insurers to build and evolve business-critical applications using visual development tools rather than traditional coding. Crucially, low-code doesn’t require ripping out existing systems. Instead, it enables insurers to layer new capabilities on top of their core infrastructure – adding intelligence, automation, and user-friendly interfaces without destabilising the foundations.

In practice, this means change can happen faster, at lower cost, and with less risk. Processes like first notification of loss (FNOL), for example, can be rebuilt around modern expectations: triaged instantly, validated in real time, and – for low-value, low-risk claims – automated to settlement. At the same time, audit trails are maintained, compliance rules are enforced by design, and customers are kept informed with timely, personalised updates. Service, efficiency, and governance – working together, not competing for attention.

Automation and AI: Turning governance into a competitive edge

The evolution of intelligent automation and agentic AI has opened up new possibilities for insurers – not just in streamlining operations, but in embedding trust and control into every digital decision. These systems are no longer limited to simple rule-based tasks. They can interpret data, make context-aware decisions, learn from interactions, and adapt over time. In complex environments like claims processing, delegated authority, and regulatory reporting, this level of intelligence has real operational value.

But the success of automation doesn’t hinge on speed alone. It depends on trust. In regulated sectors like insurance, automation must be auditable, transparent, and capable of upholding internal policies and external obligations. This is where low-code plays a crucial role. Rather than retrofitting governance onto a system after it’s built, these tools allow insurers to embed governance by design – integrating rules, audit trails, escalation logic, and data safeguards directly into the workflow.

This approach changes the dynamic. Governance is no longer a limiting factor or a compliance hurdle. It becomes a source of confidence. Teams know that AI-driven decisions are accountable. Regulators can trace every step. And customers receive outcomes that are fast, fair, and well-documented.

Low-code also makes it easier to align AI with real business needs. Instead of chasing innovation for its own sake, insurers can focus on solving specific challenges – like improving FNOL response times or reducing the risk of customer complaints – and use automation to enhance outcomes without compromising oversight. Because platforms are configurable, not hard-coded, insurers maintain flexibility as policies, risks, and regulatory expectations evolve.

In this context, governance isn’t the price of innovation – it’s the foundation. It ensures that transformation is not only fast and scalable, but sustainable and defensible. The result is a smarter, safer path forward – one where automation improves service, boosts efficiency, and strengthens compliance in equal measure.

Meeting customers where they are

That experience is more important than ever. Today’s policyholders aren’t comparing insurers only to each other – they’re comparing them to tech giants like Amazon, which set new standards for simplicity, speed and personalisation. Insurance doesn’t get a pass. Yet too many customers still face long response times, unclear communication channels and paper-heavy processes that feel stuck in another decade.

The right approach changes that. European insurance company, Baloise, for example, used a low-code platform to quadruple straight-through processing (STP) from 15% to 65%. This meant faster responses, reduced error rates and a better overall experience for both customers and teams. By processing over 11,000 documents a day with greater accuracy and speed, Baloise could resolve enquiries and claims faster, without adding operational burden.

The impact went beyond efficiency. Customers benefited from a smoother, more consistent service that felt faster and more seamless at every touchpoint. Internally, employees were freed from repetitive, manual tasks and empowered to develop broader skill sets and handle more diverse work. That flexibility made it easier to respond to demand and reduced reliance on siloed roles.

These kinds of results don’t come from massive system overhauls. They come from smart, focused improvements – starting with the moments that matter most to customers. Low-code allows insurers to build around those touchpoints, while keeping core systems intact and compliance fully in check.

From compromise to convergence

The key is integration – not just of systems, but of skills. Transformation efforts succeed when business leaders, consultants, and technology teams work together from the outset. Today’s automated process mapping tools allow organisations to automatically capture, visualise, and analyse real-time workflows – highlighting friction points, inefficiencies, and opportunities for simplification before taking action. This upfront clarity ensures insurers aren’t simply digitising legacy inefficiencies but reimagining how work gets done.

It’s this combination of smart technology and domain expertise that moves the needle from compromise into convergence. When transformation is designed with strategy, service, and compliance in mind – from day one – insurers can build experiences that work for everyone: customers who want speed, regulators who want rigour, and teams who want tools that actually help them do their jobs.

The truth is, the trade-off is a choice – and it’s no longer a necessary one. With the right approach, insurers can achieve all three pillars of the holy trinity. Service doesn’t have to come at the cost of efficiency. Governance doesn’t have to slow things down. And efficiency doesn’t have to mean cutting corners.

Setting a new standard

Insurers that understand this – and act on it – will lead the next chapter of the market. Not because they’ve chosen a side, but because they’ve built systems where no one has to lose. In a market that’s only getting more competitive, that’s not just good strategy – it’s business-critical.

It’s time to retire the idea that you can’t have it all. You can. And you must. Because in today’s market, the holy trinity of service, efficiency and compliance isn’t a luxury. It’s a competitive advantage.

Learn more at netcall.com and ecliptic.tech

  • Fintech & Insurtech
  • InsurTech

Author

Dr Abdulrahman Kerim

Published

27 August 2026

Estimated Read time

4Mins

Dr Abdulrahman Kerim, Computer Science Tutor at Superprof, on how AI will eventually take over time-consuming routine tasks, and will free educators up for supporting students, cultivating curiosity, and shaping meaningful learning experiences

With education systems continuing to push for more efficient and personalised learning experiences, AI is proving to be a major factor and catalyst for achieving these goals. This year alone, 60% of educators said that they use AI to assist with their workload, a jump from around 48% in 2024.

 Here are 5 key predictions of how AI is set to reshape the classroom, the evolving role of  tutors, and the overall student educational journey in 2026.

Personalised learning will replace one-size-fits-all classrooms

    Despite the progress made when it comes to the adoption of AI, classrooms still rely on fixed reading levels, static worksheets, and uniform methodologies that fail to reflect the diverse backgrounds of students, their interests, strong points, and even goals for the future.

    However, in 2026, we’re expecting to see this gap starting to close. AI systems will begin to assist with drawing student data, including assessment results and learning objectives, to automatically update and adapt course materials in real time. This means that students will receive an education that matches their level, and that’s aligned with their goals. This new process will certainly not get rid of human tutors who will still be vital to the learning process. However, their role will become of a more strategic nature where, instead of content creators, they will become learning experience designers, guiding and refining a student’s personalised learning journey.

    Immediate, insight driven, grading and feedback

      As of this year, manual grading still dominates the educational system. Tutors still spend hours reviewing assignments, and students wait for weeks for feedback. This process certainly delays and weakens the learning process overall.

      Next year, we will see a higher rate of adopting the AI-powered grading and feedback  systems, which will contribute dramatically to the process. The tools will have the ability to evaluate submissions within minutes, highlight errors, shed light on common patterns of misunderstanding, and provide tailored recommendations of improvement. The result of this will be more consistent and efficient grading, and deeper insights that will guide both students and educators alike.

      Around the clock student support

        Student support teams continue to be thinly stretched, resulting in long waits for answers to academic or administrative questions. This does not only cause frustration, but also results in missing deadlines a lot of the time.

        In 2026, AI chatbots will become more intelligent, responsive, and deeply embedded across educational platforms. These systems will provide immediate support, during or after hours, answering academic queries, clarifying assignments requirements, guiding students through enrolment processes, and reminding them of deadlines and outstanding tasks. The AI chatbot will enhance the FAQ responses with their ability to understand context, and personalise guidance accordingly. This will enhance both engagement and retention.

        Clear, consistent, and scalable assessment of soft skills

          Soft skills such as leadership, communication, and problem solving remain very hard to measure accurately. This measurement still relies on manual observation, which is often subjective, inconsistent, and nearly impossible to scale.

          In 2026, however, AI is set to start cracking the code of these competencies. By analysing group interactions, patterns of students participation, communications dynamics, and even study behaviours, AI tools will generate concise, data-driven insights in students’ performance and progress. Tutors will greatly benefit from this feature by receiving detailed summaries of strengths, and gaps, allowing them to design a more targeted and achievable approach to support.

          Rapid and customisable curriculum design

            Similar to some of the key things discussed in this article, curriculum design still remains fairly slow and fragmented, with learners looking to move into new fields often struggling to find coherent and updated learning pathways.

            In the next year, AI will significantly accelerate curriculum development. The systems will be capable of identifying gaps, recommending learning methodologies, align content with standards, and even generate fully customised learning plans. The tools will be informed by large scale data on how thousands of similar learners have progressed in their subjects, the challenges they faced, the resources that helped them navigate those challenges, and the skills that are highly in demand.

            In conclusion, AI is set to dramatically change the way people both teach and learn. However, this certainly does not mean it will replace educators. Instead, it will empower them. AI will eventually take over time-consuming routine tasks, and will free educators up for supporting students, cultivating curiosity, and shaping meaningful learning experiences.

            Learn more at superprof.co.uk

            • Data & AI
            • Digital Strategy

            Author

            Toby Norfolk-Thompson

            Published

            Estimated Read time

            5Mins

            Toby Norfolk-Thompson, co-founder and Chief Commercial Officer at Sentora, on why the UK needs to bring the political will to match the FinTech ambitions for crypto and realise the rewards of a decentralised future for finance

            The UK has long prided itself on being a global leader in financial innovation. London moved quickly to establish itself as the centre of the international derivatives markets in the 90s and then embraced the FinTech boom giving the world household names like Revolut and Monzo. The UK’s early embrace of open banking showed how tradition and technology could successfully merge. Yet, in the realm of cryptocurrency and blockchain, the story is increasingly one of missed opportunities. Over recent years, the UK’s regulatory approach has shifted towards greater restriction, stifling innovation and driving businesses abroad. As we look to the near future, it’s time for policymakers to recalibrate. Loosen the reins, foster clarity, and position the UK as a true crypto powerhouse once more.

            The turning point for Crypto

            The turning point came in 2023, when the Financial Conduct Authority (FCA) introduced stringent new rules on crypto advertising under the Financial Services and Markets Act. New advertising rules threatened firms with unlimited fines or even prison time, stifling both scams and legitimate innovation. While aimed at protecting consumers from scams—a laudable goal—the broad interpretation of these rules led to an immediate crackdown. On the first day alone, the FCA issued 146 alerts against non-compliant crypto entities, creating a chilling effect across the industry. This wasn’t just about curbing bad actors; it ensnared legitimate innovators, forcing many to rework marketing strategies or halt UK operations altogether.

            At the same time, the FCA’s crypto registration regime, requiring strict anti-money laundering (AML) and know-your-customer (KYC) compliance. Firms often having to pay hefty fees for approval, became a near insuromountable barrier. International firms were not exempt. Binance, one of the world’s largest exchanges, sharply scaled back its UK presence. By September 2023, additional requirements for collecting and sharing user data on crypto transfers further tightened the noose. These policies, while aligned with global efforts like the EU’s Markets in Crypto-Assets Regulation (MiCA), have been implemented with a rigour that prioritises caution over growth.

            Impact on innovation

            The impact on innovation has been profound. Crypto ownership in the UK has grown to encompass as much as 12% of the population, according to FCA estimates, highlighting strong public interest. Yet, restrictive policies have pushed entrepreneurs towards more welcoming jurisdictions. The UAE, Singapore, and even the US—despite its own regulatory hurdles—have capitalised on this exodus. In the UAE, for example, streamlined licensing and tax incentives have attracted billions in crypto investment. It’s not just companies leaving—skilled blockchain developers, founders and entrepreneurs are relocating to hubs like Dubai and Singapore. Meanwhile, UK-based firms cite unclear guidance and disproportionate compliance costs as reasons for relocation. One founder told me he spent more time with lawyers than engineers in 2024— a sentiment I’ve heard echoed across the industry. A 2025 Forbes piece highlighted how the FCA’s “blanket cryptoasset policy” is “crushing innovation” and making the UK “hostile to bitcoin businesses,” with companies like those in decentralised finance (DeFi) particularly affected. This isn’t hyperbole: high fines, up to 10% of global revenue for violations, create an environment where startups hesitate to experiment, fearing regulatory backlash.

            In the present day, these trends persist amid a backdrop of economic recovery post-Brexit and inflation challenges. The government’s draft legislation in April 2025 extended existing financial regulations to crypto firms, aligning more closely with the US than the EU. On paper, this aims to “drive growth and protect consumers,” with rules covering exchanges, custodians, and even stablecoins. However, the reality is a regulatory patchwork that burdens smaller players. Compliance asymmetry—where issuers must navigate fragmented rules across jurisdictions—favours large firms with deep pockets, crowding out innovators. Upcoming rules, such as mandatory transaction reporting by 2026, risk pushing retail users off regulated platforms altogether.

            Tax policies exacerbate the issue. Recent increases in capital gains taxes on crypto holdings, combined with AI-driven transaction tracking, and new HMRC reporting rules due in 2026, signal a surveillance-heavy approach that could alienate everyday investors. A 2017 study by the National Bureau of Economic Research found  that even a 1% rise in top marginal tax rates can reduce innovation output by 2%. In the UK, this translates to fewer patents, fewer startups, and a steady brain drain abroad.

            Next steps for Crypto evolution in the UK

            If the UK doesn’t change course, it risks cementing its reputation as a laggard, especially if the US, under evolving leadership, eases its stance. Meanwhile, Europe’s MiCA offers a unified framework attractive to firms looking for certainty. Without change, the UK risks irrelevance in a sector projected to reach trillions in value. Yet, opportunities abound. The government’s own Innovation Strategy envisions the UK as a “global hub for innovation by 2035.” To realise this, several steps are essential.

            First, to appoint a dedicated blockchain envoy, as recommended by UK trade bodies, to coordinate policy and attract investment. This role could bridge the gap between regulators and industry, ensuring voices from CryptoUK and Innovate Finance are heard.

            Second, to soften enforcement on advertising and registrations, providing clearer guidance and grace periods for compliance, as urged by Economic Secretary Andrew Griffith in 2023. By introducing phased implementation and advisory sandboxes, the UK could emulate the UAE’s VARA framework, which has licensed over 50 major players like Kraken since 2022, boosting investor confidence while fostering innovation. This shift would signal that the UK values progress over punishment.

            Third, to incentivise innovation through tax breaks for crypto R&D and sandboxes for testing DeFi and stablecoins, balancing protection with progress, and allowing safe experimentation.

            Fourth, to align with global standards like the Financial Stability Board’s recommendations, but adapt them to encourage rather than hinder, as the UAE does with federal and free-zone regulations, enabling rapid adaptation to market needs.

            Finally, to expand open banking to include crypto, addressing flaky implementations and limited scope to unlock seamless integration. This would mainstream blockchain, allowing instant transfers and reducing reliance on volatile proxies.

            The Crypto crossroads

            The UK stands at a crossroads. Its restrictive path has hindered the very innovation it seeks to champion, but reversal is possible. By prioritising pro-growth policies, the government can reclaim its FinTech crown and harness crypto’s potential to boost the economy. As a company committed to advancing blockchain and the digital asset industry, we urge policymakers: act now, or watch the world pass us by. The ambition is here, I can say with certainty. What’s missing is the political will to match it. The future of finance is decentralised. Let’s ensure the UK leads it.

            Learn more at sentora.com

            • Blockchain & Crypto
            • Neobanking

            Author

            Matthew Biboud-Lubeck

            Published

            26 August 2026

            Estimated Read time

            6Mins

            Matthew Biboud-Lubeck, General Manager EMEA at Amperity, on finding the key to creating meaningful engagements that build brand loyalty, drive up the lifetime value of a customer and, ultimately, give businesses the competitive edge

            It’s no secret that personalisation is vital for building lasting customer relationships and nurturing brand loyalty. AI holds the key to creating and delivering successful personalised engagement. And travel and airline businesses are keen to adopt this technology in their customer-facing applications.

            The reality, however, is that few are currently able to scale AI across their organisation. According to industry research, published in Amperity’s 2025 State of AI for Hotels & Airlines, AI adoption is widespread across the sector, but applications remain experimental. Just 12.5% of companies say they are ready to scale their use cases.

            The challenge isn’t ambition. Nearly all (96%) businesses in the sector are planning to maintain or increase AI spending over the next 12 months. The problem lies in two major barriers that prevent AI from scaling effectively. A lack of technical expertise within teams. And low confidence in the data that AI tools rely upon, due to fragmented or unreliable customer information.

            How to reduce reliance on technical teams

            Keeping pace with rising consumer expectations means tailoring customer engagement and presenting meaningful, real-time offers based on individual preferences. Although travel businesses know that AI is going to help them achieve this true personalisation, at scale, only 35% currently use AI in guest experiences.

            One reason for this is the inability of non-technical teams to independently access data they need, and act on the information provided. AI usage among travel and airline brands remains concentrated in marketing, customer support and sales. But less than a third of professionals in these teams say they can manage customer data without the support of their IT department. This is delaying, and in many cases denying, teams the ability to tailor customer communications.

            What teams need are intuitive tools, empowered by GenAI, that simplify the process of accessing customer insight. These platforms allow non-technical people to ask questions using natural language. And discover such things as: what their most valuable customers purchase most often, what form of content they are most likely to engage with and on which channels they are doing this.

            AI training is still an important consideration for all business users, who need to understand how AI can add value, where its limitations lie and what safeguards should be put in place to ensure responsible use. But these GenAI capabilities are now helping teams to access crucial intelligence quickly and apply this to customer engagements at scale.  

            How to build confidence in customer data  

            Accessing data is one thing, but having confidence in that information is another. This is an area where travel companies struggle. Less than a quarter claim to be very confident in their ability to understand and act on customer data.

            This is often because data is separated and siloed across different systems and channels. More than half of businesses (58%) report that their customer data is fragmented or incomplete. This can impair visibility, lead to inaccurate reporting and increase the risk of human error, while also adding IT costs. It also increases the chances that duplicate customer profiles will be created across those different channels.

            Consumers commonly engage with travel brands across several touch points including email, mobile, apps and in-store. They will also use various identifiers, such as abbreviated names, alternative email addresses, etc., when they do so. Companies need to deploy AI tools that can unify this data and stitch together information coming from different channels. If they don’t, there is the real possibility that they will end up sending conflicting or irrelevant communications to the same person. The consequences of this can be damaging as it risks annoying and alienating customers.

            Building a solid customer data foundation

            To enable fast and accurate omnichannel communications, it’s vital for companies to have a customer data platform (CDP) that is enabled by identity resolution. To build high levels of confidence in the operational identities required for personalisation and broader marketing audience targeting, companies will need to blend deterministic and probabilistic matching strategies.

            The research shows, however, that just 18% of travel businesses have this identity resolution technology in place. This is preventing companies from accurately viewing booking histories, identifying behaviour patterns and unlocking the potential of AI for the tailored engagements necessary for building brand loyalty and increasing the lifetime value of a customer.

            The evidence shows that organisations with a CDP are much better placed to ensure customer data is ready for use in marketing or analytics. The latest generation of CDPs will automatically consolidate data across various communication channels, detect and resolve identity issues. And provide travel businesses with a true, 360 degree profile of each customer, based on all interactions across each channel.

            These platforms are also enabling businesses to advance their AI deployments. More than half (54%) of businesses with a CDP report daily use of AI – this compares to 28% for those without. Half of CDP-enabled businesses use AI in guest-facing deployments, compared to less than a fifth (19%) of those without. Businesses are also five times more likely to have adopted AI across business units, when they have a CDP.

            Preparing for an Agentic AI era

            Next generation CDPs are also helping companies prepare for another major trend reshaping customer data management – the rise of agentic AI. Agents are already capable of observing behaviour, interpreting what it means and taking action autonomously.

            For example, imagine a situation where a flight has been delayed or cancelled. In this scenario, a combination of agents can now work together to provide customers with a solution. In an instant, one can spot the problem, another will find alternate solutions, while a third will send the best options to the customer. The customer doesn’t need to wait – they simply receive a notification in real-time. Some human orchestration is still required and guardrails are important to ensure communications hit the mark. Most important to note, however, is that it only works if those agents have access to accurate data.

            The travel sector is clearly at an inflection point on its road to full AI adoption. It’s moving from pilot projects and early stage experimentations to broader deployments and scaled execution. Success in customer facing applications, however, will depend on an organisation’s ability to provide non-technical teams with access to high quality, unified customer profiles.

            Modern CDPs help businesses to make faster, smarter decisions that will help to drive growth. With these solid data foundations in place, businesses can deploy and scale AI-enabled customer facing applications more readily. This is the key to creating meaningful engagements that build brand loyalty, drive up the lifetime value of a customer and, ultimately, give businesses the competitive edge.

            Learn more at amperity.com

            • Data & AI

            Author

            Ross Osbourne

            Published

            25 August 2026

            Estimated Read time

            5Mins

            Ross Osborne, CEO of UK Payments at Rippling, on why the future of finance will be defined less by incumbency and more by execution velocity

            As with most industries today, developments in technology are reshaping how people and companies bank. Traditional banks with in-person branches are no longer the default. Global FnTtech investment reached $116 billion in 2025, underscoring the scale of this shift. Meanwhile, firms like Revolut – recently securing its banking licence and targeting a valuation above $100 billion in a future IPO – highlight the continued rise of digital-first challengers.

            But beneath the growth story, much of this innovation is still concentrated in a small number of large platforms. They are repeatedly solving similar core problems – payments, accounts, onboarding – that traditional banks already spent decades building infrastructure for. The result is less a reinvention of banking, and more a reallocation of who delivers the same underlying services.

            Where FinTechs are outperforming traditional banks is not in vision, but in execution. The key difference is structural: legacy institutions are constrained by layers of governance, compliance, and internal process. This means even simple changes can take multiple steps to implement. FinTechs, by contrast, are built for rapid iteration and direct deployment. This allows them to respond to customer demand in real time.

            That speed matters. Modern businesses, especially those now operating in an AI-enabled environment, expect financial services to operate at the same cadence as the rest of their technology stack. Over time, that responsiveness becomes a competitive advantage, not just in product delivery but in attracting talent and capital. However, compressing decision cycles also concentrates operational and compliance risk, which needs to be managed deliberately rather than assumed away.

            Building beyond legacy systems

            Legacy banks are constrained by decades-old systems and processes that create a blockade of bureaucracy. Even getting a single decision over the line requires navigating endless layers of middle management and committee approvals. In that environment, processes and innovation can take twice as long as necessary.

            In contrast, FinTechs have emerged in the space that banks left behind. They don’t have the traditional overheads of corporate structures, meaning they don’t just solve problems but become the solution itself. What we see in the FinTech space is super-efficiency. Operations are conducted at pace, stripped of the performative meetings and red tape that haunt traditional institutions. There is a brutal focus on output over ‘process for the sake of process’. This speed is their greatest competitive advantage. This agility means FinTechs can iterate rapidly on pricing and features, compounding their advantage in customer experience and innovation.

            Banking without borders

            Digital-first banking is inherently global. As businesses expand across markets, an online platform means that it is more accessible, with seamless cross-border transactions, currency conversion, and international payment processing. JP Morgan predicts that international transfers are expected to increase 5% annually until 2027. Underpinning the demand for easier access to cross-border banking.

            The question is whether legacy payment rails can absorb that growth without friction eroding already thin transaction margins. As volumes scale, operational complexity tends to compound faster than efficiency gains. Particularly in systems not designed for real-time global settlement.

            Against this backdrop, the lower operating costs associated with purely digital platforms enable FinTechs to offer competitive pricing and innovative, tailored financial products. This enhanced accessibility is not just about geography; it’s also about democratising finance, ensuring that banking services can keep pace with the rapid expansion and complex operational needs of an interconnected world economy.

            Technology-based solutions for technology-based customers

            Consumers now expect banking to mirror the best consumer apps they use every day: instant, intuitive, and mobile-first. Research comparing online to traditional banking finds customers cite convenience, time saving, and accessibility as primary reasons for shifting to digital channels.

            Digital-first and mobile-only FinTechs are designed around these expectations from day one, whereas incumbents are still retrofitting branch-centric models to a digital world. Even as large banks have reduced their branches by about 15% over a decade, customer relationships have deepened digitally. Trust has become more associated with brand experience, transparency, and app reliability. Neobanks and FinTechs often rate highly in app-store reviews and NPS.

            While legacy institutions sit on mountains of siloed data that they struggle to process, FinTechs are leveraging AI to provide real-time financial insights and automated wealth management. This transition from passive storage to active intelligence transforms the bank from a mere vault into a proactive partner. By the end of 2026, predictive analytics will be the baseline expectation, allowing agile players to anticipate customer needs before a single click is made, further cementing FinTech’s role as the architects of modern commerce.

            The ecosystem of banking is also changing – shifting from product-centric to platform-based models, including embedded finance, marketplaces, banking-as-a-service. Yet many traditional banks are structurally and culturally less prepared for platform thinking, compared to FinTech or big-tech players.

            Nurturing talent-driven innovation

            This shift isn’t just about technology; it’s also about talent. Forecasts in London suggest FinTech job vacancies could grow by about 37% year‑on‑year in 2026. High-velocity environments are a magnet for and a product of top-tier talent. The best engineers, product managers, and thinkers want to work where their impact is immediate.

            FinTechs offer just that. Not having to work through legacy systems means innovators are given the chance to shape processes themselves, creating output a lot faster than traditional banks, but also promoting continuous growth. By attracting the right talent who value autonomy and pace over stability, fintechs create a self-sustaining cycle of innovation that traditional banks simply cannot match with their current structures.

            The rise of FinTechs

            The evolution of the financial sector has reached a definitive tipping point where speed and agility are table stakes. As we move through 2026, the contrast between legacy institutions and FinTech disruptors has never been sharper. While traditional banks remain anchored by the weight of their own bureaucracy and theatrics, FinTechs are capitalising on a leaner, more intentional model that prioritises output over process.

            Ultimately, the rise of FinTechs isn’t just about better apps or lower fees. It reflects a shift in what determines success in financial services: the ability to decide quickly and execute at speed. The winners will be those that can do both consistently – not those with the longest history or the deepest legacy advantage.

            For modern businesses and consumers, that recalibration is already underway. The future of finance will be defined less by incumbency and more by execution velocity.

            Learn more at rippling.com

            • Digital Payments
            • InsurTech
            • Neobanking

            Author

            Peter Pugh-Jones

            Published

            24 August 2026

            Estimated Read time

            5Mins

            Peter Pugh-Jones, EMEA Field CDO at Confluent, explains why as more AI projects move into production, that gap will become harder to ignore. The conversation will shift away from what AI could do and towards what organisations are actually able to support in practice

            AI is no longer an experiment in financial services. It’s already embedded in day-to-day operations.

            Banks are rolling out AI-driven customer service, investing in automation, and exploring how generative and agentic systems can improve decision-making. Around 92% of global banks are already using AI in at least one core function, which suggests the industry is well on its way.

            As these initiatives scale, however, a different reality starts to emerge.

            What works in a pilot often becomes more complex, not to mention expensive, when rolled out across the business. In many cases, the issue is not the AI itself, but the environment it is being deployed into.

            There is no ‘magic bullet’ for AI in financial services. Its impact is shaped by how well organisations can access, connect, and act on their data — increasingly through real-time approaches such as data streaming.

            When AI meets reality

            This is particularly visible in customer service.

            AI-powered chatbots and virtual assistants are now widely used to handle routine queries, categorise transactions, and guide users through basic processes, and in the right conditions they can deliver real efficiency gains.

            Scaling those systems, however, is a different challenge altogether. Despite the level of investment, many of these systems continue to rely on fragmented or outdated information, which sees the experience quickly starts to break down.

            For example, customer context can be missing or inconsistent. Conversations don’t carry across channels. Employees are forced to double-check or correct outputs, removing much of the efficiency they were meant to deliver.

            In a sector where trust matters, that’s difficult to ignore.

            Legacy systems are still doing most of the damage

            Frameworks like the EU AI Act are shaping how organisations deploy AI, particularly in high-impact use cases such as credit scoring, fraud detection, and customer risk assessment. Requirements around transparency, auditability, and data governance are raising the bar for how AI systems are built and monitored. And rightly so, given the importance of trust in financial services.

            In practice, though, legacy infrastructure is often the bigger issue. When data is fragmented across systems or difficult to access in real time, it becomes far harder to evidence how decisions are made or ensure models are operating on complete, up-to-date information.

            In many cases, it is these underlying data limitations — rather than regulation itself — that slow progress. With many banks reliant on systems built up over decades through mergers, upgrades, and workarounds, they struggle to create a single, reliable view of a customer or transaction — something AI depends on.

            This also helps explain how FinTech startups can outmaneuver industry giants. Free of technical or technological baggage, they can build around modern data architectures from the start and bring new ideas to market more quickly.

            AI can’t reach its potential, or in some cases even run, in a legacy environment. Sticking with the customer service example, one such example is personalisation.

            Personalisation only works if the data is current

            Personalisation has been a goal in financial services for years, but in many cases it has been closer to segmentation than true individualisation. If AI is going to change that, it needs a flow of data that can tell it exactly what’s happening in the moment.

            Hyper-personalised experiences depend on real-time context — understanding a customer’s behaviour and situation as it evolves, often enabled by data streaming to ensure systems are working from the most up-to-date information. Without that, interactions quickly become disconnected.

            A customer might start a query in an app, follow up by email, and then call support, only to repeat the same information each time. This is not a limitation of AI, but of how information moves between systems.

            When that flow is in place, interactions become far more seamless, with context carrying across channels and experiences feeling more consistent.

            AI should support people, not replace them

            There is a lot of discussion about AI replacing human roles in financial services, but a fully automated model is unlikely.

            AI is highly effective at handling repetitive, high-volume tasks, like triaging customer queries or processing transactions. But more complex interactions still require human judgement, empathy, and context — particularly in areas such as lending, investments, or financial advice.

            Customer expectations also play a role. While digital channels continue to grow, many customers still value the option to speak to a person, particularly in more sensitive or high-stakes situations.

            As a result, the most effective model is increasingly a hybrid one. AI handles routine processes and surfaces insights, while people focus on more complex and sensitive interactions — provided both are working from the same reliable, up-to-date information.

            Getting the foundations right

            As AI adoption continues, the conversation is starting to shift.

            Early investment has focused on what AI can do. Attention is now turning to what is needed to make it work properly.

            That starts with data. Organisations need to ensure data is connected, governed, and available when it is needed. Too often, this work is treated as something to address later, when in practice delaying it tends to create more complexity and cost over time.

            There is also a cultural element to consider. In many organisations, established ways of working can slow progress just as much as technology. Addressing that is just as important as modernising systems, particularly when AI initiatives span multiple teams.

            A question of readiness

            Financial services organisations are being asked to move quickly while maintaining high levels of control and accountability. That is not easy.

            The organisations that make progress will be those that focus less on AI as a standalone capability and more on the conditions that allow it to work. If those conditions are not in place, results will remain inconsistent.

            As more AI projects move into production, that gap will become harder to ignore. The conversation will shift away from what AI could do and towards what organisations are actually able to support in practice.

            In most cases, it comes down to a simple question: is the data ready?

            Learn more at confluent.io

            • Artificial Intelligence in FinTech
            • Data & AI

            Author

            Frank Jaquez

            Published

            Estimated Read time

            4Mins

            Frank Jaquez, Head of Talent & Culture at Skillsoft, on why an AI skills strategy built without skills visibility is not just incomplete; it is fundamentally blind

            Organisations are accelerating their adoption of AI, with 33% of UK businesses planning investment and training in 2026. As investment rises and competitive pressure mounts, leaders increasingly recognise that AI capability will distinguish high-growth performers from the rest. Yet a key question remains unanswered: are workforces equipped with the skills to use AI effectively and productively?

            Without clear skills visibility – a transparent picture of required skills & capabilities, existing strengths, emerging gaps and role-specific proficiency – AI strategies rest on assumptions rather than evidence. Leaders are forced to guess who can adapt, which teams need development and whether to prioritise upskilling, reskilling or the deployment of AI agents.

            Too often, organisations launch AI initiatives without understanding whether their employees can use these tools effectively. Or translate them into measurable outcomes. The result is predictable: pilots lose momentum, adoption slows, and ROI remains uncertain. When organisations lack skills visibility, decisions around investment, talent and transformation become guesswork. And guesswork is no foundation for a successful AI strategy.

            Why skills visibility must come first

            The foundation of any effective AI strategy is a clear view of workforce capabilities. Understanding what employees can do today, identifying the skills they will need tomorrow, and how those capabilities align to business goals. It also requires clarity on what AI can currently deliver and the new capabilities it introduces into the organisation. When skills are measurable and tied directly to real tasks, organisations can move decisively from experimentation to execution.

            However, this clarity cannot be achieved through learning content alone. High-quality learning is essential, but when it sits apart from roles, skills and outcomes, it cannot provide the clarity, validation or evidence of proficiency that leaders now require. Completing training does not automatically translate into performance, confidence or capability.

            The scale of this challenge is reflected in recent data. Nearly a third of UK employers lack a clear view of the skills their workforce will need in the next two to three years. Whilst 60% identify workforce planning as a critical or high priority, only 25% base their approach on skills. This gap highlights a growing disconnect. Between the urgency of AI adoption and the visibility organisations have into the workforce capabilities required to sustain it.

            Building a connected ecosystem

            To achieve genuine visibility, organisations need to start by identifying the critical skills required to deliver their strategy, and then integrate skills mapping, assessment, development and measurement into a single, connected system. When these elements operate within one environment, leaders gain a consistent, evidence-based view of workforce capability. This highlights areas of risk and readiness as priorities shift.

            Skills-aligned learning pathways ensure development efforts directly build the capabilities that matter most. When those skills are connected to clear, strategic goals, employees understand how their development ties to business priorities. Clear milestones and measurable indicators give leaders confidence that learning is translating into real-world performance where it is needed most.

            AI-powered learning is becoming central to this approach. By analysing skills data, identifying gaps in real time and recommending targeted development aligned to roles and organisational priorities, AI enables a dynamic, continuous ‘skills supply chain’. Instead of relying on infrequent skills audits, organisations benefit from an adaptive system that evolves alongside the business.

            This model supports skill development at scale. It educes reliance on costly external hiring,. This helps organisations to unlock business results by developing and redeploying existing talent through more strategic upskilling and reskilling.

            Linking skills to business priorities

            Skills visibility is most powerful when it is tightly linked to organisational priorities. When leaders understand which skills matter most and how their workforce measures against them, they can make informed decisions about where to invest and how to focus development.

            This alignment enables organisations to build capabilities that will drive the biggest impact, accelerate AI adoption through capability-led learning pathways, prioritise roles critical to future growth and track workforce readiness with confidence.

            A connected skills supply chain ensures these insights continuously inform both development and execution, creating a workforce that is responsive, targeted and future ready.

            AI transformation depends on skills visibility

            Technology may enable AI transformation, but people determine whether it drives meaningful results. Without a transparent understanding of workforce capability, even the most ambitious AI strategy risks becoming costly and ineffective. Skills visibility is the differentiator between organisations that keep pace with AI and those that lead.

            Organisations that invest in understanding their people, their current capabilities, their growth potential and how their skills align to business needs will be best positioned to unlock AI’s full value. A strategy built without skills visibility is not just incomplete; it is fundamentally blind.

            Learn more at skillsoft.com

            • Data & AI
            • Digital Strategy

            Author

            Dan Brightmore

            Published

            21 August 2026

            Estimated Read time

            5Mins

            Read in brochure format

            Welcome to the latest issue of Interface magazine! Click here to read the latest edition! First United Bank: Combining Community…

            Welcome to the latest issue of Interface magazine!

            Click here to read the latest edition!

            First United Bank: Combining Community Banking Values with Modern Technology

            Executive Vice President and transformation leader Tadd Tobkin reveals the evolution of community banking helping First United Bank customers ‘Spend Life Wisely’.

            “Our goal is not to become a technology company that happens to be a bank.  It is to become a great community bank with the modern capabilities required to serve people for generations…”

            NBME: Embracing Decisive Leadership and Human-Led Technology

            Andy Farella, CIO of the National Board of Medical Examiners (NBME), talks human-centred leadership and how the organisation is leveraging technology for its betterment. “We’ll always have appropriate humans in the loop – they’ll just be leveraging AI in thoughtful ways.”

            Ultra Clean Technology: Back-to-Basics Cybersecurity in an Advanced World

            Mandy Huth, CISO of Ultra Clean Technology, believes in embracing a back-to-basics approach to cybersecurity because cyber-attacks are a case of ‘not if, but when’. “I love that security is ever-evolving and ever-changing… Every decision my team makes should be to help UCT win, because that’s how we all win.”

            The EBRD: Building Global Technology Foundations for the Future

            Our cover story revisits the work of Chief Information Officer Subhash Chandra Jose. He reveals the strategy, leadership and continued purpose at the heart of a digital transformation across The European Bank for Reconstruction and Development (EBRD) global network. Under his leadership the digital journey has become as much about culture and collaboration as it is about cloud, artificial intelligence and cybersecurity.

            “We have made a lot of advancements on AI and cloud and all the greatest and latest technologies. But we mustn’t forget what actually makes the technology experience on the ground. It’s about network and connectivity.”

            BSP: Securing the Future of Digital Banking for Fiji

            By placing customers, community and practical innovation ahead of technology for its own sake, BSP is building a digital banking platform designed for long-term growth. CIO Omid Saberi explains how sixteen years of transformation have helped create the technological foundations for Fiji’s largest corporate bank while preparing customers for an increasingly digital future.

            “Corporate banking is where you change a country because you’re enabling businesses to grow.”

            OSB Group: Building the FinTech Foundations for Faster, Safer Growth

            For Debra Bailey, Chief Information Officer at OSB Group, working on technology transformation with Publicis Sapient is not about chasing the latest innovation. It is focused on outcomes: giving colleagues better tools, helping brokers move faster, improving customer journeys and creating the operating model needed for sustainable growth.

            “Publicis Sapient architected this overall proposed platform and the FinTechs that we work with. It’s a very logical and open architecture.”

            Publicis Sapient: AI Belongs in the Engine room, Not Just the Front Office

            Following the launch of its Core Modernisation Playbook, Publicis Sapient‘s Dave Murphy, Head of Financial Services- EMEA & APAC, explores why so many banks struggle to modernise despite investing heavily in AI. The challenge is no longer knowing what to transform, but how to execute transformation safely.

            “The banks that move first will not simply modernise faster. They will build the AI-ready foundations that determine how quickly every future innovation can be delivered.”

            Get your copy of the latest White Paper from Publicis Sapient here

            Also in this issue, we learn from Expereo why network resilience must be front of mind to drive digital transformation and TDK Ventures offer a solution to AI’s power problem. And check our events guide with all the key dates for your diary for global networking opportunities at the latest tech conferences across the globe this autumn.

            Click here to read the latest edition!

            • Cybersecurity
            • Data & AI
            • Digital Strategy
            • Fintech & Insurtech

            Author

            Dan Brightmore

            Published

            Estimated Read time

            3Mins

            Global financial institutions and FinTech infrastructure providers are delivering seamless cross-border payments

            TerraPay, the global money movement company has announced a collaboration with Deutsche Bank. It will provide access to the bank’s correspondent banking network, technology and product capabilities, supporting more efficient and reliable movement of money across borders. The milestone highlights the growing role of partnerships between global financial institutions and FinTech infrastructure providers. It is delivering more efficient, reliable and seamless cross-border payments.

            Through this collaboration, TerraPay gains access to Deutsche Bank’s correspondent banking network, payments capabilities and foreign exchange services. This strengthens TerraPay’s settlement infrastructure and enhances its ability to facilitate cross-border payments for customers around the world. The initiative is expected to support greater efficiency, speed and reliability in USD settlement while further strengthening TerraPay’s global payments network.

            The future of cross-border payments

            “The future of cross-border payments will be built through collaboration between global financial institutions and purpose-built fintech infrastructure. Our relationship with Deutsche Bank brings together the strength of one of the world’s leading banks with TerraPay’s global payment network, creating new opportunities to improve settlement efficiency, expand market reach and deliver superior payment experiences for customers around the world.” 

            Ambar Sur, Founder & CEO, TerraPay

            The collaboration lays the groundwork for future opportunities between the two organisations as they explore ways to further enhance global payment connectivity and support the evolving needs of customers worldwide.

            “We are pleased to support TerraPay as it continues to enhance its global cross-border payments infrastructure. By combining Deutsche Bank’s correspondent banking, payments and foreign exchange capabilities with TerraPay’s network, this partnership will help facilitate more efficient and reliable payment flows. It reflects our commitment to supporting Fintech clients in the Middle East region across the evolving payments landscape and connecting businesses and communities through our global network.” 

            Majed Julfar, Chief Country Officer for the UAE, Deutsche Bank

            About Deutsche Bank

            Deutsche Bank provides retail and private banking, corporate and transaction banking, lending, asset and wealth management products and services as well as focused investment banking to private individuals, small and medium-sized companies, corporations, governments and institutional investors. Deutsche Bank is the leading bank in Germany with strong European roots and a global network.

            Deutsche Bank AG, Dubai (DIFC) Branch is a branch of Deutsche Bank AG located and registered in the Dubai International Financial Centre (DIFC) in the Emirate of Dubai, United Arab Emirates, with registered no. 00062. Principal place of business in the DIFC: Dubai International Financial Centre, ICD Brookfield Place, Floor 35, PO Box 504902, Dubai, United Arab Emirates. Deutsche Bank AG, Dubai (DIFC) Branch is regulated by the Dubai Financial Services Authority (“DFSA”) and is authorized to provide Financial Services to Professional Clients only, as defined by the DFSA.

            About TerraPay

            TerraPay simplifies global money movement, providing a single connection to one of the most expansive cross-border payment networks, regulated across multiple markets. Our network enables payments to receiving and sending countries worldwide, reaching a vast network of mobile wallets, bank accounts, and cards. We make money transfers instant, reliable, transparent, and fully compliant for its partners, connecting them 7.5Bn+ bank accounts, in 156+ countries.  We work behind the scenes as the trusted partners for some of the world’s most innovative financial players, from banks and digital wallets to MTOs, corporates and fintech platforms. On a mission to create a borderless financial world, TerraPay operates Xend – the first of its kind payments interoperability network, enabling secure, real-time cross-border payments, to and from 3.7B wallets through a single, unified connection. TerraPay is headquartered in London, with offices in cities including, Dubai, Milan, Miami, Singapore, Bogota, Johannesburg, Kampala, Bangalore.

            • Digital Payments

            Author

            Simon Ritter

            Published

            Estimated Read time

            5Mins

            Simon Ritter, Deputy CTO at Azul, on how the Java ecosystem is adapting (quickly) to the needs of AI applications

            If you ask most people in IT which programming language is most commonly used for AI applications, they’ll almost all answer Python. Which raises the obvious questions: why, and why not Java instead? 

            Python is actually older than Java by over 4 years, so its AI popularity is not because it is newer than other alternatives. Python’s origins have nothing to do with AI (or even numerically intensive applications). It was originally developed as a high-level scripting language for writing system utilities and applications on the Amoeba distributed operating system (which I doubt most people have even heard of). Python is also a poor choice for numerically intensive operations, which is a lot of what AI and LLMs need to do.

            The real reason for Python’s popularity is that the mathematicians working on AI found it easy to learn. It could serve as a simple interface to high-performance computing libraries, typically written in complex low-level languages such as C and C++. This initial popularity led to many industry-standard frameworks, such as PyTorch and TensorFlow, being written in Python.

            Does this mean Java has no place in AI?

            Far from it, as was demonstrated recently by Azul’s AI4J online conference. This brought together nine AI proponents, most of whom are Java Champions, to present various ways Java can be used in this area.

            The general theme that came across was that “Java owns the enterprise data”. Given Java’s enduring popularity for server-side applications, this is a statement that’s hard to dispute. This underscores the importance of Java when considering Retrieval Augmented Generation (RAG). Training an LLM uses a wide array of data and can deliver a model that is easy to communicate with in natural language. To change this from being smart to helpful (another quote from the conference), you need to access enterprise data in real time. The most valuable data for an organisation is contained in systems like those for ERP and CRM, as well as a plethora of databases. Most of that data is managed by Java-based applications.

            What we end up with is the usual layered IT architecture, each layer providing an abstraction of the one below it. As was explained in one session, the LLM sits at the bottom, with vector search (or naïve RAG) above that and Graph RAG above that. 

            Java delivers powerful technologies

            Java can deliver powerful technologies that fit well into this critical space. Instead of searching flat text files, a RAG system can use tools like Neo4j and Cyrock.AI to model complex relationships among concepts, people, and documents. This allows the AI to fetch deep, interconnected context that standard vector databases might miss. Enterprise Java application platforms like Jakarta EE and Spring are rapidly adapting to the needs of AI systems; Josh Long of Pivotal and James Ward of Amazon provided details of how Spring and AWS Bedrock can be used in this way.

            Another significant takeaway from the AI4J sessions was the use of predictive, or analytical AI rather than the high-profile generative AI (or GenAI). One session quoted a report from Deloitte on the State of AI that said that “30% of GenAI projects will be abandoned due to a lack of clear business value”. Predictive AI has many benefits for an enterprise, not least of which is its deterministic quality. This means it does not hallucinate (which you really don’t want when making mission-critical, enterprise-wide decisions).

            AI with context

            Context for AI was another recurring theme in these sessions. Better coordination of input to an LLM-based AI system is critical to success. Providing that bridge from smart to helpful is what enterprise users need the most.

            This, in turn, led to the conclusion that one of the most important aspects of any AI system is performance. In its over thirty-year history, Java has gone from a platform teased for being slow to one that, using techniques like just-in-time (JIT) compilation, can even outperform native C and C++ code.

            The AI4J sessions discussed a number of ways that Java delivers optimum performance:

            • Project Panama for easier Java integration with lower-level C++ frameworks such as CUDA and ONNX Runtime.
            • Virtual threads, capable of delivering massively greater scalability for applications that spend a lot of time blocking, which is what AI applications do as they wait for tokens to be processed.
            • Project Babylon, which is exploring ways to target specific hardware architectures at runtime. Moving beyond JIT, this allows code to be targeted at GPUs or even FPGAs as and when necessary.
            • Azul’s Prime JVM that delivers lower latency and higher throughput through different garbage collection (GC) and JIT implementations. All whilst maintaining full adherence to the Java SE specification, making it a drop-in replacement for other JVMs. No code changes or recompilations required.

            Overall, the AI4J conference provided a fascinating insight into how the Java ecosystem is adapting (quickly) to the needs of AI applications.

            About Simon Ritter

            Simon has been in the IT business since 1984 and holds a Bachelor of Science degree in Physics from Brunel University in the UK. Simon joined Sun Microsystems in 1996 and started working with Java technology from JDK 1.0; he has spent time working in both Java development and consultancy. Having moved to Oracle as part of the Sun acquisition, he managed the Java Evangelism team for the core Java platform. Now at Azul, he continues to help people understand Java as well as Azul’s JVM technologies and products. Simon has twice been awarded Java Rockstar status at JavaOne and is a Java Champion. He represents Azul on the Java SE Expert Group, OpenJDK Vulnerability Group and Adoptium Steering Committee. He is also the author of OpenJDK Migration for Dummies.

            About Azul

            Azul is the trusted leader in enterprise Java for today’s AI and cloud-first world. Its open source-based Java platform empowers organizations to optimize the entire Java lifecycle to accelerate performance, strengthen security, reduce licensing and cloud costs, and boost developer productivity. Azul powers mission-critical systems for 36% of the Fortune 100, 50% of the Forbes Top Ten World’s Most Valuable Brands, and the world’s top 10 financial trading companies. Learn more at azul.com and follow @azulsystems.​

            • Data & AI
            • Digital Strategy

            Author

            Todd Clyde

            Published

            20 August 2026

            Estimated Read time

            4Mins

            Both Amazon and eBay have announced the introduction of Pay by Bank as a payment method in the UK. If…

            Both Amazon and eBay have announced the introduction of Pay by Bank as a payment method in the UK. If there was ever any doubt, this signals that Pay by Bank is beginning to solidify itself as a mainstream global payment option.

            Initially a “made in Europe” alternative to card payments, Pay by Bank is now a genuinely disruptive force, challenging traditional payment methods and offering faster payments, stronger security, and a simpler user experience.

            A recent Token.io survey demonstrated that 91% of respondents reported strong merchant demand, while Open Banking Limited estimates a £4.4 billion opportunity. For UK businesses, Pay by Bank could unlock huge savings through lower transaction fees and improved reconciliation processes. This includes an estimated £331 million from online payments, £40 million from in-store transactions, £110 million from one-off bill payments, and £78 million from recurring billing.

            Recent data from Open Banking Limited also shows Pay by Bank is a safer way to pay, with fraud rates 2.4x lower than payments industry norms. During 2025, approximately one in 6,000 open banking payments were fraudulent, compared with one in 2,500 across the broader payments industry. 

            Pay by Bank

            Today, popular Pay by Bank use cases include credit card repayments, current account top-ups and savings account funding, with adoption set to expand significantly as new schemes emerge.

            Yet as Pay by Bank scales, a familiar pattern is revealing itself: fragmentation.

            Across the UK and Europe, multiple industry-led and regulatory-led Pay by Bank schemes have emerged, each bringing its own functionality, geographic reach, dispute frameworks and commercial models. In these regions, open banking regulation created a foundation for every use case, but not every capability that merchants, billers and consumers need, including recurring payment mandates and dispute resolution frameworks.

            Multiple Pay by Bank schemes reflect a payments ecosystem maturing beyond a one-size-fits-all model; this is not a flaw but a sign of healthy competition. Fragmentation is the inevitable consequence of a market scaling at pace, yet the fragmentation competition causes needs to be addressed.

            Looking at the financial challenges, as more Pay by Bank schemes emerge, payment service providers (PSPs) could find themselves needing to integrate with multiple networks, each with its own technical, operational and commercial requirements. This increases developmental costs, maintenance requirements and operational complexity.

            Greater Efficiency, Lower Operating Costs and Wider Adoption

            If left unchecked, those costs risk being passed through the payments value chain, reducing some of the economic advantages that have made Pay by Bank so attractive. The industry should instead aim for greater efficiency, lower operating costs and wider adoption.

            Limiting competition between schemes is not the goal, simplifying access to them is. Rather than integrating separately with every Pay by Bank network, PSPs can connect through a single infrastructure provider that enables access to multiple schemes via one integration. Those efficiencies can then be passed on to merchants, helping ensure that fragmentation drives innovation, not unnecessary cost.

            Yet, the key is not to resist fragmentation, but to abstract both its technical and commercial complexity. Success is achieved for those that access multiple PBB schemes through a unified layer, benefiting from the reach and functionality of different networks without managing them directly.

            Pay by Bank’s next chapter will be defined less by whether it succeeds and more by how the ecosystem scales. The rise of multiple schemes is evidence of a market attracting investment, innovation and competition, all hallmarks of a maturing payments category.

            Fragmentation is therefore a sign of progress. The task ahead is to ensure that complexity is abstracted away, enabling PSPs and merchants to benefit from the reach of multiple schemes without the operational burden that comes with them.

            About Token.io

            Token.io is the leading Pay by Bank infrastructure provider. Powering major Pay by Bank schemes in the UK and Europe, Token.io’s turnkey infrastructure makes it simple for banks, platforms and payment companies to grow revenue, reduce costs and expand to new markets with Pay by Bank. Unrivalled acceptance, industry-leading conversion rates, and a uniquely collaborative partnership and distribution model make Token.io the market benchmark, as recognised by independent analysts. The company’s partners include three of Europe’s five largest financial institutions.

            Learn more at Token.io

            • Digital Payments
            • Neobanking

            Author

            Raphael Guilley

            Published

            19 August 2026

            Estimated Read time

            4Mins

            Raphael Guilley, SVP Consulting – FIME, on why institutions that establish robust frameworks for secure, compliant, and trusted agent-mediated transactions will be best positioned to lead in the agentic era

            The integration of artificial intelligence (AI) into financial services is progressing beyond advisory tools and assistants toward autonomous economic actors capable of initiating transactions independently. This development, often described as agentic commerce, presents a fundamental transformation in how payment systems operate and poses significant strategic, regulatory, and technological challenges.

            Defining Agentic Commerce

            Agentic commerce refers to economic activity that is initiated and executed by autonomous AI agents with the authority to transact without ongoing human intervention. These agents possess decision-making logic, clearly defined permissions, and access to financial resources. Two principal models have emerged: agents acting on behalf of individuals within defined parameters and direct machine to machine payment interactions. While the former retains human oversight, the latter represents autonomous payment flows between AI entities.

            This shift is distinct from traditional automation or decision support. It implies that software components are granted agency, including identity, credentialing, and access to value, enabling them to transact continuously and autonomously within predefined constraints.

            Evolution of Payments Infrastructure

            The payment industry has historically evolved from cash to plastic cards, then to digital wallets and mobile channels. Each stage redefined user behavior and market economics by reducing friction and expanding transactional capabilities. Agentic commerce represents a qualitative inflection point: economic actors that operate in real time and without direct human initiation will change not only how payments occur but the nature of economic participation itself.

            Major industry stakeholders are investing in this future. Global card networks have announced initiatives designed to support agent-mediated transactions, indicating that the conceptual frontier of autonomous payments is now entering strategic product roadmaps.

            Trust, Identity, and Control

            A central question in the agentic era is trust. How can payment systems reliably authenticate and authorize autonomous agents? Traditional governance models are built around human identity and accountability; they do not readily extend to autonomous software entities. The emerging concept of know your agent (KYA) extends conventional identity frameworks to encompass machine identities, enforceable delegation scopes, and behavioral reputations.

            Robust agent identity is a prerequisite for liability assignment and risk management. Without verifiable identity frameworks, the industry cannot effectively prevent or remediate unauthorised transactions initiated by compromised or malfunctioning agents. Furthermore, the probabilistic nature of AI decision-making introduces complexity in ensuring that agent actions remain aligned with user intent and regulatory expectations.

            Risk Management and Governance

            Agentic payments require robust governance constructs that balance autonomy and control. Delegation scopes must be clearly defined and tied to explicit user consent, while spending limits and risk parameters should be configured according to institutional policy. Real-time monitoring with anomaly detection tailored to agent behavior is essential, along with immediate revocation mechanisms to address errant agents. Comprehensive audit trails are required to ensure regulatory compliance and support dispute resolution. These capabilities must be integrated into AI payment frameworks to mitigate the risks associated with nondeterministic AI outcomes and to preserve compliance with existing risk, privacy, and financial crime obligations.

            Smart Wallets as Governance Interfaces

            In agentic commerce, the wallet evolves from a simple token repository into a governance boundary and enforcement engine. Smart wallets encapsulate digital value alongside programmable constraints, including merchant restrictions, behavioural policies, and regulatory triggers. Merchants and service providers could enforce acceptance criteria that require authenticated agent credentials and minimum trust thresholds.

            This architecture enables a new class of programmable identity-linked reputation systems. If an agent violates trust thresholds, network participants could restrict its access, similar to consumer credit blacklisting in traditional finance.

            Regulatory and Institutional Challenges

            The introduction of agentic payments presents systemic challenges for policymakers, payments infrastructure operators, and regulatory authorities. Most existing payment rails were designed for human-initiated interactions and must evolve to support continuous, high-frequency autonomous transactions at scale. Determining responsibility for incorrect or malicious agent behavior requires new legal constructs, as traditional liability models for chargebacks, arbitration, and consumer protection do not readily apply when the initiating actor is non-human. Additionally, anti-money-laundering and sanctions compliance frameworks are predicated on human behavior and identity. Autonomous agents transacting across borders challenge existing compliance models and demand new risk-profiling and enforcement mechanisms.

            Strategic Imperatives for the Payments Ecosystem

            Agentic commerce is not a fringe trend but an emerging paradigm with implications for competitiveness and resilience. Payment networks must support real-time settlements, micro and nano transaction capabilities, and agent-aware fraud detection. Regulatory sandboxes should accommodate agentic payment use cases to inform policy and technical standards. Identity layers and standards, including ISO 20022 extensions, must incorporate agent flags and authentication metadata. Central banks and domestic schemes should explore programmable digital currencies usable by autonomous agents under defined compliance protocols. Failure to proactively adapt could result in external platforms capturing market share in agentic transaction flows.

            Conclusion

            Agentic commerce represents a transformative step in the evolution of payments. Autonomous AI agents will increasingly act as economic participants, triggering transactions on behalf of users and other systems. For the payments industry, this shift demands a rethinking of identity, governance, liability, and infrastructure. Institutions that establish robust frameworks for secure, compliant, and trusted agent-mediated transactions will be best positioned to lead in the agentic era.

            Learn more at fime.com

            • Artificial Intelligence in FinTech
            • Digital Payments

            Author

            Oz Nicco-Annan

            Published

            18 August 2026

            Estimated Read time

            5Mins

            Oz Nicco-Annan, CFO at InfraPartners – the prefabricated data centre solutions specialist – on why developers should align deployment with evolving demand, while also adapting flexibly to keep pace with AI

            Behind the excitement of AI infrastructure, from hyperscale data centres to GPU compute mega-campuses, lies a growing concern. While long-term demand for AI is widely expected to grow, the shape and timing of that demand is still taking form. The market is shaped by rapid chip cycles, fluctuating pricing and shifting workload demands, and the old ‘build it and they will come’ approach has resurfaced. Only now, the stakes are far higher and there is an expectation to get more certain on when ‘they will come’.  

            Financial analysts are beginning to flag the tension between rapid AI infrastructure expansion and the pace at which demand is materialising. Some reports suggest AI infrastructure spend is outpacing realistic demand projections, and there is an increasing focus on how quickly demand can convert into committed, revenue-generating workloads.  

            Yet projections do point to strong long-term growth. While questions are emerging about who bears the risk if utilisation levels fall short, the answer is not to halt or slow the development of vital infrastructure. Instead, utilising more flexible and phased deployment models will enable operators to better align capacity with usage and meet AI ambitions. Therefore, infrastructure can be deployed, adapted and expanded in line with realistic, evolving workload requirements. 

            The demand complexities  

            There is a widely held assumption that demand for AI compute will be effectively unlimited. That if capacity is built, it will inevitably be used. This assumption is likely because projections point to sustained growth in training and inference workloads, with data centre capacity demand expected to grow by around 20–25% annually through 2030.  

            In reality, AI demand is complex and still evolving. Enterprise adoption remains uneven, many organisations are still in pilot (or experimentation phases) and not all workloads require large-scale, always-on infrastructure. Recent research shows that only around 20% of companies have scaled AI capabilities across their organisations, highlighting the gap between investment and real, sustained usage. At the same time, AI interest is undeniably accelerating, driven by rapid advances in generative models and increasing enterprise confidence. Most importantly, there is clear risk of over-cautious investment that could leave markets underprepared and unable to keep pace with national and commercial AI ambitions.  

            Speed versus commercial discipline  

            Historically, large data centre developments were underpinned by long-term agreements with hyperscalers or enterprise tenants before construction began. These commitments provided a clear route to secure revenue, reducing exposure to market volatility. Across the last few years, however, projects are increasingly moving forward while long-term customer commitments and workload requirements are still taking shape. 

            This shift in commercial models is partly driven by the speed of the market. The pressure to deliver capacity quickly has intensified, particularly as new entrants such as GPU-as-a-service providers compete alongside established hyperscalers. In this fast-paced environment, the ability to deploy infrastructure rapidly can outweigh the discipline of securing customers in advance. But this comes with risk that needs addressing.  

            Without committed users, developers are exposed to two key challenges. First, the utilisation risk; the possibility that capacity cannot be filled. Yet this is only likely if the facility is designed around specific architectures or hardware configurations that do not align with actual customer requirements. Where data centres were once built to operate for 10 to 15 years with minimal change, operators are now dealing with hardware refresh cycles closer to three to five years. The right technology for today’s infrastructure may not be fit for tomorrow.   

            The second challenge is slower-than-expected customer adoption. Capacity may ultimately be utilised, but not at the pace required to support the investment case. This issue can extend timelines, delaying revenue generation and placing pressure on project economics. Delayed customer adoption, whether due to economic conditions, regulatory constraints or shifts in AI development priorities, can leave newly built facilities underutilised for extended periods. 

            Rethinking deployment models 

            How does the industry respond to these risks? In short, we must rethink how infrastructure gets deployed. Instead of committing to large, monolithic builds, there is a growing case for more upgradeable and phased approaches. Upgradeable data centres, built offsite with advanced manufacturing processes, allow operators to align capacity deployment more closely with confirmed demand. This technique reduces the risk of overbuilding; organisations can start small and add capacity as required, rather than building for a future that may not play out in the way expected. Delayed customer adoption suddenly becomes less of a concern. Critically, upgrades can also be performed while the rest of the site remains operational, avoiding prolonged downtime and revenue loss. 

            As AI hardware evolves with increasing power densities and shifting cooling requirements, this upgradability also allows infrastructure to adapt and remain relevant as technology changes unfold. AI is evolving much too fast for rigid designs and, instead, flexibility should be built in to ensure this infrastructure can cater for the needs of tomorrow. In other words, we should be manufacturing building blocks that have the capability to evolve and scale, therefore reducing risk around utilisation. Rather than designing and building an AI factory that works only for the hardware we know, developers should be looking at power and structural systems that can scale to higher densities, or support a transition to new cooling methods, without the need for a full re-build or retro fit.  

            From a financial perspective, this level of upgradability means the model can shift from large upfront capital commitments towards more incremental investment. Developers can then scale in line with both technological developments and customer demand. 

            Demand-led infrastructure 

            How effectively capacity is matched to real, sustained demand will be crucial in the coming years. The risk of building without guaranteed users is not new, but the scale at which it is now occurring is. The industry must quickly learn the art of balancing speed with discipline. 

            Developers should align deployment with evolving demand, while also adapting flexibly. Understanding who is committed to using these facilities is critical to determining the sustainability of the AI boom. But it’s also key that we design facilities not just for today’s technology, but for tomorrow’s unknowns, ensuring they can evolve at the same pace as AI itself. 

            Learn more at infrapartners.llc

            • Data & AI
            • Digital Strategy

            Author

            Dan Brightmore

            Published

            Estimated Read time

            3Mins

            Real-time bank-to-wallet cross-border payments now available as Inter advances its mission to build the financial infrastructure connecting Brazil, the United States, and the world

            Digital wallets have become the default way people send and receive money across large parts of the world, but the banks that hold and move the majority of global capital have largely stayed outside that ecosystem, reliant on legacy rails not built to reach wallet users directly. Inter, a global financial technology company serving 45M customers worldwide, today announced it has extended its cross-border payment infrastructure to reach 3.7 billion digital wallets worldwide through a strategic partnership with TerraPay, a global money movement company.

            Real-Time Bank-to-Wallet Transfers

            This integration connects Inter’s regulated infrastructure in Brazil and the United States directly to Xend – TerraPay’s global wallet interoperability network, enabling real-time bank-to-wallet transfers without requiring new connectivity layers.

            TerraPay recently launched Xend for real-time wallet interoperability across financial ecosystems, empowering last mile localised payouts, cross-border wallet payments and merchant payments. 

            Inter serves as the licensed settling institution for these transactions in both BRL and USD, combining its existing payment capabilities — PIX, InterPAY, Same Day ACH, and Real-Time Payments — with TerraPay’s global wallet reach to deliver most transactions in under one minute.

            “Our focus has always been building infrastructure that behaves like software, not legacy banking. This partnership connects that infrastructure to billions of digital wallet destinations worldwide, demonstrating Inter’s long-term strategy to be the platform that links Brazil and the US to the global financial system, for our customers and for the enterprises that move money at scale.”

            Ralph Boragina, Head of Cross-Border Payments, Inter

            Payments at Scale

            The scale of the opportunity reflects where payments are heading. The total value of digital wallet transactions is projected to grow from $9.85 trillion in 2026 to $23.4 trillion by 2031, a nearly 140% increase over five years. For platforms and enterprises managing payouts at scale, the infrastructure to reach that wallet economy has been the missing piece.

            “The next phase of cross-border payments will be defined by interoperability between banks and digital wallets,” said Ralph Koker, Head of Europe and Americas, TerraPay, “Inter’s infrastructure, together with regulated, real-time, and built for scale,  is exactly what that future requires. This partnership extends what Inter has already built to reach billions of digital accounts globally, through existing rails, without friction.”

            The announcement marks the latest step in Inter’s international infrastructure build-out, which includes its position as one of Brazil’s largest PIX payers, a leading FX operator by contracts registered with Brazil’s Central Bank, and the holder of a federally authorized banking branch in the U.S. Through TerraPay’s network, Inter is now positioned to serve the full spectrum of cross-border payment needs, from individual customers to high-volume enterprise disbursements, within a single regulated operating environment, settling in both BRL and USD.

            About Inter

            Inter (Inter&Co Inc./NASDAQ: INTR) is a global financial technology company providing banking, credit, investments, payments, and lifestyle solutions to more than 45 million customers worldwide. Inter leverages technology to unlock simplicity, offering mortgages, credit, gift cards, investments, and international payments through Banco Inter S.A., Brazil’s first digital bank, and a growing global footprint. Recognised by Forbes, CNBC, and others as one of the world’s leading fintechs and digital banks, Inter is guided by the Rule of 50 — a commitment to growing profitably and with discipline as it expands globally.

            Learn more at inter.co.

            About TerraPay

            TerraPay simplifies global money movement, providing a single connection to one of the most expansive cross-border payment networks, regulated across multiple markets. TerraPay’s network reaches 3.7 billion mobile wallets and 7.5 billion bank accounts across more than 156 countries, serving as a trusted infrastructure partner to banks, digital wallets, money transfer operators, corporates, and fintech platforms. TerraPay is headquartered in London, with offices in Dubai, Milan, Miami, Singapore, Bogotá, Johannesburg, Kampala, and Bangalore.

            • Digital Payments
            • Neobanking

            Author

            Chris Wallis

            Published

            17 August 2026

            Estimated Read time

            5Mins

            Chris Wallis, Founder & CEO of Intruder, on why the case for AI pentesting is well established but the work now is in building the programmes around it that will stand up to scrutiny

            The cybersecurity landscape is accelerating at a pace that would have seemed unthinkable just a few years ago. The number of published common vulnerabilities and exposures (CVEs) surged 263% between 2020 and 2025, while mean time-to-exploit has collapsed, with some projections putting it at minutes by late 2026.

            As attackers are moving faster than ever, and exploit windows are shrinking, penetration testing needs to evolve. In the post-Mythos landscape, AI pentesting is providing a much-needed new approach to the challenges stretched security teams face in identifying and remediating vulnerabilities and other risks across their digital estates.    

            Where the scanning/pentesting distinction is blurring

            Security teams have historically worked with two very distinct approaches to assessing risk.

            Vulnerability scanners cover a lot of ground cheaply and can run on a continuous basis, but what they cannot do is tell you whether a finding matters in your environment. They detect, but do not interpret.

            Penetration testing works differently. A skilled tester gets inside an application, understands how it is built and looks for weaknesses the way an attacker would. The findings tend to be higher quality and better contextualised, but the model has obvious constraints. It is expensive, happens infrequently, and whatever it surfaces reflects a snapshot of the environment at one point in time. A lot can change between tests.

            That gap has always been a problem, but it’s become a critical threat in the age of AI. With a growing volume of vulnerabilities and threat actors able to identify and exploit them faster than ever, the old scheduled pentest approach lags dangerously behind. The recent arrival of models like Mythos that can identify huge volumes of flaws at lightning speed has accelerated the issue to breaking point. In the long term the pentest as we know it is set to become a thing of the past. 

            How pentesting is set to evolve

            The question is no longer whether AI will change pentesting, but how completely it will do so. As AI-powered tools become capable of autonomously identifying vulnerabilities at machine speed, the assumptions that have underpinned security testing for decades are coming under pressure.

            For now, the pentest report isn’t going anywhere. In the short term the cost and time required to run a pentest will fall, but what it produces still needs to meet established expectations. We are at the beginning of the AI adoption curve, not the end of it. Compliance frameworks move slowly, and auditors expect deliverables that conform to standards built up over many years.

            What is changing is the frequency and the trigger for pentesting. Rather than a single substantial engagement each year, the emerging model is one of smaller, more targeted assessments initiated automatically when something in the environment changes, such as when code is shipped, a new service is exposed, or a configuration is modified. The barriers of cost and expertise that made continuous pentesting impractical are eroding and, with them, the case for accepting the gaps that annual testing leaves behind.

            This is the direction that pentesting is heading; a transformation into something that runs in the background continuously, surfacing findings as the environment evolves rather than at a fixed point in time.

            Providing context and speed by leveraging AI

            AI pentesting agents can support the work that human analysts do, particularly with triage, investigation and validation which are the slowest parts of the remediation cycle. For the 42% of midmarket security teams already stretched, overwhelmed or consistently behind, that is time they do not have.

            That investigative depth shows up in practice. In our own testing, an AI system correlated a vulnerability on a user’s laptop with that user’s cloud infrastructure permissions, a finding no conventional scanner would have surfaced because it can’t reason across those two layers at the same time. That kind of contextual correlation has not been possible before, and it has a direct impact on how effectively a team can prioritise its workload.

            Pentesting agents can investigate a wide range of issues

            While AI-powered pentesting is in its infancy, it’s already clear where the model is especially powerful.

            When it comes to information disclosure risks, it provides investigative depth that goes further than any scanner. Rather than simply flagging that configuration details or storage buckets are exposed, an AI agent can review what is accessible, evaluate how an attacker might use it and, in some cases, attempt to verify whether exposed credentials are valid.

            On injection flaws, an AI agent can reproduce the finding using multiple techniques (error-based, timing-based and UNION-based) to confirm whether an attacker could manipulate the application’s commands or queries to gain unauthorised access.

            The difference also shows when it comes to client-side issues such as clickjacking Here, a scanner will flag any page missing the relevant headers, but an AI agent can assess whether the page is actually frameable in a way that poses genuine risk.

            These capabilities matter, but the principle of human oversight in all AI deployments still holds true. 

            The end of pentesting as we know it?

            The long-term trajectory points toward continuous testing becoming the default rather than the exception. As the tooling matures and the user base grows, new ways of validating security posture will gain acceptance with auditors, insurers, and compliance bodies. The annual pentest, built on the assumption that depth and frequency are mutually exclusive, will look increasingly like a product of its time.

            Getting there will require the industry to resolve some genuinely difficult questions. Demonstrating the rigour of a continuous programme to an auditor is a different challenge to presenting a point-in-time report. The signals that should trigger an automated assessment, the liability implications for insurers, and the appropriate degree of AI autonomy versus human control are all areas where standards and expectations are still forming.

            Teams adopting AI security testing today are operating ahead of that settled framework. Going in with clear criteria and a defined approach to oversight is essential, not because the tools aren’t capable, but because the context in which they operate is still evolving. The case for AI pentesting is well established. The work now is in building the programmes around it that will stand up to scrutiny.

            Learn more at intruder.io

            • Cybersecurity
            • Digital Strategy

            Author

            Cien Solon

            Published

            Estimated Read time

            6Mins

            Cien Solon, CEO & Co-Founder of LaunchLemonade, on why the future of financial services will be shaped by those who can balance AI innovation with responsibility

            Every industry is racing to implement artificial intelligence. In financial services and other heavily regulated sectors, that race has been cautious. From automated underwriting and fraud detection to hyper-personalised customer experiences, AI offers FinTech companies unprecedented opportunities to innovate and scale. Yet alongside this promise comes the growing weight of regulation.

            Different approaches to AI governance are already taking shape. In Europe, the EU is introducing a structured, risk-tiered framework that sets clear obligations depending on how AI is used. The UK is pursuing a more flexible, principles-based approach, allowing individual regulators to apply guidance tailored to their sectors. The US has a more decentralised model, blending federal direction with a growing patchwork of state-level rules. For FinTech founders operating across borders, this diversity can feel complex and, at times, overwhelming.

            With these frameworks emerging, many fintech founders are beginning to hesitate. Questions around compliance costs, legal complexity and potential liability are causing some to reconsider or delay AI adoption altogether. For smaller fintech firms and scale-ups, the concern feels especially acute. Is the risk worth the reward?

            It is. And founders who treat regulation as a strategic advantage, rather than a barrier, will be the ones who pull ahead.

            Regulation as a Trust Multiplier

            In financial services, trust is everything. Customers entrust FinTech platforms with their most sensitive data and critical financial decisions. Investors and partners demand reliability, transparency and accountability. Regulators expect firms to operate with integrity and control.

            Robust AI governance can enhance trust across all of these stakeholders. FinTech companies that proactively adopt transparent and responsible AI practices signal maturity beyond their size. They demonstrate operational discipline alongside technical capability.

            This matters especially now, when AI systems can appear opaque or unpredictable. Clearly communicating how AI is used, what decisions it informs, what data it relies on, and how risks are mitigated allows fintechs to differentiate themselves in a crowded market.

            Compliance, when done well, builds credibility. And credibility, in financial services, is a powerful competitive advantage.

            The Myth of ‘Compliance Paralysis‘

            One of the most common misconceptions among FinTech SMEs is that AI regulation requires building entirely new compliance infrastructures from scratch. This belief often leads to what I call “compliance paralysis,” a reluctance to act because the problem feels too big to start.

            In reality, much of AI governance maps directly onto frameworks that fintech companies already use. Risk management, model validation, data protection, auditability. These are familiar disciplines. AI extends them into new domains, but the foundations are already there.

            Take model risk management. The steps a compliance team follows to validate a human-led underwriting process can be adapted to validate an AI-driven one. The logic is the same. The documentation requirements are the same. What changes is the technology performing the task, and the speed at which it operates.

            I see this with our own clients. Regulated businesses assume they need to start from zero when it comes to AI governance. When we walk them through what they already have in place, the gap between current practices and regulatory expectations is almost always smaller than they feared.

            The Agility Advantage of FinTech SMEs

            Large banks may have dedicated compliance teams and significant legal budgets, but they also carry the weight of legacy systems. Retrofitting AI governance into complex, decades-old infrastructure is both costly and time-consuming.

            Fintech SMEs have the advantage of agility. They can design and implement responsible AI practices from the ground up, embedding governance directly into their products and processes from day one.

            This is exactly how we built LaunchLemonade. Every AI agent on our platform operates within a governance framework by default. Audit trails, data handling rules, transparency requirements. They are baked into the architecture, because bolting them on later is always harder and more expensive.

            This compliant approach delivers real advantages. Long-term costs stay lower because governance is structural rather than retrofitted. Adapting to evolving regulatory requirements becomes faster because the foundations are already solid. And AI-enabled products reach the market ready to meet governance standards, rather than needing months of remediation after launch.

            Regulation, in this sense, levels the playing field. Larger incumbents may have more resources, but smaller firms can move faster and design smarter. Those that embrace this will outpace competitors who delay.

            Practical Steps for Leaders

            For finance leaders looking to navigate AI regulation without overwhelming their teams, the key is to focus on pragmatic, high-impact actions.

            Start by classifying AI risk early. A chatbot handling customer queries carries a very different risk profile from an algorithm making credit decisions. Understanding where your use cases sit on the risk spectrum allows you to prioritise governance efforts where they actually matter.

            Be transparent by default. Inform users when AI is being used, explain its role in decision-making, and provide avenues for human review where appropriate. Clear communication builds trust and reduces regulatory friction.

            Leverage what you already have. Map AI governance onto your existing risk and compliance processes rather than building a parallel system. This reduces duplication and accelerates implementation.

            Build lightweight governance structures. You do not need a large compliance team to implement effective oversight. Documenting model assumptions, maintaining audit trails and assigning internal accountability can go a long way. At LaunchLemonade, we see regulated SMEs achieve meaningful governance with lean teams precisely because they focus on these fundamentals.

            And know when to bring in expert input. Legal and regulatory expertise is valuable, but it does not need to be engaged at every step. Focus external support on high-risk use cases or areas of genuine uncertainty.

            The Cost of Standing Still

            If financial SMEs retreat from AI because of regulatory anxiety, innovation will not stop. It will simply concentrate in the hands of large incumbents and major technology companies with the resources to navigate complex compliance landscapes.

            That concentration has broader implications. Competition reduces. Innovation cycles slow. Diversity in financial products and services narrows. Opportunities to improve financial inclusion and accessibility are missed.

            Fintech and finserve has historically been a driver of disruption, challenging traditional models and expanding access to financial services. AI represents the next wave of that transformation. If smaller players step back now, they risk ceding that ground entirely.

            Regulation as an Enabler

            The conversation around AI regulation in financial services needs a shift in perspective. Regulation creates the guardrails that allow new technologies to scale safely and sustainably. It builds the trust required for widespread adoption. And it ensures that innovation benefits broadly, across industries and communities, rather than being concentrated among those who can afford to figure it out alone.

            For fintech and finserve founders, the real question is how to innovate responsibly and strategically. Those who embrace that question will find that regulation is part of the answer.

            AI is too important an opportunity for FinTech to ignore. Regulatory frameworks may seem daunting, but they are neither insurmountable nor inherently restrictive. When approached thoughtfully, they strengthen trust, streamline operations and unlock new avenues for growth.

            Financial SMEs are uniquely positioned to lead in this space. Their agility, focus and ability to embed governance from the outset give them a distinct edge over larger, slower-moving competitors. The future of financial services will be shaped by those who can balance innovation with responsibility. The firms that recognise this will define the age of AI, rather than simply surviving it.

            Learn more at launchlemonade.app

            Cien Solon is the CEO and Founder of LaunchLemonade, a secure governed platform of AI agents for regulated industries. An experienced AI transformation leader, Cien has been building AI-powered solutions since 2018 and working with generative AI since 2022. Her mission is to ensure that smaller businesses in regulated sectors can adopt AI confidently, without the enterprise price tag. LaunchLemonade was included in Entrepreneur UK’s Top 100 Startups to Watch, and Cien was shortlisted in the Technology category of the 2025 Investec Early-Stage Entrepreneur of the Year Awards.

            • Artificial Intelligence in FinTech
            • Data & AI
            • Digital Strategy
            • Fintech & Insurtech

            Author

            Dan Brightmore

            Published

            14 August 2026

            Estimated Read time

            2Mins

            Standard Bank reported a 10% increase in first-half headline earnings to $1.62 billion, supported by stronger fee and trading income and a decline in credit impairment charges

            Africa’s largest bank by assets reports that headline earnings for the six months ended June 30 rose to $1.62 billion, equivalent to 26.1 billion South African rand, from the same period a year earlier. The result reflected stronger activity across its corporate and investment banking operations and moderate growth in lending.

            Net interest income increased 4% to about $3.32 billion, as stronger deal activity and loan growth supported revenue. However, net interest margins narrowed to 4.72% as lower interest rates and increased pricing competition put pressure on lending profitability.

            Fee and commission revenue rose 7% to approximately $1.14 billion, driven by higher corporate debt financing activity and increased banking transactions. Trading revenue also increased 8% as market activity provided an additional boost to the bank’s non-interest income.

            Credit impairment charges fell 12% to about $439 million, improving the group’s credit loss ratio to 73 basis points from 93 basis points a year earlier. The lower impairment charge provided a significant contribution to earnings growth as credit conditions improved across its markets.

            Strong Performance

            The bank’s performance came despite a more challenging interest-rate environment. Lower rates can reduce the amount banks earn from the difference between lending and deposit rates, making growth in fees, trading and other non-interest revenue increasingly important.

            Standard Bank’s return on equity remained strong at 19.8%, reflecting its ability to generate substantial earnings relative to shareholders’ capital. The group has continued to focus on expanding its digital banking, payments and corporate banking operations across Africa.

            The lender also increased its interim dividend by 10% to $0.56 per share, equivalent to 9.02 South African rand, signalling confidence in its earnings and capital position.

            The results come as South Africa’s banking sector benefits from improving economic conditions and lower credit stress, although weaker interest margins are creating pressure on traditional lending income.

            Standard Bank has maintained its full-year outlook as it seeks to build on the momentum from the first half. The group expects continued growth across its banking businesses while remaining focused on controlling costs and managing credit risks.

            The earnings performance reinforces Standard Bank’s position among Africa’s largest financial institutions, with its diversified operations across South Africa and other African markets helping offset pressure from weaker margins in traditional banking.

            Learn more at standardbank.co.za

            • Digital Payments
            • Neobanking

            Author

            Dan Brightmore

            Published

            12 August 2026

            Estimated Read time

            5Mins

            Gaby Diamant, Co-founder and CEO of BridgeWise, on how and why AI will play an even larger future role in the wealth space

            Artificial intelligence is now firmly embedded across investment research, portfolio construction and advice. Its capabilities have evolved alongside the expectations users have for it. Today’s investors and advisors are not satisfied with speed alone. They want to understand how and why decisions are made.

            This shift is placing explainability at the centre of modern investing. As algorithms take on a greater role in shaping financial decisions, transparency is becoming critical to building trust, meeting regulatory expectations and supporting better outcomes for both advisors and investors.

            AI adoption is reaching a pivotal point

            AI adoption across financial services continues to accelerate – particularly because of its ability to process vast amounts of both structured and unstructured data. From earnings reports, market sentiment, global news and social media discussion, AI can analyse information in minutes, replacing what once required teams of analysts working over long periods.

            This shift is significant in scale, with BridgeWise’s State of AI for Wealth 2026 report finding 78.3% of respondents globally already use AI tools as part of their investment research, making it no longer early experimentation but mainstream behaviour. In addition, real world applications are already in use, with large asset managers now using AI to scan thousands of company filings during reporting seasons to identify shifts in guidance or any emerging risks much faster than manual methods. This is transforming how opportunities are identified and how risk is assessed.

            However, it is important to acknowledge that faster and more informed outputs may not be the better option if they aren’t explainable and traceable. These outputs are not enough if advisors cannot understand how conclusions were reached.

            Why is explainability and traceability essential?

            Explainable AI refers to systems that clearly show how outputs are generated, including which data points were used and how different factors were weighed in decision making. This is a critical step in financial services, as each decision and piece of advice directly impacts a client’s investments, risk and long term goals.

            The main challenge lies in trust.

            Despite growing trust in AI, nearly half of all survey respondents, 49.5%, say their main concern is that it may provide incorrect or risky advice, making this the single biggest barrier to adoption. Without clear reasoning behind every decision, even the most accurate systems struggle to gain trust over a human advisor.

            Regulators are also increasing scrutiny with the UK’s Financial Conduct Authority making it clear that advisors remain responsible for decisions made using AI and must ensure their outcomes are explainable and auditable. The EU AI Act does the same, introducing stricter requirements around advice transparency, record-keeping and human oversight for high risk systems and investments.

            The growing focus places traceability at the centre and is making it no longer acceptable to know just what decision was made, they must now be able to show how and why it was made. Decisions are regularly reviewed, audited and challenged in investing, so advisors moving forward with decisions that do not have a clear record of how a recommendation was generated, face regulatory non-compliance and risk customer relationships.

            Explainability is no longer a competitive advantage, it is an expectation.

            How is AI supporting better human decision making?

            Explainable AI is reshaping how advisors interact with technology. Rather than replacing humans, these processes are being used to enhance human capabilities by accounting for global market data. When models outline their reasoning clearly, advisors can apply their own expertise, experience and knowledge to hold more informed conversations with their clients. This becomes important in the wealth management space, where decisions must align with long term goals.

            If an AI model were to suggest reducing exposure to a specific sector, having it be explainable allows an advisor to understand what is driving this, whether it be macroeconomic, company changes or market sentiment. Having this context enables clearer thought processing and stronger client relationships.

            Among more advanced users, the role of AI is shifting from validation to discovery, with 44% of frequent users primarily relying on AI to identify new investment opportunities. Again here, before investing in an unknown stock, trust must be established.

            Bridging the trust gap in investing

            Explainability plays a key role in bridging the trust gap – when systems can provide clear, traceable insights to help advisors and investors understand the outcome, they can mentally validate it, check its logic and understand its reasoning – whether they agree with it or not.

            By sharing transparency with a solid user experience, users can then become active and see AI as supplementary and enhancing to their investing model, not predatory or inaccurate. 26.9% and 24% of users respectively cite that proof of data accuracy and human oversight would increase their confidence in using AI for investing.

            Explainable models address both these needs by highlighting the source of information and allowing a human to externally verify, helping build confidence in the face of skepticism.

            Using AI for a more transparent future in investing

            There is no doubt AI will play an even larger future role in the wealth space, but expectations and regulations are shifting. With an increasing demand for investors wanting to understand the reasoning behind recommendations, regulators are placing greater emphasis on accountability and transparency for firms.

            Explainable AI supports this shift by making decisions easier to understand, giving advisors and investors clearer visibility into how outcomes are reached and providing greater confidence in the decisions they make or advise.

            Learn more at bridgewise.com

            • Artificial Intelligence in FinTech
            • Blockchain & Crypto

            Author

            Dan Brightmore

            Published

            11 August 2026

            Estimated Read time

            4Mins

            Read in brochure format

            Read the latest issue here The EBRD: Building Global Technology Foundations for the Future Our cover story revisits the work…

            Read the latest issue here

            The EBRD: Building Global Technology Foundations for the Future

            Our cover story revisits the work of Chief Information Officer Subhash Chandra Jose. He reveals the strategy, leadership and continued purpose at the heart of a digital transformation across The European Bank for Reconstruction and Development (EBRD) global network. Under his leadership the digital journey has become as much about culture and collaboration as it is about cloud, artificial intelligence and cybersecurity.

            “We have made a lot of advancements on AI and cloud and all the greatest and latest technologies. But we mustn’t forget what actually makes the technology experience on the ground. It’s about network and connectivity.”

            BSP: Securing the Future of Digital Banking for Fiji

            By placing customers, community and practical innovation ahead of technology for its own sake, BSP is building a digital banking platform designed for long-term growth. CIO Omid Saberi explains how sixteen years of transformation have helped create the technological foundations for Fiji’s largest corporate bank while preparing customers for an increasingly digital future.

            “Corporate banking is where you change a country because you’re enabling businesses to grow.”

            First United Bank: Combining Community Banking Values with Modern Technology

            Executive Vice President and transformation leader Tadd Tobkin reveals the evolution of community banking helping First United Bank customers ‘Spend Life Wisely’.

            “Our goal is not to become a technology company that happens to be a bank.  It is to become a great community bank with the modern capabilities required to serve people for generations…”

            OSB Group: Building the FinTech Foundations for Faster, Safer Growth

            For Debra Bailey, Chief Information Officer at OSB Group, working on technology transformation with Publicis Sapient is not about chasing the latest innovation. It is focused on outcomes: giving colleagues better tools, helping brokers move faster, improving customer journeys and creating the operating model needed for sustainable growth.

            “Publicis Sapient architected this overall proposed platform and the FinTechs that we work with. It’s a very logical and open architecture.”

            Publicis Sapient: AI Belongs in the Engine room, Not Just the Front Office

            Following the launch of its Core Modernisation Playbook, Publicis Sapient‘s Dave Murphy, Head of Financial Services- EMEA & APAC, explores why so many banks struggle to modernise despite investing heavily in AI. The challenge is no longer knowing what to transform, but how to execute transformation safely.

            “The banks that move first will not simply modernise faster. They will build the AI-ready foundations that determine how quickly every future innovation can be delivered.”

            Get your copy of the latest White Paper from Publicis Sapient here

            Also in this issue, Mangopay examine the evolution for payments with stablecoins; Lexis Nexis explain why single customer view is not just a data capability; but a core enabler of InsurTech innovation; TreviPay explore the future for B2B payments and Austriacard Holdings advise on strong governance in AI adoption.

            Want more? We also round up the key FinTech events and conferences across the globe.

            Enjoy the issue!

            Read the latest issue here

            • Artificial Intelligence in FinTech
            • Digital Payments
            • InsurTech

            Author

            Dave Murphy

            Published

            10 August 2026

            Estimated Read time

            6Mins

            Following the launch of its Core Modernisation Playbook, Publicis Sapient’s Dave Murphy, Head of Financial Services – EMEA & APAC, explores why so many banks struggle to modernise despite investing heavily in AI. The challenge is no longer knowing what to transform, but how to execute transformation safely

            Most banks have spent the last decade applying AI where its impact is most visible, in chatbots, fraud scoring and personalised product recommendations. The results have been real, and in places significant, however, they have also been contained. AI has been working at the edges of the organisation, improving the surfaces that customers and compliance teams interact with, while the actual machinery of the bank carries on untouched. The core systems, the batch processing logic, the settlement rules written in COBOL three decades ago, have continued to run exactly as they always have, insulated from the transformation happening around them.

            Meeting the AI Execution Challenge

            That insulation is starting to break down. For CTOs weighing where AI investment should go next, the implications are considerable. The question is no longer whether banks need to modernise. Every major institution already knows it needs real-time payments, AI-ready operations, API-enabled architectures and cleaner data foundations. The challenge is execution. How do you modernise decades of critical systems without introducing unacceptable operational risk?

            That is where AI is beginning to change the equation. Rather than focusing solely on customer experience or analytics, it is being applied to the engineering process itself. The discovery, extraction, transformation and validation work that has traditionally been the slowest, riskiest and most expensive phase of core modernisation is becoming increasingly automated. This is where the greatest opportunity now exists.

            The Core Modernisation Playbook

            Publicis Sapient explores this shift in its recent Core Modernisation Playbook. At the centre of that approach is Sapient Slingshot, a platform designed to apply AI across the engineering lifecycle rather than simply the coding stage. Slingshot applies AI across the entire modernisation lifecycle, from understanding legacy systems through to transformation and continuous validation within a governed workflow. The objective is not simply to write code faster. It is to make large-scale modernisation more predictable, auditable and easier to execute.

            One global bank recently used this approach to analyse three million lines of COBOL and produce verified functional specifications in eight weeks, work that would previously have taken well over a year and required scarce legacy expertise that is becoming increasingly difficult to find. What changed was not the strategy or destination. It was the execution model.

            Core banking code written over thirty or forty years rarely comes with a user manual. Business rules become embedded directly in software, operational workarounds accumulate over time, and critical knowledge often exists only in the minds of engineers who built the systems. As those engineers retire, banks are not simply losing people. They are losing the institutional knowledge needed to modernise safely.

            This reflects a broader shift in how modernisation is delivered. Instead of treating discovery, code generation and testing as separate phases owned by different teams, they become part of a connected execution model. Understanding legacy systems, building modern services and validating every change happen as part of the same continuous process. That reduces risk while improving speed, which is exactly what heavily regulated organisations require.

            AI-Powered Business

            AI-powered business rule extraction is a direct response to that problem. Rather than depending on interviews with legacy experts and time-consuming manual code walkthroughs, automated analysis can surface the logic embedded in legacy programs, cross-reference it against actual processing behaviour, and generate specifications that engineers who’ve never touched the original COBOL can read and act on. In the case above, specification accuracy came in at 95%, and the time needed to analyse individual batch feeds dropped from 35 days to five. That compression changes the shape of what a modernisation programme looks like in its early phases. Work that used to occupy teams for the better part of two years can be substantially completed in months, and what comes out the other end isn’t an informal summary but a set of audit-ready specifications and, in this instance, more than 200 implementation-ready backlog items that become the foundation for everything that follows.

            Establishing Strong Foundations For AI

            Foundations matter because the validation phase of modernisation is where most programmes break down. There’s no shortage of ambition or strategic clarity in banking transformation. Most major institutions have roadmaps, target architectures and detailed business cases. What consistently falls apart is proving, with sufficient rigour, that a rewritten system behaves identically to the one it’s replacing.

            Core banking processes handle exceptions, regulatory edge cases and accumulated business logic in ways that are difficult to fully enumerate up front, let alone test exhaustively by hand. Traditional testing models depend heavily on manually created test cases, subject-matter-expert review and long validation cycles. Coverage gaps are hard to detect, particularly when the legacy behaviour they’re meant to validate against is poorly documented in the first place. Banks end up completing the development work and then getting stuck proving the new system does what it’s supposed to.

            AI-driven testing changes that equation by generating and executing test cases at a scale manual teams can’t match, covering standard transactions, edge cases and downstream dependencies systematically rather than sampling them. That doesn’t remove the need for human judgement on what ‘correct’ looks like, but, it does remove the bottleneck of generating enough coverage to have confidence in the answer.

            Modernisation Strategy

            None of this means banks can modernise the way digital natives do. Core banking platforms sit inside dense ecosystems of payments networks, risk engines, regulatory reporting systems and third-party providers. A change in one system can create consequences dozens of steps downstream, and banks must modernise around uptime, auditability and customer trust in a way a retailer redesigning a checkout flow simply doesn’t. That’s precisely why execution has become the defining challenge. Every bank already knows what it wants to build. The competitive advantage now lies in how effectively it modernises the foundations that make those ambitions possible.

            Sapient Slingshot – Deploying An Execution Engine

            What AI applied to the engineering process offers is a way to close that gap without pretending the underlying complexity has gone away. Understanding what a legacy system actually does, generating modern code and architecture that preserves the business behaviour underneath it, and validating the result continuously rather than at the end of a multi-year build: these are the three things that have historically made core modernisation slow, expensive and prone to stalling.

            Applying AI across the engineering lifecycle does not remove the need for governance, auditability or human judgement. It strengthens them by making documentation, traceability and validation part of the execution process rather than activities completed afterwards. This is where platforms such as Sapient Slingshot are emerging as execution engines for modernisation, enabling banks to modernise with greater speed, control and confidence rather than simply generating code more quickly.

            For CTOs, the practical takeaway is less about any single tool and more about where in the lifecycle AI is being pointed. The customer-facing layer of banking has benefited from a decade of AI investment. The engine room is only just beginning. The banks that move first will not simply modernise faster. They will build the AI-ready foundations that determine how quickly every future innovation can be delivered.

            The Core Modernisation Playbook

            Get your copy of the latest White Paper from Publicis Sapient here

            • Artificial Intelligence in FinTech
            • Digital Strategy
            • Fintech & Insurtech

            Author

            Dan Brightmore

            Published

            7 August 2026

            Estimated Read time

            3Mins

            Conducted in partnership with Visa, the study reveals how community banks can attract and deepen relationships with small- and medium-sized businesses (SMBs)

            Primax, a provider of payment processing services and value-added solutions for banks, today announced the release of its second annual Banking in Focus research. This year’s study reveals a significant opportunity for community banks to strengthen relationships with small- and medium-sized businesses (SMBs) as their financial needs evolve.

            Banking in Focus

            According to the study -conducted in partnership with Visa -, 86% of SMBs work with multiple banking partners. They use an average of 2.8 financial institutions for their business banking needs. With SMB relationships increasingly spread across providers, community banks have an opportunity to capture more of the relationship by becoming a trusted partner throughout the business lifecycle.

            “Community banks have a strong foundation with small businesses built on trust, relationships and personalised service. This year’s Banking in Focus research shows that the path forward is not replacing those strengths, but building on them with the capabilities SMBs need to manage and grow their businesses. By continuing to evolve alongside their customers, community banks can deepen relationships and strengthen their role in the SMB market.”

            Bill Hampton, President, Primax

            Key findings from this year’s study include:

            • Community banks have a strong foundation to build on: SMBs that bank with community financial institutions report high satisfaction with transparent fees and customer service. Yet community banks are less likely than larger providers to serve as businesses’ primary financial institution. This suggests an opportunity to strengthen awareness of their business banking capabilities and reinforce their value as businesses grow.
            • Growing SMBs need more sophisticated financial support: As businesses scale, their needs become more complex, with greater demand for capabilities such as payment processing, payroll, treasury management and financial advisory support. Community banks that evolve alongside their business customers are better positioned to retain relationships over time.
            • Personal and business banking relationships are closely connected: More than half (53%) of SMBs surveyed use the same primary financial institution for both personal and business banking. Highlighting the opportunity for community banks to deepen relationships as entrepreneurs start and scale their businesses.
            • Access to credit and payments can determine a business’s ability to grow: One in three SMBs, and 39% of lower middle market businesses, say payment and credit challenges affect their ability to maintain cash flow and invest in growth. Underscoring the critical role financial institutions play beyond traditional banking services.
            • Digital banking and security are baseline expectations: Robust digital banking, fraud protection and card offerings have become table stakes for SMBs. Community banks can differentiate by pairing these essentials with the personalised service and business expertise for which they are known.

            Community Banking Strategy

            The study also outlines strategies for community banks looking to acquire, deepen and retain SMB relationships. These include leading with payments, leaning into their core strengths, supporting SMB credit needs and engaging early and often as businesses grow.

            Primax’s 2026 Banking in Focus research was conducted in partnership with Visa. It surveyed 600 U.S. small- and medium-sized business owners and decision makers. The audience was evenly split, with 300 respondents from micro and small businesses with less than $10 million in annual revenue. And 300 respondents from lower middle market businesses with $10 million to $50 million in annual revenue. All participants were 21 years of age or older, with a mix of gender and age groups.

            The full 2026 Banking in Focus white paper is available for download on the Primax website.

            About Primax

            Primax provides banks with payment processing services and an expansive array of value-added technology and solutions. Primax’s customisable solutions include risk management, mobile and online card management, data and analytics, loyalty programs, marketing, strategic consulting, delinquency management and contact centre services. These help banks profitably grow their portfolios and deliver an unparalleled experience to their accountholders. With a longstanding commitment to service excellence, Primax has been designing and providing support services for banks throughout the U.S. and the Caribbean for over 40 years.

            For more information visit primax.us

            • Digital Payments

            Author

            Todd Latham

            Published

            6 August 2026

            Estimated Read time

            6Mins

            Todd Latham, CEO at Attest, a B2C research and insight platform, on why trust in financial services is earned and the need to act quickly on inisghts

            You would be hard pressed to find a leadership team that wouldn’t say maintaining trust with consumers is a top strategic priority in its decision-making.

            Since the financial crisis, financial services firms have worked hard to rebuild trust, gradually climbing back into neutral territory from a position where the industry ranked among the least trusted sectors. 

            But if the past few years have shown us anything, it’s that trust in financial services is still fragile. And it can be lost far more quickly than it is built.

            While the outlook for financial services firms has certainly improved, trust remains delicate. Consumers are firmly in the driving seat and can even dictate whether firms retain their social licence to operate, as we saw during the collapse of Credit Suisse in 2023. 

            Cybersecurity incidents on the rise

            At the end of 2025, that fragility was laid bare once again. Following a spate of cybersecurity incidents and the rapid rise of AI-enabled fraud, Attest data shows that half of British consumers said they trusted financial institutions less as a result.

            And unlike in many other industries, when it comes to finance, consumers are far more willing to act on that loss of trust. One in five have already switched providers because of it. More than a third (39%) say trust is the single most important factor when choosing a financial product, far ahead of the next most important factor, lowest fees (18%).

            But even this doesn’t tell the full story. Because ‘trust’, now more than ever, does not mean one thing to all consumers.

            It is not a single, shared concept that can be measured and managed in the same way across an entire customer base. It is fluid, context-dependent and increasingly shaped by individual expectations, experiences and life stages.

            A perfect storm reshaping trust 

            What makes the current moment particularly challenging is not just the scale of change, but its speed.

            Consumers are moving faster than institutions, adopting new technologies, forming new expectations and redefining what good looks like, often ahead of the industry’s ability to respond.

            At the same time, the environment in which trust is built has fundamentally changed. Financial services are no longer experienced through single, contained relationships. The environment is now embedded across digital journeys, shaped by interactions that extend beyond traditional banking channels, and influenced by a wider ecosystem of providers, platforms and technologies.

            This has two important consequences. First, trust is not built in one place. It is shaped across multiple touchpoints, many of which sit outside a firm’s direct control. Second, the criteria by which trust is judged are shifting. Being secure or reliable is table stakes. Increasingly, trust is evaluated through experience: how intuitive something feels, how clearly it communicates, and how well it fits into a customer’s everyday life.

            Against a backdrop of rising fraud, rapid AI adoption – spanning a wide range of consumer comfort levels – and ongoing economic pressure, the result is a more dynamic, more complex and more demanding landscape than financial services has faced before.

            From brand promise to lived experience

            Traditional banks continue to hold an advantage in baseline trust. Attest data shows that 77% of consumers trust traditional banks, reflecting decades of institutional credibility, regulatory oversight and scale.

            However, that trust is far from unassailable and newer players are gaining ground. Over half of consumers now trust neobanks (54%), and while overall trust in cryptocurrency platforms remains lower at a quarter (24%), it rises significantly among younger and more cosmopolitan audiences – 41% among 25–34-year-olds and 44% among Londoners. 

            These figures point to a broader shift: trust is becoming more fluid, contextual, and influenced by experience rather than legacy.

            FinTechs, in particular, are redefining what trust looks like. They are building it through intuitive design, frictionless onboarding and customer-centric experiences, leading to 15% of millennials putting complete trust in fintech apps, much higher than older cohorts. Increasingly, trust is less tied up with who provides a service and more about how well it works.

            A generation-driven redefinition of trust

            Exposure to different macro conditions is also shaping how different cohorts think about trust, and who they place it with.

            With traditional financial life stages breaking down, younger consumers are not following the same patterns as previous generations; whether that is buying a home in their late 20s, building wealth in predictable ways, or engaging with financial products at expected milestones. These disruptions mean that the models many institutions still design around fail to reflect reality.

            At the same time, a significant generational wealth transfer is underway. It’s estimated that more than £5 trillion in assets will be passed down the generations by 2050. Yet one in ten younger consumers are not financially confident or well-prepared to manage it. They are actively seeking guidance, often from a fragmented mix of sources – from social media to AI tools – leaving a gap for financial institutions to position themselves as trusted educators and advisors in an increasingly noisy landscape. Research we carried out around savings and investments found 87% of 25-34-year-olds expect financial services firms to help them understand investment products.

            AI adds another layer to this dynamic. Its perceived value differs significantly by cohort. Older consumers are more likely to see AI as a tool for protection, for example detecting fraud and safeguarding their money. Younger consumers, meanwhile, are more open to AI playing a proactive role, whether that’s analysing spending, recommending savings strategies or even informing lending decisions.

            Trust is contextual and should be understood in terms of who the customer is, what they need at that moment, and how their expectations are being shaped by the world around them.

            From insight to action

            Understanding that complexity is one thing. Responding to it is another.

            Most financial services organisations are already sitting on vast amounts of data, tracking behaviours, sentiment and shifting expectations in near real time. But too often, insight remains observational – something that informs strategy decks rather than shaping lived customer experiences.

            In an environment where trust is continuously tested, passive understanding has limited value. The real differentiator is how quickly and confidently organisations can translate that understanding into decisions customers experience directly.

            That requires a shift in mindset. Not from data to insight, but from insight to execution. It means shortening the distance between what customers say, what organisations learn, and what ultimately changes as a result.

            Today, trust isn’t built through broad brand narratives or periodic improvements. It takes shape in the moments that customers notice most: when something goes wrong, when a decision needs explaining, when speed and simplicity matter.

            Ultimately, trust in financial services is now earned – and lost – in everyday customer interactions, making an organisation’s ability to act quickly on insight just as critical as the insight itself.

            Learn more at attest.com

            • Cybersecurity in FinTech

            Author

            Cian Fernando

            Published

            5 August 2026

            Estimated Read time

            5Mins

            Cian Fernando, CEO of Aqua Global, explains why as increased sanctions expand, the ability to evidence compliance at the transaction level will soon become a baseline expectation for banks

            From 2028, the top 40 EU banks will face direct scrutiny under the Anti-Money Laundering Authority (AMLA). This development has been a long time coming. Cross-border flows are accelerating, and financial crime is evolving just as fast. The European Banking Authority found in 2025 that 70% of competent authorities report high or rising money-laundering and terrorist-financing risk in the financial sector.

            While the AMLA’s direct supervision will only initially cover 40 banks, the move sends an industry-wide signal. The bar is rising on how banks are judged, how controls are evidenced, and how financial crime frameworks stand up under pressure. These expectations won’t just stop at the largest institutions, but will increasingly shape expectations across the wider market.

            For banks relying on translation tools to bridge the gap between legacy infrastructure and new messaging standards, the clock is ticking. Many legacy systems weren’t designed to store structured AML data or provide end-to-end traceability. When regulators come knocking, fragmented records leave banks exposed to fines, remediation, and reputational damage. Those who wait risk reacting under pressure rather than staying ahead of the curve.

            From Screening to Structured Evidence

            The AMLA was established in 2024 to strengthen and harmonise anti-money laundering and counter-terrorist financing (AML/CFT) supervision across the EU. For years, oversight has been national, creating inconsistencies in assessment and enforcement. AMLA’s mandate is to create greater consistency, convergence and accountability, reducing gaps that financial crime can exploit.

            To support this, European authorities developed a first package of Regulatory Technical Standards (RTS). The RTS specifies standardised data points and criteria that national supervisors will use to assess money-laundering and terrorist financing risk. This involves a three-step process: assess inherent risk, evaluate AML/CFT controls, and determine a residual risk score.

            The decision to directly supervise 40 of the EU’s highest-risk cross-border banks from 2028 marks the next phase. It signals that this harmonised methodology will not remain theoretical, but will be brought into direct EU-level application. The direction of travel is clear. Scrutiny is becoming more centralised, more consistent and more exacting, with less room for interpretation or inconsistency.

            Supervisors will now assess not only the presence of controls but also how effectively those controls are applied and how risk assessments translate into action. Banks must be able to show precisely how decisions were made, who approved them, and what follow-up steps were taken. Without a complete, end-to-end record, it is impossible to demonstrate that controls are effective or that residual risks are properly managed in a defensible way.

            Meeting AMLA’s expectations will be challenging for many banks. Translation tools – used to convert payment message formats to meet evolving requirements – were never designed to capture structured AML verification data or link compliance decisions to individual transactions. They can reformat messages, but they do not embed verification records into the core systems. As a result, critical AML information ends up sat across multiple platforms. Some data remains in the originating platform, some in separate AML systems, whilst workflow decisions may be recorded elsewhere entirely.

            The result is fragmentation. Data is split across systems, teams, and workflows. Critical AML information is scattered, making it difficult – if not impossible – to create a single, coherent view of a payment and its associated AML checks. Under AMLA’s new standards, this kind of disconnected record-keeping will be a major liability.

            Recent enforcement action shows how quickly these weaknesses can translate into real consequences. In July 2025, the Financial Conduct Authority fined Barclays £42 million for weaknesses in financial crime controls, citing failures in customer due diligence and ongoing monitoring. The AMLA can impose similarly strong penalties – up to €10 million or 10% of a company’s annual turnover. Banks that delay upgrading their AML frameworks not only risk steep fines, but lasting damage to client trust should they fall victim to control failures they cannot clearly explain.

            Modernising Payments to Meet AML Demands

            As regulatory expectations tighten, banks face a choice. They can continue layering temporary fixes onto legacy infrastructure, or rethink how compliance is built into payments altogether. Stop-gap solutions may appear cost-effective in the short-term, but they often add complexity, increase operational risk and make evidencing compliance harder over time.

            A more sustainable path is to embed compliance directly into the transaction lifecycle. That means ensuring AML evidence – screening results, verification references and workflow decisions – sits within the payment record itself, creating a clear and defensible audit trail. Payments platforms that integrate seamlessly with AML and sanctions providers can capture responses in real time, reducing delays and manual intervention. They also standardise data across message formats, allowing banks to build transparency into everyday operations. Banks that take this approach will be better placed to meet rising supervisory expectations; those that delay may find short-term workarounds turning into long-term constraints.

            The AMLA Wake-Up Call

            AMLA’s supervision of 40 institutions is a starting gun for banks. As increased sanctions expand, the ability to evidence compliance at the transaction level will soon become a baseline expectation. Banks need to move now to get their ducks in a row and ensure their systems are fit for purpose. Those that embed verification, real-time monitoring, and structured workflows into their core processes will navigate scrutiny with confidence. Those that delay risk chaos – scrambling to trace transactions, facing fines, and suffering reputational damage when the rules inevitably tighten across the board.

            Learn more at aquaglobal.co.uk

            • Cybersecurity in FinTech
            • Digital Payments

            Author

            Dan Brightmore

            Published

            4 August 2026

            Estimated Read time

            4Mins

            Graeme Donnelly, Founder & CEO of 1st Formations, on how small businesses can master cash flow and target growth

            As British small businesses navigate a landscape of rising digital subscriptions and fluctuating utility costs, many are overlooking thousands of pounds in potential savings. While large corporations chase complex R&D credits, many limited companies are often leaving money on the table through sheer habit. 

            In partnership with leading company formation agent, 1st Formations, we have identified 10 of the most effective, yet frequently ignored, financial hacks specifically tailored for UK SMEs. 

            As Graeme Donnelly, founder and CEO of 1st Formations, puts it: “In 2026, operational efficiency is the new profitability. Many directors are so focused on top-line growth that they ignore the ‘silent drain’ of legacy bank fees, incorrect VAT categories, and dormant software subscriptions. Saving £200 a month through smarter digital choices is the equivalent of adding thousands to your annual turnover without the cost of acquisition. It’s about being as lean as you are ambitious.” 

            1. The power of the ‘sector group’ 

            Many directors believe the Federation of Small Businesses (FSB) or local Chambers of Commerce are only for established firms. In reality, the membership fee is often offset by the discounts they provide on business insurance, legal HR support, and software packages like Xero or Microsoft. 

            2. The voluntary VAT advantage 

            If your turnover is under the £90,000 threshold, you aren’t required to register for VAT, but doing so voluntarily allows you to reclaim VAT on everything from stock and web hosting to digital ads. While it does introduce an extra layer of administration, the ability to reclaim input VAT often far outweighs the effort involved. It also levels the playing field when pitching to larger corporate clients. 

            3. Stress-test your small businesses VAT flat rate 

            The VAT Flat Rate Scheme can significantly reduce your administrative burden, but many businesses are stuck in the wrong sector category. Reviewing your percentage, for instance 14.5% for IT consultancy vs. 7.5% for certain retail, could instantly boost your margins. 

            4. Hunt for ‘hyper-local’ micro-grants 

            While national grants get the headlines, local Growth Hubs and Local Enterprise Partnerships (LEPs) often have pots of £500–£5,000 earmarked for digital upgrades or energy efficiency. These are often poorly advertised but easier to secure than larger funds. 

            5. Kill the manual spreadsheet 

            Directors often miss out on tax deductions by forgetting small receipts. Using real-time scanning apps like Dext (for receipts), Pleo (for spending rules), or Expensify (for reimbursements) ensures every deductible expense is captured. Connecting these directly to your bank feed via QuickBooks or Xero turns a year-end headache into a streamlined process and makes bookkeeping easier. 

            6. Look beyond the “big four” banks for funding 

            Traditional loans are increasingly difficult for micro-companies to secure. Modern alternatives like Tide’s Funding Options, Uncapped (for revenue-based growth), or invoice finance platforms like Kriya offer the flexibility SMEs actually need without the long-term fixed commitments many traditional loans carry. 

            7. Ditch the merchant account legacy 

            If you’re still paying monthly rental fees for a card machine, you’re overpaying. Switching to fintech providers, such as Square, Zettle, or SumUp, eliminates fixed monthly costs and integrates sales data directly with your bookkeeping. 

            8. Master the ‘work from home’ deduction 

            If you run your limited company from home, you can charge relevant costs back to the business. These are essential business costs, such as a portion of your broadband bills, mobile phone contracts, and mileage or petrol costs. 

            Whether using the flat-rate method (£6/week) or a formal rental agreement to claim a portion of mortgage interest and utilities, ensure you are documenting this correctly to stay HMRC-compliant. 

            9. The quarterly subscription audit 

            The ‘SaaS drain’ is real. Using tools like Cledara to flag duplicate project management tools or unused licences can shave hundreds off your monthly outgoings. To ensure this doesn’t slip through the cracks, set a recurring calendar reminder to audit your tools every quarter. Always negotiate at renewal; most providers have “retention rates” they don’t advertise.  

            10. Stop paying for basic banking 

            Many small firms are still paying monthly fees for basic banking, something digital-first providers have all but eliminated. Digital-first banks, such as Starling or Monzo Business, offer fee-free banking with superior app integration, saving both money and administrative hours. 

            Learn more at 1stformations.co.uk

            • Digital Payments
            • Digital Strategy
            • Fintech & Insurtech
            • Neobanking

            Author

            Robbie Tilleard

            Published

            3 August 2026

            Estimated Read time

            4Mins

            Robbie Tilleard, GM EMEA at Lorikeet, on why you don’t need a year, a team of twenty engineers, or a bespoke model trained on your data to deploy an AI in CX

            Revolut launched its AI financial assistant in April this year. Starling launched one three weeks before that. An in-app AI financial assistant has become table stakes for FinTechs overnight.

            The good news is you don’t need a year to get there. All you need is a plan and the right data.

            Eight Years or Eight Days                                                                  

            Revolut’s AI assistant took a year from announcement to launch, and Starling describes their assistant as the culmination of eight years of work. Both were building the infrastructure before it existed off-the-shelf. 

            FinTechs we work with deploy an AI assistant in weeks, not months. They don’t build from scratch. They buy infrastructure that’s already solved the hard parts… Knowing when to escalate, handling ambiguity without hallucinating, navigating the tone of a conversation where someone has had their card blocked and is not in the mood for corporate language.

            Sound familiar? It should. Every technology shift in financial services has looked like this for a period. Early movers build bespoke, the rest wait for the case studies, and then someone works out you don’t need to build when you can buy much faster. Then it simply comes down to having the right data in place, connecting your systems, and putting in place a clear implementation plan. Financial services knows the playbook. The difference is that the loop has simply gotten a lot faster with AI.

            Why Not Wait Longer Then?

            Customer expectations have moved, shaped by AI interactions people have outside financial services (support queries answered in seconds, refunds processed mid-conversation), and the gap between ChatGPT responses and waiting for a queued ticket to a human agent is becoming visible in churn data. The FinTechs that move first will set the bar for what customers expect from anyone in the same category.

            There is also a regulatory dimension. In the UK, the FCA moved Consumer Duty into more active supervision this year. The review published in March 2025 found that vulnerable customers continue to receive worse outcomes than other customers, particularly where firms have primarily digital customer journeys, and that most firms have underestimated the depth of outcomes monitoring required.      

            The reaction to AI in many compliance teams looks a lot like the reaction to autonomous vehicles. Every time a Waymo crashes, the genuine safety improvements (fewer accidents overall, faster hazard detection, no fatigue) get less coverage than the single incident. 

            Yet AI is going to help Consumer Duty over time, not hinder it. Autonomy with the right guardrails produces more consistent outcomes than human handling at scale. An AI system can understand a customer’s problem immediately, resolve it on the spot where it can, and route to the right internal team where it can’t, without queue times, without agent fatigue, without twenty different people interpreting the same policy differently. This ensures the core customer care team remains focused and can prioritise the cases with the most need. And this isn’t a far off future for consumers. The infrastructure to build it already exists off the shelf.   

            The European Dimension

            There are additional points to consider if you operate in Europe. The EU AI Act’s full compliance requirements for high-risk AI sees financial services explicitly on the list. 

            Being compliant means documented risk management, transparency mechanisms, human oversight baked into the architecture and a traceable record for every decision that the system makes. The cost of getting this wrong is high: up to €35 million or 7% of global revenue for the most serious breaches.

            Most organisations aren’t close to achieving requirements. More than likely they’ve rolled out AI as an experiment (that’s good!) but without a plan to get it scaled in production (not so good). Systems built purely to retrieve and respond weren’t designed with auditability or explainability in mind and it isn’t something you can bolt on later. 

            If you operate in Europe and have not mapped your current customer experience AI stack against these requirements, the time is now.

            What You Actually Need                                                                                                

            You don’t need a year, a team of twenty engineers, or a bespoke model trained on your data. You need focused use cases, infrastructure that handles the compliance layer, and a team willing to run a tight pilot with clear success criteria and a plan to scale.

            You can move now. The companies that don’t are still lacing up.

            Learn more at lorikeetcx.ai

            • Artificial Intelligence in FinTech
            • Cybersecurity in FinTech
            • Data & AI
            • Digital Strategy

            Author

            Diederik Wirtz

            Published

            31 July 2026

            Estimated Read time

            4Mins

            Diederik Wirtz, Director of Business Development at Aryza, explains why your RCSA data contains far more intelligence than any quarterly heatmap conveys

            Every organisation running a Risk and Control Self-Assessment programme is sitting on a goldmine of intelligence. Most treat it like a compliance filing cabinet. Updated periodically, reviewed by committee, archived until the next cycle. Heatmaps get produced, RAG statuses get refreshed, and everyone moves on.

            Meanwhile, the COO is trying to understand operational fragility, the CFO is modelling downside scenarios, and the CEO is preparing for a board challenge on risk appetite. All receive dashboards that tell them what happened, but almost nothing about what is likely to happen next.

            This is the gap between compliance-grade GRC and enterprise-grade GRC. The difference is whether your risk and control data produces outputs executives can use to make decisions or simply confirms that an assessment process took place. The signals are already in your RCSA data, the question is whether your tools are designed to extract them.

            The Problem with Snapshots

            Traditional RCSA programmes produce point-in-time snapshots. A risk is assessed, a control evaluated, a residual score assigned. But snapshots tell you almost nothing about trajectory, volatility, or the compounding effects of interconnected control weaknesses.

            Consider a risk assessed as “medium” with controls rated “effective” for four consecutive cycles while three of its five mitigating controls have seen their effectiveness scores decline marginally each time. No single assessment raises an alarm. But the trend line tells a different story: gradual erosion that will eventually result in a control environment that can no longer hold.

            This is the slow drift, the first and most fundamental signal organisations miss. It doesn’t show up on a heatmap. But it is visible the moment you analyse RCSA data longitudinally. The executive insight is simple: “This process area has looked stable for two years, but the controls underpinning it are weakening at a rate that will become material within 12 months.” That is enterprise-grade intelligence. A static heatmap is not.

            Correlation Patterns Hiding in Plain Sight

            Most RCSA frameworks assess risks individually within assigned business areas. A control weakness in procurement is evaluated separately from a similar weakness in vendor management. Each looks manageable in isolation.

            But when you map data across domains, clusters emerge. Control effectiveness scores for anything related to manual data handling might have declined across four different business units simultaneously, a pattern revealing a systemic issue invisible to siloed programmes. These cross-domain correlations are precisely what a COO needs when deciding where to invest in process improvement, or a CRO needs when presenting enterprise-wide exposure to the board.

            The Overconfidence Indicator

            When a business unit consistently rates it controls as highly effective while simultaneously reporting rising operational incidents or audit findings in the same risk category, that disconnect is one of the clearest indicators of assessment bias and it is remarkably common.

            The data to identify this gap already exists. RCSA scores sit in the GRC platform. Incident data sits in the loss event database. Audit findings sit in the internal audit tracker. The challenge has always been bringing these datasets together. When you do, the mismatches become immediately apparent and points directly to the areas of your control environment most likely to fail under stress. For an executive, this is not a technical finding. It is a credibility issue.

            RCSA: From Assessment to Simulation

            The real step change comes when you move from descriptive analysis to predictive modelling. Monte Carlo simulation transforms RCSA utility by running thousands of scenarios against your actual assessment data, applying random variation within defined probability distributions to produce a range of probable outcomes rather than a single point estimate.

            The practical applications are directly executive-relevant. In merger and integration planning, simulation allows executives to see the impact of colliding control environments before they materialise. For capital allocation, it reveals which control improvements deliver the greatest reduction in probable exposure, enabling resource decisions based on quantified impact rather than qualitative judgment. For regulatory stress testing, it provides evidence that controls have been tested against plausible scenarios, grounded in operational data.

            Signals Most Organisations Miss

            Several patterns consistently emerge when RCSA data is examined with greater rigour:

            • Concentration risk in control ownership — a small number of individuals responsible for a disproportionate share of highest-rated controls, creating single points of failure invisible to traditional reporting.
            • Assessment fatigue — scoring clustering around previous assessments as cycles progress, indicating the programme may be generating the appearance of oversight rather than reliable information.
            • Phantom controls — controls receiving regular effectiveness ratings that have never been independently tested or are linked to processes that have fundamentally changed since the control was designed.
            • Velocity mismatches — risk categories (cyber, regulatory change) evolving faster than the assessment cycle, monitoring them, signalling where continuous monitoring should replace periodic review.

            Your RCSA data contains far more intelligence than any quarterly heatmap conveys. Monte Carlo simulation, longitudinal trend analysis, and cross-domain correlation mapping turn RCSA from a compliance exercise into genuine executive decision-support. The signals are already in your data. Analysis is what locates them.

            Learn more at aryza.com

            • Cybersecurity
            • Cybersecurity in FinTech

            Author

            Luke Trayfoot

            Published

            30 July 2026

            Estimated Read time

            5Mins

            Luke Trayfoot, Global Head of Strategic Partnerships at YouLend, on how embedded finance can help create a more resilient and inclusive lending ecosystem for SMEs

            Bank‑to‑business lending growth is expected to halve to just 3.5% this year, driven by global tensions, higher funding costs and ongoing economic uncertainty. For many small and medium‑sized enterprises (SMEs), this means tighter credit conditions and a growing risk of a funding gap. And at a time when fast, flexible capital is essential.

            Traditional lenders will remain central to SME finance, but slower lending growth naturally limits how quickly banks can meet demand. SMEs often need capital at short notice to manage cash flow, invest in stock or respond to new opportunities. When bank lending slows, the pressure on these businesses increases. This is where alternative lenders and embedded finance providers are stepping in, offering models that work alongside the traditional system to ensure SMEs still have access to funding when they need it most.

            A New Reality for SME Embedded Finance?

            The slowdown in bank lending does not mean traditional finance is stepping back from the SME sector. UK Finance data shows that while gross SME lending rose for the second consecutive year in 2025, approval volumes have begun to flatten. Meanwhile, average loan sizes are trending downward, which signals that traditional lending capacity is tightening. What this really highlights is the need for a more diversified and resilient lending ecosystem. Alternative lenders, from online credit platforms to embedded finance providers, are becoming an important part of that shift by offering additional, data‑driven routes to capital.

            These lenders are not here to replace banks. They are absorbing the demand that banks cannot meet quickly enough. Especially for SMEs that need fast decisions or more flexible repayment options. By using real‑time business performance data, alternative lenders can assess risk in a more dynamic way and offer funding that reflects how SMEs actually trade, rather than relying solely on historical, static credit files.

            This becomes particularly valuable during periods of economic uncertainty. When traditional credit conditions tighten, SMEs with strong underlying performance can still access the capital they need, which helps prevent stalled growth or operational disruption.

            Platforms as Distribution Hubs

            E‑commerce marketplaces, payment providers and business software platforms are quickly becoming some of the most important channels for SME finance. A trend reflected in the scale of the market, with global embedded finance transactions projected to exceed $7 trillion by the end of 2026. These platforms sit at the centre of day‑to‑day business activity, which gives them a clear view of trading patterns, cash flow and overall performance. Because of that, they are in a strong position to offer funding at the exact moment a business needs it, whether that is during checkout, inside a dashboard or alongside a payment settlement.

            For SMEs, this creates a much smoother path to capital. They can access funding without the paperwork or delays that often come with traditional loan applications. For platforms, it deepens customer relationships and opens the door to new value‑added services that support long‑term loyalty.

            The Infrastructure Powering Embedded SME Finance

            Behind the scenes, specialised FinTech providers supply the underwriting, risk modelling and compliance infrastructure that allows capital to move responsibly through non-financial channels. This infrastructure layer is what enables these business platforms to offer financing without becoming lenders themselves, while ensuring SMEs receive funding that is both fast and sustainable. It’s also creating a more standardised, interoperable foundation for SME credit delivery across the digital economy.

            Government data shows that late payments continue to impact the UK economy to the tune of £11 billion annually, which creates a significant cash‑flow friction. By combining platform‑level insights with advanced risk analytics, embedded finance providers can tailor funding to real‑time business performance. This reduces default risk, improves capital efficiency and strengthens the resilience of the wider SME lending ecosystem, ultimately leading to better outcomes for both SMEs and the capital providers supporting them.

            Smarter Underwriting is Becoming the Backbone of SME Finance

            Real‑time performance data and AI‑enabled underwriting are reshaping SME finance by making funding decisions more accurate, more inclusive and more responsive to how businesses actually operate. Instead of relying solely on traditional credit scores, lenders can assess SMEs based on real trading activity such as sales patterns, seasonality and customer behaviour. This moves underwriting closer to the operational reality of a business, rather than a backwards-looking snapshot.

            This approach opens the door for more businesses to access capital, including those that may be underserved by conventional credit models. It also helps ensure funding decisions remain stable even when market conditions are volatile, supporting business continuity and long‑term resilience. As these models continue to mature, they are becoming a core part of how lenders manage risk, price capital and build a more adaptive SME finance ecosystem.

            Where SME Finance Goes from Here

            As traditional lending growth slows, there is a real opportunity to build stronger partnerships between banks, FinTechs and digital platforms so SMEs can continue to access the capital they need. Embedded finance is becoming a mainstream, complementary channel that broadens access, speeds up decision‑making and supports day‑to‑day business growth.

            The priority now is scaling these models responsibly. That means keeping transparency front and centre, maintaining strong risk frameworks and ensuring long‑term stability as more capital flows through embedded channels. If the industry can combine the strengths of traditional finance with the agility of modern FinTech infrastructure, we can create a more resilient and inclusive lending ecosystem for the SMEs that drive economic growth.

            Learn more at youlend.com

            • Embedded Finance
            • Fintech & Insurtech

            Author

            Koert Grasveld

            Published

            Estimated Read time

            6Mins

            Koert Grasveld, Senior Director – Payments at TerraPay, on why the experience of moving money in the travel industry will continue to converge with the experience of booking travel itself

            When a traveller in Barcelona makes arrangements for their next trip to Bali, the experience feels immediate. A few clicks, a confirmation, and the journey is set. But behind that seamless user interface lies a financial chain of considerable complexity: airlines, online travel agencies, hotel wholesalers, local operators, and payment providers spanning multiple currencies, time zones, and regulatory environments that rarely align neatly with one another.

            The scale of what flows through this system is considerable. UN Tourism estimates that between 1.52 billion arrivals, international tourism generated USD 1.9 trillion in receipts globally in 2025. Each of those journeys generates its own trail of payment obligations: commissions settled across continents, supplier disbursements sent into markets where banking infrastructure varies enormously, refunds processed across multiple currencies and time zones.

            For decades, the infrastructure handling all of this evolved slowly. Payments moved through chains of correspondent banks, accumulating fees and delays at each step. Not only does this result in uncertain settlement times, but payees have to deal with opaque FX markups and the headache of manual reconciliation. The system functioned, but its inefficiencies were absorbed rather than resolved because they were built into margins and managed through workarounds that became institutional habits.

            Today, that model looks completely different.

            The Infrastructure Behind the Booking

            Global travel is, by its nature, a uniquely cross-border industry. For example, in the course of operating an international flight, an airline may be required to settle fees with airport authorities in different jurisdictions, while the process of an OTA reconciling commissions with hotel partners could involve layers of intermediary wholesalers. Even the process behind a tour operator disbursing payments to local guides and experience hosts could take place in multiple markets where banking infrastructure varies enormously

            In all these examples, businesses are forced to navigate the same underlying complexity, just from different points in the chain. As travel volumes rebounded following the pandemic and consumers increasingly moved to booking trips, accommodation, and experiences online, payment volumes grew sharply with them, and the limitations of legacy infrastructure became harder to absorb.

            The core friction is structural. Traditional correspondent banking was designed for large, infrequent interbank transfers, not for the high-volume, lower-value disbursements that characterise modern travel payments. Each intermediary in the chain adds time, cost and increases uncertainty about final settlement amounts, leaving travel finance teams managing a system that was never built for the demands now placed on it.

            The gap between ambition and reality is reflected in the G20’s cross-border payments roadmap, launched in 2020 with the goal of making international payments faster, cheaper, more transparent, and more inclusive. That those goals still feel ambitious reveals the scale of the infrastructure challenge. Data from the BIS and Financial Stability Board show that only 35% of global cross-border retail payments currently settle within one hour, against a target of 75%.

            Faster Payments

            Yet progress is visible where it matters most: central banks across Southeast Asia have been actively interlinking domestic fast payment systems – with the BIS-led Project Nexus. For example, bringing together the central banks of India, Malaysia, the Philippines, Singapore, and Thailand to connect their domestic instant payment systems through a single standardised platform

            What is changing, then, is not simply the speed of individual transactions (though that’s still important). The more significant evolution is the emergence of payment networks that connect directly to local infrastructure – domestic rails, mobile wallet platforms, real-time payment schemes – rather than routing everything through the correspondent banking chain.

            When funds move through fewer intermediaries and settle directly into local systems, outcomes improve across several dimensions simultaneously: settlement becomes more predictable, cost structures become clearer, and payment data can travel with the transaction rather than being lost along the way. For travel companies with supplier networks across dozens of markets, that combination has direct consequences for the commercial relationships at the heart of the business. Suppliers who receive payments reliably and with clear remittance information tend to be more flexible partners, and that trust has real commercial value in an industry where supplier relationships frequently determine product availability and preferential terms.

            Beyond the Bank Account

            Perhaps the least visible dimension of this evolution, from a corporate finance perspective, is the growing importance of mobile wallets as a payout endpoint. In many of the emerging markets that represent the travel industry’s most significant growth corridors – across Southeast Asia, sub-Saharan Africa, and parts of Latin America — mobile money infrastructure has developed ahead of, or independently from, the traditional banking system.

            McKinsey has characterised Southeast Asia as a “wallet-first” region, where more than six in ten people remain unbanked yet smartphone penetration is high and wallet adoption is accelerating across urban and rural markets alike.

            For travel companies expanding into these corridors, the ability to disburse to wallet endpoints determines whether payment can reach local partners in a form that is immediately usable. The World Bank estimates that remittance flows to low- and middle-income countries reached USD 685 billion in 2024 — surpassing both foreign direct investment and official development assistance combined, and illustrating how consequential payment access is for communities whose incomes depend on cross-border flows. The same logic applies, at a different scale, to the independent operators and service providers who form the supply base for much of global tourism.

            The Collaborative Architecture of What Comes Next for Payments

            The transformation underway in cross-border travel payments is not the work of any single institution or technology. Instead, it reflects a convergence of regulatory intent, infrastructure investment, and commercial innovation that is gradually reshaping how money moves across borders. And the direction, even where the pace remains uneven, is clear. Payment networks, banks, and fintech providers building the next layer of cross-border infrastructure are doing so with interoperability and reach as design goals, creating conditions in which the geographic and institutional barriers that have historically constrained travel payments are eroded rather than simply worked around.

            For travel companies, this evolving infrastructure represents an opportunity to reconsider payment operations not as a cost centre to be minimised, but as a capability that can differentiate supplier relationships, support expansion into new markets, and provide a clearer picture of financial performance across a complex global business.

            In the years ahead, as real-time payment adoption expands across emerging markets, as mobile wallet networks deepen their reach, and as data standards improve the quality of information travelling alongside transactions, the experience of moving money in the travel industry will continue to converge with the experience of booking travel itself: faster, more transparent, and far more connected to the destinations it ultimately serves.

            Learn more at terrapay.com

            • Digital Payments
            • Neobanking

            Author

            Nell Walker

            Published

            Estimated Read time

            3Mins

            Ivalua finds it takes 25 days to replace a failed supplier, with 74% of businesses left exposed to shortages or disruption in that time

            A new report from Ivalua, the enterprise AI platform for procurement, has found that in the past 12 months, 70% of organisations have had between one and five critical suppliers fail, while 41% say they’re one supplier failure away from a supply chain crisis.

            Ivalua’s report – ‘Building Resilience from the Ground Up: Anchoring Supplier Risk in a Unified System of Record’ – surveyed 800 supply chain and procurement decision makers. It found that organisations take 25 days on average to identify and onboard a replacement supplier, during which 74% are exposed to shortages or significant operational disruption. More concerning still, 57% of organisations admit they are reacting to these events rather than preventing them.

            Reactive behaviours to disruption mean that much of this supply chain fragility goes undetected until it is too late. Over half of organisations (53%) have little to no visibility into the cybersecurity posture of their suppliers, and 51% admit that a major supplier cyber incident would catch them entirely by surprise. Other areas of limited visibility include ESG performance and risk (53%), financial health (44%), operational capacity and delivery performance (41%), and compliance status, including certifications and regulatory requirements (40%).

            “Supplier failure is no longer an exception; it is a cost of doing business. The organizations suffering the most aren’t necessarily those with weaker suppliers, but those blind to risks until it’s too late,” says Jarrod McAdoo, Director at Ivalua. “With inflation, tariffs, and rising costs already stretching operations, a single supplier failure can be the tipping point that pushes an already strained supply chain into collapse.” 

            Closing the visibility gap

            Organisations are also grappling with fragmented data, making it difficult to identify risks before they escalate. The findings show that 45% of organisations have no single, trusted view of supplier risk across the business. Crucially, 76% still rely on manual processes for due diligence and risk checks on critical suppliers, with 58% agreeing that their reliance on spreadsheets leaves them exposed to human error. As a result, blind spots remain: over half (53%) report low or no visibility into their sub-tier suppliers, and almost three quarters (73%) say they would like to spot supplier distress earlier to prevent disruption.

            AI plays an important role in helping to predict and holistically plan for supplier failure, but only 39% of organisations are currently using AI in supplier due diligence or risk monitoring. Further, AI’s potential is limited by the quality of underlying data, with 50% agreeing that their supplier data is not AI-ready, limiting their ability to scale analytics and surface risk early.

            “The goal is not to predict supplier failure. That is a pipe dream, considering the complexity of modern global supply chains. The goal is to build the capability to anticipate it by spotting early signals and to be ready to act on contingency plans with confidence,” says McAdoo. “AI can make a real difference, but is only as effective as the data it’s working with. Organisations without a strong, unified supplier data foundation will struggle to deliver meaningful value from AI investment, regardless of how sophisticated the tools are. The organisations that fix this will be the ones still standing when future disruption hits.”

            Find out more about how to build a proactive and anticipatory approach to supplier risk management in the full report.

            • Collaboration & Optimization

            Author

            Errol Rodericks

            Published

            29 July 2026

            Estimated Read time

            5Mins

            Errol Rodericks, Director of product marketing EMEA and LATAM at Denodo, on why the future of insurance will be defined by whether humans and AI agents have the right data, in the right form, to make the right decisions

            AI adoption across the UK insurance sector continues to accelerate, yet the impact is falling short of the industry’s expectations. The narrative is now shifting beyond systems that analyse data and generate recommendations towards agentic AI. Capable of taking action, whether that means initiating claims processes, identifying potential fraud in real time, refining underwriting decisions or triggering next-best actions. Many see this as the next major leap forward for insurance, and they may be correct, but not for the reasons most people think.

            While AI development certainly doesn’t lack pace, most of the insurance industry remains constrained by a more fundamental issue. AI cannot make trusted business decisions until it first understands the business. That understanding depends on trusted, relevant data being delivered in the right context and at the right time. Without it, insurers will struggle to give AI systems meaningful autonomy, however advanced the models become.

            This is not because insurers lack data or technology. Over the past decade, the industry has invested heavily in data lakes, advanced analytics and AI tools, and integration and data engineering pipelines. Yet despite these investments, many organisations continue to struggle to move AI beyond proof-of-concept projects and into day-to-day operations.

            Meeting the Challenge

            The challenge is not data availability. It is ensuring data is trusted, relevant and readily accessible when decisions need to be made.

            Few industries experience this challenge quite like the insurance industry. Decision-making depends on information drawn from multiple policy, claims and customer systems, alongside external data sources such as telematics, weather and credit data. When that information is fragmented or delayed, even the most advanced AI systems are left working with an incomplete picture, making it difficult for AI to understand the wider business context behind every decision.

            Agentic AI does not solve this problem. It exposes it.

            Access Alone Is Not Enough

            Many organisations respond by building a shared data foundation consisting of a unified layer where humans and AI agents can access the same information. While this is directionally right, it is incomplete. The challenge is not that organisations lack a shared data layer. They struggle to deliver the right version of data for each decision, at the moment it matters.

            Insurance operates on multiple, decision-specific views of data, each with distinct requirements:

            • Claims decisions depend on real-time, enriched incident data.
            • Underwriting relies on forward-looking risk models and external signals.
            • Fraud detection requires cross-entity patterns and behavioural analysis.
            • Customer servicing depends on a simplified, current policyholder context.

            These are not variations of the same dataset; they are purpose-built representations of data, shaped by different latency, governance, and semantic needs, which becomes even more critical with agentic AI. Different agents operate at different points in the decision lifecycle, and require different data, in different forms, at different times.

            A shared data layer can improve access to information, but access alone does not guarantee better decisions. Context transforms data into business understanding, and business understanding is what enables trusted decisions.

            Turning Data Into Action

            This is where many AI strategies stall. Most architectures are designed to store, process, and analyse data, but not to activate it at the point of decision. There is a fundamental gap between data being available and data being usable within real-time workflows.

            Agentic AI operates directly in this gap. Without access to live, governed, and contextually aligned data, agents operate with partial understanding, and their outputs become unreliable. This is why many AI initiatives remain stuck in experimentation.

            To move forward, insurers need to rethink how data is delivered. Not as raw datasets or reports but as data products. A reusable, governed, and outcome-aligned data asset designed to support a specific decision or workflow is what’s needed. Instead of exposing raw data, insurers should deliver contextualised, decision-ready views, with embedded governance and policy controls, consistent business semantics, and real-time access to internal and external sources.

            For example:

            • A claims data product unifying FNOL, policy data, repair estimates, and external signals.
            • A fraud data product combining claims history, network relationships, and behavioural indicators.
            • An underwriting data product integrating internal risk data with third-party enrichment.

            These are not static datasets. They are decision-ready data assets, designed to deliver the right information, in the right context, for a specific business outcome.

            Why Data Quality Matters Now More Than Ever

            For agentic AI to deliver value, data must be live, governed at access, semantically consistent, and traceable. This is where a logical data layer becomes critical, not just as an integration approach, but as a way to connect distributed data in real time, apply governance dynamically, and deliver consistent, business-ready views across systems. This enables both humans and AI agents to act with confidence, without introducing further fragmentation.

            The insurers that lead in 2026 will not be those with the most advanced models. They will be the ones that connect AI directly to business outcomes. That means starting with the outcome, such as reducing claims cycle time, improving fraud detection, increasing underwriting precision, or enhancing customer experience, and working backwards to define the decisions, data and systems required to support them.

            This is how AI moves from experimentation to operational impact.

            Where AI Initiatives Succeed or Fail

            The next phase of AI in insurance will not be defined by advances in model performance. It will be determined by how well AI understands the business it is supporting. That requires trusted, relevant data, but it also needs the business context that allows AI to interpret that data correctly and make decisions insurers can trust.  

            Agentic AI accelerates this realisation. It makes clear that data must be trusted, contextual, available at the moment of decision, and aligned to outcomes. The goal is not simply to give AI more information. It is to help AI understand how the business works well enough to make confident, consistent decisions. Those who solve this will scale AI successfully, and those who do not will continue to pilot without transformation.

            The future of insurance will not be defined by whether humans and AI agents share the same data. It will be defined by whether they have the right data, in the right form, to make the right decisions. That requires a shift from shared data to decision-ready data, from access to activation, and from experimentation to measurable outcomes. The real inflection point for AI in insurance will come when organisations enable AI to understand the business well enough to make decisions that customers, employees, and regulators can trust.

            Learn more at denodo.com

            • Artificial Intelligence in FinTech
            • Data & AI
            • InsurTech

            Author

            Dan Brightmore

            Published

            22 July 2026

            Estimated Read time

            9Mins

            CPOstrategy spoke with some of the key speakers and leaders in attendance at DPW in New York… From AI and operating models to talent and strategic influence, procurement leaders gathered with a shared message: the profession isn’t simply adopting new technology, it is redefining what procurement itself should become

            The theme of DPW New York 2026 – Recode – could hardly have been more appropriate. Across conversations with procurement leaders representing industries as diverse as financial services, media, pharmaceuticals, luxury retail, technology, industrial manufacturing and commercial real estate, one message emerged repeatedly: procurement is entering the most significant period of transformation in its history.

            Artificial intelligence may dominate the headlines, but for practitioners leading transformation programmes inside global organisations, the conversation has already moved beyond the technology itself. Instead, attention is turning towards operating models, organisational design, talent, governance and the role procurement will play as businesses navigate increasingly complex commercial environments.

            Rather than viewing AI as the destination, many see it as the catalyst for fundamentally redesigning procurement.

            For several of the speakers CPOstrategy spoke to, ‘Recode’ wasn’t simply about software, it was about rethinking the function from the ground up.

            Rina Patel, SVP – Chief Procurement Officer at Versant Media, described the company’s recent spin-off from Comcast NBCUniversal as an opportunity to reshape procurement into a trusted advisory function involved in deciding whether initiatives should happen in the first place, rather than simply executing purchasing decisions.

            Others echoed that sentiment. Danielle Salyers, VP for Strategic Sourcing & Enterprise Contracting, at Allied Solutions, who has built procurement organisations from scratch on multiple occasions, viewed the theme through the lens of establishing entirely new operating models. At Semrush, VP for Global Procurement Alejandro Fernandez is living through a different kind of recode as the company’s procurement organisation becomes integrated into Adobe, forcing a rethink of how teams, processes and technologies come together.

            Despite the different business contexts, the conclusion was remarkably consistent: procurement’s traditional operating model no longer reflects the realities of modern business.

            Beyond Cost Savings

            If there was one topic that united almost everyone we spoke to, it was the growing belief that procurement has outgrown its historical identity as a cost-cutting function.

            Savings remain important, but few leaders now see them as sufficient for measuring success.

            Patel argued that procurement should instead be judged on whether supplier decisions generate genuine business value, improve return on investment, enable revenue and support broader organisational objectives. Salyers similarly described cost savings as an output of procurement rather than its defining purpose, pointing instead to resilience, supplier strategy, risk management and business enablement as the measures that matter most.

            Mercury’s Head of Procurement Bobbi Bachynski perhaps captured the changing mindset most directly. Working in a fast-growing fintech business, she sees procurement less as a savings function and more as business development tool, measuring success through the speed at which the organisation can launch products, enable teams and support growth.

            That broader definition of value reflects the changing pressures organisations face today.

            From geopolitical instability and tariffs to ongoing supply chain disruption, procurement has increasingly become central to helping organisations manage uncertainty. Angela Zou, Chief Procurement Officer at Cushman & Wakefield, noted that global disruption has elevated procurement’s importance within executive discussions, while Marc Behring, Director – Procurement Excellence at Messer Americas, explained that procurement’s ability to respond quickly to changing market conditions has become just as valuable as negotiating favourable contracts.

            The profession is steadily moving away from being measured purely on how much money it saves towards how effectively it enables organisations to operate.

            AI Starts with Better Processes

            Despite the excitement surrounding artificial intelligence throughout DPW, one point generated almost universal agreement. AI cannot repair broken procurement.

            Again and again, the executives we spoke to stressed that organisations must first simplify processes, improve governance and clean their data before expecting technology to deliver meaningful transformation.

            Fernandez described AI as a magnifying glass that simply exposes existing weaknesses. Deploying sophisticated technology on top of poor-quality processes only embeds those problems more deeply into the organisation.

            Others used even stronger language. Salyers warned that “if you automate chaos, you just get chaos faster,” while Zou argued organisations must “clean house before you automate or optimise”. Bachynski cautioned against allowing excitement around AI to distract organisations from building strong procurement foundations, preferring what she described as a measured and intentional approach rather than chasing hype.

            Several leaders also highlighted the importance of data quality.

            Behring believes that process and data remain the essential foundations for successful AI adoption because intelligent systems can only generate meaningful outcomes when they are built on structured, consistent information. Without that foundation, organisations risk investing significant time and money only to conclude that AI itself has failed, when the real issue lies in poor underlying processes.

            The message from DPW was clear: successful transformation begins long before organisations deploy their first AI agent.

            Humans Remain at the Centre

            Although AI dominated almost every conversation, none of the procurement leaders we spoke to suggested technology would replace human expertise. Instead, the future appears to be one of partnership.

            Routine, rules-based and transactional work is increasingly expected to migrate towards automation, freeing procurement professionals to focus on judgement, creativity, negotiation, supplier relationships and strategic decision-making.

            Fernandez believes relationship management and stakeholder trust remain fundamentally human activities. Salyers described the ideal future as automating repeatable work while preserving human involvement wherever judgement, ethics and commercial trade-offs matter.

            Sandeep Dhar, Senior Director – Center Global Category Management Leader at Johnson & Johnson, sees the operating model evolving into one where AI agents perform tactical activities while procurement professionals concentrate on strategy. Sean Park VP – Procurement, AP & Transformation at Arm similarly views AI less as a replacement than as a decision-support capability, enabling procurement teams to analyse suppliers, identify strategic partners and generate insights more rapidly than traditional reporting tools allow.

            A Different Kind of Procurement Professional

            As technology changes, so too does the profile of the procurement professional.

            Several of the leaders we spoke to suggested that tomorrow’s procurement teams will require fundamentally different capabilities from those traditionally associated with category management and sourcing.

            Fernandez believes the rise of large language models will reduce dependence on narrow category specialisation, instead rewarding analytical thinkers, creative problem-solvers and builders who are comfortable experimenting with new technologies.

            Behring agreed that procurement professionals will increasingly need to understand how AI works, develop stronger technology skills and continuously adapt as new capabilities emerge. For him, the challenge extends beyond technology itself to identifying what the future procurement skillset should actually look like.

            That evolution also requires organisations to rethink how they develop talent. Zou spoke about freeing procurement professionals from routine work so they can build stronger judgement and advisory capabilities, while Dhar argued that curiosity, continuous learning and asking better questions will become defining characteristics of successful procurement leaders.

            Several executives also highlighted the importance of confidence. Patel encouraged procurement professionals at every level to become bolder, understand the businesses they support more deeply and build the commercial credibility needed to influence decision-making. Rather than waiting to be invited into strategic discussions, procurement leaders should demonstrate why they belong there by speaking the language of growth, revenue and business outcomes.

            Technology is Changing, but Leadership Matters More

            Despite AI dominating conference agendas, many leaders suggested that technology alone will not transform procurement.

            Leadership remains the decisive factor. Gary Levitan, VP – Head of Procurement at Louis Vuitton, argued procurement earns influence through a compelling vision rather than software. Digital tools may strengthen procurement’s ability to execute, but they do not secure executive credibility on their own.

            Others made similar observations. Several leaders stressed every organisation faces different priorities, making it dangerous simply to copy another company’s technology roadmap. Bachynski warned against benchmarking blindly, arguing that procurement functions should be designed around the unique commercial realities of their own businesses rather than following industry trends.

            For Park, transformation succeeds when stakeholders understand the entire journey rather than experiencing disconnected process changes over time. Bringing employees into the transformation early, explaining the bigger picture and managing change carefully are just as important as selecting the right technology.

            That emphasis on governance, communication and organisational change repeatedly surfaced throughout our conversations. AI may accelerate procurement, but successful implementation still depends upon people understanding, trusting and adopting new ways of working.

            Why DPW Matters

            While technology formed the backdrop to almost every conversation, many attendees described the greatest value of DPW as something far more human. Connection.

            Time and again, these leaders spoke about the opportunity to compare experiences with peers facing similar challenges, distinguish genuine innovation from marketing hype and learn from successes as well as failures.

            Fernandez described the event as an opportunity to separate “signal from noise” by hearing directly from practitioners rather than relying solely on vendor demonstrations. Zou highlighted the value of seeing collaborative customer and supplier transformation stories while building relationships with peers on similar journeys.

            For Bachynski, DPW represents procurement’s “Super Bowl”. Not because of the product demonstrations, but because it creates opportunities for honest conversations about what is really happening inside organisations. Those candid exchanges help procurement leaders avoid repeating mistakes while accelerating successful transformation.

            Others shared similar experiences. Behring pointed to the importance of understanding how peers are deploying AI and evaluating emerging technologies, while Park noted discussions with fellow procurement leaders often proved just as valuable as conversations with software providers. Dhar reflected on DPW’s rapid growth over recent years as evidence that procurement itself has become a far more influential discipline than it once was.

            Perhaps the strongest message came from Salyers, who described DPW as a place where people challenge conventional thinking, inspire one another and return to their organisations better equipped to lead change.

            Procurement’s Defining Moment

            Although each executive represented a different organisation, industry and stage of transformation, they revealed an unusually consistent picture of procurement’s future.

            The function is no longer content with measuring success purely through cost reduction. Instead, procurement leaders are redefining their role around business enablement, resilience, commercial insight, supplier innovation and enterprise growth.

            Artificial intelligence will undoubtedly accelerate that evolution, but only where organisations first establish strong processes, clean data and effective governance. Automation will increasingly handle transactional work, allowing procurement professionals to concentrate on the uniquely human capabilities that technology cannot replicate: judgement, relationships, creativity and strategic thinking.

            The challenge now is not simply adopting AI. It is recoding procurement itself.

            That was the unmistakable message running through DPW New York 2026. The organisations that succeed will not necessarily be those deploying the greatest number of AI tools. They will be those prepared to rethink operating models, develop new skills, redefine success and position procurement as an indispensable strategic partner to the business.

            For a profession long associated with purchasing and cost control, that represents a profound shift. If the conversations at DPW are any indication, procurement’s next chapter has already begun. And this time it is writing the code rather than simply following it.

            Read more of our DPW coverage in the latest issue of CPO Strategy

            Author

            Dan Brightmore

            Published

            Estimated Read time

            4Mins

            Russell Gammon, Chief Innovation Officer at Alphatax, on why tax needs to operate on a shared foundation where information can flow and be updated consistently

            The modern CFO has more responsibilities than ever before, with a 2024 study finding that over 80% had taken on additional demands in the previous two years. As the report points out, “CFO is not a finance role. It is a strategic business role whose mandate is finance.”

            With multiple priorities competing for time and attention, tax sits at the centre of many of the metrics CFOs are accountable for, from cash flow and risk exposure at the operational end of the scale to corporate reputation at the strategic level. Tax functions are under growing pressure to meet these expectations, with workloads increasing even as resources and budgets in many organisations remain flat or decline.

            Under these conditions, tax teams are forced into a reactive, deadline-led operating model, and it’s understandable that work is completed in cycles rather than as part of an ongoing, connected process that continuously inputs into business decisions. The problem this creates, however, is that it perpetuates a clear disconnect between the undeniable importance of tax and the role it can and should play in day-to-day financial management.

            A Taxing Set of Problems

            It’s a situation that has evolved over a considerable period of time. For instance, most CFOs progress through finance-led career paths, with limited exposure to the depth and breadth of tax as a defined discipline.

            In addition, tax is highly specialised and fragmented. It covers multiple complex, often interrelated requirements, each with its own processes and requiring access to specific subject matter experts. Even within tax teams, knowledge is distributed across individuals.

            Tax also operates in a context where interpretation is often required, and clear-cut answers are not always immediately available. Bring these issues together, and it’s hardly surprising that many organisations view tax as a risk-sensitive function that must exist in its own bubble, rather than as one integrated into broader financial strategy or front and centre for the CFO.

            The many and varied supporting technologies used across the tax function inevitably reflect this fragmentation. A large, expanding list of multiple-point solutions is used to address specific requirements rather than to provide a unified view of the tax function. This means tax data is almost inevitably spread across disparate systems and teams, and as any tax professional knows, the potential for inconsistencies and errors is ever-present.

            Much of the work required to manage this complexity remains manual, and even where integration exists, insights are often buried within compliance outputs rather than being fed back into planning or decision-making.

            Empowering the CFO

            For CFOs, this situation almost inevitably draws them towards retrospective processes and analysis, when what they need is forward-looking insight. In practical terms, this reinforces a cycle of reactive intervention and increases the likelihood that opportunities to improve tax outcomes are missed.

            This is increasingly at odds with the role CFOs are expected to fulfil, particularly given the emphasis on agile decision-making and the high levels of accountability that come with the job. How, for example, can a CFO be expected to make the best decisions about business expansion or investment when they do not have a clear, connected view of the organisation’s tax position?

            Addressing these challenges should start with a commitment to break down the process, expertise and information silos that have historically defined the tax function; away from managing tax as a series of separate activities and towards a more integrated approach.

            Tax needs to operate on a shared foundation where information can flow between different areas and be updated consistently. CFOs should be empowered with a coherent view of the organisation’s tax position and what is happening at any given point in time, without relying on the need for remedial work or retrospective reporting.

            With better, integrated visibility at their disposal, the CFO can shift their focus towards strategic planning and opportunities, while the tax team can also address potential risks before they escalate.

            Data Insights

            A key part of this approach is making better use of the data generated through compliance processes, treating it not simply as an output but as a source of insight that can also be fed back into planning and forecasting.

            Clearly, technology has an important role to play in facilitating this transformation, particularly in reducing the manual effort associated with routine tasks and improving the accessibility of data across the organisation. Don’t forget, the objective is not to remove human judgment from tax, but to ensure that specialists can focus their time on supporting the wider business.

            In this context, the tax function takes on a different, more strategic role, with CFOs empowered to draw it into their decision-making processes rather than engaging with it only at the point of reporting, as so many still do today.

            Learn more at alphatax.com

            • Artificial Intelligence in FinTech
            • Digital Payments

            Author

            Hugh Scantlebury

            Published

            Estimated Read time

            5Mins

            Hugh Scantlebury, CEO and Founder of Aqilla , on why the finance teams that benefit most from AI will likely be the ones that strike the right balance between automation and oversight

            At first glance, finance and accounting appear to be ideal environments for AI integration. The work is structured, rules-driven and built on numerical data, which AI can automate and process at scale and speed. As such, it’s ideal for much of the repetitive work — such as invoice capture, reconciliations, reporting, and anomaly detection. These tasks still consume so much time and reduce space and capacity for strategic and creative thinking.

            But as confidence in AI grows across the sector, and as the technology is integrated into accounting and finance software, is there a risk that organisations once fearful of the technology may go to the other extreme? What might happen if they lean so deeply into AI that finance and accounting professionals become distant and removed from the numbers?

            These questions matter because the decisions that sit behind the numbers are rarely driven by pure logic. Financial strategy is shaped by risk appetite, leadership judgement, organisational priorities and sometimes even internal politics.

            So many entrepreneurial success stories include a fateful risk, gamble or moment of inspiration that defies established financial wisdom. A founder deciding whether to invest in growth or a charity balancing financial sustainability with its mission does not rely on numbers alone. That means a surprisingly high number of commercial decisions have a human dimension that AI cannot, and arguably should not, replace.

            Balancing AI and Human Strengths

            So how can organisations maintain the human intuition and instinct that sits behind so many corporate transactions while embracing AI? The answer is to use the technology to remove repetition rather than people from core financial processes. In practical terms, that means using automation to reduce cognitive load so people can focus on interpretation, creativity and decision-making — the human parts of finance and accounting. 

            That’s a sensible approach because poorly implemented automation accelerates errors and obscures decision-making processes. It can also cut humans out of the process exactly at the point where instincts and experience are most needed. In that situation, AI-enabled systems simply become faster at making poor decisions. And those poor decisions end up costing time as well as money.

            Once that happens, the space, time and resources that finance and accounting leaders are trying to create for more strategic work will rapidly shrink. By contrast, when humans actively guide AI systems, review outputs and set boundaries, automation becomes a powerful extension of human capability rather than a substitute for it.

            The Limits of Automation in Decision-Making

            In finance and accounting, numbers often create a sense of objectivity and certainty. But financial reporting still involves interpretation, context and judgement. A technically correct output is not always the same thing as the right commercial decision for a business, its employees or its long-term strategy.

            As such, it’s important that accounting and finance professionals at every level do not lose a grip on their data when engaging with AI and automation. Aside from losing an understanding of the systems and processes behind the outputs, there’s still an ethical responsibility to deploy AI in a way that preserves human oversight, authority and compliance in financial reporting. That’s because, for the first time, we’re asking technology, in the form of AI, to provide an opinion on our data — not just deliver the logic and the numbers.

            For that reason, finance leaders still need visibility into how AI-generated outputs are reached, the ability to challenge them when necessary, and a clear understanding of the underlying data behind the results. That’s important because AI output is ultimately based on prediction, and prediction is not the same as established and quantifiable truth.

            Keeping Score 

            One possible middle ground is confidence scoring and validation workflows. Rather than unquestioningly trusting every AI-generated output, organisations can introduce processes that flag lower-confidence results for human review before action is taken. That creates a more balanced relationship between automation and oversight, while also giving finance teams clearer visibility into how reliable or complete AI-generated outputs actually are. The goal should be confidence in AI-supported workflows, not unquestioning reliance.

            That visibility, however, should not sit solely with grads and junior finance staff. They need to understand the manual calculations and processes sitting behind automated systems so they can properly challenge the results. But the same principle applies at senior levels too. Experience and seniority should not create distance from the underlying logic behind the numbers. If anything, AI makes that visibility even more important.

            Otherwise, organisations risk creating the worst of both worlds: juniors who can’t challenge AI outputs because they haven’t learned the manual processes, and complacent seniors who know the manual systems and assume AI is following them. It means organisations can end up with more information at their fingertips than ever before, while simultaneously becoming more detached from the underlying data and logic behind it.

            That risk becomes even more significant at senior levels, where financial decisions often carry wider operational, commercial and strategic consequences. The more detached leaders become from the logic behind the outputs, the greater the impact when reliability or credibility issues emerge within the data.

            Conclusion

            Productivity gains from AI and automation have the potential to create more space for higher-value thinking rather than remove people from the process. The information AI surfaces can ultimately help senior leaders make more creative and strategic decisions by revealing connections, patterns and insights that may previously have remained hidden within the data. 

            Leaders have always relied on summaries, dashboards, and reporting layers to help them make decisions. But AI dramatically widens the gap between decision-makers and the operational reality beneath the numbers. The danger is not simply inaccurate data. It’s overconfidence. When systems appear highly intelligent and highly efficient, organisations can gradually stop questioning how conclusions are reached in the first place.

            Ultimately, organisations may wish to focus on people-led automation rather than handing complete control to systems. The goal should be confidence in AI-supported workflows, not unswerving reliance on automated outputs — with AI acting as an extension of human capability rather than a replacement for judgement.

            The finance teams that benefit most from AI will likely be the ones that strike the right balance between automation and oversight. Hand over the repetition and the manual processing, but don’t lose visibility into the data itself. Because if organisations surrender that understanding completely, all the entrepreneurial instinct, creativity and commercial judgement in the world may no longer be enough to compensate for the decisions being made underneath them.

            Learn more at aqilla.com

            • Artificial Intelligence in FinTech
            • Data & AI

            Author

            Nell Walker

            Published

            2 July 2026

            Estimated Read time

            3Mins

            Ivalua finds businesses are caught in a skimpflation sandwich, quietly trading down on quality for consumers while their own suppliers do the same to them

            A new report from Ivalua, the enterprise AI platform for procurement, has found that 52% of businesses say cost pressure is driving ‘skimpflation’ in their supply chains – quietly trading down on component or ingredient quality to protect margins. In the UK, this rises to 64% – the highest of any market surveyed.

            The report finds businesses are struggling to balance cost, risk and resilience. Constrained by manual processes and limited supplier visibility, the fastest route to saving is skimpflation. This is happening on two fronts: businesses are either reworking product specifications to bring the cost of goods down (59%) or switching to cheaper suppliers or goods outright (36%). The savings, however, rarely reach the consumer. Among businesses that made those changes, just 10% cut their prices, while 43% raised them and 47% left them unchanged. The result is a quiet trade-down where shoppers pay the same, or more, for products and goods made to a lower standard.

            The squeeze is showing up elsewhere too, as more than half (53%) of businesses experienced delayed product launches, likely tied to supplier quality issues, poor collaboration or missed warnings of disruptions. And consumers aren’t the only victims of the scourge, as 49% of businesses are seeing skimpflation from their own suppliers.

            “Skimpflation is the silent inflation. It never shows up in the headline CPI figure but consumers feel it every time something breaks sooner, wears thinner or runs out faster,” says Alex Saric, Smart Procurement Expert at Ivalua. “But short-term cost and quality decisions can conceal long-term risk. Businesses spend years building brand loyalty, then a single reformulated product or cheaper component undoes it. The firms cutting hardest today aren’t just protecting margins. They’re borrowing against their reputation, and that debt comes due the moment consumers notice the difference.”

            Brittle foundations behind the squeeze

            Businesses are also rethinking where they buy from as cost pressure, shortages and trade disruption reshape supply chains. Over the past year, organisations have replaced, reduced or exited suppliers across every major region, including Eastern Europe (44%), China (43%), Western Europe/UK (40%) and The Middle East (32%).

            Supplier cost inflation (33%) ranks as the biggest trigger for these regional shifts, closely followed by shortages of critical components (32%) and efforts to reduce tariff and trade exposure (30%). The pressure of managing a supply base in constant flux across every major region is forcing businesses to make decisions faster than traditional processes allow.

            To address this, 76% of organisations are using or experimenting with AI, with a further 14% planning to adopt it. Among those already using it, 89% say it has been effective at identifying and qualifying new suppliers and sourcing hubs. The catch is that almost half (44%) admit their supply chain data isn’t AI-ready. Until that gap closes, AI won’t fix supply chain problems, it will expose them.

            “When businesses can’t see across their supply chain, cost pressure forces their hand. They cut components, switch to cheaper suppliers and hope customers turn a blind eye,” concludes Saric. “AI changes that by giving procurement teams the visibility to find savings and qualify suppliers before quality problems reach the customer. But it only works on clean, connected data. Run it on spreadsheets and scattered systems and it doesn’t surface better decisions – it just makes the wrong ones faster. Get the foundations right and businesses stop having to choose between protecting their margins and protecting their customers.”

            • Risk & Resilience

            Author

            Apurv Gupta

            Published

            26 June 2026

            Estimated Read time

            6Mins

            Apurv Gupta, Head of Financial Services Industry at independent management and technology consultancy, BearingPoint, explains that while insurers have modernised underwriting and pricing, broker management remains manual – but must react to the impact of AI solutions

            The UK insurance industry has made significant strides in modernising almost every function it touches. From actuarial pricing and underwriting analytics to claims handling and customer engagement, through data and, increasingly, AI. Distribution, however, hasn’t had the same focus. This is not through a lack of ambition. But rather a longstanding assumption that because broker management is fundamentally a relationship business, the only way to change it is to continually focus on those relationships.

            That assumption deserves revisiting. In a world increasingly shaped by AI, insurers with the most up-to-date intelligence on their brokers, including insights into how and when to engage with them, will be best placed to capture a disproportionate share of broker-led business. By combining information from multiple sources, these insurers can take a more informed and targeted approach to distribution.

            The signals from the UK market are worth paying attention to. A landscape where 83.5% of commercial premium is broker-led. The broker channel intermediates £10.8bn in commissionable revenue (growing 9.4% YoY in 2025). Meaning insurers’ addressable distribution pool is potentially expanding faster than their ability to manage it. This suggests that a more proactive, data-informed approach is not just advantageous, it is becoming essential.

            Insurers Are Relying on Historic Data, Intuition, and Inconsistent Strategies

            Across the UK market, distribution leaders often don’t have a clear view of which of their top 50 brokers are likely to grow their portfolios in the next 12 months. Or where their biggest opportunities lie. Equally, assessments of which brokers are at risk of moving business to competitors tend to rely on anecdote rather than evidence.

            The issue isn’t that insurers lack data — it’s that their broker intelligence is fragmented, inconsistent and disconnected from outcomes. As a result, growth and retention decisions are driven by intuition or the history of the relationship.

            The consequences are predictable and measurable. CRM systems log activity, not insight. They record that a key account manager visited a broker; they do not tell you whether that visit changed anything. What insurers need is a single, structured, real-time view of broker performance, pipeline and engagement — enabling truly data-led distribution management.

            What broker management capabilities exist in the UK Insurance Market today?

            The UK insurance market is already looking toward the next generation of broker distribution, and the direction is clear. Insurers are investing in fully integrated Broker Relationship Management platforms. Here, underwriting is natively connected to distribution, enabling real-time policy updates to flow to brokers without manual intervention. Alongside this, momentum is building behind dedicated broker portals offering a single window onto a broker’s entire book. Visibility of every in-force policy, the ability to raise new cases, and self-serve access to commission statements and bespoke deals.

            Broker marketing is also emerging as a strategic priority. Broker Distribution Managers are increasingly seeking the ability to run targeted, locally tailored campaigns for their own broker cohorts, rather than relying on generic global activity. And looking further ahead, insurers are exploring white-labelled distribution propositions. These allow brokers to sell policies under their own brand. A model that deepens broker loyalty while extending the insurer’s reach into segments it could not otherwise serve directly.

            The competitive benchmark for these capabilities is already being set. In 2025, Aviva was rated the top UK insurer by brokers in 10 of 12 service categories. Including e-trading, extranet capabilities, and underwriting flexibility. The distance between the leader and the median across these categories is substantial. And it represents a measurable, addressable gap for any insurer willing to invest.

            The softening market is sharpening the urgency. Insurance Times’ survey of 850 UKGI brokers identified softening market conditions — defined by rising supply, intensifying competition, and downward pressure on premium — as the number one concern for 2025, particularly among larger brokers with GWP above £10m. As pricing power erodes, brokers have greater choice of capacity, and the basis on which they allocate business shifts accordingly.

            This trajectory signals a structural shift insurers must plan for now. As cost pressures push pricing toward a practical floor, insurance will increasingly look like a commodity to brokers. The winners won’t be those competing hardest on price, but those competing most effectively on distribution capability and engagement — making proposition differentiators clear and delivering everyday value-add services that materially ease brokers’ working lives.

            How is AI changing the Broker Distribution landscape?

            The scale of the AI opportunity in insurance is now well-quantified. One recent study estimated that Generative AI could unlock $50–70bn in incremental global insurance revenue, with distribution intelligence ranked among the highest-value application domains. UK carriers are already demonstrating what disciplined deployment looks like in practice. Aviva has put more than 80 AI models into production across its claims function, delivering over £60 million in savings in 2024 alone and reducing liability assessment times by 23 days. Vitality, meanwhile, has announced a strategic partnership with Google to build Vitality AI — an initiative that will extend beyond its core insurance operations to revitalise its broker distribution capability.[N(1] [N(2] [AG3] 

            Broker Management Use Cases

            Broker management is ready to evolve from an operational function into a strategic growth capability where the use cases are both numerous and immediately actionable.

            • Prioritised opportunity lists for Business Development Managers (BDMs). AI can continuously rank live opportunities by likelihood-to-win and commercial value, so BDMs approach every broker conversation with a clear, evidence-based agenda rather than relying on memory or intuition.
            • Automated broker pack generation. Pre-meeting broker packs — covering portfolio performance, recent activity, share-of-wallet trends, and talking points — can be generated automatically in minutes, replacing hours of manual preparation.
            • Meeting management. AI can handle scheduling, note-taking, and follow-up actions, freeing BDMs to invest the majority of their time in the person-to-person relationship building that ultimately drives placements.
            • Continuous market sensing. AI agents can scan industry news daily and push real-time alerts into the Broker Relationship Management (BRM) platform — flagging broker consolidations, divestments, leadership moves, and emergent opportunities — ensuring insurers stay ahead of structural market changes without manual effort.
            • Smarter broker segmentation. AI can construct dynamic broker segments based on behaviour, portfolio mix, and engagement signals, ensuring the right message reaches the right broker at the right moment.
            • Next Best Action. AI can analyse broker portfolios, pipeline activity, and relationship data, and surface actions such as which broker to engage, which opportunity to prioritise, where cross-sell opportunities exist, and what risks exist in the pipeline.

            The shift that AI offers within broker marketing is significant. In an era of pervasive personalisation, broker marketing should be approached with the same hyper-personalised, behaviour-driven discipline that B2C marketing already demands. Winning a broker’s attention — and, more importantly, holding it — requires content that is timely, relevant, and tailored to that individual’s interests and current commercial priorities. AI makes this practical at scale, monitoring broker engagement patterns across digital touchpoints and using those signals to surface the most relevant content through the broker portal, email channels, and BDM conversations.

            The Time for Incremental Improvement is Over

            Broker management is not lagging because the industry lacks ideas, use cases, or technology. It is lagging because it has not yet been treated as a priority.

            Insurers that intend to lead in broker distribution need to make a deliberate shift now:

            • Elevate broker management from a support function to a board-level growth agenda
            • Assign clear ownership for broker intelligence and decisioning
            • Commit to building (or buying) a system (like BrokerVue8) that actively tells teams what to do next — not just what has already happened

            Because that is ultimately the dividing line.

            Not between insurers who “use AI” and those who don’t — but between those whose distribution teams operate with direction, and those who continue to operate on best guess.

            And in a market where brokers decide where business flows, that difference will not remain invisible for long.

            Learn more at bearingpoint.com

            • Blockchain & Crypto
            • InsurTech

            Author

            Martin Jakobsen

            Published

            25 June 2026

            Estimated Read time

            5Mins

            Martin Jakobsen, Managing Director at Cybanetix, on why SOC training and progression will be vital to meet the challenge of AI-driven cyber attacks

            The accepted wisdom in cybersecurity is that the Security Operations Centre (SOC) has become overwhelmed. There’s some truth in this. Yes, the time taken to detect and remediate has grown, with IBM stating this now stands at 241 days. Yes, alert fatigue remains an issue, with SOC analysts snowed under 10,000 alerts per day leading to confirmation bias whereby positive alerts are discounted. And yes, alert volumes are rocketing due to a thriving black-market economy in attack kits and services, not to mention the emergence of AI-enabled attacks. Yet the SOC can adjust to these challenges and move with a rising tide if it adopts a paranoid posture.

            One of the key attack trends over the past year is for threat actors to gain and maintain access legitimately. It’s for this reason we’ve seen a rise in credential theft (with an 800% increase in 1H2025) and the use of living off the land techniques (LotL). The attacker can log-in and use legitimate, trusted tools to evade detection and move laterally across the network to escalate those privileges. But the very fact these attacks don’t leave telltale footprints can make them much harder to detect. The SOC needs to rely on tools such as endpoint detection and response (EDR) and monitor tool usage to look for anomalies and that means listening to the less noisy network activity and low-severity alerts.

            SOC: Selective Hearing

            However, most SOCs focus on the high-severity alerts to such an extent that they even measure their success against the resolution of these incidents. SOC service level agreements typically specify a mean time to detection (MTTD) of 30 minutes and a mean time to respond (MTTR) of 15 minutes but the small print will often reveal this only applies critical alerts. Medium level alerts will typically be responded to within 1-2 hours and those low-level alerts that could indicate a LotL attack? Those are relegated to a window of 12 hours or more.

            To capture this range of alerts will require the SOC to process ten times the usual volume of events. This presents the SOC with a quandary. How do you increase the monitoring and the ingest of alerts without submerging a SOC team who may already be drowning? For that to happen, the SOC must stop being selective and instead become more efficient.

            To begin with, automation must be extended. Many teams will already be using Security Orchestration Automation and Response (SOAR) which can be configured to automatically remediate and close cases. But there are numerous other processes that can also benefit from automation. These can see the application of threat intelligence, automatic threat hunting and telemetry gathering, artificial testing, correlation with other alerts, and entity mapping, all of which can be used to enrich cases to the point where the SOC analyst doesn’t need to gather any further information when picking up the case.

            Recovering from Cybersecurity Incidents

            If we take the pre-defined playbooks that contain the recipe for how to detect, contain, eradicate and recover from specific cybersecurity incidents, for example, these can be automated to respond in a cascading effect. This sees one playbook used to trigger another depending on what the first has discerned, so that sub-playbooks iterate through lists of entities for each part of the case. Assuming 4,000 alerts coming into the SOC, triggering 100 playbooks per alert, with each playbook performing around ten actions such as enrichment lookups, correlation queries and containment steps, that’s equivalent to over four million triggered automations being executed per day before these cases are passed to a human analyst.

            AI and the Analyst

            AI, too, has a role to play. It currently enables the translation of free text questions into syntactical queries of Security Incident and Event Management (SIEM) and EDR data and can also be used to explain alerts, investigations and findings using human-friendly language and to summarise cases. It also lends itself to detection engineering, facilitating the faster creation of playbooks through the creation of code and detection syntax. With many detections sharing the same remedial actions, using AI to recommend a course of action makes sense, reducing analyst workloads substantially. And looking to the future, we can expect it to be used to optimise SOC operations by building playbooks on the fly and through the use of predictive investigative outcomes.

            Applying these methods of automation enables any alert to be dealt with much more efficiently. The alert will initially be put through a process of enrichment, with threat intelligence used to compare the alert with known threats. SIEM and EDR searches will then correlate information, after which any associated alerts pertaining to the same entities (i.e. IPs, users, accounts, devices etc.) are linked to the case for the analyst to review. Using these processes, it’s possible to automate 65-70% of all SOC activity, dramatically reducing case workloads and allowing the SOC to process those massive alert volumes.

            Human in the Loop

            Yet, while automation is critical to adopting a paranoid posture capable of responding to evolving threats, it’s no substitute for the skills of the SOC analyst. Human expertise is invaluable in evaluating, verifying and deciding upon the best course of action and the likelihood is that those skills will become even more pertinent as AI becomes more widely used. This is because the technology can become so goal-driven that it becomes blinkered and can make incorrect deductions. In one instance, an AI agent went on to misinterpret a threat and produce a fictitious kill chain and mitigation advice, revealing the need for a human in the loop (HITL) and traceability in AI.

            Paranoid monitoring does require the generation of more alerts for investigation. But we do have the tools and automation capabilities to handle those volumes so that this uptick needn’t equate to a massive increase in alert fatigue. What it does mean is that the role of the analyst will subtly change. They will be augmented by these processes and need to develop additional skillsets associated with validating these outputs, so the SOC will need to continue to invest in continuous training and progression, particularly if they want to hang on to that talent which remains in short supply. But unless we make these changes, the SOC won’t be able to keep pace with emerging threat patterns and will almost certainly fail to meet the challenge of AI-driven attacks.

            Learn more at cybanetix.com

            • Cybersecurity
            • Cybersecurity in FinTech
            • Digital Strategy
            • Infrastructure & Cloud

            Author

            Jean-Philippe Avelange

            Published

            24 June 2026

            Estimated Read time

            4Mins

            Jean-Philippe Avelange, CIO at Expereo, on why network resilience must be front of mind to drive digital transformation

            Digital transformation has become fluent in the language of applications. Boards are asking for AI, cloud migration, automation, better customer experiences and more real-time data. Teams are being asked to modernise faster, operate more efficiently, and make digital investment show measurable returns. But what many enterprises are still learning is that the bottleneck is not always the application, the data, or the platform team. It is often the network underneath them.

            For years, connectivity has been treated as little more than plumbing. Something to procure, renew and occasionally upgrade when a site opens or a contract ends. That assumption held up reasonably well when enterprise technology was slower, more centralised and more predictable. But it has become harder to defend now that applications are distributed, users are everywhere, data moves constantly between environments, and businesses expect digital services to perform in real time.

            Expereo’s Enterprise Horizons 2025 research found that only half of organisations believe their networks are ready to support new technology initiatives. This is an issue for any organisation expecting transformation to move from a programme plan to something employees and customers can depend on every day. Once new tools scale across sites, providers, clouds and regions, the network either becomes an enabler of change or the place where momentum starts to slow.

            The same research found that more than half of companies had experienced financial impact from network downtime or poor performance in the previous 12 months. That is lost productivity, lost revenue and lost confidence in the digital services that a business is trying to build around.

            Resilience is not the same everywhere

            The instinct, when faced with a fragile network, is often to add more – more bandwidth, more redundancy, another circuit. This creates the appearance of resilience while driving up costs and doing little to improve the experience where it actually matters. At the same time, most organisations still treat every location as equally important. But a headquarters, a manufacturing site, a contact centre and a small regional office are fundamentally different in how they operate and the risk they carry. Yet they are often supported by similar connectivity models.

            Resilience needs to reflect that reality. The network should be designed around the role each site plays in the business. Some locations need diverse fibre paths or wireless failover to maintain continuous operations. Others need strong performance guarantees and rapid remediation. Others require a simpler, reliable and cost-appropriate baseline.

            The goal is not to make every site identical – it is to make every site fit for purpose.

            Standardisation still plays a role, but not in forcing every location into the same architecture. The more effective approach is to define clear site categories, each with a pre-defined level of resilience. That turns expansion into a repeatable process, rather than a constant redesign exercise.

            Seeing where failures actually happen

            Another gap sits in visibility. Most IT teams monitor applications closely, but far fewer have a clear view of the network paths on which those applications depend. When issues arise, teams are often troubleshooting without knowing whether the problem sits with the user, the site, the provider or the cloud environment. By the time the cause is identified, the impact has already been felt.

            Failures rarely occur in isolation. They happen across the path between users, sites, providers and cloud platforms. Without visibility into those paths, problems are misdiagnosed, escalated slowly or incorrectly attributed to the application itself. Network observability changes this dynamic. It provides early warning rather than post-incident analysis, showing where performance is degrading, where traffic is being routed inefficiently and where user experience is being shaped by infrastructure rather than software. This visibility is also critical for data sovereignty. As organisations adopt cloud, AI and third-party services, understanding how data moves becomes as important as knowing where it is stored. In many cases, the network is the only place where the view exists.

            From network to advantage

            As digital transformation becomes embedded in day-to-day operations, connectivity moves from a background concern to a business-critical capability. The question is no longer whether the network matters, but whether investment in it is aligned with business risk. Adding more bandwidth or blanket resilience does not solve the problem – being more precise does. Resilience needs to be applied where it matters most, observability needs to be built into the network layer and design decisions need to reflect how each part of the organisation actually operates.

            Connectivity has long been treated as plumbing, but in reality, it underpins digital transformation. It determines whether applications perform consistently, whether users trust the tools they are given, and whether organisations can adapt without repeatedly rebuilding their foundations. The organisations pulling ahead are not treating the network as an afterthought. They are designing resilience into the transformation from the start.

            Learn more at expereo.com

            • Data & AI
            • Digital Strategy
            • Infrastructure & Cloud

            Author

            Dan Brightmore

            Published

            23 June 2026

            Estimated Read time

            2Mins

            Read in brochure format

            Welcome to the latest issue of Interface magazine! Click here to read the latest edition! U.S. Merit Systems Protection Board:…

            Welcome to the latest issue of Interface magazine!

            Click here to read the latest edition!

            U.S. Merit Systems Protection Board: Driving Operational Exellence

            Interface revisits the important work of the U.S. Merit Systems Protection Board – an independent, quasi-judicial agency in the Executive branch that serves as the guardian of Federal merit systems. CIO Craig Thomas, explains the strategy behind the completion of Phase II of its case management journey, application stabilisation challenges and the deliberate rollout of secure artificial intelligence tools.

            “At MSPB we field IT that is necessary, useful, cost-effective, manageable and scalable. If the technology doesn’t add value, doesn’t help us meet our mandate, I don’t want it… We see the active deployment phase as an incubator for support methodology, data and reporting strategy, and road-mapping the next five years of your application – use the active deployment phase, don’t just firefight.”

            Morgan Street Holdings: Cybersecurity for Strategic Operations

            Morgan Street Holdings is a privately owned investment and operating company whose portfolio includes global supply chain specialist HAVI, marketing and sourcing firm tms, consumer products brand Stanley, and hospitality and vending business Continental Services. Collectively, the organisation employs more 10,000+ people globally across more than 50 countries. Business Information Security Officer, Douglas Darden, talks building cyber resilience to enable the business and deliver cybersecurity for strategic operations.

            “It’s not in us as a cyber team to just say no. Give us the opportunity to conduct a risk assessment and the cybersecurity function can become a business enabler.”

            Also in this issue, we hear from Confluent on why AI is changing what data privacy means; learn from Kore.ai on how unmonitored AI agents are becoming enterprise AI’s biggest risk; and Xsolla explain why the real AI debate in video games isn’t about jobs, it’s about creativity.

            Click here to read the latest edition!

            • Cybersecurity
            • Data & AI
            • Digital Strategy
            • Infrastructure & Cloud
            • People & Culture

            Author

            Gareth Hewitt

            Published

            Estimated Read time

            5Mins

            Gareth Hewitt, Founder of LemonEdge, on why the real lesson from DORA is that resilience has to run through the whole operating model, from the infrastructure underneath it to the controls and workflows used every day

            The EU’s DORA regulation has exposed how firms operating in private markets face a set of risks that is not purely about IT.

            Designed to reduce financial sector reliance on a small number of infrastructure vendors, the regulation came into force last year. Since implementation it continues to throw a spotlight on risks arising from continued use of manual methods and over-reliance on individual employees’ subject matter expertise.

            The regulation is forcing firms to examine how their operating models work under pressure and where risk resides inside workflows. 

            A private equity or private credit manager may sit on robust cloud infrastructure at one end of its technology stack, while still relying on spreadsheets, manual handoffs and offline approvals at the other. In that kind of environment, resilience is only as strong as the least controlled part of the workflow.

            The real risk often sits inside the workflow

            This is where risk accumulates in an organisation – in processes that are not properly systemised. This is amplified when they rely on manual intervention or a small number of people who know how to make them work.

            A month-end close supported by spreadsheets and a sequence of offline steps may function well enough in normal conditions. However, it looks far less resilient when deadlines tighten, a key individual is unavailable or an investor query lands at exactly the wrong moment.

            DORA, in effect, acts as a pressure test on whether resilience has been built into the operating model or simply assumed. Private markets firms have lived with this kind of exposure for years, often because experienced teams compensate for process weaknesses. Regulation leaves no room for that comfort. The question now is whether the workflow holds up under strain.

            With DORA, accountability has moved to the top

            One of the clearest shifts under DORA is the level at which accountability now sits. Historically, operational resilience could be treated as a technology matter and delegated accordingly. If a firm had a CTO, responsibility was often there. If it did not, it shifted into finance or operations. DORA has since changed that dynamic. Responsibility now sits with the management body, which is held accountable for the ICT risk management framework. It means resilience is now a governance issue with clear ownership at the top of the business.

            That changes the conversation both inside firms and with suppliers. Firms increasingly want hard evidence from their providers. They want to understand incident processes, failover arrangements, access controls and recovery expectations in practical terms. They also want to know when documentation was last reviewed, whether controls have been tested – and what would happen if a system failed.

            The weakest point is often internal

            For many private markets firms, however, the central issue is not whether a major external provider is resilient in principle. It is whether the firm’s own workflows can withstand increased pressure.

            Key-person risk is at the heart of this issue. Many firms have built effective processes around capable individuals who know every workaround and hidden dependency. That may keep the machine running, but it is not the same as resilience. If knowledge is not captured and supported by the underlying platform, the process is weaker than it appears, severely increasing dangers when individuals leave or are unavailable.

            The same applies once essential work starts moving outside the core platform into spreadsheets, email chains or offline approvals. Control weakens, visibility drops, auditability becomes harder and recovery becomes more uncertain. In private markets, where reporting expectations are high and investor scrutiny remains intense, that is not a side issue. It goes to the heart of whether the operating model is fit for purpose.

            That matters particularly in fund accounting, where complexity has only increased. Reporting expectations are higher, structures are more demanding, and firms need stronger control across workflows. A system built for a different era often pushes work outside the main platform at exactly the point where clarity and control matter most. Once reporting becomes a scramble and key numbers are being pieced together manually, the workflow has already become more fragile than it should be.

            DORA is the prompt, not the end goal

            Operational resilience has often been treated too narrowly as a compliance cost. In practice, stronger resilience usually brings clearer process ownership, better visibility across workflows and faster responses when investors, auditors or regulators ask harder questions. It also reduces key-person risk and makes growth easier to support because the process does not have to be rebuilt every time complexity increases.

            DORA will not solve every vulnerability in private markets operations, nor was it meant to. What it does is raise the bar for accountability, testing and supplier oversight. It shines a harsher light on brittle processes that have survived largely through habit and human intervention. It also makes clear that operational resilience depends not just on policies and providers, but on whether the underlying systems and platforms are strong enough to support controlled, auditable workflows under pressure.

            That is the real wake-up call. Resilience has to run through the whole operating model, from the infrastructure underneath it to the controls and workflows used every day. That’s the real underlying lesson of DORA that private markets firms need to heed.

            Learn more at lemonedge.com

            • Cybersecurity
            • Cybersecurity in FinTech
            • Digital Strategy
            • InsurTech

            Author

            Paul Dawalibi

            Published

            22 June 2026

            Estimated Read time

            5Mins

            Paul Dawalibi, CEO of Innovation City – Ras Al Khaimah, on why the next wave of transformation will emerge from the places most deliberately built to welcome it, equip it, and champion it without reservation

            The machines are accelerating faster than our institutions can comprehend. AI has not merely arrived, it has detonated, compressing years of progress into weeks. And forcing every industry to reckon with what comes next. Yet in this moment of unprecedented velocity, a paradox persists. The very founders and teams best positioned to shape this future often find themselves fighting systems built for a slower, more predictable age.

            They battle outdated regulations and navigate prohibitive costs. They operate in environments designed for the previous generation of business. And too often, brilliant ideas die not from lack of vision, but because the ecosystem around them was never built to support the speed, scale, and sovereignty that modern innovation demands.

            For decades, the world assumed that real ambition naturally flowed to the great megacities. Places like London, New York and San Francisco. Those places brought extraordinary revolutions. But today, they exact a hidden tax: on time, on capital, on mental bandwidth, on the simple ability to focus. Talent and capital are voting with their feet, seeking environments where the default setting is momentum rather than friction.

            The next chapter will not be written in those places. It will be written in deliberately designed sanctuaries. Hyper-focused ecosystems where resources, regulation, and human support are engineered not just to enable innovation, but to accelerate it. These are not free zones or business parks in the traditional sense. They are living systems built on three pillars, delivered with a philosophy we call white glove.

            The Three Pillars That Change Everything

            The first pillar is sovereign resources. For AI builders, the bottleneck has never been ideas. It has been access to the compute, talent, and infrastructure that turn ideas into reality at speed. In a true innovation sanctuary, these are not scarce commodities to be fought over. They are shared foundations. Dedicated data centers. Curated networks of specialists. Proximity transforms isolated founders into powerful collective intelligence. When the raw materials of the future are abundant and aligned, what once felt impossible begins to feel inevitable.

            The second pillar is regulatory intelligence. Speed without standards is chaos. Standards without speed are stagnation. The ecosystems that will define this era understand that regulatory frameworks must be as dynamic as the technologies they govern. They anticipate the needs of digital assets, tokenisation, and agentic systems. And move from years and millions in friction to days and focused investment. They protect what matters – trust, ethics, sovereignty – while ruthlessly eliminating what does not.

            The third pillar is the gravity of focus. Breakthroughs rarely emerge from fragmented attention. When the cost of living and operating is dramatically lower than in legacy hubs – often 50 to 60 percent less – entire teams can relocate together. Families put down roots. Deep work becomes the norm rather than the exception. The spontaneous collisions that fuel genius stop being lucky accidents and become the fabric of daily life. This is how movements are built.

            White Glove is Not a Service. It is a Stance.

            What elevates these ecosystems from merely functional to truly transformative is the white glove philosophy. A level of personalised, proactive championship that goes far beyond standard facilitation.

            It means that when a company arrives, the ecosystem does not simply clear a path. It walks beside them. Talent acquisition becomes strategic matchmaking. Regulatory navigation becomes clear before questions are asked. The search for that critical first customer becomes an active introduction to the right partners at the right moment. Because in the end, validation from the market is more powerful than any pitch deck or funding round. It is proof that the world is ready for what you have built.

            We have seen this model work before. Y Combinator did not merely advise – it obsessed over every detail of a startup’s trajectory. Singapore did not merely welcome industry – it engineered precise conditions for the sectors it chose to lead. The common thread is simple but rare. An ecosystem that measures its own success by the velocity and impact of the builders it attracts.

            Innovation City: Building the Sanctuary

            In Ras Al Khaimah, we are not constructing another free zone. We are creating a sanctuary for those who intend to build what comes next.

            Strategically positioned less than an hour from Dubai, within reach of a third of the world’s population, at the crossroads of continents – we have chosen this place because it still believes in agility, in partnership, in the radical idea that the role of an ecosystem is to remove obstacles, not create them.

            Here, we are putting the white glove philosophy into practice at scale. We are building dedicated AI infrastructure so that computational power serves the many, not just the largest. By crafting regulatory frameworks for digital assets and real-world tokenisation that respect both innovation and responsibility. We are creating the conditions for families and teams to thrive – because great work is sustained by great lives.

            More than any single asset, we are cultivating a culture. A place where ‘how do we make this happen?’ replaces ‘why it cannot be done”. Where bureaucracy is recognised as the enemy of progress, not its protector. Where the builders who are willing to run through walls find an entire community running with them.

            This is what happens when an ecosystem decides that its highest purpose is to amplify human ambition.

            The Future Belongs to Those Who Build it

            The paradox we face is not inevitable. It is the result of design choices made in a different era. And design choices can be remade.

            The great hubs of the past deserve their legacy. But the next wave of transformation will emerge from the places most deliberately built to welcome it, equip it, and champion it without reservation. That work is underway.

            In Ras Al Khaimah and in other rising sanctuaries around the world, a new standard has been established. Not for what is merely permitted, but for what becomes possible when every barrier between vision and execution is removed.

            The future will not ask for permission. It will go where it is most fiercely supported. We are building that place. And we invite every founder, every team, every leader who shares this conviction to build it with us. The age of accidental ecosystems is over. The age of designed destiny has begun.

            Learn more at innovationcity.com

            • Artificial Intelligence in FinTech
            • Blockchain & Crypto

            Author

            Dan Brightmore

            Published

            19 June 2026

            Estimated Read time

            6Mins

            Craig Gravina, CTO at Semarchy, on why successful AI at scale requites the transparency and trust only proper governance foundations can deliver

            Across the globe, organisations are aggressively approving AI budgets, hiring teams, and deploying models, yet the promised returns remain elusive for many. AI investments continue to grow, but measurable business value is not keeping pace with the level of spend.

            The numbers tell a striking story. Semarchy’s 2026 research of C-suite executives reveals that 99% of UK organisations claim AI readiness – with 69% saying they’re completely ready. Yet 56% making significant investments cite data management as their top challenge, while only 33% are prioritizing it for investment. This 23-point gap reveals the fundamental issue: organisations are building AI applications whilst underfunding the data infrastructure those applications require.

            Globally, fewer than 2% of enterprises successfully avoid data quality problems, even though 74% planned to increase AI spending in 2025.

            This isn’t a failure of technology talent or algorithms; it’s a governance breakdown. The path to faster AI ROI doesn’t run through better models, but through better governance and transparency, embedded at the center of every AI initiative from the outset.

            The Hidden Cost of Ungoverned AI

            When AI operates without governance, the primary risk shifts from technical to reputational. Whereas technical failures are recoverable, reputational damage is far harder to restore. Consider consequential AI decisions: credit denials, medical recommendations, hiring shortlists. When these go wrong publicly, the fallout extends far beyond the technical team. When regulators or customers ask how they make their decisions, organisations without governance have no credible answers.

            The regulatory pressure only amplifies this risk. Global frameworks such as the EU AI Act, emerging UK regulations, and sector-specific rules increasingly require explainability and traceability. Companies that cannot show how AI reached its conclusions risk fines, restrictions, and forced remediation.

            Public misfires, such as biased outputs or visible system breakdowns like Grok AI’s high-profile failure on X last year, can rapidly erode customer trust and brand equity. For consumer tech brands especially, reputational cost can exceed technical cost exponentially. Internally, unexplained or unreliable AI outputs erode confidence, resulting in disengaged business users, stalled initiatives, and data leaders losing credibility.

            These aren’t exceptional cases. They’re the predictable result of deploying AI without governance infrastructure.

            The Governance Misconception

            Many organisations still treat AI governance as a compliance checkpoint, bolted on just before deployment to satisfy legal or risk requirements. This narrow interpretation is exactly what makes governance feel like an obstacle. Organisations feel forced into a false choice: move fast and bypass governance or be compliant and accept delays.

            In this scenario, projects slow to a crawl. Data teams spend months preparing ‘clean’ datasets specifically for AI, only to sit in approval queues. By the time governance clears, business conditions have changed or stakeholders have moved on to new priorities.

            The answer isn’t less governance; it’s different governance. Governance should be intrinsic to how data is defined, managed, and delivered. When governance “travels with the data,” models consume information that already carries quality checks, lineage, access controls, and semantic context already present. AI initiatives don’t wait for approval gates – they consume governed data from the start. There’s nothing to retrofit or remediate later, because the controls were in place from day one.

            This shift from governance-as-gate to governance-as-infrastructure is the key divide between organisations that struggle to realise AI ROI and those that achieve it.

            Transparency as a Strategic Asset

            Leadership often frames transparency as a compliance requirement, but in AI, it’s just as critical for development speed. When data lineage is clear, AI teams don’t have to spend weeks figuring out where data came from, how it was transformed, or whether they can trust it. They can focus on building and refining models instead of investigating data provenance, shifting effort from detective work to value creation.

            Transparency also removes technical friction. With explicit semantic context, models interpret data correctly without layers of custom preprocessing or extensive feature engineering. Consistent access controls across data sources prevent last minute security reviews from stalling projects at critical milestones.

            Transparency enables continuous improvement cycles. When you can see exactly which data influenced a decision, you can diagnose errors with precision rather than guesswork. You can identify which specific data sources introduce noise or bias into model outputs, and measure whether data quality improvements affect AI performance in production, creating feedback loops.

            Organisations that try to reconstruct this visibility only during audits pay for it repeatedly in delays, extended debugging, and failed deployments. By contrast, those that build transparency from the start move faster through development, testing, deployment, monitoring, and iteration. Transparency then becomes a competitive advantage rather than compliance burden.

            What Real AI Success Looks Like

            Real AI success isn’t a single breakthrough model. It’s the ability to deliver value repeatedly and at scale. Successful organisations move from proof of concept to production without major rework or last-minute governance scrambles.

            In these organisations, AI consumes the same governed data as everyone else. Agents and models access data through shared interfaces and policies, alongside business users and operational systems. There’s no special ‘AI data prep’ phase, no separate pipelines to maintain, and no governance gap to close before go‑live. AI is simply another consumer of trusted data.

            Explainability comes by default. Because lineage, transformations, and quality metrics are already present in the data, teams can trace any decision without forensic effort. When governance is built into data flows, AI teams can retrain on fresh data without re-running compliance reviews, keeping models current as business conditions change. Iteration cycles shorten from months to weeks or days.

            This foundation also enables decentralised experimentation with centralised trust. Teams can launch AI initiatives against shared, governed data products without creating shadow pipelines.

            Getting There: Three Practical Shifts

            Reaching this future state doesn’t require a multi-year overhaul or new tech stack. It needs a focused effort in how data is delivered to every consumer, including AI, with governance built in rather than added later.

            First, stop treating AI data preparation as a separate workstream. If AI teams need specially cleaned and packaged data, you’ve created handoffs that introduce delay and governance risk. Instead, provide data that all consumers can trust and use immediately.

            Second, embed semantic context with the data. AI needs business context and meaning, not just schemas. What does ‘customer’ mean in this specific context – prospect, active user, former customer? What business rules apply, and what relationships matter for decision making? This semantic layer enables AI to interpret data correctly without custom workarounds.

            Third, make lineage and quality observable by default. When lineage, quality scores, and transformation history are always available, debugging, compliance, and continuous improvement happen without emergency efforts.

            Yet our research reveals the challenge: whilst 56% of UK organisations cite data as their top AI challenge, only 33% are prioritising investment in it. This misalignment between stated priorities and actual investment is precisely why AI initiatives continue to struggle, regardless of confidence levels.

            The ROI Equation

            The companies that achieve AI ROI faster aren’t the ones with the biggest budgets or the most sophisticated models – they’re the ones that eliminated the friction between data and AI consumption through proper governance infrastructure.

            When governance is intrinsic, projects don’t wait for approval gates. With built-in transparency, debugging is fast, and audits are painless. The result? Shorter time to value, lower risk of public failure, reduced regulatory exposure, and AI investments that deliver measurable impact. These aren’t abstract benefits – they show up in project timelines, deployment success rates, and business KPIs.

            The technology is ready. The question is whether your data governance is ready to support AI at scale. Success requires the transparency and trust that only proper governance foundations can provide.

            Learn more at semarchy.com

            • Data & AI
            • Digital Strategy

            Author

            Simon Pamplin

            Published

            Estimated Read time

            4Mins

            Simon Pamplin, CTO at Certes, on how Quantum computing will eventually force a reckoning with the cryptographic assumptions on which the financial system is built.

            Financial institutions hold some of the most sensitive and long-lived data of any sector. Customer identities, decades of transaction records, credit histories, and proprietary trading information must be kept confidential, not just today, but far into the future. The cryptographic systems guarding that data, however, were built for a different era; one in which quantum computing was purely theoretical. That era is ending.

            Recent guidance from both the G7 Cyber Group and the UK’s National Cyber Security Centre has made the stakes explicit: financial organisations should complete their transition to post-quantum cryptography by 2035. For institutions managing sprawling, decades-old technology estates, that deadline is less reassuring than it might first appear. Ten years sounds like breathing room. In practice, for organisations of this complexity, it is barely enough.

            Quantum Risk: The Threat Doesn’t Wait for the Technology

            The most common mistake in discussions about quantum risk is treating it as a problem for tomorrow. The assumption is that until a quantum computer powerful enough to crack modern encryption actually exists, there is no immediate danger. That assumption is dangerously flawed.

            Sophisticated adversaries are already operating on a longer time horizon. The strategy, widely referred to as “harvest now, decrypt later,” involves systematically collecting encrypted data today and storing it until quantum computing capabilities catch up. No quantum computer is needed to execute this phase of the attack, only patience and storage.

            For the financial sector, the implications are particularly serious. The data being harvested now, customer records, transaction histories, and commercially sensitive communications, could have a useful life measured in decades. By the time it is decrypted, the individuals and organisations it concerns will still be very much affected by its exposure. This is not a theoretical future liability. It is a risk quietly accumulating in the background of every financial institution that has not yet begun to act on it.

            Why Updating Financial Systems Is So Hard

            The cryptographic methods underpinning most financial infrastructure today, primarily RSA and elliptic curve cryptography, derive their security from mathematical problems that are extraordinarily difficult for conventional computers to solve. Quantum computing threatens to render those problems tractable, potentially dismantling encryption that currently appears robust.

            Responding to that threat, however, means working through some of the most technically complex environments in any industry. Core banking platforms were often built 20, 30, or 40 years ago. Cryptographic functions are frequently embedded deep within application logic, firmware, or hardware, in payment terminals, ATMs, and proprietary systems that were never designed with replaceability in mind.

            Replacing or updating these components is not a software patch. It can mean recertifying devices, replacing physical infrastructure, or undertaking large-scale application rewrites, all while maintaining continuous service to customers and satisfying stringent regulatory requirements. Progress is possible, but it is measured in years, not quarters.

            Rethinking Security from the Data Outward

            The quantum threat is also accelerating a broader rethink of how financial institutions approach security architecture. Perimeter-based defences, firewalls, VPNs, and network segmentation have consistently shown their limitations when attackers can move laterally through systems using legitimate credentials or compromised access. Relying on the boundary to keep data safe has a poor track record.

            A more resilient approach centres protection on the data itself. By applying strong controls directly to information as it moves across systems, organisations can maintain security even within legacy environments that cannot be immediately upgraded. Sensitive data remains protected regardless of where it travels or which network it traverses.

            Alongside this, crypto agility, the ability to swap out cryptographic algorithms and rotate keys without overhauling entire systems, is becoming a fundamental requirement rather than a nice-to-have. The transition to post-quantum standards will not be a one-time event. Cryptographic best practice will continue to evolve, and institutions that have built flexibility into their architecture will be far better positioned to keep pace.

            The Cost of Inaction

            The financial sector has a well-established capacity to absorb and adapt to technological change. It has navigated the transition to digital banking, the rise of real-time payments, and successive waves of cybersecurity threats. The quantum challenge is different in one important respect: the timeline for harm is already running.

            Regulatory expectations are being set now. Adversaries are collecting encrypted data now. And the internal processes required to modernise a major financial institution’s cryptographic infrastructure take years to complete. The gap between when action is needed and when it can realistically be delivered is not wide, and it is narrowing.

            Preparation does not demand panic or the immediate replacement of every system. It demands a clear-eyed audit of where sensitive data lives, an honest assessment of which information carries long-term confidentiality requirements, and a commitment to building security architectures that can evolve as the threat landscape does.

            Financial institutions that move early will not simply reduce their quantum exposure. They will emerge with stronger, more adaptable security postures and with the trust of customers, regulators, and partners intact.

            Quantum computing will eventually force a reckoning with the cryptographic assumptions on which the financial system is built. The question is no longer whether that reckoning is coming. It is whether institutions will be ready when it arrives.

            Learn more at certes.ai

            • Artificial Intelligence in FinTech
            • Blockchain & Crypto
            • Cybersecurity in FinTech
            • Data & AI
            • Digital Strategy
            • Fintech & Insurtech

            Author

            Dan Brightmore

            Published

            17 June 2026

            Estimated Read time

            3Mins

            The combined company is expected to generate approximately $3 billion in annual revenue and process more than $500 billion in annual payment volume for more than 2.4 million customers

            Nuvei and Payoneer have entered into a definitive agreement under which Nuvei will acquire Payoneer. Under the terms of the agreement, Nuvei will acquire all of the issued and outstanding shares of common stock of Payoneer Global Inc. for $7.40 per share in cash, representing a total transaction equity value of approximately $2.75 billion.

            “The acquisition of Payoneer marks a defining step in Nuvei’s evolution into a global financial infrastructure leader,” said Phil Fayer, Chairman and Chief Executive Officer of Nuvei. “By combining complementary capabilities, we can offer businesses a more complete platform to accept payments, send funds, issue cards, manage treasury and FX needs, and access embedded financial services – at scale.”

            Nuvei & Payoneer: Cross-Border Payments

            As commerce becomes more complex across local and cross-border markets, businesses need infrastructure that can support the full transaction lifecycle. This transaction directly addresses that need by combining Nuvei’s leading payment acceptance capabilities with Payoneer’s cross-border payouts, multi-currency accounts and banking network, along with same-day and real-time settlement in more than 150 markets.

            Together, the companies create an always-on, unified financial infrastructure built on trusted rails, supporting customers that do business across the world’s leading digital commerce platforms, including Amazon, eBay, Walmart, Airbnb, Fiverr, Upwork, Etsy, ByteDance, Shopify, and WooCommerce.

            A key component of this infrastructure is Payoneer’s established regulatory footprint across major jurisdictions around the world. Payoneer holds multiple licenses and authorisations, including licensing for online payment services in mainland China and authorisation in principle as a cross-border payment aggregator in India under the Reserve Bank of India’s regulatory framework.

            Agentic Commerce and Stablecoins

            The transaction also strengthens Nuvei’s ability to support emerging financial models, including agentic commerce, stablecoin payments, and platform-native financial services. These capabilities are expected to help businesses move funds more seamlessly across payment types, settlement networks, and jurisdictions.

            “For two decades, Payoneer has earned the trust of millions of businesses in markets where trust takes years to build,” said John Caplan, Chief Executive Officer of Payoneer. “We have transformed our business with extraordinary results, and our combination with Nuvei will extend what we can offer customers. Together, we will reach more businesses, in more markets, with a more complete platform.”

            About Nuvei

            Nuvei is building the infrastructure for every payment, everywhere. Its modular, flexible, and scalable technology enables leading companies to accept next-generation payments, offer all payout options, and benefit from card issuing, risk, and fraud management services. Connecting businesses to their customers in 190+ countries, with local acquiring in 52 markets, 150 currencies, and over 720 alternative payment methods, Nuvei provides the technology and insights that help customers and partners succeed locally and globally.

            About Payoneer  

            Payoneer is the financial platform for cross-border business and global payments. It empowers millions of businesses with the financial tools and services they need to grow and transact globally with confidence. Payoneer makes it easier for businesses, particularly in emerging markets, to connect to the global economy, pay and get paid across borders, manage their funds across multiple currencies, and grow their businesses.

            • Digital Payments
            • Neobanking

            Author

            Peter Pugh-Jones

            Published

            12 June 2026

            Estimated Read time

            5Mins

            Peter Pugh-Jones, EMEA Field Chief Data Officer at Confluent, on why the challenge for companies will not be building AI systems but standing behind the decisions those systems make

            For years conversations about data privacy have centred on conventional dangers, like breaches from outside, or leaks from within. The assumption has been that the biggest risk lies in information either being inadequately protected or improperly handled.

            As we move deeper into the AI era, that picture is shifting. A more complex privacy challenge that we’re now faced with comes from how data is used once it sits inside an organisation.

            AI is a crucial part of everyday business operations. It automates processes and shapes decisions. Data is no longer simply stored or shared; it’s interpreted and acted on at incredible speed, in ways that are difficult to trace or explain. 

            Decision-makers recognise the importance of accessing this data. 80% of business decision makers say without up-to-date data, businesses can’t be fully confident in decisions.

            As such, the systems and rules that dictate how AI is used have never been more important. Business leaders need to be able to understand and explain how automated decisions are reached and the data that informed it.

            Governance has to sit at the centre of the privacy conversation — not as a compliance exercise, but as the framework for the responsible application of AI. Without that foundation, organisations are likely to expose themselves to regulatory and reputation risk that they’re simply not equipped to avoid. 

            Privacy Now Depends On Explanation

            Governments worldwide recognise the need for these AI frameworks. Europe’s AI Act has drawn the most attention, but equivalent regulatory approaches are appearing in markets from South Korea and the UK.

            The result is that companies deploying AI systems are held to higher standards than ever when it comes to demonstrating that they use AI in a lawful, appropriate manner. As such, many businesses are discovering a gap between deploying AI and understanding it.

            Implementing a model that produces useful outputs is relatively straightforward. Explaining how those outputs were created and which data shaped them is much harder. Where that clarity is missing, privacy becomes an operational problem.

            Organisations may need to pause or redesign systems already in production because they cannot explain how those systems reached their conclusions. Some companies may even find themselves withdrawing products already released into the market. 

            So: what challenges are these organisations facing?

            The Segmentation Challenge AI Introduces

            One significant obstacle is segmentation. Companies need to understand who their systems interact with and which rules apply to each, and this isn’t always clear. 

            Businesses have historically segmented audiences by geography, demographics or behaviours. AI creates a more demanding requirement in that the systems using it must recognise when different safeguards should apply to different groups of people. 

            Regulation is pushing this further especially around children and vulnerable users. Content recommendations and automated responses that are acceptable for one group can become inappropriate for another. 

            The difficulty is that many AI systems are trained on broad datasets gathered across the internet. That breadth makes it harder to understand whether information came from a reliable source, or whether it should appear in a particular context. Without stronger segmentation, organisations risk losing control over how AI behaves in real interactions — and if that AI behaves inappropriately, it’s likely to lead to a breach of regulation. 

            Why Governance Cannot Be An Afterthought

            Another mistake appears repeatedly in businesses deploying AI and modern data platforms. Governance is treated as something that can be dealt with later once systems are already running, with isolated but exciting pilots running well on smaller data sets often leading businesses to try and run before they can walk. 

            The challenge is even sharper in real time environments where data moves constantly between applications and teams. When governance is not designed into those flows from the beginning it quickly becomes a bottleneck.

            In practice, the most effective point to govern data is when it first enters the business. Once information is already moving through systems, feeding models and generating outputs introducing controls becomes far more complex. 

            Many companies only realise months later that they’re lacking certain controls needed to manage risk. If they were never properly built in, it’s an expensive, time-consuming, incredibly nuanced task to put the genie back in the bottle.

            The Shared Responsibility For AI Decisions

            As with any cybersecurity approach, the human link can also be the weakest in the chain.

            Responsibility for AI risk is often treated as if it sits neatly with one team. In many companies the assumption is that specialists are handling it, whether that is data scientists, a compliance function or a single executive role. The underlying thought is: “this is not my problem — someone else is dealing with it.”

            That assumption creates problems. AI systems are trained on vast datasets and their behaviour is not always easy to predict without close scrutiny. When governance is treated as a delegated task, important questions can slip through the cracks unanswered. 

            Decisions informed by AI still sit with the business using the technology and ultimately with its leadership. Without senior oversight and a clear understanding of how systems are built, trained and governed privacy risks tend to surface long after those systems have become part of everyday operations. 

            The Question Organisations Should Be Asking

            Organisations have the capability to build powerful AI systems. The harder question is whether those systems can stand up to scrutiny.

            That means understanding where data originates, recognising differences between users and putting governance in place before automation begins to scale. It’s not enough to just co-opt an impressive new platform and give it access to your data.

            In an AI driven environment privacy does not sit somewhere at the end of the process. It shapes how systems are designed, deployed and managed from the beginning. 

            In the years ahead, the challenge for companies will not be building AI systems. The challenge will be standing behind the decisions those systems make. 

            Learn more at confluent.io

            • Data & AI
            • Digital Strategy

            Author

            Konstantinos Stavropoulos

            Published

            9 June 2026

            Estimated Read time

            5Mins

            Konstantinos Stavropoulos, Product Manager at AUSTRIACARD HOLDINGS, on why the firms that succeed will be those that adopt AI with the right balance of control, flexibility and practicality

            As FinTechs accelerate their adoption of artificial intelligence, the question of capability – what AI can do, and how it can improve efficiency – is typically overshadowed by the question of governance: how and where should this technology run. For an industry built on trust, regulation and the responsible handling of sensitive data, this goes to the heart of how FinTechs manage risk, protect privacy and intellectual property, and maintain control over their operations including cost.

            A Down-to-Earth AI Approach for FinTechs

            AI is transforming the way that organisations automate processes, with companies such as Tag Systems – a member of AUSTRIACARD HOLDINGS, and the biggest payment card provider for UK FinTechs – experiencing and supporting this transition. From document handling and onboarding to cross-checking and company-specific workflows, It offers an efficient automation alternative to manual, repetitive and time-consuming activities. However, in regulated business domains such as the fintech industry, processes that involve sensitive customer data or company-specific intellectual property need to be protected.

            Most widely used AI models (LLMs, Large Language Models) run in vendor-managed cloud environments. This approach may have enabled rapid adoption, but it requires organisations to send data beyond their own well-guarded setting. Even when some protections are in place, question marks around sovereignty (and how data is stored and potentially used by 3rd parties in the cloud) remain.

            For FinTechs, these are not abstract concerns. They are vital governance considerations. Therefore, solutions are needed to bring AI closer to (ideally, inside) the organisation, similar to that offered by AUSTRIACARD’s GaiaB™ – originally ‘GenAI in a box’, and also meaning ‘Second Earth’. By combining powerful (Dell) hardware with a cutting-edge agentic AI software platform, GaiaB™ Appliance keeps artificial intelligence in-house to ensure sovereignty, compliance, and control. A down-to-earth approach to adopting it.

            Can Fintechs Risk Sharing Their Data and Intellectual Property?

            When AI-driven tasks and processes run in a 3rd party managed environment, risk is also (partly) transferred to that 3rd party. In other words, organisations must rely on an external provider, not only for performance, but also for security, availability and compliance alignment. When AI-driven tasks and processes run in-house, that is not the case. For most fintechs, particularly those operating in tightly regulated environments, this is increasingly attractive.

            One of the key advantages of running artificial intelligence in a controlled, local or private environment (potentially, even air-gapped, which is also supported by solutions such as GaiaB™ Appliance) is that data and intellectual property stay where they belong. Instead of sending information to external providers, organisations can process it internally, within systems they already govern. This reduces exposure, simplifies oversight, and provides a clearer answer to the fundamental challenge of making the most of AI without losing control of sensitive information.

            Can AI be Predictable for FinTechs?

            Many organisations begin experimenting with AI through online usage-based services (AI token – fundamental AI unit of data – dependent). This may appear to be an attractive OPEX proposition, but costs that appear manageable at first can escalate when it gets embedded in business processes and usage scales. Due to the nature of tokens, AI consumption charging models are typically unpredictable, particularly for smaller firms and those operating on tight margins. Such businesses may well become reliant on services for which they have not budgeted.

            A more controlled deployment model means that organisations do not have to pay, or understand what they would need to pay, every time they use AI. Instead, they can invest in well-defined capabilities, which they can scale as needed. For example, GaiaB™ Appliance offers four hardware options (Tiny, Small, Medium, and Large), which can be used to create a modular and scalable AI infrastructure. Cost predictability enables FinTechs to align AI investment more closely with business needs, and avoid cost escalation.

            How FinTechs can Target Optimal, Cost-Effective AI Performance

            When AI is delivered as an external online service, performance can vary depending on network conditions, service levels or pricing tiers. In contrast, when AI runs within a FinTech’s own controlled local environment, the organisation can target more consistent performance. Consistent levels of latency and availability matter when AI is embedded into mission-critical operational processes.

            In business domains where requirements evolve quickly, AI strategies need to be adaptable, not fixed. Equally important is the need to avoid dependency on any single AI model provider. AI is evolving rapidly. Models improve, new approaches emerge, and what is considered state-of-the-art today may not be optimal and cost-effective tomorrow. FinTechs should be able to adapt, switch and tailor models without being locked into a rigid framework. In these terms, flexible solutions of modular and scalable nature and of AI model independence are invaluable.

            The AI Way Forward for FinTechs

            Most organisations are trying to improve – not replace – human decision-making with artificial intelligence. Interestingly, some of the most popular use cases today are the least glamorous: automating workflows, routing information, extracting and classifying data, and supporting employees in repetitive time-consuming tasks. Such applications deliver considerable value from AI by improving productivity and allowing people to focus on higher-value work. Similar to other industries, the future of AI in FinTech entails the concept of agentic AI which has been gaining traction. Instead of relying on a single model or an autonomous/semi-autonomous AI agent, organisations can deploy multiple specialised agents that handle specific tasks.

            To adopt AI successfully as a strategic growth lever within a secure governance framework, FinTech leaders need to balance cost and benefit, risk and reward. Can the organisation maintain control over its data? Does it manage risk and meet regulatory expectations? Can it control costs as usage scales? Can it adapt as technology evolves? Ultimately, can it deploy artificial intelligence in a way that supports real business outcomes?

            The firms that succeed will be those that adopt AI with the right balance of control, flexibility and practicality.

            Learn more at austriacard.com

            • Artificial Intelligence in FinTech
            • Embedded Finance

            Author

            Berkley Egenes

            Published

            Estimated Read time

            6Mins

            Berkley Egenes, Chief Marketing & Growth Officer at Xsolla, on why the real AI debate is about creativity and ensuring that machines support human expression, rather than replace it

            The conversation around artificial intelligence (AI) in video games has quickly zeroed in on jobs. Headlines warn of artists, writers, and designers being displaced by tools that can generate concept art at the click of a button, produce dialogue on demand, or compose background music in seconds. That fear is understandable; we’ve seen similar concerns in journalism, marketing, and content  production, but it’s also limiting. Because when we focus primarily on economic displacement, we miss the far deeper challenge: what happens to creativity when generative AI begins to produce the art, dialogue, music, and even mechanics that shape a game’s identity? 

            The real debate isn’t jobs. It’s creativity.

            From Efficiency Tools to Artistic Authors

            In 2024, a survey by the Game Developer Conference (GDC) found that nearly half of game developers were already experimenting with generative AI tools for writing, art, or code. Industry analysts estimate that by 2030, up to 70% of mid-sized and large studios will integrate AI into their asset pipelines. These numbers signal rapid adoption, but they also risk setting expectations that AI is first and foremost an efficiency multiplier. 

            And it is, at least on the surface. Game development is famously deadline-driven and resource-constrained. Developers often spend years polishing art assets, scripting dialogue trees, and composing music suites. Generative AI promises dramatic productivity gains: conceptual environments in minutes, ambient soundscapes generated automatically, even procedurally expanded quests.

            But under the hood of that promise is something more complex. Creativity in games isn’t just about completing tasks faster. It’s about voice, intention, and authorship. 

            When Enhancement Changes the Creative Equation

            This is where the idea of job enhancement becomes more complicated than it first appears. 

            Generative AI unquestionably enhances creative roles. It can allow a single environment artist to produce in days what previously took weeks. It can enable a narrative designer to draft and iterate on branching dialogue trees at unprecedented speed. In pure productivity terms, enhancement looks like empowerment: the same people, equipped with better tools, producing more ambitious worlds. 

            But enhancement is not neutral. When AI dramatically increases output capacity, it changes the nature of the job itself. 

            Imagine a studio with ten environment artists. Before generative AI, they produced a carefully curated set of handcrafted scenes over two years. With generative AI tools, the same team might produce twice as many environments in half the time. That is job enhancement in its clearest form: no layoffs, no replacements, just expanded capacity. 

            But creative identity does not scale linearly with asset count. More backgrounds do not automatically mean a stronger artistic vision. In fact, abundance can dilute intentionality. 

            As AI enhances production, the human role often shifts from originating every element to curating, refining, and selecting from machine-generated options. The artist becomes part creator, part editor. The writer becomes part storyteller, part prompt engineer. The designer becomes part world-builder, part systems orchestrator. That shift is subtle, but profound. 

            When creators move from author to editor, the center of gravity in the creative process changes. Enhancement increases capability, but it can also redistribute authorship. The critical question is now whether AI makes creatives more productive. It does. The question is whether enhanced productivity deepens creative vision, or gradually distances humans from the expressive core of the work. 

            Why Creativity Matters More Than Efficiency

            People care deeply about the emotional resonance of games. A compelling narrative arc, a haunting melody, a dialogue choice that feels meaningful – these aren’t just outputs, they’re expressive decisions. They reflect human imagination and carry cultural and artistic weight. 

            Creativity in games operates at multiple levels, like visual storytelling, narrative voice, mechanics design, and audio identity. Generative AI tools can do all of these. Large-language models can produce thousands of lines of dialogue in seconds; image synthesis tools can generate environments, characters, and textures from prompts; music AI can compose adaptive scores that fit different gameplay contexts.

            The Limits of Machine Creativity

            When AI generates a piece of music, art, or a story beat, it fundamentally recombines patterns learned from human-created data. There is no subjective inspiration, no lived experience, no intentional aesthetic choice behind the work, only statistical inference. That distinction is more than philosophical. It affects the game’s identity.

            Consider two hypothetical scenarios:

            1. A studio uses AI to generate placeholder environment concept art that artists later refine
            2. A studio uses AI to generate final environment assets with minimal human input

            In the first scenario, AI accelerates workflow, but human creativity still sets the artistic direction. In the second, the machine’s biases, training data limitations, and opaque generative patterns increasingly define visual identity, potentially diluting the studio’s artistic voice.

            This pattern repeats across other domains. AI-generated dialogue might be serviceable, but will it capture the nuance of lived human experience in a way that players recognize as authentic? These are not questions with easy answers, but they are central to the cultural value of games.

            The False Comfort of Economic Framing

            Much public discourse has focused on job displacement. Partly because economic arguments are easier to quantify. People can point to employment numbers, wages, and productivity curves.

            Creative impact, on the other hand, is harder to measure. When we frame the AI debate primarily in terms of jobs, we implicitly assume that creative work is fungible, that one person’s artistic contribution is interchangeable with another’s, including a machine’s. But in the arts, identity matters. Players choose games not just because they work well, but because they express something unique – a vision, a style, a tone. AI might replicate style, but it can’t originate meaning.

            Redefining Roles, Not Replacing Them

            That’s not to say generative AI has no place in game development. If used thoughtfully, it can be a powerful collaborator, a brainstorming partner, a rapid prototyping tool, and a way to push creative boundaries. 

            For example, writers using AI to generate dozens of narrative alternatives in seconds may help them explore more possibilities before committing. Whereas level designers using AI to test countless iterations of mechanics can see what feels most engaging.

            In these cases, AI augments human creativity, not replaces it. It expands the sandbox in which creators play. But this requires deliberate choices. Studios need creative leadership that understands when AI is a tool and when it’s overreaching. There must be clear boundaries around authorship and safeguards to ensure that AI outputs serve the human vision, not obscure or subsume it. 

            Creativity as a Competitive Advantage

            There’s another economic point that circles back to creativity: in a crowded market, artistic distinction is a competitive advantage. According to industry analysis, the global video game market is projected to exceed $300 billion by 2027, but growth is slowing in core segments, leading studios to compete fiercely on creative differentiation rather than sheer production volume. 

            In a world where everyone has access to the same AI tools, technical efficiency alone won’t distinguish one game from another. Games that lean too heavily on generic, AI-generated material risk becoming indistinguishable. 

            So the paradox emerges: AI might make game creation easier, but that very ease could erode the uniqueness that makes games worth playing.

            The Future of AI in Games Should Be Artistic, Not Automatic

            We should absolutely have conversations about job training, economic transition, and the ethics of data usage. But those debates should not eclipse the most important question: how do humans and machines collaborate to create meaning?

            Creativity is not an economic output. It’s a human act of expression. In video games, where storytelling, mechanics, visuals, and sound converge, that act of expression is the beating heart of the medium. 

            If we allow AI to take over the mechanics of creation without guarding the human spark that animates it, we risk more than job shifts; we risk a homogenized cultural future where games lose their soul. 

            The real AI debate is about creativity and ensuring that machines support human expression, rather than replace it. 

            Learn more at xsolla.com

            • Data & AI
            • Digital Strategy

            Author

            Helen Richardson

            Published

            28 May 2026

            Estimated Read time

            4Mins

            Helen Richardson, Insurance Senior Product Manager, UK and Ireland, LexisNexis Risk Solutions, on why SCV is not just a data capability, it is a core enabler of InsurTech innovation

            Insurance protects what matters, from our health to our homes, yet access to that protection can remain uneven. The UK Government’s Financial Inclusion Strategy highlights that renters, the self-employed, and lower-income households still face affordability and accessibility barriers.

            Cost is only part of the issue. A more fundamental challenge is how well insurance providers and insurtechs understand their customers. When customer visibility is limited, delivering personalised, relevant products at scale becomes difficult.

            A Single Customer View

            This is where a single customer view (SCV) becomes critical. For InsurTechs, SCV acts as a foundational data layer – similar to customer data platforms in banking and payments. It is powering intelligent products and underpinning the data-driven models. These drive growth and differentiation for the FinTech and open opportunities for customers.

            In many traditional insurance environments, customer data remains siloed across products. There is little interaction between motor, home, health, pet and travel. The result is fragmented, incomplete customer profiles. For InsurTechs who are building digital-first, scalable models to support their customers, this fragmentation of information could directly constrain automation. AI performance and real-time decisioning can be affected.

            A single customer view changes this. By unifying data, it enables a continuous, contextual understanding of the customer. Mirroring FinTech approaches to dynamic profiling, adaptive pricing, and real-time risk assessment. This is already evident in InsurTech models such as usage-based, on-demand and embedded insurance.

            With this broader view, pricing aligns more closely to actual risk. Interactions become more relevant, and customer journeys are significantly streamlined. Customers no longer need to re-enter data or navigate disjointed processes. Crucially, SCV can also support embedded insurance. It is one of the fastest-growing InsurTech opportunities, by allowing protection to be delivered seamlessly within digital ecosystems.

            Data Connectivity

            Delivering this requires accurate data connectivity, with identity resolution at its core. Solutions such as LexID® for Insurance, a scalable linking technology leveraging a unique identifier, enable organisations to reconcile fragmented records into a single, trusted profile. For InsurTechs, identity resolution can help make real-time, data-led experiences operationally viable at scale.

            SCV also plays a critical role in recognising vulnerability. People may become vulnerable for many reasons, whether due to health conditions, financial capability, resilience, or significant life changes like redundancy or separation. These indicators rarely appear one place, but when they surface, they need to be understood across the entire organisation.

            Linking data across functions such as sales, underwriting and claims ensures that every team works from the same version of the truth. This reduces the risk of incomplete or conflicting information shaping decisions and allows insurance providers to adjust how they communicate and respond, ensuring that customers -particularly those in vulnerable situations – are treated with appropriate care. In practice, this means fewer repeated questions, fewer disjointed interactions, and a more consistent, empathetic experience. A well-executed SCV therefore supports both inclusion and compliance with Consumer Duty, while delivering transparency and auditability required in regulated, digital-first environments.

            When these insights are embedded into a unified view, organisations can respond dynamically to life events – adapting products, communications and support in real time. This reflects the broader shift toward event-driven, responsive insurance models, leading to stronger engagement, improved retention and better customer outcomes.

            Robust Governance

            None of this is possible without robust data governance. Building an SCV requires strong controls around consent, accuracy and transparency, as well as collaboration with data partners who can responsibly connect and enrich data from multiple sources in a compliant and ethical way.

            When done well, identity resolution transforms disconnected data into actionable intelligence across the lifecycle – from quote to renewal to claims. This can enable more accurate pricing, frictionless journeys, improved fraud detection as well as higher retention, while supporting regulatory expectations around fairness and vulnerability.

            These capabilities are central to modern InsurTech propositions. SCV enables earlier detection of financial strain, deeper behavioural insights and stronger predictive models, enhancing both risk selection and customer engagement.

            Looking ahead, the direction is clear. The Financial Inclusion Committee has called for protection that reflects real lives – circumstances, transitions, and evolving risks. InsurTechs could have a strategic advantage, but only if they can see the customer clearly across every interaction.

            A robust single customer view makes that possible. It bridges fragmented systems, unlocks scalable personalisation, and enables the next generation of embedded, adaptive insurance.

            In that sense, SCV is not just a data capability, it is a core enabler of InsurTech innovation, scalability, and competitive advantage.

            Learn more at lexisnexis.co.uk

            • Embedded Finance
            • InsurTech

            Author

            Chris Tredwell & Charis Thomas

            Published

            Estimated Read time

            5Mins

            Chris Tredwell, Chief Operating Officer and Charis Thomas, Chief Product Officer at Aqilla, on why the question is no longer whether to adopt AI, but whether processes, governance structures and training pathways are ready for the workforce

            Have you ever got into an old car with a Gen-Zer? If they were driving, chances are you wouldn’t have got very far. A recent survey has found that 39% of 14–29-year-olds couldn’t identify an ignition key. Proof, if it were needed, that once technology advances, old ideas are quickly forgotten. This isn’t just happening in cars. The internet and social media have produced their own native generations – people who have never known a world without those technologies.

            The same pattern is starting to emerge with AI. That means Gen Z and Millennials are about to experience a similar shift. The first wave of true AI natives will soon enter the workforce – a cohort that has never known a world without AI. 

            AI- The New Normal

            People’s reactions will largely depend on their experience with AI. But one thing is certain: these graduate and entry-level employees won’t need to be convinced of its value. They’ve already seen what it can do, so if it’s missing, disbelief – or frustration – is likely to follow. It’s a bit like broadband. Here in the UK, it’s simply the standard we all expect. We don’t stop to think about how that connectivity reshaped our lives, helped us work from home or allowed us to stream high-definition media.

            Many organisations are still in the early or experimental phases of AI adoption. They might be using the technology to automate basic email inbox management and take meeting minutes. Meanwhile, those further ahead of the curve are exploring more advanced tools and assessing where automation can be safely deployed, particularly for reporting and analysis.

            But AI natives won’t see these use cases as experimental. In fact, they probably wouldn’t even refer to them as use cases. It’s just normal, like using a search engine rather than visiting a library to carry out research.

            Prompting New Behaviour

            Perhaps the biggest difference, however, is where organisations may integrate AI into their existing workflows, AI natives are more likely to structure work around it from the outset.

            For them, work tends to start within an AI system, defining the objective clearly, setting constraints, and effectively “briefing” it, before iterating quickly and refining outputs as they go. For AI natives, this kind of prompt-based mindset isn’t a specialist skill; it’s simply how they approach tasks.

            This is a fundamental shift. For AI natives, the question isn’t “Should we use AI here?” It’s “Why can’t I use it for this piece of work?” When their expectations collide with more cautious, process-led environments, friction is almost inevitable. Not because one approach is right and the other is wrong, but because both sides are starting from completely different assumptions.

            Skills Transfer and Mentoring

            But how does the need for AI natives to understand and work through basic manual processes coexist with intuitive prompt-based thinking? Should AI use come with experience-based restrictions in the finance sector? For example, do your three years first, and then you can use the tools.

            It’s probably not what AI natives want to hear, but there is logic behind the approach. Learning the manual processes behind automation will enable new recruits to apply the necessary checks and balances to system outputs — putting them in a position to verify data rather than passively accept it.

            Without taking this step, there’s a real risk that people will lose the ability to question the outputs they’re working with. That, in turn, has implications for how people are taught. Whether in an educational setting or on the job, that training needs to help AI natives understand the logic behind the systems they’ll be working with.

            Lurking in the Shadows 

            If AI natives encounter friction when trying to use the technology, there’s a risk they’ll seek informal workarounds. Organisations have seen similar patterns before with personal devices or early cloud adoption. With AI, the risks are more focused on data, traceability, and accountability than on system access and security, though these remain important considerations.

            Rather than restricting AI use, organisations are already beginning to reshape it by building oversight into how systems are used. That might mean making the AI’s working assumptions more visible and requiring humans to validate outputs. It might also require AI systems to signal their confidence in those outputs and to request manual checks. Over time, this reduces risk and creates an environment where people can work confidently with AI without losing sight of who is responsible.

            This approach also challenges a common narrative. Much of the current discussion around AI focuses on job displacement, but the reality is more nuanced. The issue isn’t a simple replacement of human intuition and experience, but how those qualities evolve alongside increasingly capable systems.

            Rather than removing the need for people, this managed shift reinforces it. Greater emphasis is placed on human-in-the-loop models, in which individuals with a deep understanding of AI can interrogate, challenge, and interpret system outputs.

            A different starting point

            So, what happens next? AI tool adoption, for sure. But it goes far deeper than that. Getting ready for AI natives means shifting to a different starting point – and learning to “live in the prompt”.

            As AI natives begin entering the workforce and eventually move into leadership roles, the expectation won’t be that AI is introduced; it will be that it is already there. That shift reshapes how people think, learn, and approach tasks from the outset. It will also change how tasks are conceived, carried out and reviewed. The ability to configure, interrogate and challenge systems will become as important as the ability to interpret their outputs.

            For organisations, the question is no longer whether to adopt AI, but whether their processes, governance structures and training pathways are ready for a workforce that already assumes it – and will expect to work that way from day one.

            Learn more at aqilla.com

            • Artificial Intelligence in FinTech
            • Data & AI

            Author

            Dan Brightmore

            Published

            Estimated Read time

            4Mins

            Read in brochure format

            Welcome to the latest issue of Interface magazine! Click here to read the latest edition! Precisely: Reframing Cybersecurity for the…

            Welcome to the latest issue of Interface magazine!

            Click here to read the latest edition!

            Precisely: Reframing Cybersecurity for the AI Era

            This month’s cover star, Marcus Johnston, deploys a leadership philosophy that blends empathy, operational discipline and pragmatic innovation as Precisely scales cyber solutions to meet the demands of regulation and accelerating digital risk. Learn how this CISO is reframing Cybersecurity for the AI era. “I view the role of InfoSec as a trusted partner advisor in the goals of the company to innovate, to introduce new technology, new platforms, to constantly be on the watch for changes in the threat environment.”

            Ireland Department of Agriculture: Growing a Culture of IT Transformation

            Louise McKeever, former Chief Information Officer and Head of Operations at Ireland Department of Agriculture, discusses the changes she drove at the agency and how a culture of IT transformation has gone on to thrive. ““My vision is that the Department’s data has consistency, is securely accessible, understood, and trusted… We have modernised our technology landscape, strengthened our capabilities, and most importantly, made a real difference in how we serve the Department and the sector.”

            Washington State Dept of Natural Resources: Building Trust & Enabling Transformation

            Washington State’s Department of Natural Resources (DNR) has been on a journey of building trust to enable transformation. We go inside that technology journey with CIO Liz Lewis-Lee on a mission to deliver reliable, effective technology. “At the DNR, all of our technology has to work to support all of the people all of the time, both in the office and out in the field.”

            Virginia SCC: Building a New Cybersecurity Landscape

            Glendon Schmitz, Chief Information Security and Privacy Officer at Virginia State Corporate Commission (SCC), discusses the exciting challenges of building a successful cybersecurity function. ““If everything’s a priority, nothing is. So understanding one’s lack of resources from a funding perspective and also the human side is important.”

            Redefining Technology Leadership Through People, Culture and Business Outcomes

            From the Music Industry to the Boardroom, IT leader Angie Fyke has blazed a trail for women in tech – she tells Interface her story of redefining technology leadership through people, culture and business outcomes from telcos to retail and today at Ontario Medical Supply. “The feedback I value most is when teams say they’re clearer, more confident, and able to move faster – that’s when you know change is working.”

            Also in this issue, we learn from Claroty why critical infrastructure can’t afford to bolt on resilience; hear from NetApp on why defending against AI-powered cyberattacks requires AI-powered defence; and OVHcloud explain why Blockchain is crucial for the future of trust in AI.

            Click here to read the latest edition!

            • Cybersecurity
            • Data & AI
            • Digital Strategy
            • Infrastructure & Cloud
            • People & Culture

            Author

            Rob Demain

            Published

            22 May 2026

            Estimated Read time

            5Mins

            Rob Demain, CEO at e2e-assure, on why the clock is ticking when it comes to IT/OT convergence for Critical National Infrastructure (CNI) and associated organisations

            Many critical national infrastructure (CNI) operators lack the ability to protect their infrastructure despite the UK being subjected to daily sub-threshold cybersecurity attacks, according to the Strategic Defence Review 2025. It’s a situation that the Network and Information Systems (NIS) regulations, introduced back in 2018, sought to prevent. But since its inception, just over half of the operators of essential services have updated or strengthened their existing policies and processes, leaving many woefully unprotected. 

            In desperate need of reform, NIS is set to be superseded by the Cyber Security and Resilience Bill (CSRB), which is expected become law later this year, at which point a consultation on implementation proposals will commence followed by secondary legislation and an adjustment period for stakeholders. The bill will broaden the scope to include other organisations deemed critical to the national economy i.e. data centres, Managed Service Providers (MSPs) and critical suppliers. Plus, the government reserves the right to extend those categories still further as part of its ‘future proofing’, which will enable changes to be made to the act to accommodate emerging threats and potential targets. 

            New Demands 

            All of these new entities will need to comply with the Cyber Assessment Framework (CAF), which lays out expected cybersecurity and resilience outcomes. First published in 2018 to support NIS, it has undergone a number of revisions since, with v4.0 released in August 2025. This version places a far greater emphasis on proactive security and decision making based on real threat intelligence. As well as adding new contributing outcomes on understanding threats and secure software development and support, it also expands the sections on security monitoring and response and recovery, while an entirely new category has been added on threat hunting.   

            All of this points to a far greater emphasis on being able to demonstrate assurance and proactively monitor all aspects of CNI infrastructure and that means more scrutiny of both IT and Operational Technology (OT). Until recently, securing OT wasn’t seen as a priority. These systems were chiefly concerned with maintaining system availability and minimising downtime. But their increased integration with IT systems to connect with the industrial Internet of Things (IIoT) and deliver real-time monitoring, for example, are exposing these systems to attack, with threat actors able to move laterally from one environment to the other.  

            The threat posed by IT/OT convergence is well known, but it continues to be the Achilles heel of CNI, as revealed by the Volt Typhoon attack. This saw Chinese nation state actors maintain persistence across CNI in the USA since at least 2021 through the use of Living off the Land techniques, illustrating just how insidious and sustained these cybersecurity attacks can be. 

            Securing IT/OT Systems 

            It’s these types of threat the CAF addresses through its risk and asset management requirements. Organisations must risk assess systems with respect to their dependencies and interactions with other systems such as IT/OT, and document and understand those dependencies. But other complementary frameworks can also be used to map IT/OT system security, such as the ISMS within ISO27001 from an IT perspective and IEC 62443 from the OT side, in addition to ISO/IEC 27019 for process control systems. 

            Being able to follow these frameworks will require organisations to increase their security monitoring of both IT and OT and the transparency of their processes. They will need to transition from being reactive to proactive, and become resilient and risk informed, which will mean many will have to change their approach. These are really the only options available to them in this respect if they are to move the resilience needle. 

            The first is to decentralise and harden OT systems while keeping them segregated from IT. However, hardening alone can’t keep pace with digital transformation. Many OT assets cannot support multi-factor authentication (MFA) or accommodate rapid patching because they are downtime sensitive. So, surface hardening alone won’t confer the resilience needed long term.  

            The second option is to manage IT and OT together by giving everything an identity in a converged environment, but to do that you need to move the monitoring of OT into the Security Operations Centre (SOC). Centralised monitoring allows threats to be detected across both IT and OT networks, for teams to monitor east-west traffic, and to correlate alerts that might otherwise appear unrelated. And it’s this centralised management that will provide the visibility and control needed to improve IT/OT resilience.  

            Converged Cybersecurity 

            Such a converged SOC doesn’t just offer continuous visibility over IT, industrial control systems (ICS), OT and cloud environs, but also the real-time triage of critical alerts. These might include unauthorised PLC logic changes, unsafe set-point writes, abnormal OT protocol behaviour, lateral movement in ICS DMZs, OT malware,or unauthorised remote access into OT environments. These alerts are then grouped by operational impact, such as whether they present a safety critical risk or could lead to service degradation, so that they can be prioritised. Weekly threat hunts and detection surface validation over distributed environments provide the threat hunting capabilities needed to meet the CAF requirements and the SOC evidences and provides that all important audit-ready compliance mapping to meet the demands of other frameworks too such as IEC 62443, and ISO/IEC 27019. 

            Whether standing up a converged SOC internally or outsourcing, this capability is the most efficient way to adapt to the tightening regulations, particularly as we can expect ‘future proofing’ to lead to yet more demands. The emphasis is now firmly focused on the proactive monitoring of both IT and OT systems together, given their growing dependencies, so it makes sense for those organisations in scope – as well as those who could soon be – to begin to move their OT monitoring from the plant and into the all-seeing all-knowing enclave of the SOC.  

            Learn more at e2e-assure.com

            • Cybersecurity
            • Cybersecurity in FinTech
            • Digital Strategy
            • Fintech & Insurtech

            Author

            Adnan Patka

            Published

            21 May 2026

            Estimated Read time

            5Mins

            Adnan Patka, Enterprise Manager – Blockchain, AI and Web3 at OVHcloud, on why the convergence of blockchain and AI heralds a brighter future for both

            Anthropic’s Mythos team recently assessed various LLM technologies, scoring ChatGPT against Gemini, Grok, Claude and others. The metrics included trustworthiness, but also user deception, sycophancy, encouragement of user delusion and co-operation with human misuse.

            Clearly, there’s no doubt that AI can be an enormously positive transformational force: the development of AlphaFold, whose founders won the Nobel Prize, showed us that very clearly. But when AI is being assessed for encouraging ‘user delusion’, it’s clear that it still has a PR problem.

            In fact, AI has more than just a PR problem: research from McKinsey highlighted that although almost two thirds (64%) of decision-makers reported that AI was accelerating innovation within their organisation, less than half (39%) could actually report positive financial impact on the business as a result.

            The AI Trust Gap

            AI doesn’t exist in a vacuum, and the interplay of AI with other technologies is beginning to come into focus. We see that blockchain has great potential to complement AI, providing the transparency and trustworthiness that it needs. In a recent study of Web3 professionals, almost three-quarters (70%) said that blockchain had the potential to fill AI’s trust gaps.

            But where else are these issues coming from?

            It’s overly glib to say that people and businesses don’t always trust AI. What our research told us was that 32% of Web3 experts believe that privacy is AI’s main challenge. Historically, AI systems have been shown to be biased, and data has often been collected by AI systems for training or other uses – sometimes without user permission. One in seven (14%) of the study also reported that they believed AI needed more transparency to be trustworthy.

            Blockchain’s Potential for Supporting AI

            Blockchain is well-placed to help tackle these issues. Blockchain was originally created as a database with clear traceable links between items, without the need for a single governing authority.

            For example, consider an AI system which optimises food supply chains, allowing a supermarket to maintain lower prices. In an environment where people may distrust AI, they may suspect that the system is simply buying lower-quality stock and shipping it more affordably, potentially meaning shorter shelf-lives and less tasty tomatoes! However, blockchain systems can help to track the provenance of these items, proving where the tomatoes were grown, when, by who, and the subsequent shipping and treatment of them, all in a publicly accessible ledger.

            Blockchain systems also automatically check the validity of transactions and link them together in a way that makes fraud very difficult. Changing one transaction generally requires changing the entire ‘course of history’ or having significant control (specifically, controlling over half of the nodes) of an entire blockchain, which is usually unfeasible in most large, public blockchains today. 

            Delivering Benefits with Blockchain

            There are also a number of cases where blockchain can bring functionality benefits to AI systems. For example, as we saw, privacy is a significant concern for AI users, but blockchain systems are able to authenticate securely without exposing user credentials, through self-sovereign identity. This is especially useful where AI systems need to interface with other tools that might not be as trustworthy as the core system.

            Blockchain principles can also support data exchange in federated learning systems, where data is shared between different machine learning platforms. Blockchain can establish the validity of this data while still preserving its integrity and user privacy. This means that AI can be trained on data that is consistent and robust – without breaking trust.

            There are a huge number of applications for this kind of technology, but unfortunately, it’s not all plain sailing.

            Blockchain’s Challenges

            The industry is undergoing a huge change today. Interest in stablecoins – a crypto asset which is attached to the value of a more stable asset like a traditional (fiat) currency or other physical commodity – has quadrupled since October 2025. Large, well-established organisations are considering what private and public blockchains can do for them, which could ultimately have a positive and stabilising impact on the regular bear and bull cycle that the crypto market experiences.

            However, some more negative parts of blockchain’s legacy persist. Almost two thirds (61%) of our research sample said that concerns about blockchain’s credibility were having an impact on its integration into the enterprise ecosystem. This includes blockchain’s links with the more unsavoury side of cryptocurrency, including cybercriminal activity. Over half (54%) simply said that there wasn’t enough understanding about blockchain’s capabilities and what it could really do, holding IT decision-makers back from making the most of it.

            At the same time, this isn’t a one-way street. AI’s capabilities – in particular, automated, agentic AI – can carry out blockchain activities without needing user input. For example, AI can issue transactions or analyse data and summarise it for monitoring systems. There is tremendous potential for AI to support many different parts of the blockchain ecosystem, summarising trends, automatically carrying out tasks, or executing smart contracts without human intervention.

            A Mutually Beneficial Relationship

            Blockchain and AI have enormous potential to support each other. There are some organisations already starting to integrate blockchain into AI and vice versa, but it will still be a number of years before the relationship is mature. That said, two-thirds of the Web3 experts we surveyed (67%) believed that it’d take roughly one to four years, with only eight percent saying that it might take five to six years.

            Indeed, given the enormous benefits that blockchain can bring to AI, not to mention the rapidly increasing acceptance of blockchain technologies across the financial sector in particular, the convergence of these two technologies is almost inevitable. If we can push past the challenges, the future is bright for both blockchain and AI.

            Learn more at ovhcloud.com

            • Artificial Intelligence in FinTech
            • Blockchain & Crypto
            • Data & AI
            • Digital Strategy

            Author

            Adam Gale

            Published

            Estimated Read time

            4Mins

            Adam Gale, Field CTO for AI & Cybersecurity at NetApp, on why cybersecurity in the AI era will depend on two capabilities: detecting abnormal behaviour as early as possible and ensuring trusted data can always be restored

            How can we not talk about cyberattacks when talking about AI? Barely a week passes without another story on AI-generated scams, deepfake voice fraud or ransomware becoming more sophisticated. Security teams are starting to see the effects in their own environments too. In many organisations, the first sign is simply the number of alerts appearing in security dashboards each morning.

            Generative AI tools are lowering the technical barriers that once limited cybercrime. Cybercriminals can use it to generate convincing phishing emails in almost any language, code malware variants in minutes, or automate the process of scanning infrastructure for vulnerabilities. In fact, cyberattacks are now being generated at industrial scale. The good news is that AI can be an equally powerful tool for organisations in defending against this deluge.

            When threats multiply faster than defences

            It’s important to understand how quickly – and how far – Ransomware-as-a-Service has come in recent years. Large language models (LLMs) can now generate convincing phishing campaigns tailored to specific roles or individuals. Threat actors are already using them to scrape publicly available data and craft highly personalised emails that mimic internal communications. Malware authors are experimenting with AI to produce polymorphic code that changes its behaviour between executions, making signature-based detection far less effective.

            Voice cloning is another rapidly emerging risk. In several reported cases, help desks and IT support teams have been persuaded to reset credentials after receiving calls from voices that sounded identical to employees or senior executives. What previously required careful social engineering can now be automated and scaled.

            Many of these incidents succeed because cybersecurity platforms were built around a fundamental assumption: threats evolve incrementally and can therefore be detected by identifying known patterns. These include signature-based detection, rule-based alerting and static threat indicators. However, the problem is that this model breaks down quickly when cybercriminals can generate new variations faster than those signatures can be updated.

            Additionally, monitoring tools generate huge volumes of warnings, many of which turn out to be harmless, but IT security teams still need to investigate them. When hundreds of alerts appear each day, this creates a risk of alert fatigue which may see genuine threats hidden in a sea of false alarms. As a result, malicious actors are now operating at machine scale while many defensive processes still rely on human triage, and this mismatch is becoming one of the defining problems in modern cybersecurity.


            Fighting AI with AI

            On the bright side, all that AI-powered automation can also be used for defence – not just offence. For example, AI can strengthen cybersecurity by examining activity across systems and data and identifying behaviour that deviates from normal operations.

            Instead of relying only on known hacking techniques, AI-driven systems can monitor how data is accessed and modified across environments. If behaviour changes unexpectedly, systems can raise alerts or trigger automated responses. Detecting anomalies early matters even more as breaches become more automated, because the earlier suspicious activity is identified, the easier it is to contain the impact.

            Automation also reduces the burden on security teams. Analysts spend less time reviewing routine alerts and focus more on investigating genuine threats. As organisations increasingly rely on AI systems, protecting the data those systems depend on becomes central to cybersecurity strategy.

            This is especially crucial today, as modern enterprise data environments are highly distributed. Data is constantly moving between on-premises systems, cloud platforms, analytics pipelines and AI training environments. In turn, every transfer, replication process or API connection creates another potential attack surface.

            This also means that storage plays an elevated role in supporting cyber resilience. Capabilities such as storage-level anomaly detection, autonomous ransomware protection and immutable snapshots allow organisations to identify suspicious data activity and preserve trusted recovery points. If systems are compromised, clean versions of data can be restored quickly without paying ransoms or rebuilding environments from scratch.


            Building Confidence in AI Environments

            AI is rapidly industrialising cybercrime. Malicious actors can now generate new techniques, test them and deploy them at a pace that traditional security operations struggle to match. Therefore, defence strategies must adapt accordingly. That means shifting from signature-based detection to behavioural analysis and from manual investigation to automated response. In other words, defending against AI-powered threats will increasingly require AI-powered security.

            Ultimately, cybersecurity in the AI era will depend on two capabilities: detecting abnormal behaviour as early as possible and ensuring trusted data can always be restored. In a world where attackers can generate new threats in minutes, organisations that cannot protect and recover their data quickly will struggle to trust their systems at all – including the AI tools they increasingly rely on.

            Learn more at netapp.com

            • Cybersecurity
            • Data & AI

            Author

            Brian Gaynor

            Published

            20 May 2026

            Estimated Read time

            5Mins

            Brian Gaynor, European Chief Executive at BlueSnap, on why the future of European payments sovereignty may ultimately lie in fundamentally reshaping the terms under which existing networks operate

            The recent move by UK bank bosses to explore alternatives to Visa and Mastercard has reignited a long-running debate about payments sovereignty in Europe. At its core, the initiative represents a recognition that the continent’s financial infrastructure is overly dependent on two US-controlled networks, a vulnerability that carries both economic and geopolitical risk.

            The strategic logic is sound. Across Europe, governments are increasingly scrutinising their dependence on American technology and infrastructure. From enterprise software to cloud services, the push to develop sovereign capabilities is gaining real momentum, exemplified by moves such as the German state of Schleswig-Holstein’s decision to phase out Microsoft 365 in favour of open-source alternatives. It was only a matter of time before payments, arguably one of the most critical layers of economic infrastructure, came under the same lens.

            As the world’s two dominant payment processing networks, Visa and Mastercard exert enormous influence over global commerce. For the UK and Europe, that level of concentration raises legitimate concerns about market control, pricing power, and the strategic vulnerability of relying on infrastructure governed from abroad. Financial institutions and regulators are right to take these concerns seriously and to explore what alternatives might look like.

            But recognising the problem is one thing. Solving it is quite another. The difference between payments and other areas of technology is fundamental, and in its current form, Europe’s bid for greater payments autonomy faces formidable obstacles. Obstacles that no amount of political will alone can overcome.

            Beyond a technology problem

            Payments are fundamentally different from other areas of technology. Unlike enterprise software, there is no straightforward ‘open source’ equivalent waiting in the wings. Building a viable alternative to global card schemes is not just a technical undertaking; it requires merchants, consumers, and banks to adopt it simultaneously.

            To date, the most credible European initiative aimed at reducing US dependency has been Wero, a unified European account-to-account digital wallet and instant payment system which is being rolled out across the region. However, it remains limited in scope. It is not yet available across the entire EU, has only recently begun supporting eCommerce payments, and does not yet offer key capabilities, such as Near Field Communication (NFC). 

            Interoperability with other local schemes is planned, but meaningful traction will take time and will likely require sustained government backing. Even then, success is far from guaranteed.

            The adoption paradox

            The fundamental issue is adoption. For any new payment system to succeed, it must achieve simultaneous scale among consumers and merchants. Without a sufficiently large number of users on both sides, any new payment scheme will struggle to compete. Consumers and merchants in the UK and Europe are deeply accustomed to card payments, and there is little incentive to switch unless the alternative offers a significantly better experience. At present, it is not evident that this is forthcoming – card acceptance is deeply embedded, secure and cost-effective, making it a difficult incumbent to displace. Indeed, previous regulatory interventions limiting interchange fees in Europe have made card processing extremely cost-effective and removed a typical entry point for a challenger to win on price. 

            Comparisons to systems like Unified Payments Interface (UPI) in India or Pix in Brazil are often cited, but they are misleading. In both cases, adoption was driven by the shift away from cash rather than by the displacement of well-established card networks. In both countries, card penetration was much lower than it is in the UK and Europe. By contrast, the UK and Europe already have highly mature card ecosystems, making behavioural change far more difficult.

            Pragmatism over ambition

            Instead of pursuing outright replacement, businesses should prioritise building resilience. Governments are already advising citizens to keep cash on hand for emergencies, highlighting concerns about systemic vulnerabilities, ranging from connectivity failures to wider infrastructure risks. For merchants, this means diversifying payment options and working with providers that can offer alternative routing in the event of scheme outages.

            In the longer term, the most realistic outcome is not the displacement of Visa and Mastercard, but their transformation under regulatory pressure. Government intervention could drive greater localisation of processing within the UK and Europe, enabling the use of local technology and intellectual property rights to operate schemes more locally while remaining aligned with global networks. Such a shift would resemble arrangements that existed before Visa Inc. acquired Visa Europe, restoring a measure of regional control without severing ties to the wider global payments ecosystem. It would also address the core concern driving this debate: that critical financial infrastructure should not be entirely subject to decisions made outside of Europe.

            This kind of structured adaptation would allow governments to address concerns about sovereignty and resilience while preserving the considerable benefits of globally integrated payment networks, a pragmatic compromise that serves the interests of all stakeholders.

            The future

            The ambition to develop credible alternatives to the Visa-Mastercard duopoly is justified, and the UK banking sector is right to be raising the issue. But the expectation that new schemes will meaningfully supplant entrenched global networks underestimates just how deeply embedded card payments are in European economic life. Unlike in markets such as India or Brazil, where digital payment adoption was built on replacing cash, the UK and Europe face the far harder challenge of displacing systems that already work well for most users. For Visa and Mastercard, adaptation under sustained regulatory and political pressure is the far more likely outcome than displacement. The future of European payments sovereignty may ultimately lie not in building something entirely new, but in fundamentally reshaping the terms under which existing networks operate.

            Learn more at bluesnap.com

            • Digital Payments
            • Neobanking

            Author

            Nick Haan

            Published

            Estimated Read time

            5Mins

            Nick Haan, Field CTO at Claroty, on how resilience will define which organisations stay operational when the next wave of disruptive attacks hits

            In October last year, Spain experienced one of the most dramatic infrastructure failures in modern European history. A sudden collapse in grid frequency left more than 50 million people without power, grounded flights, halted trains, and shut down businesses across the Iberian Peninsula. Fortunately, it was down to a technical issue known as an overvoltage event, not a hostile act by threat actors. 

            But the incident still delivers an important lesson: when critical infrastructure fails, the consequences will quickly cascade.

            Geopolitical tensions are running high and state-sponsored actors increasingly target operational technology environments directly. The conditions for a similarly disruptive incident – this time deliberate – are building. 

            Withstanding these disruptive incidents requires building resilience into critical infrastructure at a foundational level. Not bolting it on as an afterthought. But with so many facilities designed and built for a different age, where do operators start? 

            The infrastructure threat is no longer theoretical

            State-sponsored threat actors are no longer simply probing IT networks for data – they are targeting the operational technology that controls physical processes, including energy generation, water treatment, transport systems, and manufacturing. 

            The list of targets is expanding too, as critical infrastructure now encompasses airports, telecoms networks, hospitals, and commercial data centres. All of these areas are under increased cyber threat, and that makes it even more important that operators get on top of it now.

            Data centres in particular will be a growing concern. Modern hyperscale facilities consume enormous amounts of power, making them uniquely dangerous nodes on the grid. If you bring a data centre down in one go, the impact on the grid will be immense. 

            Taking a centre offline creates a spike in one direction, while bringing it back up creates another. A coordinated cyberattack targeting multiple facilities simultaneously wouldn’t just take those sites offline, but could also destabilise the wider grid they draw from.

            The problem with bolting security on

            Most infrastructure operators are well aware of the increasing cyber threat, but it competes with many other challenges for resources.

            A core difficulty facing most operators is that they are not starting from a blank page. The systems that control physical operations were built for reliability and longevity, not security. Many have been running for decades, and some predate the internet entirely. 

            Replacing them wholesale is not a realistic option, and even widespread retrofitting may not be possible where taking a system offline could interrupt critical services.

            But that doesn’t mean that nothing can be done. The real opportunity in retrofit scenarios lies not in the operational technology (OT) assets themselves but in the infrastructure that surrounds them. Replacing a complete factory won’t happen – some of those components will remain old. But the switches, the firewalls, the IT-type infrastructure that underpins OT operations: that is what needs replacement. 

            Without modernising that network layer, even strong security tooling cannot do its job. If you know you need to do segmentation but haven’t got the switches to enforce it, what are you going to do with the information?

            This is where resilience-by-design comes in. Organisations that treat cybersecurity as something to be addressed after the operational priorities are settled will find themselves permanently catching up – technically constrained, financially stretched, and exposed. 

            Resilience by design in practice

            For organisations building new infrastructure, the opportunity to get this right exists – but only if security is treated as a design requirement from the earliest stages. The architectural choices, the network topology, the equipment specifications: these need to account for cybersecurity years before the first brick is laid, not at commissioning when the options have already narrowed.

            But inbuilt resilience is still achievable for existing operators. The starting point is understanding what you actually have. An up-to-date asset inventory is the foundation of everything else, because you cannot protect what you cannot see. 

            From there, the question that should drive every security investment decision is simple: which systems would cause the greatest disruption if they stopped working? Security programmes built around that question will always outperform those built around compliance checklists.

            Organisational structure matters too. Bringing IT and OT under a single point of security responsibility is essential – one person accountable across both domains, with the authority and tenure to make decisions that outlast their own role. Security decisions made by people who know they are moving on in two years tend to reflect that horizon.

            Simplify, don’t accumulate

            Most critical infrastructure domains tend to operate at a slow pace, where changes are big but take time to build momentum. This is a poor fit for the fast-paced and increasingly hostile nature of the cyber threat landscape.

            However, there are immediate steps that operators can take now, without a major investment programme. Launching a programme to reduce redundancy and overlap is a quick win, especially for remote access systems. 

            Over time, most operational environments accumulate technology in layers. Each new vendor relationship, each maintenance contract, each operational requirement brings its own tools and its own access pathways. In one customer environment, we found 80 different remote access solutions in active use. 

            Reducing that to say, five or 10, is already a major security improvement – the complexity cost of those 80 solutions, in monitoring burden, policy management, and sheer number of potential entry points, far outweighs any operational convenience they provide.

            The principle is straightforward: stop adding more and start strengthening what you already have. Vendors naturally tend to push their own tools and access requirements, but organisations need to push back. Prioritise resilience, simplify the ecosystem, and eliminate the fragility that attackers are counting on. 

            Preparing for the next wave of digital risk

            The Iberian Blackout may not have been a hostile act, but it demonstrated what happens when critical infrastructure proves more fragile than anyone anticipated. The threat environment is not getting easier, and the interconnected systems that underpin daily life – energy, transport, communications – leave little margin for complacency.

            Those that have built security in from the foundation will be better placed to withstand incidents, recover faster, and continue serving the people and industries that depend on them. Those who haven’t will find that bolting it on after the fact is slower, more expensive, and less effective than doing it right from the start. In the year ahead, resilience won’t just protect systems – it will define which organisations stay operational when the next wave of disruptive attacks hits.

            Learn more at claroty.com

            • Cybersecurity
            • Digital Strategy

            Author

            Dave Silke

            Published

            19 May 2026

            Estimated Read time

            5Mins

            Dave Silke, Managing Director, EMEA & APAC at Centripetal, on reframing cybersecurity as a leadership discipline

            Cyber resilience is no longer a technology problem; it’s a leadership one. For many executives, cybersecurity risk has become background noise. It’s ever-present and, fair to say, it’s routinely acknowledged, but it’s rarely confronted with the urgency it demands. Breaches appear in the news with unsettling regularity, followed by familiar language such as “sophisticated attackers” and “unprecedented scale”. But once the moment passes, business for most continues as normal.

            This quiet normalisation of cyber incidents is not resilience. It is inertia, and it is costing organisations far more than balance sheets can show. The average global cost of a data breach, recorded by IBM, reached $4.44 million in 2025. This was driven primarily by operational disruption, lost business and recovery costs, but broken customer trust is harder to quantify and plays a significant role.

            Yet despite the scale of impact, many boards still treat cybersecurity as a technical line item. Something to insure against or react to when needed. It is viewed as a risk to manage rather than a capability to lead. That mindset is the real vulnerability.

            Inertia in the C‑suite

            Leadership inertia rarely comes from ignorance. It often emerges from success. Many organisations have spent decades refining governance, compliance, and audit processes that worked well in a slower, more predictable world. But today’s threat landscape is shaped by automation, AI, and global cybercrime‑as‑a‑service, and those same mechanisms can quietly hold organisations back.

            We remain deeply comfortable analysing failure. Forensic investigations, root cause reports and post‑incident reviews feel constructive, like we’re doing something about it. But in cybersecurity, understanding the last breach rarely prevents the next one. Attackers do not repeat themselves for our benefit, they adapt, and with AI, they now move faster than any human response cycle can match.

            Forensic investigations are simply about giving shape to chaos. But what if we didn’t need to let the chaos in in the first place?

            The Imbalance Between Speed and Focus

            Modern cyber threats have scaled and ransomware groups now operate like businesses. Initial access brokers sell compromised credentials and AI and ransomware-as-a-service lowers the barrier to entry. At the same time, leadership attention is finite; CIOs and CTOs are balancing cloud migrations, regulatory change, AI adoption, supply chain risk and workforce transformation often while CISOs and security teams drown in alerts that they cannot realistically action.

            The result is a dangerous disparity between leadership and attackers who operate at machine speed. We ask security teams to “do more” with dashboards overflowing with indicators, logs and alerts. But intelligence without application is noise, and noise breeds complacency.

            Intelligence was Once a Government Privilege

            For decades, threat intelligence was the domain of governments and military organisations. It relied on classified sources, cross‑agency collaboration, and the ability to process vast volumes of data in real time. That gap has now closed, and enterprises can now access the same categories of intelligence – from strategic, operational and tactical – previously reserved for nation states. Platforms enriched by AI and machine learning can ingest billions of indicators, correlate them at wire speed, and apply protections instantly across networks, cloud environments and endpoints.

            This matters because over 99% of exploited vulnerabilities are already known at the time of attack. Security AI and automation now demonstrably change outcomes. Yet, despite availability, intelligence‑driven security is still framed by many leaders as “advanced”, “complex” or “future‑state”. That framing is itself a symptom of inertia.

            Cultural resistance plays a significant role here. Research consistently shows that organisations reward risk avoidance over proactive change – even when data shows the cost of inaction is greater. The World Economic Forum’s Global Cybersecurity Outlook 2026 report highlights that organisational readiness and culture gaps are a major weakness in cyber security.

            Leadership signals matter. When cyber risk is discussed only after incidents, teams learn that defence is reactive by design. When budget cycles plan for recovery rather than prevention, attackers are given implicit permission to succeed.

            On the other hand, boards that treat intelligence as a strategic asset rather than a defensive afterthought consistently outperform peers in resilience and recovery. McKinsey’s 2025 report ‘Competitive advantage through cybersecurity’ shows that organisations embedding cyber security into strategic decision‑making achieve stronger long‑term performance and faster incident containment.

            Leaving the “When, Not If” Era Behind

            The phrase “it’s not if but when” has done more harm than good, as it removes agency. It tells leaders that breaches are inevitable, regardless of the level of effort or investment.

            That is no longer true. Threat intelligence can prevent 99.99% of breaches. Threat‑informed security allows organisations to see hostile infrastructure before it is weaponised, block known threats before they touch production systems, and reduce noise so that human teams can focus on what truly matters.

            For senior technology leaders, the challenge is not adopting another platform; rather, it is reframing cyber security as a leadership discipline. Every generation of leaders is defined by the risks it chooses not to accept, and today’s leaders are navigating a moment where intelligence – both human and technological – offers genuine leverage against an overwhelming threat landscape.

            Choosing to remain reactive is no longer prudent. It is a decision with consequences that ripple through employees, customers, communities and markets.

            Learn more at centripetal.ai

            • Cybersecurity
            • Digital Strategy

            Author

            Daniel Broadhurst

            Published

            Estimated Read time

            4Mins

            Daniel Broadhurst, Commercial Director at Finova, on why the revolution in retail savings distribution may be quiet, but it’s reshaping competitive dynamics in ways that deserve front-line strategic attention, not afterthought status

            After two decades working across banking and FinTech, I’ve watched the same investment debate play out repeatedly. When budgets are tight, modernise lending first. Lending drives visible revenue, attracts broker attention, dominates board discussions. Savings, by contrast, gets treated as operational infrastructure: important, but rarely strategic.

            But that thinking is starting to shift, and for good reason. Rising funding pressures, increasingly rate sensitive customers and the rapid expansion of savings aggregator platforms are reshaping how deposits are gathered and retained. As a result, some institutions are beginning to question whether lending should always be the starting point for transformation.

            How the Market has Evolved

            Savings marketplaces such as Hargreaves Lansdown Active Savings and Flagstone have fundamentally changed how deposits move around the market.

            Customers can now compare and open accounts across multiple banks through a single digital interface. For savers, that feels completely normal, no different from comparing flights or switching energy suppliers. For banks and building societies, however, it shifts control. Funding is no longer driven by brand strength or branch presence, but by visibility and competitiveness on a digital marketplace.

            This became particularly obvious when rates rose sharply in 2022 and 2023. Savers became far more sensitive to small differences in pricing, and institutions without marketplace presence found themselves invisible to active customers. Today, not being present on marketplace platforms means being invisible to active customers. And if you are present but slow to update pricing or onboarding, you could fall out of contention within days.

            At the same time, funding economics have changed. In a higher-for-longer rate environment, small delays in repricing can have a material P&L impact. If rate changes require manual workflows and weeks of coordination, cost of funds or deposits move elsewhere. Marketplace platforms also reduce acquisition costs by reaching customers who are actively searching for high yields, rather than relying on expensive brand campaigns or branch networks.

            For many mid-tier lenders and building societies, savings has shifted from being a passive funding pool to an actively competitive marketplace.

            Where Technology Makes the Difference

            But participating in these marketplaces requires more than just a commercial agreement. Without the right technology infrastructure, institutions risk being shut out of what’s quickly becoming a standard distribution channel for retail savings.

            To genuinely benefit from these marketplaces, institutions need systems that can keep up: digital onboarding, rapid rate updates, automated account opening.

            Institutions still reliant on manual processes or inflexible legacy systems struggle to compete at that speed. There’s a growing gap between those who can use marketplaces as a flexible funding channel and those held back by their architecture. Technology doesn’t just improve efficiency anymore. It determines who can compete.

            Should Savings Come First?

            None of this diminishes the importance of lending transformation. But it does challenge the assumption that lending must always come first.

            Savings transformation projects tend to be shorter and less operationally complex. There’s no underwriting engine to rebuild, no broker integration to manage, no capital modelling layer to rework. That typically means faster implementation and less organisational disruption.

            The commercial case is fairly straightforward. Automation reduces operational costs, marketplace participation expands distribution, faster repricing lowers funding drag. These outcomes are measurable and predictable. Lending transformation ROI, by contrast, often depends on volume growth assumptions that may not hold in volatile markets.

            For institutions with stable lending operations but facing funding pressures, prioritising savings can be a pragmatic strategic choice, not a secondary consideration.

            Turning Technology into an Advantage

            The most forward-thinking institutions aren’t simply digitising existing savings processes. They’re rethinking product and distribution design from the ground up.

            Modern platforms make it far easier to introduce and refine savings products without the delays that traditionally slow banks down. Instead of taking months to launch something new, institutions can configure products in days, adjust pricing by balance or customer segment, and manage promotional rates automatically, all from the same underlying system. The same core platform can support direct, branch and marketplace channels, creating consistency across distribution whilst avoiding duplication behind the scenes.

            This kind of flexibility means banks can respond to changing market conditions without triggering complex internal processes. Products can be tested, inflows monitored and pricing refined as conditions evolve, rather than being locked into rigid structures that are difficult to adjust.

            What this Means for Banks

            Of course, technology alone doesn’t deliver this shift. The institutions seeing the greatest benefit are those treating savings as a strategic capability rather than a back-office function.

            This isn’t about choosing savings over lending indefinitely. Both sides of the balance sheet need modern infrastructure. But the sequencing decision matters. Institutions investing in savings capabilities now are positioning themselves for stronger funding flexibility, better data, and access to distribution channels that are rapidly becoming standard.

            The revolution in retail savings distribution may be quiet, but it’s reshaping competitive dynamics in ways that deserve front-line strategic attention, not afterthought status.

            Learn more at finova.tech

            • Digital Strategy
            • InsurTech
            • Neobanking

            Author

            Maxime Vermeir

            Published

            18 May 2026

            Estimated Read time

            6Mins

            Maxime Vermeir, Vice President of AI Strategy at ABBYY, on how organisations can build a faster and more resilient approach to KYC compliance

            “Know Your Customer” (KYC) used to feel as painfully slow as dial-up internet, but it doesn’t have to be that way any more.

            KYC is the process organisations use to verify the identity of their customers, assess risk, and ensure compliance with regulations such as AML (Anti-Money Laundering) and counter-terrorist financing laws.

            Today, it’s about more than compliance. It’s a critical trust and customer-experience driver. Financial institutions, insurers, and fintechs use KYC to build confidence, protect their brand, and deliver frictionless onboarding experiences that feel more like Apple Store checkout than DMV purgatory.

            However, organisations face an increasingly challenging landscape.

            Regulatory pressure is intensifying. The July 2027 EU AML Regulation, for example, will harmonise standards across all member states, introducing stricter requirements for beneficial ownership data and ongoing due diligence.

            At the same time, customer expectations have shifted dramatically. People expect to open accounts or complete onboarding in minutes, not days. Fraud and identity theft are growing. Deepfakes, synthetic IDs, and digital manipulation make verification more complex and costly than ever.

            Against this backdrop, many organisations struggle to keep pace. Inefficient processes, fragmented systems, and manual checks create delays, increase risk, and damage the customer experience. We examine the six deadly sins of KYC compliance and how organisations can address them to build a faster and more resilient approach.

            Fragmented, Siloed KYC Workflows

              KYC processes often span multiple systems, from CRM to AML, to onboarding portals and case management. A lack of integration between these different areas creates more work, with data capture often duplicated, and SLAs missed. Teams have no unified view of onboarding performance.

              Disconnected databases, inconsistent standards, and repetitive customer documentation cause onboarding friction, high operational costs, and gaps that fraudsters can exploit with alarming speed.

              To combat these risks, organisations need to implement end-to-end visibility across fragmented workflows. The easiest way to achieve this is through AI-powered Process Intelligence tools. These tools can reveal bottlenecks, improve communication between teams, and avoid the risk of repeating time-consuming work.

              Manual Document Handling and Validation Bottlenecks

              Most onboarding delays occur during document intake and validation. Human review teams spend hours checking IDs, proof of address, and corporate records, often working across multiple systems and formats. This introduces inconsistencies, with decisions likely varying between reviewers, and increases the likelihood of errors or missed details.

              The process is also resource-intensive and difficult to scale. As volumes increase, manual reviewers either become bottlenecks or require additional headcount, pushing up operational costs. Long cycle times mean customers wait, which can lead to drop-off, frustration, and reputational risk to the organisation.

              Automating document classification, extraction, and validation can mean the difference between success and failure, even for complex, multi-page corporate KYC packs. These systems leverage intelligent workflows and advanced data processing to accurately sort documents, extract critical information, and standardise it in real time.

              This not only reduces manual effort but also significantly minimises human error, identifying missing fields and inconsistencies before submission. AI tools for regulatory automation and fraud checks enable higher rates of first-pass compliance and faster document processing. This means less time spent on manual reviews and a faster overall process.

              Lack of Process Visibility and Control

              Compliance and operations teams at financial services organisations often lack real-time visibility into where a customer’s onboarding file sits in the process or how long it has been at each stage. Information typically spreads across systems, inboxes, and manual trackers, making it difficult to build a clear view of progress.

              As a result, it’s difficult to pinpoint the problem when delays happen. This lack of transparency makes it harder to meet SLAs or prepare for audits. Teams may only realise there’s an issue once deadlines are missed or escalations occur.

              Process Intelligence provides real-time monitoring of onboarding KPIs, including time per stage, rework rates, and failure points, and allows teams to simulate process improvements. It creates a complete digital audit trail of every step, supporting both operational management and regulatory compliance.

              Better visibility makes it easier for organisations to maintain control, prove compliance, and deliver a predictable customer experience.

              Inconsistent Execution Across Regions and Business Lines

              In many businesses, each branch or business unit follows slightly different onboarding procedures, often shaped by local practices, legacy systems, or different interpretations of compliance requirements. While these variations may seem small individually, together they increase fragmentation across the organisation.

              This can lead to inconsistent customer experiences and non-uniform compliance documentation. One customer may be onboarded quickly, while another similar customer faces delays or repeats because a different team or location handles them. Over time, this erodes trust and makes the organisation look disjointed and unpredictable.

              Best-practice workflows must be standardised enterprise-wide, and all data and documentation should adhere to consistent formats and validation rules across jurisdictions. This is where Process Intelligence excels, benchmarking and comparing process execution across teams, countries, and products, and highlighting deviations from policy.

              Slow Remediation and Periodic Review Cycles

              When periodic reviews or remediation campaigns begin, teams struggle to find and validate the information they need. Customer records may be spread across multiple systems or stored in inconsistent formats, making it difficult to quickly identify what is missing.

              Manual checks only make things worse. Reviewing large volumes of records is time-consuming and repetitive, increasing the likelihood of human error. As workloads increase during remediation campaigns, these risks multiply.

              A better approach is event-driven (pKYC) automation. Instead of relying on periodic reviews, it detects changes in customer data and automatically triggers the right review workflows. Intelligent document processing (IDP) can quickly revalidate and update documents, while process intelligence tools track progress, flag exceptions, and ensure tasks are completed on time.

              Proving Compliance and Audit Readiness

              Regulators increasingly expect organisations to demonstrate full transparency across their KYC processes, including clear data lineage, time-stamped actions, and explainable decision-making. This is particularly true where AI or automation is involved.

              It is no longer sufficient to show that checks were completed. Firms must be able to evidence exactly how data was collected, transformed, verified, and used at every stage of the customer lifecycle. However, many organisations lack this end-to-end audit view. KYC processes are often fragmented across multiple systems, and as a result, audit trails are incomplete or difficult to reconstruct.

              Process Intelligence maintains a comprehensive record of every process step, decision, and exception, while IDP provides field-level traceability, showing where each data point came from and how it was verified.

              Using AI-powered tools that combine process intelligence with document processing makes KYC faster, easier, and more accurate. It means customers can be onboarded more quickly and mistakes are reduced, making the whole KYC process easier to track and audit. Organisations can trust that they comply with regulations while building trust among customers and giving them a smoother, better experience.

              Learn more at abbyy.com

              • Cybersecurity in FinTech
              • Digital Payments

              Author

              Andrea Babayan

              Published

              12 May 2026

              Estimated Read time

              3Mins

              Andrea Babayan, Demand Growth Strategist at Ipsotek (an Eviden business), on why the real competitive advantage will not belong to the hub with the most advanced analytics stack but the one that can demonstrate proportionality, resilience and clarity of purpose

              Transportation hubs are not simply adopting new technologies. They are reconfiguring how they operate. Digital identity systems, AI-driven video analytics, environmental sensors, drones and upgraded communications platforms are converging into core processes.

              What was once framed as incremental innovation is now embedded in passenger throughput, dispatch coordination, platform management and perimeter oversight. Modernisation is no longer an upgrade cycle. It is a redesign of the operating model.

              The more difficult question is not what these systems can detect, but whether institutions are evolving with equal deliberation.

              Identity Border Systems as Throughput Infrastructure

              Border modernisation makes the shift explicit. Europe’s Entry/Exit System (EES) illustrates how biometric identity is no longer merely a security control. It is a flow determinant. When enrolment, verification and exception handling are integrated into primary processing lanes, identity becomes inseparable from capacity planning and operational resilience.

              The expansion of touchless identity verification in US airports reflects the same structural move. Identity confirmation now shapes staffing equations, lane geometry and peak-period modelling.

              When these systems perform, throughput improves. When they stall, queues lengthen – and confidence declines.

              Not all identity applications carry equivalent risk. Verification within controlled checkpoints differs materially from open-ended identification in public space. Proportionality must therefore be embedded at design stage. Governance is not a constraint imposed after deployment; it defines the parameters within which identity systems can scale without undermining trust.

              In jurisdictions such as the European Union, where the AI Act establishes a formal framework for high-risk systems, institutional readiness must include formal risk assessment, auditability and transparent oversight mechanisms as foundational design requirements – not retrospective safeguards.

              Border digitisation is not merely technological enhancement. It is throughput engineering under regulatory and operational accountability.

              Sensor Convergence and Institutional Responsibility

              Beyond borders, transport networks are integrating AI-powered video analytics with environmental sensors, access control systems and communications infrastructure. The objective is coordinated situational awareness – not visibility for its own sake.

              Crowd density analysis can inform service adjustments. Platform anomalies can trigger structured escalation. Perimeter events can be validated through multiple inputs before operational response.

              This convergence strengthens detection capability. It also expands institutional responsibility. As data streams intersect, purpose limitation, retention discipline, interoperability standards and escalation protocols must be clear. Integration increases capability – and scrutiny.

              Governance is not a brake on innovation. It is the framework that allows it to scale responsibly. Alert volume creates noise. Decision quality creates advantage.

              The Operational Decision Core

              Modernisation is increasingly coalescing into centralised operational environments – decision cores that synthesise data across security, passenger flow, maintenance, access control and communications.

              This direction aligns with the formalisation of collaborative operational models such as the Airport Operations Centre (APOC), which positions airport coordination as a structured, cross-stakeholder decision environment rather than a collection of siloed control rooms.

              What was once compartmentalised becomes interconnected. The value lies not in dashboards, but in how insights shape staffing allocation, capital prioritisation, service recovery and commercial performance. Yet greater visibility does not eliminate boundaries. Cross-functional insight should improve coordination, not dilute accountability. Rich operational data must remain tied to defined purposes.

              As AI systems transition from pilot to operational dependency, resilience becomes decisive. Designing for stress is therefore as important as designing for efficiency. Compliance cannot be retrofitted. Interoperability cannot be improvised. Institutional maturity cannot be assumed.

              What Comes Next

              Transportation hubs are becoming decision environments embedded within physical infrastructure. Cameras function as sensors; identity shapes throughput; and analytics informs operational judgement. But the defining difference over the next decade will not be technological sophistication; it will be institutional discipline.

              In my view, the real competitive advantage will not belong to the hub with the most advanced analytics stack. It will belong to the one that can demonstrate proportionality, resilience and clarity of purpose – consistently, transparently and under pressure. Smarter systems are inevitable. Smarter institutions are a choice.

              Learn more at ipsotek.com

              • Data & AI
              • Digital Strategy

              Author

              Dan Brightmore

              Published

              Estimated Read time

              5Mins

              Q&A – Inez Berkhof-Hollander, EMEA Vice President at the global B2B payments network TreviPay discusses the future for B2B payments

              Your research with 550 senior UK and European business buyers found that AI is now widely used in B2B payments. Where is it being implemented, why, and are there any risks here?

                “What we’re seeing is that AI adoption in B2B payments is less about experimentation and more about removing friction from complex, high‑volume processes that were historically manual. We see AI currently mostly used on the AP side, not so much on the AR side yet.

                Today, AI is most commonly applied in three areas: invoice processing and data extraction; payment routing; and reconciliation. The undervalued opportunity is still on the AR side. For exception handling, dispute management and in credit and risk decisioning. These are all pain points where accuracy, speed and scale really matter. Particularly in industries with high invoice volumes or complex payment terms.

                The business driver is clear… Suppliers want faster time to cash. Buyers want fewer errors and disputes. And finance teams want better cash predictability.

                That said, the risk isn’t the technology itself – it’s how it’s governed. In B2B, when AI decisions sit close to credit, compliance and customer relationships; black‑box models without explainability, or automation without appropriate guardrails, can introduce operational and regulatory risk. The most effective applications we see are those where AI augments human decision‑making. Rather than replacing it entirely, there is clear auditability and accountability built in.

                In other words, AI is already delivering tangible value in B2B payments, but the winners will be those who treat it as an enterprise capability, not just a standalone feature.”

                Looking wider – the research also examined friction within the payments process. What were the most important areas that suppliers can leverage to their competitive advantage?

                  “One of the clearest signals from the research is that payment experience has become inseparable from the overall customer experience and the commercial relationship.

                  The biggest opportunities sit at the intersection of flexibility and predictability. Suppliers that make it easy for buyers to pay – through compliant and accurate invoicing, aligned payment terms, and transparency throughout the lifecycle – are easier to do business with as they reduce disputes and accelerate cash flow. Hence, ultimately they will increase customer loyalty.

                  What’s interesting is that many of these levers are not new, but they are finally being treated as strategic. Invoice accuracy, payment visibility, dispute resolution, and alignment between sales and finance are no longer just operational hygiene; they are differentiators. In competitive markets, the ability to offer consistent terms across regions, customized invoicing or reporting or to accommodate how buyers want to pay without creating internal complexity, is increasingly decisive.

                  From an O2C perspective, friction often appears at handovers: from order to invoice, from invoice to payment, and from payment to reconciliation. Suppliers that invest in smoothing those transitions – rather than optimising individual steps in isolation – are better positioned to compete on experience without eroding margin.”

                  Your report shows 82% of buyers value invoice customisation. Why has something traditionally seen as back-office admin become such a decisive competitive factor?

                    “Because invoices are no longer just accounting documents – they’re a key part of the buyer experience.

                    In B2B, invoices often trigger downstream processes on the buyer side: approval workflows, ERP/PO matching, compliance checks, and even cash forecasting. When invoices don’t align with a buyer’s internal requirements – whether that’s formatting, data fields or references – friction is inevitable. That friction shows up as delayed payments, disputes, and strained relationships.

                    What the 82% figure really tells us is that buyers are under pressure themselves. Finance teams are expected to do more with less, manage risk more actively, and support the wider business – all while maintaining control. Invoice customisation helps them do that.

                    For suppliers, this is a powerful insight. Meeting buyers where they are, instead of forcing one‑size‑fits‑all processes, has moved from ‘nice to have’ to a strategic necessity. It’s also a clear example of how O2C capabilities directly support revenue protection and growth – not just operational efficiency.”

                    What was the regional difference in your data that surprised you the most?

                      “What stood out most was not just what differs by region, but why.

                      Across Europe, Pay by Invoice (or ‘Net Terms’) remains dominant, but the expectations around speed, visibility and automation vary significantly. In some markets, buyers are primarily focused on control and compliance; in others, on efficiency and working capital optimisation. Regulatory maturity, banking infrastructure and ERP penetration all play a role in shaping those expectations.

                      What surprised me was how consistently these regional nuances translate into different definitions of ‘good experience’. The markets that move fastest are not necessarily those with the most advanced technology, but those where finance, procurement and payments strategies are more closely aligned.

                      For suppliers operating pan‑European or globally, this reinforces the importance of flexibility. A single market‑specific approach rarely scales. The winning strategies are built around a common O2C backbone, with local adaptability layered on top.”

                      Pay by Invoice remains dominant in Europe, but you also highlight digital wallets and even stablecoins. How do you see the payment mix evolving over the next 3-5 years?

                        “Pay by Invoice will remain the backbone of B2B payments in Europe for the foreseeable future. And that’s not a sign of stagnation, but of trust in a model that supports credit, risk management and commercial flexibility at scale.

                        Where we will see change is behind the scenes. Greater digitisation, faster settlement, and better integration between invoicing, payments and reconciliation will progressively modernise how Pay by Invoice operates.

                        When it comes to alternative instruments like stablecoins, the conversation is still early, but it’s becoming more serious. Their potential value lies in settlement efficiency and cross‑border use cases, rather than replacing core commercial constructs like trade credit. Whether they become relevant at scale will depend on regulation, risk frameworks and clear economic benefit for both sides of the transaction.

                        What’s important is that B2B payment evolution will be pragmatic, not disruptive for its own sake. Buyers and suppliers will adopt new rails where they reduce friction or risk – not because they’re new, but because they meaningfully improve the O2C lifecycle.

                        Learn more at trevipay.com

                        • Artificial Intelligence in FinTech
                        • Digital Payments

                        Author

                        Alex Taylor

                        Published

                        11 May 2026

                        Estimated Read time

                        4Mins

                        Alex Taylor, UK Managing Director at Mangopay, on why the future of payments will not be defined by how quickly money moves, it will be defined by how intelligently it flows

                        Stablecoins are the payments industry’s latest obsession. But if you listen closely, most of the conversation sounds strangely familiar: faster settlement, cross-border transfers and quicker access to funds.

                        Speed is consistently positioned as the key benefit, but it’s no longer a differentiator because speed, on its own, isn’t transformative. Payments have been getting faster for years. Real-time rails are expanding globally, card networks continue to optimise settlement cycles, and even legacy infrastructure is evolving to reduce friction. Competing on speed alone is no longer enough to change how platforms operate.

                        What stablecoins introduce is something far more significant: programmability.

                        Beyond Movement to Logic

                        Traditional payments move money from A to B and execute instructions. But programmable money does something different; it embeds logic into the transaction itself.

                        This means payments can be conditional, automated, and responsive. Funds can be released based on events, split across multiple parties according to predefined rules, or held in escrow until specific criteria are met. The payment is no longer a final step in a process – it becomes part of the process itself.

                        For platforms and marketplaces, this is where the real opportunity lies.

                        Most platform-based payment flows aren’t simple transactions. They involve multiple stakeholders, dependencies, and conditions that sit outside the actual payment. Today, managing that complexity requires layers of reconciliation, manual intervention, and often fragmented infrastructure.

                        Programmability changes that. Revenue can be split instantly at the point of transaction, escrow can be automated without bespoke workflows, and payouts can be triggered dynamically based on factors like delivery, milestones, or performance.

                        It’s fair to say that stablecoins bring control into multi-party payments and rethink how money moves through a platform.

                        The Missing Layer: Governance

                        There is, however, a tendency to treat programmability as inherently beneficial. But without control, programmable money may introduce new risks just as quickly as it creates new possibilities.

                        Stablecoins operate on transparent, public blockchains. Once a wallet address is linked to an identity, transaction histories become visible. That raises immediate questions around privacy, compliance, and data protection.

                        At the same time, automation increases the stakes. If a rule is poorly defined or exploited, the impact spreads across the entire funds distribution flow. This is why programmability needs a governance layer built around it. Identity verification, compliance controls, and user-level permissions are what make programmable money usable in a regulated environment.

                        Why Wallets Matter More Than Tokens

                        The real bridge between fiat and stablecoins is actually the wallet, not the token. Programmable wallets provide an environment where both fiat and crypto can coexist under the same set of rules. A platform can manage revenue splits, fund holding, and payouts across different currencies and rails within a single system. It can integrate stablecoins where they add value, without reworking its entire payments stack or becoming a crypto-native business.

                        A Targeted Role for Stablecoins

                        The role of stablecoins in payments will be significant, but not universal. They are particularly well-suited to use cases that require transparency and efficient multi-currency handling. Cross-border treasury management, platform-based fund distribution, and real-time settlement between multiple parties are clear examples.

                        At the same time, traditional rails will continue to serve many use cases effectively. The future is not about replacing existing rails but adding stablecoins to the payment mix where it makes sense.

                        Platforms will need to decide which rails to use when and manage these decisions seamlessly behind the scenes.

                        Privacy Will Influence Adoption

                        Public blockchain infrastructure is transparent by design. While this enables traceability, it also creates tension with growing expectations around data protection and regulatory compliance.

                        Stablecoins do not offer anonymity in the way cash does. Once identity is linked to a wallet, activity can be traced. For mainstream adoption, this model will need to evolve within a framework that balances transparency with privacy.

                        So again, we return to the role of infrastructure. Without the right controls in place, programmability becomes difficult to scale in regulated markets.

                        Stablecoins: From Speed to Intelligence

                        So the value of stablecoins is reflected in how they allow money to move based on a certain logic.

                        For platforms, it unlocks a different way of operating. Payments become embedded into workflows, automated at scale, and aligned with how value is actually created and distributed.

                        Stablecoin adoption is not a main concern. The focus should be on integrating them into a governed, programmable infrastructure that makes them usable in the real world. Because ultimately, the future of payments will not be defined by how quickly money moves, it will be defined by how intelligently it flows.

                        Learn more at mangopay.com

                        • Blockchain & Crypto
                        • Digital Payments
                        • Neobanking

                        Author

                        Nell Walker

                        Published

                        7 May 2026

                        Estimated Read time

                        5Mins

                        How Sonata Software is driving CPL Aromas’s technology transformation

                        When CPL Aromas needed a software partner with deep technical expertise to guide its transformation, it chose Sonata Software. Theirs is a relationship that’s been going strong for eight years and counting – since Alfred Muthunathan, CIO at CPL, joined the team. “He was looking to transform the IT landscape,” says Uttam Hazari, Head of UKI & Europe at Sonata Software. “But before he could embark on that journey, it was important to ensure that the foundation was stable first.”

                        The goal was very simple: to ensure CPL could continually strengthen its system performance and improve reliability across its global operations. “It was growing at a rapid pace,” says Hazari. “However, the systems and data weren’t well integrated. It was important for us to work collaboratively with CPL to not only analyse the processes, but also stabilise the systems and the applications, and allow the company to regain its confidence.”

                        This early engagement reflects Sonata’s Frontier Firm approach – combining deep engineering expertise with business-aligned transformation to create resilient digital foundations.

                        A transformational relationship

                        Alongside the ERP support, Sonata partnered with CPL Aromas to develop its first B2B customer portal, enabling users to securely access orders and shipment information, thereby improving transparency and reducing manual interactions. “The early successes formed the foundation for a long-term digital transformation partnership built on trust, responsiveness, and delivery excellence,” Hazari adds.

                        This momentum reflects Sonata’s broader approach to building long-term, value-driven digital partnerships. 

                        “Sonata Software is committed to building strategic partnerships that help organisations accelerate their digital transformation journeys. Today, our clients are looking for initiatives that move faster, reduce risk, and deliver clear, measurable business impact. As a Microsoft Frontier Partner, we bring deep expertise in translating advanced Cloud and AI capabilities into tangible outcomes. Our expansion across the UK and Europe marks an exciting phase of growth for Sonata, and our transformation journey with CPL Aromas is a key pillar of that strategy. We are privileged to have CPL Aromas as both a valued customer and a trusted partner.” – Anthony Lange, Chief Revenue Officer at Sonata Software

                        What began as a simple collaboration has evolved into a multi-phase, enterprise-wide transformation journey over the past eight years. A couple of years in, Sonata’s role expanded. “As part of that, we helped CPL rebuild and transform their core infrastructure,” says Hazari. “We also helped to implement Azure Site Recovery, stabilise IMS operations, strengthen identity and endpoint security, and establish modern monitoring with Azure Sentinel. And this is just the beginning.”

                        From ERP to infrastructure management to full enterprise modernisation, Sonata has been with CPL every step of the way, including the end-to-end global migration from the legacy ERP platform to Microsoft Dynamics 365 across multiple legal entities. “As we speak, the engagement has matured into a data-driven, AI-first innovation spanning across Dynamics 365 and the customer engagement platform, RPA automation, and agentic development,” says Hazari. 

                        AI-first

                        Developing an AI-first approach is at the core of CPL’s current and ongoing focus, and it’s an area where Sonata has played a major role. “Sonata has been working very closely with CPL Aromas in its AI-first evolution – not just technically, but strategically as well,” says Hazari. 

                        One of the key contributions to this has been building a modern digital core through the Dynamics 365 ERP program, enabling real-time visibility, standardised processes, and scalable operations. “These things are a prerequisite for effective AI deployment,” says Hazari. “Without that, you can’t just bring in AI to do the magic. Data is the most important ingredient. If the data isn’t right, the AI won’t be either. Then came automation at scale through RPA initiatives, which reduced the manual workload and increased process efficiency.”

                        More recently, Sonata has been working with CPL to pioneer its agentic AI adoption. “One of the recently deployed agents is a custom planner agent, which is designed to automate one of CPL’s most complex manufacturing processes,” Hazari explains. “This represents a major leap towards AI-driven decision intelligence across operations. Together, these initiatives have established the technology, data, and automation foundation for CPL Aromas’ AI-first strategy.”

                        These capabilities exemplify how Frontier Firms operationalise AI – embedding intelligence into core processes to drive real-time, autonomous decision-making.

                        The four pillars

                        Sonata is now helping CPL deliver value across four strategic dimensions. These consist of:

                        • Operational resilience and security
                        • Standardisation of global processes and transparency
                        • Enterprise-wide data platform
                        • Agentic AI-driven decision-making

                        “Bringing these initiatives together is transforming CPL into a digitally agile and AI-ready enterprise,” says Hazari. “That’s the value we’ve been able to deliver.”

                        The goal is for this relationship to continue for many more years. Sonata will continue to contribute towards CPL’s incredible growth, and as this collaboration progresses, it will be centred on CPL’s AI-first operating model.

                        “Scaling agentic AI beyond the initial successes is very important,” says Hazari. “That’s across all business functions, whether it’s finance, quality control, demand forecasting, supply chain optimisation, and even customer engagement. The idea is to help CPL in democratising AI across business functions, to reach a state where they’re able to create their own workflows and generate their own agents.”

                        As well as this, it’s about continuous automation and expansion to further reduce manual effort wherever possible from current processes. “Lastly, we’re working on enhancing digital sustainability,” says Hazari. “This includes ensuring that systems remain scalable, secure, and ready for emerging AI technologies. Technology is changing; AI is changing very rapidly. It’s extremely important for Sonata, as the strategic partner, to make sure we’re able to keep CPL Aromas one step ahead in this journey.”

                        Author

                        Dan Brightmore

                        Published

                        Estimated Read time

                        3Mins

                        Read in brochure format

                        Welcome to the latest issue of Interface magazine! Click here to read the latest edition! Appian: Why AI is Putting…

                        Welcome to the latest issue of Interface magazine!

                        Click here to read the latest edition!

                        Appian: Why AI is Putting Better Business Within Every Organisation’s Reach

                        Our cover story highlights how AI is putting better business within everyone’s reach. Mark Talbot, Director – CS AI Initiatives at Appian, reasons that as organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it. “Instead of concentrating control and decision rights in a small, central group, modern AI tools give more agency to the people closest to the work. They can see what is not working, imagine better approaches, and use AI to help redesign and improve the processes they rely on every day.”

                        CPL Aromas: How a Leading Fragrance House is Using AI to Amplify Creativity

                        In the world of retail, a leading fragrance house uses AI to amplify creativity. Alfred Muthunathan, CIO at CPL Aromas, explains how the family-owned business is using AI as a strategic capability to support creativity and accelerate innovation. “We didn’t bolt AI onto our systems; we redesigned the organisation, so AI is native to how we operate… Our new system takes away the workload from perfumers and has allowed us to create something that always keeps the nuances of our industry at its core.”

                        Vibrant Capital: Scaling AI on Main Street

                        Shadman Zafar, Founder & CEO of Vibrant Capital, is building a CIO-led model for enterprise transformation. Vibrant Capital is an operator-led investment and company-building platform focused on scaling AI in the real economy. “We don’t spray investments across hundreds of AI startups. We curate a portfolio with purpose – selecting companies that solve the real mission-critical problems CIOs face in scaling AI adoption.”

                        Also in this issue, we learn about the supply chain transformation journey at Swiss sportswear brand On, unpack the latest AI readiness research from Snowflake and hear from Hitachi Vantara about the importance of strong data foundations for the best utilisation of AI.

                        Click here to read the latest edition!

                        • Cybersecurity
                        • Data & AI
                        • Digital Strategy
                        • People & Culture

                        Author

                        Dan Brightmore

                        Published

                        Estimated Read time

                        7Mins

                        Snowflake’s UK research reveals that while many continue to invest in artificial intelligence (AI), the country’s businesses are in the early stages of finding productivity gains at scale

                        UK productivity remains a longstanding economic challenge, with policymakers consistently positioning AI as a key lever for growth and competitiveness.

                        Snowflake’s findings show AI investment and experimentation amongst businesses though impact so far is varied. 45% of UK organisations say AI is delivering early gains or in specific use cases, though 23% are already seeing it delivering productivity improvements at scale. Buoyed by this thought, most organisations expect AI investment to increase over the next 12 to 24 months, with just 1% planning to decrease spend. Businesses continue to back thetechnology as a strategic priority, demonstrating confidence that productivity breakthroughs will come.

                        The UK AI Report

                        Conducted by YouGov on behalf of Snowflake, the report surveyed 500 senior decision-makers including CEOs & CFOs from large UK organisations, spanning key industries including public sector, manufacturing and financial services. These industries are seen as being vital to the Government’s AI and productivity ambitions. Its 2025 AI Opportunities Action Plan aims to boost the UK economy by £47 billion annually, estimating that widespread AI adoption could increase national productivity by up to 1.5% each year.

                        Dr Fabian Stephany, Economist & Departmental Research Lecturer at the Oxford Internet Institute (OII), University of Oxford commented: “I am encouraged to see early evidence that AI is beginning to generate measurable productivity gains for UK firms. Since the introduction of generative AI, many observers have been asking when these gains would materialise, and the findings suggest that this moment may now be arriving. This is consistent with what research would predict: technological breakthroughs rarely translate immediately into productivity improvements, as organisations need time to adapt their workflows, governance structures and capabilities.“

                        From AI Promise to Productivity Reality

                        The data suggests that while belief in AI’s potential is strong, execution at scale is proving more complex. Findings indicate that the primary obstacles to AI-led productivity are not technological. Instead, organisations point to structural and operational barriers as factors slowing the move from pilots to enterprise-wide transformation.

                        Top barriers include a lack of skilled workforce, poor data quality, organisational silos and unclear leadership or strategic direction. Technology itself ranks below many of these internal challenges, cited by just 19% of respondents. Responsibility for AI governance is also often fragmented across executive, technology, data and business leaders. While executive leadership typically holds responsibility for investment, there is no clear governance owner, limiting accountability and slowing decision-making.

                        This suggests that many UK organisations are pursuing AI at a measured pace, building progress through smaller, targeted use cases while strengthening internal structures, with broader productivity gains likely to follow as foundations mature.

                        In debates about national productivity, AI is positioned as a growth engine. For business leaders, these gains will manifest themselves at both their top and bottom line with cost reduction as a clear goal. Nearly half (44%) say that cost reduction matters most as a key measure of success, while 26% say the same of revenue growth.

                        The Executive Confidence Gap

                        The research also reveals cautious confidence on AI deployments among senior leaders. Only 24% of organisations say AI initiatives are identified and prioritised using a rigorous framework aligned to business objectives. Meanwhile, 40% expect AI to take two years or more to materially improve productivity.

                        Around 60% say ethics and safety concerns influence their decisions to adopt and scale AI. This reflects a responsible approach to deployment, particularly in regulated and risk-sensitive sectors.

                        Jennifer Belissent, Principal Data Strategist, Snowflake, said: “UK organisations clearly believe in AI’s long-term potential, and continued investment runs parallel to this belief. This research shows, however, that belief alone is not enough. Productivity gains require clear ownership, strong data foundations and alignment between AI initiatives and measurable business objectives. AI has the potential to be a real driver of UK productivity and economic growth. But unlocking that potential depends on getting the fundamentals right – governance, data and clear accountability.”

                        A Varied Industry Picture

                        The research highlights clear differences in AI maturity and confidence across key UK industries, although productivity gains remain uneven.

                        • Financial services is more advanced on governance and strategic alignment, but regulatory and reputational concerns are slowing the move from structure to scale.
                        • Manufacturing shows strong belief in AI’s long-term productivity potential, yet expects slower returns due to skills gaps and integration challenges.
                        • Retail lags on confidence and delivery, with AI often confined to isolated use cases amid persistent data quality issues and fragmented ownership.

                        In the public sector, organisations are the most risk-aware and governance-led, but also anticipate longer timelines before productivity gains are realised.

                        • 53% cite safety & reliability of AI outputs as the top concern affecting confidence in AI.
                        • Two thirds (66%) say ethics and safety significantly shape adoption decisions.
                        • 52% say AI will not materially improve productivity for at least two years.

                        While this cautious approach prioritises trust and accountability, it may mean productivity gains take longer to come to fruition.

                        Turning Point for UK Enterprise AI

                        Across industries, many are still realising how best to drive AI productivity at scale and the skills needed to make this a reality. While levels of governance maturity and risk appetite differ, the journey to broader productivity gains is shared.

                        Dr Stephany added: “The report’s finding that skills shortages are a key barrier to adoption strongly resonates with findings from my research group (SkillScale) at the Oxford Internet Institute, University of Oxford. AI systems are only as powerful as the people who develop, maintain, apply and govern them. In SkillScale’s research, we find that workers with AI-related skills command a wage premium of around 23% in the UK, have higher chances of finding a job, and are more likely to receive additional job benefits. These patterns reflect the strong and growing demand for talent capable of working with artificial intelligence. Expanding access to AI skills and training will therefore be critical if organisations want to sustain and scale these productivity gains and ensure that the benefits of AI are broadly shared.”

                        Jennifer Belissent concluded: “The research paints a clear picture. The foundations for AI success in the UK are in place. Organisations are investing, experimenting and strengthening governance frameworks. However, to close the gap between ambition and measurable productivity gains, businesses need stronger alignment, clearer ownership and more robust data foundations. If AI is to play the transformative role policymakers and business leaders expect, the focus must now shift from experimentation to disciplined execution.”

                        Methodology

                        The research was conducted by YouGov on behalf of Snowflake among 500 senior decision-makers from large UK organisations with 250 or more employees across manufacturing, financial services, retail, the public sector and other industries. Fieldwork was conducted in January 2026.

                        Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 13,300 customers around the globe, including hundreds of the world’s largest companies, use Snowflake’s AI Data Cloud to build, use and share data, applications and AI. With Snowflake, data and AI are transformative for everyone.

                        Read the full report here

                        • Data & AI
                        • Digital Strategy

                        Author

                        Nell Walker

                        Published

                        6 May 2026

                        Estimated Read time

                        2Mins

                        Exiger returns to the Great Scotland Yard Hotel for a new Executive Forum

                        AI-native supply chain management platform, Exiger, is returning to the Great Scotland Yard Hotel for an exclusive executive forum. On the 15th of July at 6:30pm, Exiger will host a discussion entitled ‘Human rights in the supply chain: From obligation to operational discipline’. Leaders and experts from across supply chain will gather together at the event to dig deep into this topic – because human rights risk goes far beyond being a reputational issue. 

                        SCS/CPOstrategy readers can click here to request a place at the Exiger Executive Forum

                        Supply chains are under intense scrutiny, and businesses are being held accountable for every single part of that chain. The aim of Exiger’s Executive Forum is to examine this topic, and how the topic of human rights due diligence must now be a continuous oversight. The conversation will revolve around how to embed human rights intelligence into everyday supply chain decisions. 

                        The speakers for this event are:

                        Tim Fowler

                        Host & moderator, Client Executive, Exiger

                        Koray Köse

                        CEO and Chief Analyst, Köse Advisory & Senior Fellow, GlobSEC Geotech Centre & Board Member, Slave-Free Alliance

                        Tim Nelson

                        Co-Chair & CEO, Hope for Justice & Slave-Free Alliance

                        Erika Peters

                        Head of Customer Success & Strategic Accounts, Exiger

                        During the evening’s discussions, the panel will also explore insights on:

                        • The need to evolve beyond static human rights policies
                        • The structural realities of forced labour risk
                        • The limitations of traditional compliance models
                        • Moving from reactive remediation to proactive risk governance

                        Click here to request a space and join other supply chain professionals at the Exiger Executive Forum

                        Author

                        Kartik Venkatesh

                        Published

                        Estimated Read time

                        4Mins

                        Kartik Venkatesh, Global Head of Innovation at GBG, argues that cyber resilience must be built around people, not systems

                        For years, financial institutions built walls around their systems – firewalls, encryption, and network monitoring designed to keep the intruder out. But in 2026, the new battleground for fraud isn’t in the network or the cloud; it’s in the individual. The fastest developing point of attack is no longer infrastructure – it’s identity.

                        Generative AI-driven fraud in the U.S. alone is expected to hit $40 billion by 2027, according to Deloitte. The tools of deception have been democratised, industrialised, and globalised. So as AI agents, synthetic identities and deepfakes blur the line between real and imitation, financial services must shift their focus away from ‘how do we protect our systems?’ to ‘how do we trust our people?’

                        The Globalisation of Fraud

                        Fraud has always been an international sport, but AI has made it an industry. Cyber threats often spread rapidly across regions and industries, shape-shifting thanks to advances in technology. CIFAS reported a record number of fraud cases in the first half of 2025 and pointed to the availability of AI tools as a key reason.

                        GBG’s Global Fraud Report found that 96% of fraud prevention professionals are concerned about the industrialisation of fraud, with 79% seeing a significant rise in sophistication over the past year. Fraudsters today are not lone opportunists; they’re coordinated, tech-savvy, and now powered by automation.

                        Deepfakes epitomise this shift. Using advanced generative AI techniques (including GANs and diffusion models), criminals can create hyper-realistic videos and images that mimic genuine human behaviour. Popular apps like Deep-Live-Cam and Reface offer plug-and-play capabilities that can inject fake faces into authentication systems in seconds. This isn’t a fringe risk, 71% of fraud professionals in EMEA have expressed concern about deepfake threats, while regulators like the EU are responding through frameworks such as DORA (the Digital Operational Resilience Act) which explicitly push institutions toward operational and identity resilience.

                        These attacks strike at the heart of trust. The questions financial institutions must now answer are deceptively simple: ‘Can I trust that the person I see on the screen is real?’ ‘Can I trust that this ID is not fake?’ The barrier of resilience is set at how confidently and quickly they can say “yes.”

                        Synthetic Identity: The Art of Invisible Fraud

                        Synthetic identities – fabricated personas built from real and fake data – represent one of the most profitable forms of digital fraud today. Deloitte estimates the average payoff from a synthetic identity fraud case in the U.S. is between $81,000 and $98,000, with fraudsters now using generative AI to make these identities eerily convincing.

                        Unlike stolen identities, synthetics evolve. They age in the system, build credit, and interact with institutions just like genuine customers. What makes this form of fraud particularly insidious is its invisibility. Unlike with traditional identity theft, the identity doesn’t belong to a real person, so there is no one to report suspicious activity.

                        That’s where behavioural identity comes in. The future of verification isn’t about what a person has (an ID) or knows (a password), but how they behave. Micro-patterns in typing rhythm, device use, and even navigation paths form part of a behavioural fingerprint that’s extremely difficult to replicate at scale. When combined with biometric and contextual data, this builds a dynamic, living model of trust – a security perimeter shaped not by static credentials, but by human nuance.

                        Beyond Biometrics: Building Trust Without Friction

                        Security works best when you barely notice it. Yet seamlessness must never mean vulnerability. That’s the paradox at the centre of modern digital finance: how do you protect without obstructing?

                        Biometric identity verification offers an answer. Face and voice recognition systems, combined with document authentication, are now essential tools in fighting fraud and meeting regulatory expectations. But not all biometrics are equal. As deepfake technology advances, even the most human identifiers – our faces, our voices – can be mimicked.

                        To defend against this, leading institutions are adopting liveness detection – technology that identifies subtle biological markers like blood flow, skin texture, and light reflection to confirm a user’s genuine presence. Passive liveness detection achieves this invisibly, verifying authenticity without forcing the customer to blink or nod into a camera. It’s the intersection of security and simplicity: frictionless defence at machine speed.

                        This kind of advanced biometric intelligence doesn’t just deter fraud, it strengthens customer relationships. Real-time, omnichannel verification lets people move effortlessly between digital and physical channels, while AI-driven anomaly detection flags risks before they become breaches.

                        Done right, identity verification becomes both a safeguard and a customer experience advantage.

                        A New Kind of Resilience

                        Throughout history, disguise has been the villain’s oldest trick. Today, AI gives that trick infinite new faces. But the same intelligence that enables deception also empowers defence.

                        At GBG we’ve seen firsthand how behavioural and biometric intelligence are reshaping fraud defence, shifting from static verification to continuous authentication.

                        Cyber resilience in finance will no longer hinge on perimeter walls or system redundancies. It will depend on the ability to recognise the genuine in a world of perfect forgeries. Behavioural, biometric and contextual intelligence – combined into a single trusted identity fabric – is the foundation of that future.

                        Learn more at gbg.com

                        • Cybersecurity in FinTech

                        Author

                        Dan Brightmore

                        Published

                        5 May 2026

                        Estimated Read time

                        4Mins

                        Asia’s FinTech ecosystem reached new heights with breakthrough announcements and industry-defining moments that will reshape the region’s financial landscape

                        Money20/20, the world’s leading FinTech show and the place where money does business, concluded three days of industry‑shaping conversations. High‑impact networking, and packed stages were seen across three days from April 21–23 at the Queen Sirikit National Convention Center (QSNCC) in Bangkok, Thailand.

                        Money20/20 Asia – 2026

                        This year’s show welcomed over 4,500 attendees, a 40% increase in its third year. With over 360 speakers delivering over 100 hours of content across four stages. A very busy Money Hall show floor brought together delegates, sponsors and exhibitors from 90 countries. Across the show floor, companies reported a surge in commercial activity, with hundreds of partnership meetings, product demos, and investor conversations driving tangible deal‑flow throughout the three days. 

                        “Money20/20 Asia is where Asia does business. The record attendance we’re seeing, combined with our relentless focus on quality – of audience, conversations, content, and experience – is translating into real partnerships and outcomes for the leaders shaping the future of financial services in the region. It’s why we are firmly established as Asia’s #1 quality FinTech show.” Danny Levy, Money20/20’s Executive Vice President & Managing Director for Money20/20 Asia and Middle East.

                        Headline Sessions

                        AI, cross‑border payments, digital banking, and the accelerating rise of digital assets, including stablecoins, tokenisation, and CBDCs shaped the headline sessions at Money20/20 Asia. Leaders from global banks, FinTechs, and regulators unpacked these themes across discussions on trust in banking, invisible finance, intelligent infrastructure, the future of digital banks, and ASEAN’s sustainability transition. 

                        “Our goal is not just to digitise finance, but to humanise it,” said Daranee Saeju, Assistant Governor, Bank of Thailand, reinforcing the event’s focus on people‑centred innovation and trusted digital ecosystems. 

                        These themes came to life across the agenda’s most talked about sessions. On Day 1, Technology, Trust and the Future of Banking in Asia brought together Scarlett Sieber of Money20/20 and Kattiya Indaravijaya of KASIKORNBANK to explore how modern infrastructure and AI‑driven intelligence are redefining trust and resilience in banking. “Uncertainty is a new normal. Banking will exist, but the way banks have to do the business will totally change,” said CEO Indaravijaya, Chief Executive Officer.

                        Day 2 spotlighted the rapid evolution of digital assets with The Rise of Blockchain and Stablecoin Payment Rails, featuring leaders from Uquid, Pantera Capital, VelaFi, and Trace Finance in a forward looking discussion on next‑generation cross‑border payment rails.

                        On Day 3, The New Paradigm of Cross‑Border Money Flows and International Expansion saw speakers from Money20/20, TenPay Global (Tencent), Fiserv, and Ant International unpack how Asia’s payment giants are building interoperable, multi‑rail systems to power global expansion. 

                        Three Days of Expert Inisghts

                        Along with KASIKORNBANK, the programme saw strong participation from leading global and regional banks, with senior executives from Standard Chartered, Deutsche Bank, J.P. Morgan, Citi, and DBS Bank present across the three days.

                        FXC Intelligence and Money20/20 presented new findings from The New Era of Asia’s Cross‑Border Payments, underscoring Asia’s rapid growth, rising interoperability, and the convergence of key technologies shaping cross‑border finance. The Press Lunch additionally marked the debut of Money20/20’s new book, The New Intersection of Money – Where TradFi and DeFi Converge, exploring how traditional and decentralised finance are now co‑creating the future of financial services. 

                        Money20/20 Asia spotlighted five rising startups Boost Capital, TrustPlus AI, Continuum, Eazy Digital, and zkMe, Money20/20’s Startup pitch winner during its Startup Media Panel. The show also hosted the Startup and Investor Park, where APAC startups met global investors and competed for a Golden Ticket to the 2026 Startupbootcamp Sustainability Singapore Accelerator. The program supports founders with pitching opportunities, investor access, and pathways to scale across Asia’s fast-growing fintech markets. 

                        Policy20 at Money20/20 Asia 2026 brought together more than 80 of Asia’s top policymakers, regulators, and industry leaders to address the rise of sovereign intelligence in global finance. In a closed-door Governors’ and Chairs’ Roundtable, participants aligned on three priorities: embedding regional values into global standards, building harmonised but sovereignty‑respecting cross‑border rails, and advancing intelligence‑led governance through AI and real‑time data. 

                        After a highly successful third year in Bangkok, Money20/20 Asia has confirmed its return to the Queen Sirikit National Convention Center next year from 27-29 April 2027. 

                        Author

                        Dan Brightmore

                        Published

                        30 April 2026

                        Estimated Read time

                        5Mins

                        CoinCover’s Chief Commercial Officer Anthony Yeung on why trust and confidence remain the key barrier to adoption with digital assets

                        Stablecoins, tokenisation and decentralised finance (DeFi) have woken traditional financial institutions to the potential of digital assets. This is no longer a fringe idea; there are clear signs that digital assets are rapidly becoming a fixture of the financial system – as 86% of institutional investors already have exposure to digital assets and nine major banks announced plans to launch a euro-dominated stablecoin in September 2025. Consumer demand is also growing, with 820 million crypto wallets live in 2025.

                        The scale of demand is undeniable, and the opportunity is clear. However, the question now becomes: can financial institutions offer digital assets with the same standards of security, continuity and recoverability that customers and regulators expect?

                        The Institutional Opportunity

                        Consumer demand for digital assets is growing, and financial institutions are alert to the commercial opportunities. But the real engine of adoption is broader than retail investment alone. Stablecoins, in particular, are shifting digital assets from a speculative use case into payments infrastructure, with clear relevance for cross-border transfers, settlement and treasury operations. The total value of issued stablecoins is forecast to reach more than $2 trillion by 2028, a surefire sign of their growing role in mainstream financial infrastructure.

                        The opportunity for banks and financial institutions is enormous, but it’s also the responsibility of these institutions and regulatory bodies to ensure that digital assets can be accessed in a secure and resilient way. There is clear cause for concern, with an estimated one in five bitcoin – $350 billion worth – now permanently inaccessible due to loss of access. Consumer trust and confidence, therefore, remains one of the main barriers to the widespread adoption of digital assets, and for the sector to mature and scale, and for traditional financial institutions to take full advantage of this opportunity, customers will need assurance that innovation will not come at the expense of their financial security.

                        Balancing Regulation and Innovation

                        The UK’s digital asset ecosystem is growing rapidly, and regulatory requirements and expectations are developing alongside it. This represents a significant shift in how the UK manages digital assets, and it puts pressure on institutions around compliance, accountability and transparency.

                        The Financial Conduct Authority (FCA) has made it clear that the UK is moving towards a comprehensive set of digital asset regulations. The Cryptoasset Regulations 2026, which fully come into force in October 2027, signals a point of maturation for the market, but it also leads to increased pressures for institutions to demonstrate compliance and accountability. Equally, the Bank of England is also pushing forward with plans for stablecoin regulation in association with the FCA under the UK European Market Infrastructure Regulation. This clearly demonstrates that for the UK Government and regulators, consumer protections and preventing loss of access to assets are high on the agenda.

                        Institutions are also more likely to scale activity where rules and responsibilities are clear. Done right, regulation doesn’t slow innovation, it gives institutions the structure they need, and as demand is moving in the right direction, there is only one more missing piece.

                        Embedding the Right Infrastructure

                        Regulation creates the right foundations, but customer trust is earned in the “what if?” moments: what if a customer loses access? What if a key-holder leaves the firm? What if a critical wallet becomes inaccessible during a market stress event?

                        Scaling adoption isn’t just about building the right products, it’s about whether customers, counterparties and regulators believe the system will protect them when something goes wrong. That’s where the trust gap appears.

                        The expectation across traditional financial assets is that access can be fully recovered if a mistake is made – the ‘forgotten password’ principle – and customers demand the same for digital assets. If you lose your login, misplace your device or make an operational mistake, there is a governed process to restore access and keep critical assets safe. Crypto’s design often flips that expectation; when private keys or seed phrases are lost, access is permanently lost, even if the assets remain visible on-chain. This creates an institutional issue – key loss and process failure are predictable failure modes of any human system, and you cannot build institutional trust on that fragility.

                        Self-Custody

                        The problem isn’t self-custody itself, it is that self-custody at scale becomes an operational risk transfer. Self-backup remains the number one method by which self-custodied consumers protect their digital assets, but it is not sustainable at institutional scale, and it is unlikely to satisfy regulatory expectations as oversight tightens.

                        Institutions need robust recovery technologies and the infrastructure to build confidence and scale effectively – for their own assets and that of their customers. This needs to be incorporated from the start, not added on when something goes wrong, at which point it is already too late. This is particularly relevant as digital assets move towards mainstream adoption, where consumers cannot be expected to be cognisant of the risks of self-custody – principally, the risk of digital assets in a wallet being permanently lost due to a lost seed phrase. Having a viable recovery method as part of an institution’s core infrastructure helps to offset these concerns and puts institutional and consumer security first. Without it, institutions face risk of high-profile losses, inconsistent outcomes and a persistent perception that digital asset innovation comes at the cost of safety.

                        In practice, this means layered protection against lost access, matched to the risk profile of each use case, and for consumers, it means a governed path back to access when a seed phrase or device is lost without opening the door to fraud. Institutions need wallet disaster recovery that quickly restores operations and has clear controls over who can trigger recovery is vital. This is a business continuity issue, not a ‘nice-to-have’. A firm that cannot access its own wallets is operationally frozen.

                        The Path Ahead

                        The institutional opportunity in digital assets is real and growing. But adoption will ultimately be defined by whether traditional finance can deliver trust at scale. Regulation is heading in the right direction, and the UK is establishing a clear framework, but regulation needs to be accompanied by progress in infrastructure. Firms need recovery capabilities that protect assets and maintain access when errors, failures or disruptions occur – the ‘forgotten password’ feature that we are used to in in traditional finance. 

                        Pair comprehensive regulation with resilient recovery frameworks, and institutions can finally offer digital assets with the assurance customers expect – that innovation won’t come at the cost of security.

                        Learn more at coincover.com

                        • Blockchain & Crypto
                        • Cybersecurity in FinTech

                        Author

                        Dan Brightmore

                        Published

                        29 April 2026

                        Estimated Read time

                        6Mins

                        Andrew Power, Head of UK&I at Tricentis, on why the right approach to AI can deliver the foundation for more resilient, predictable systems

                        Artificial intelligence is reshaping software delivery in financial services. Code that once took teams weeks to develop can now be generated and deployed in a matter of hours. This isn’t just about faster delivery; it changes the fundamentals of how software is built and how it behaves in production.

                        Financial institutions have moved quickly to integrate AI across core systems, from customer operations to anti-money laundering (AML) and software development to capture efficiency and innovation gains. UK parliamentary evidence shows adoption is already widespread, with the majority of firms using AI, and more planning to follow.

                        But as adoption spreads and becomes more embedded within key systems, so does exposure. Risk is no longer confined to individual defects, but shaped by how quickly those defects can spread across interconnected environments.

                        AI has removed the limits on how quickly software can be created, but not on how confidently it can be trusted, and financial institutions can now generate and deploy code faster than they can safely validate it.

                        This creates a new paradox: AI is both accelerating the pace of software change and increasing the speed and scale at which failures can materialise.

                        Machine-Speed Failure

                        AI-driven development shortens the distance between change and consequence. Software updates can move through the pipeline from creation to production with significantly less friction. However, this also reduces the time available to identify, flag and contain any issues before they have an impact.

                        AI-driven software changes don’t just move fast, they scale fast. Unlike traditional failures, these are systemic risks. A single misstep in an AI-generated update can propagate unpredictably.

                        For financial services, this is especially significant when key systems are deeply interconnected, spanning complex layers of infrastructure, integrations, and third-party services. Even a minor defect can propagate quickly across systems, amplifying its impact.

                        What would once have been contained can now escalate, cascading across systems and causing wider disruption that affects customers, operations and, in some cases, market activity. In financial services, this is not just a technical issue but a business risk with direct implications for customer trust, regulatory compliance and financial stability. The challenge is no longer simply identifying defects but maintaining confidence in what is being deployed.

                        This risk is already being felt across the sector. Institutions are accelerating delivery to meet customer expectations and competitive pressures, but often without corresponding advances in validation. Tricentis’ research shows 68% of financial services organisations anticipate outages or serious incidents due to poor software quality.

                        Regulatory Pressure for AI is Increasing

                        The issue is also drawing attention from regulators. Earlier this year, the UK Treasury Committee warned that current approaches to AI in financial services are inadequate and could expose customers and the wider system to “serious harm”, highlighting the need for stronger guardrails, clearer accountability and more robust oversight to deploy it safely.

                        Traditional resilience frameworks were never designed for systems evolving in real time, and AI can no longer be treated as a marginal technology risk. It must become central to how organisations manage and assess resilience.

                        This marks a shift from software quality being an engineering concern to a board-level issue of operational resilience. If machine-speed change is the new operational hazard, then failure to address it becomes a strategic issue rather than a technical one. With that in mind, financial leaders must acknowledge AI’s dual role as both a driver of risk and a mechanism for preventing it.

                        AI as Both a Safeguard & Source of Risk

                        AI also offers the most effective and scalable way to manage the risks it introduces. Advanced AI-driven validation, continuous monitoring and risk-prioritised testing can identify issues earlier than any manual process, helping reduce the likelihood they reach production.

                        In effect, the same AI that accelerates software creation must now be applied to validation and governance – operating at the same speed and scale.

                        The same capabilities that facilitate rapid software production can be applied to validation and governance, continuously evaluating system behaviour, detecting anomalies and prioritising testing based on potential business impact, rather than volume. This allows organisations to move beyond rigid approaches and towards more adaptive, responsive quality models that more accurately reflect the way AI behaves.

                        Instead of relying on standard periodic testing cycles, systems can be validated on an ongoing basis. This enables earlier intervention before issues escalate.

                        AI can also help organisations better understand the complexity of their own systems. By analysing dependencies across applications and infrastructure, it becomes possible to identify which processes are most critical and where failures would have the greatest impact.

                        From Acceleration to Control

                        There is a clear mismatch in how financial organisations approach AI. While many are leveraging AI to accelerate development, far fewer are evolving their validation and governance to keep pace, and it’s in this gap that risk emerges.

                        This is the “confidence gap”, where organisations can create software faster than they can safely deploy it.

                        To address this imbalance, firms must treat software quality as a core component of their AI strategy. Development and validation must move forward together. Governance must adapt to continuous, AI-driven change. This requires a move from static testing and coverage metrics to continuous, risk-based validation, where software is assessed in real time based on potential business impact.

                        If AI is the engine driving software creation, validation must act as the braking system – built in, not bolted on at the end. At machine speed, gaps in control become points of failure. The aim is not to slow innovation, but to ensure it progresses in a way that is sustainable and safe. When validation keeps pace with development, firms can move quickly and competitively, whilst maintaining control over how risk is introduced and managed.

                        This is a change we are seeing across large enterprises adopting AI-driven quality approaches, where validation, monitoring and governance are increasingly orchestrated together rather than treated as separate processes.

                        Preventing the Next Outage

                        The financial sector has already seen how quickly failures can escalate in complex, interconnected environments. In March, an IT error at Lloyds Banking Group exposed the private financial information of nearly half a million customers, prompting the bank to issue £139,000 in compensation.

                        Such incidents aren’t isolated: over the last two years, more than 33 days of unplanned banking outages have been reported to Parliament, underlining the scale of the issue.

                        As AI increases the velocity of change, it also raises the stakes for getting it wrong. But the irony is that it also provides the tools needed to prevent these failures from happening in the first place. AI is both contributing to the risk of outages and becoming the most effective way to prevent them.

                        By applying AI to continuous validation, monitoring and risk detection, organisations can spot issues earlier, understand their potential impact and intervene before disruption occurs. This shifts the focus from reacting to outages to preventing them, and it’s where the paradox becomes constructive. AI doesn’t have to be a source of instability.

                        With the right approach, it can become the foundation for more resilient, predictable systems. Those that fail risk trading innovation for instability. In the AI era, speed without confidence is simply another form of risk.

                        Learn more at tricentis.com

                        • Artificial Intelligence in FinTech
                        • Cybersecurity
                        • Cybersecurity in FinTech
                        • Fintech & Insurtech

                        Author

                        Dan Brightmore

                        Published

                        28 April 2026

                        Estimated Read time

                        2Mins

                        Peer-reviewed Physical Review Research paper shows efficient preparation of financial distributions on quantum hardware

                        Haiqu, a leading developer of quantum middleware, has announced the publication of joint research with HSBC in Physical Review Research demonstrating an efficient approach to encoding real-world probability distributions into quantum circuits.

                        Quantum State Preparation

                        Quantum state preparation, the process of encoding classical data into quantum states, is widely recognised as a major bottleneck when implementing many algorithms on hardware. This challenge is particularly relevant for applications such as financial risk modelling and simulation, where complex probability distributions must be loaded onto quantum devices.

                        The research uses matrix product state (MPS) methods to construct shallow circuits that encode smooth functions, including probability distributions, directly into quantum states. It also introduces a sampling-based workflow that avoids storing the full discretised dataset in classical memory, enabling larger encoding circuits to be generated.

                        The approach was validated on finance-relevant models including heavy-tailed Lévy distributions, commonly used to capture extreme market events. 

                        On IBM quantum hardware, circuits up to 25 qubits produced samples that passed standard statistical tests, showing the method can accurately reproduce the probability distributions these models rely on in practice. 

                        Sampling-Based Workflow

                        Using the sampling-based workflow, the researchers also executed circuits up to 64 qubits, reproducing qualitative features of the target distributions under realistic device noise and demonstrating feasibility at larger scales. Similar behavior was observed in simulations up to 156 qubits, indicating the approach can extend to substantially larger problem sizes.

                        “Preparing complex probability distributions efficiently is a key step in many quantum algorithms,” said Dr. Philip Intallura, Group Head of Quantum Technologies at HSBC. “This work shows how they can be implemented with much shallower circuits, bringing practical applications such as financial risk modelling closer.” 

                        “One of the biggest practical barriers is getting realistic financial data onto today’s quantum hardware. This work shows a scalable path around that barrier and helps move quantum finance workflows from theory toward execution.” Mykola Maksymenko, Co-founder & CTO, Haiqu

                        Read the paper in Physical Review Research. 

                        About Haiqu   

                        Haiqu is an emerging leader in quantum software that supports the notion that near-term, commercially viable applications are achievable with the right software, even on current hardware. Haiqu’s hardware-agnostic software can run applications with up to 100x more operations on current devices compared to competitors. Headquartered in New York City in the United States, Haiqu’s expert team operates from US, Canada, Ukraine, UK, EU, and Singapore, contributing to the company’s mission to make quantum computing practical as soon as possible.

                        • Blockchain & Crypto
                        • Digital Payments
                        • Fintech & Insurtech
                        • Infrastructure & Cloud

                        Author

                        Dharmesh Acharya

                        Published

                        Estimated Read time

                        6Mins

                        ZeroThreat co-founder Dharmesh Acharya on why the only way to know if your defences actually hold is to challenge them with continuous penetration testing and exploit validation

                        Your security dashboard is green. No alerts. No critical flags. Everything looks fine. That feeling of calm is exactly what you should be worried about. A clean dashboard does not mean your application is secure. It often means you are measuring the wrong things.

                        The reality is, threats are growing faster than most security programs can keep up with. Over 2,200 cyberattacks happen every day globally, which is roughly one attack every 39 seconds. At the same time, attackers are no longer looking for obvious vulnerabilities. They focus on weak access points, exposed data, and chained exploits that traditional dashboards fail to capture.

                        If a threat operates outside those parameters, it stays invisible. Your logs look normal, your vulnerability scanner reads low risk and your compliance status says passing. And somewhere in your environment, an attacker could be moving quietly through systems your dashboard never touches.

                        Let’s take a look at why green dashboards can be misleading, what they are not showing you, and what real security validation actually looks like.

                        The False Comfort of a Green Dashboard

                        There is something deeply reassuring about a green dashboard. No alerts. No red flags. And no critical vulnerabilities screaming for attention. For most security teams, that view signals control. It signals safety. But here is the uncomfortable truth: a clean security dashboard does not mean your environment is secure. It often just means your tools are not seeing the full picture.

                        Most monitoring systems only report what they are configured to detect. If a threat operates outside those parameters, it stays invisible. Your SIEM logs look normal. Your vulnerability scanner shows low risk and your compliance status reads “passing.” Meanwhile, an attacker could be sitting inside your network, moving quietly, and your dashboard would never know.

                        According to IBM’s Cost of a Data Breach Report, the average breach takes 168 days to identify and 51 days to contain it in the finance industry. That is over six months of green dashboards while real damage is being done. False confidence in security metrics is not a minor issue. It is one of the most exploited gaps in enterprise security posture today.

                        5 Problems with Traditional Security Metrics

                        Traditional security metrics were built for a different era. They measure what is easy to measure, not what actually matters. And when security decisions are based on incomplete or misleading data, the entire security program becomes vulnerable, even when everything looks fine on paper.

                        1. Visibility Without Context

                        Knowing that 10,000 events were logged means nothing without understanding what those events represent. Traditional metrics track volume, not relevance. Security teams end up drowning in data while the actual threats, the ones that matter, go unnoticed. Coverage without context is just noise.

                        2. Compliance Masking Risk

                        Passing a compliance audit does not mean you are secure. It means you met a checklist. Many organizations confuse regulatory compliance with actual cyber resilience. Attackers do not care about your audit results. They look for gaps, and compliance-focused metrics rarely surface those gaps in time.

                        3. Perimeter-Focused Thinking

                        Most traditional security metrics are built around the perimeter. But the perimeter does not exist the way it once did. Remote work, cloud environments, and third-party integrations have dissolved those boundaries. Metrics that still prioritize perimeter health give a dangerously narrow view of your actual attack surface.

                        4. Lagging Indicator Dependency

                        Traditional metrics tend to be reactive. They tell you what already happened, not what is happening right now. Mean time to detect, incident counts, patch rates, these are all lagging indicators. By the time they show a problem, the damage is often already in motion. Real security needs leading indicators too.

                        5. Ignoring Unknown Assets

                        You cannot protect what you cannot see. Shadow IT, unmanaged endpoints, forgotten cloud instances, these assets rarely show up in traditional security dashboards. Yet they are among the most targeted entry points for attackers. Metrics that only account for known assets create a false sense of complete coverage.

                        Hidden Risks Your Dashboard Doesn’t Show

                        Your dashboard reflects what your tools are configured to monitor. Nothing more. Unmanaged devices, misconfigured cloud storage, dormant user accounts with excessive privileges, these risks exist outside the monitoring boundary. They do not trigger alerts. They do not show up in reports. But they are real, and attackers know exactly how to find them.

                        Lateral movement is one of the most dangerous and least detected attack behaviors. Once an attacker gains initial access, they move quietly across your environment using legitimate credentials and trusted pathways. Traditional security monitoring tools rarely flag this activity because it does not look like an attack. It looks like normal user behavior. That is precisely what makes it so effective.

                        Third-party risk is another blind spot most dashboards completely ignore. According to Verizon’s Data Breach Investigations Report, 15% of breaches involve a third party. Vendor access, supply chain integrations, and API connections create exposure points that sit entirely outside your visibility. If your dashboard is not showing you that, it is not showing you everything.

                        What a Genuinely Healthy Security Posture Looks Like

                        A healthy security posture is not about having zero alerts. It is about having full visibility, fast response capability, and continuous validation. Organisations with mature security programs do not chase green dashboards. They build systems that surface the right information at the right time.

                        According to IBM, organizations with a fully deployed security AI and automation program contained breaches 108 days faster than those without. Speed of detection and response is one of the clearest indicators of a strong security posture. That cannot be measured by looking at how calm your dashboard appears.

                        Real security health includes knowing your complete asset inventory, including cloud workloads, third-party connections, and unmanaged endpoints. It means having continuous monitoring that goes beyond compliance checkboxes. It means your team runs regular adversarial testing to find gaps before attackers do.

                        And it also means your security metrics are tied to business risk, not just technical thresholds. When a CISO can clearly explain what is protected, what is exposed, and why, that is what a genuinely healthy security posture actually looks like.

                        How to Ensure Real Security: Exploit Validation

                        Knowing you have vulnerabilities is not enough. You need to know which ones can actually be exploited, and how far an attacker could get if they tried. That is what continuous exploit validation delivers. It moves security testing from a scheduled event to an ongoing process that reflects your real-world risk exposure.

                        AI-driven automated penetration testing makes this possible at scale. Instead of waiting for an annual pentest, these tools continuously simulate real attacker behavior across your environment. They test your controls, validate your detections, and surface exploitable paths before a real threat actor finds them. Your security team gets evidence, not assumptions.

                        The result is a security program that is grounded in reality. You stop relying on what your dashboard says and start relying on what has actually been tested and verified. Continuous exploit validation closes the gap between perceived security and actual security, and that gap is exactly where breaches happen.

                        Conclusion: Stop Trusting Your Dashboards and Start Validating

                        A green dashboard does not mean you are secure. It means nothing alarming has been detected within the boundaries your tools are configured to monitor. That is a very different thing. Real security is not about how calm your dashboard looks. It is about how thoroughly your environment has been tested and validated.

                        The only way to know if your defences actually hold is to challenge them. Continuous penetration testing and exploit validation give you evidence, not assumptions. They show you what an attacker would find before an attacker actually finds it. That shift, from monitoring to validating, is what separates a false sense of security from a real one.

                        Learn more at zerothreat.ai

                        • Cybersecurity
                        • Data & AI

                        Author

                        Dan Brightmore

                        Published

                        27 April 2026

                        Estimated Read time

                        4Mins

                        Dimitrios Bougioukas, VP – IT Security Training Services at Hack The Box, on how simulations can support security teams to detect, respond, contain and mitigate in real time

                        Financial institutions are operating in one of the most heavily targeted and scrutinised cyber environments in the world. They handle vast numbers of transactions, flows of sensitive personal data and have high-value digital infrastructures. Therefore, it is no surprise that the sector has invested heavily in technology. The aim is to ensure visibility of threats, including monitoring platforms, telemetry, alerting systems and threat intelligence. Yet, despite these investments, the visibility is not always translating into effective containment.

                        The Hack The Box Global Cyber Skills Benchmark 2025 analysed performance from 795+ teams and over 4,500 players. Carried out across 40 real-world Capture The Flag challenges, highlights this imbalance. 

                        According to the report, in simulated attacks, finance teams had a strong 37.6% average solve rate. Outperforming sectors like healthcare, education and government. They demonstrated great investigation skills, scoring 71% in OSINT, 54.6% in forensics and 51.4% in coding. These figures are all good indications that financial sector cybersecurity teams are able to effectively identify and analyse suspicious activity.

                        However, the report also shows there is significant underperformance in the skills required to stop attackers once they are inside. This means the gap is not in detection skills; it is in depth of capability.

                        Cyber Attackers Get Further Than They Should

                        The weakest scores are being recorded in the areas where adversaries could inflict the most damage.

                        According to the report, persistence scored just 21.1%, privilege escalation 20.3% and collection just 10.8% across financial cybersecurity teams. These are the tactics that determine how attackers entrench themselves, escalate access and gather sensitive data before exfiltrating it. They are central to adversarial movement inside financial networks.

                        Emerging threat vectors showed even more vulnerability. Blockchain security challenges, DeFi (Decentralized Finance) and smart contract–related vulnerabilities were only solved by the teams 10.1% of the time. And in exploit development, the teams averaged just 3.9%. This exposes weaknesses in exploit awareness, with the attackers increasingly using zero-day and near-zero-day vulnerabilities.

                        The combination of strong investigative skills and weaker adversarial resilience suggests that current capabilities are more focused on post-incident analysis than on preventing or containing attacks in real time.

                        Visibility Doesn’t Equal Control

                        Financial institutions have one of the most mature cybersecurity monitoring ecosystems across any industry. They deal with enormous volumes of logs, run highly tuned detection pipelines and leverage advanced SIEM and SOAR tooling. But this visibility on its own clearly does not equal security.

                        The benchmark report found that reconnaissance and initial access had solve rates between 23.8% and 28.4%, which indicates that many attacks will not be effectively blocked. From there, attackers are often able to succeed with their persistence, privilege escalation and lateral movement tactics because defenders do not have the technical depth to disrupt the attack chain at critical points.

                        In practice, this means teams may be able to see an attack unfold, but they will struggle to break it apart before data is collected for exfiltration. Even though exfiltration itself scored a relatively high at 53.4%, a low collection score suggests most teams are not catching malicious activity upstream, when intervention matters most.

                        A Depth Problem, Not A Monitoring Problem

                        This skills imbalance stems from how training and capability development have historically been structured. Much of the financial sector’s cybersecurity readiness has been shaped by compliance, audit frameworks and classroom-style instruction. While these approaches fulfil important governance functions, they do not by themselves produce the hands-on adversarial fluency that simulation-based training supports.

                        Financial teams must move beyond compliance checklists and legacy training models because they do not provide the attacker-aligned, hands-on experience required to strengthen deeper-layer defensive capabilities.

                        Attackers do not operate in silos and neither should defenders. A phishing foothold becomes privilege escalation, which becomes persistence, which becomes lateral movement – all in a matter of minutes. Without having continuous practice in this full attack flow, teams will continue to be strong in analysis and weak in action.

                        Continuous, scenario-based upskilling is the clearest path to addressing this imbalance. The benchmark data in the report demonstrates the need for a cyber readiness model that is based on realistic adversarial simulation. These simulations don’t just replicate individual techniques; they replicate entire attack chains. This forces security teams to detect, respond, contain and mitigate in real time.

                        Learn more at hackthebox.com

                        • Blockchain & Crypto
                        • Cybersecurity in FinTech

                        Author

                        Dvir Hoffman

                        Published

                        Estimated Read time

                        6Mins

                        By Dvir Hoffman, CEO at CommBox on why organisations that will lead in the next phase of digital transformation are those that treat AI not as a feature, but as a production capability

                        Enterprise AI has entered a new phase. The experimentation cycle that defined the past few years, full of proofs of concept, innovation labs, and sandbox deployments, is giving way to a harder question: how do we operationalise AI at scale, safely and measurably?

                        Nowhere is this tension more visible than in customer service voice environments. Voice remains the most complex, emotionally nuanced and operationally demanding channel. It is also where AI has the potential to unlock some of the greatest value.

                        Recent research from McKinsey highlights that organisations embedding generative AI directly into customer operations are seeing productivity improvements of 30 to 45 percent when deployment is integrated into workflows rather than layered on top. The distinction is critical. AI succeeds not because it sounds intelligent, but because it is embedded into systems, governance and business metrics.

                        For technology leaders, the path from pilot to production is less about enthusiasm for AI and more about discipline in execution.

                        Why AI Voice Often Stalls Before Scale

                        The majority of AI initiatives do not fail because the technology underperforms. They stall because the surrounding enterprise architecture is not aligned.

                        In controlled pilots, AI voice agents can demonstrate impressive conversational capability. They answer FAQs, interpret intent and simulate human dialogue convincingly. But production environments are not defined by conversation quality alone. They are defined by operational depth.

                        When a customer calls a healthcare provider, an insurer or a retailer, the AI must do more than talk. It must: Authenticate identity securely. Retrieve and update records in real time. Execute transactions, escalate appropriately and comply with regulatory frameworks. Without direct integration into CRM systems, billing platforms, policy databases or electronic health records, AI remains superficial.

                        This is where many organisations hit friction. Production requires orchestration across telephony infrastructure, data platforms, compliance frameworks and human workflows.

                        Governance becomes another inflection point. Voice interactions carry legal and reputational weight, particularly in regulated sectors. Disclosure requirements, audit trails, escalation protocols and data protection controls cannot be retrofitted after deployment. The World Economic Forum’s 2024 work on responsible AI underscores that governance must be embedded into AI systems by design, particularly where customer trust and compliance are at stake.

                        When governance is treated as an afterthought, scaling slows dramatically.

                        There is also a measurement problem. Too many pilots are judged by narrow metrics such as intent recognition accuracy or conversation duration. Production environments are judged by business impact: containment rates, reduction in average handling time, cost-to-serve, regulatory adherence and customer satisfaction. If AI is not connected to those outcomes from the outset, executive momentum fades.

                        The shift from pilot to production requires organisations to think less about model performance and more about operational alignment.

                        Automation Without Eroding Trust

                        A common concern among executives is whether AI voice will erode customer trust. The answer depends entirely on how it is deployed.

                        Voice remains deeply human. Customers call when they want clarity, reassurance or resolution. In emotionally charged situations, such as reporting an accident, disputing a claim, or querying medical results, the experience must feel competent and controlled.

                        The most effective AI voice deployments do not attempt to automate everything. They focus on high-volume, structured interactions where resolution paths are clear and compliance rules can be embedded confidently. Appointment scheduling, policy updates, payment processing and order tracking are examples where end-to-end automation can meaningfully reduce friction.

                        In live enterprise environments, we are seeing organisations safely automate a significant proportion of inbound calls when the AI agent has direct access to real-time data and defined escalation thresholds. Customers benefit from immediate resolution, while human agents are freed to handle complex and emotionally sensitive cases.

                        Equally important is what happens when escalation occurs. AI should not disappear at the point of handoff. Instead, it should transfer context, summarise the conversation and provide agents with relevant data and next-best-action prompts. This augmentation model aligns with Gartner’s 2024 analysis of customer service technology trends, which emphasises that the greatest gains come from combining automation with agent enablement rather than pursuing full replacement.

                        Trust is reinforced when customers feel they are being served efficiently and responsibly. That requires transparency about when AI is involved, clear pathways to human support and systems that operate within strict compliance guardrails.

                        In regulated industries, explainability is no longer optional. Enterprises must be able to demonstrate how decisions were made, how data was handled and how customers can escalate concerns. When these safeguards are engineered into the platform, AI voice becomes a tool for strengthening trust rather than compromising it.

                        Where Enterprise Leaders Should Focus

                        As AI investment accelerates, CIOs and CDOs face pressure to deliver measurable value while maintaining governance standards. The lesson from organisations successfully scaling AI voice is that integration and oversight matter more than experimentation.

                        AI should be treated as infrastructure. That means prioritising deep integration with core enterprise systems from the outset. An AI voice agent that cannot execute transactions securely or access accurate, real-time data will struggle to deliver meaningful business outcomes.

                        Governance must also be operational, not theoretical. Clear escalation pathways, role-based permissions, audit capabilities and sector-specific compliance frameworks need to be embedded within the technology layer. When risk management is part of the architecture, deployment accelerates rather than slows.

                        Finally, measurement must be tied directly to business performance. Containment rates, resolution times, operational cost reductions and customer satisfaction metrics should be defined before rollout. McKinsey’s 2024 research reinforces that organisations capturing the most value from generative AI are those embedding it deeply into workflows with explicit performance targets.¹ AI that operates alongside business KPIs, rather than parallel to them, is far more likely to achieve sustained executive backing.

                        The broader transformation taking place across enterprise technology is not about AI replacing human capability. It is about rearchitecting customer engagement so that automation, data and people operate in synchrony.

                        Meeting AI Voice Demands

                        Voice remains one of the most demanding channels to modernise precisely because it sits at the intersection of emotion, compliance and operational complexity. Yet that is also why it offers such strategic value. When AI voice is integrated into core systems, governed rigorously and measured against real business outcomes, it moves from being an innovation experiment to becoming a structural advantage.

                        The organisations that will lead in this next phase of digital transformation are those that treat AI not as a feature, but as a production capability. Moving from pilot to production is not simply a technical milestone. It is a cultural one, signalling that AI is no longer an experiment on the edge of the enterprise, but a trusted component at its core.

                        Learn more at commbox.io

                        • Data & AI
                        • Digital Strategy

                        Author

                        Dan Brightmore

                        Published

                        24 April 2026

                        Estimated Read time

                        7Mins

                        The Innovate Finance Global Summit (IFGS) once again set the tone for London FinTech Week, transforming the historic Guildhall into…

                        The Innovate Finance Global Summit (IFGS) once again set the tone for London FinTech Week, transforming the historic Guildhall into the beating heart of global financial innovation on Tuesday April 21st 2026. As the flagship event of the week-long programme, IFGS brought together more than 1,500 senior leaders from over 70 countries. Cementing its reputation as one of the most influential gatherings in the FinTech calendar.

                        A mix of founders, regulators, policymakers, investors and financial institutions filled the venue’s grand halls, all drawn by a shared ambition: to shape the future of financial services. IFGS has long positioned itself as “where decision-makers meet,” and in 2026 that promise felt particularly tangible.

                        A Global Stage for a Rapidly Evolving Industry

                        The summit’s scale and structure reflected the complexity of today’s FinTech ecosystem. Across four stages and more than 100 sessions, attendees were immersed in a dense programme of keynote speeches, panel discussions and live demonstrations.

                        What distinguishes IFGS from many industry events is its ability to convene not just innovators, but the entire value chain. Fom early-stage startups to global banks, Big Tech firms and regulators. Companies such as Google Cloud, Amazon Web Services and Microsoft were prominent throughout the programme, reflecting the growing convergence of financial services and cloud infrastructure. Where policy meets practice and innovation meets implementation.

                        The agenda in 2026 was firmly aligned with the industry’s most pressing challenges and opportunities. Themes ranged from AI in financial services and open finance to stablecoins, fraud prevention and financial inclusion. Not juts abstract talking points, but the battlegrounds on which the future of finance will be decided.

                        Opening Momentum: Optimism with a Call for Action

                        The day began with a keynote from Innovate Finance CEO Janine Hirt, who set a confident yet pragmatic tone.

                        “We have built one of the most dynamic fintech ecosystems in the world here in the UK,” she said. “But leadership is not guaranteed. It requires constant innovation, investment and collaboration.”

                        Hirt highlighted the UK’s continued strength in areas such as open banking, while stressing the need to accelerate progress in digital assets, fraud prevention and global competitiveness.

                        A recorded address from Chancellor Rachel Reeves reinforced this narrative, positioning fintech as a cornerstone of the UK economy.

                        “Fintech is not just a success story—it is a strategic priority,” Reeves noted. “We will continue to back innovation, support digital assets development and ensure the UK remains a global hub for financial services.”

                        The political backing on display underlined fintech’s growing importance—not just as a sector, but as a driver of national economic growth.

                        AI, Data and the Next Wave of Innovation

                        Unsurprisingly, artificial intelligence dominated much of the conversation. Across multiple sessions, speakers from firms including NVIDIA, IBM and Accenture explored how AI is reshaping everything from customer onboarding to fraud detection and credit decisioning.

                        What stood out was the shift in tone. The discussion has moved beyond hype to implementation.

                        “We’re past the experimentation phase,” said a senior executive from Mastercard during a panel on AI in payments. “The focus now is on scaling responsibly. Embedding AI into core systems while maintaining trust and transparency.”

                        Similarly, Revolut’s leadership team pointed to AI-driven personalisation as a key differentiator in the next generation of digital banking. “Customers increasingly expect financial services to anticipate their needs. AI enables us to deliver that, but only if it’s built on strong data foundations.”

                        Closely linked to AI was the continued evolution of open banking into open finance and ‘smart data’. Speakers from the Financial Conduct Authority (FCA) and firms such as Plaid and TrueLayer emphasised the importance of interoperability and regulatory clarity.

                        “Open finance is the natural next step,” said an FCA representative. “The challenge is ensuring it delivers real value to consumers while maintaining robust safeguards.”

                        Meanwhile, stablecoins and tokenisation emerged as key themes in the context of global competition. Circle, Ripple and several UK-based digital asset firms contributed to discussions around the future of programmable money.

                        “The question is no longer if tokenisation will reshape markets, but how quickly,” said a Ripple executive during a digital assets panel.

                        The Exhibition Floor: Innovation in Action

                        Beyond the stages, the expo hall offered a tangible view of FinTech’s evolution. Established players such as Lloyds Banking Group, NatWest and Santander UK showcased innovation initiatives alongside emerging startups presenting solutions in regtech, embedded finance and payments.

                        Fintech scale-ups including Checkout.com and Adyen demonstrated new capabilities in cross-border payments and merchant services, while regtech firms, such as Entrust, highlighted advances in compliance automation and fraud detection.

                        This blend of established and emerging companies is one of IFGS’s defining strengths. It creates an environment where partnerships can form organically. Whether between a startup seeking scale or an incumbent looking to innovate.

                        The presence of regulators, notably the FCA, added another layer of value. Initiatives such as onsite ‘office hours’ enabled direct engagement between FinTech firms and regulators.

                        “Dialogue is essential,” one FCA official commented. “Events like IFGS give us the opportunity to hear directly from industry and shape regulation that supports innovation.”

                        Networking: Where Deals and Ideas Converge

                        If content is the backbone of IFGS, networking is its lifeblood. The summit’s design – from curated meetings to informal discussions in the Guildhall’s historic spaces – encouraged meaningful interaction.

                        With thousands of attendees and a high concentration of C-level executives, the event offered unparalleled access to decision-makers. Conversations between banks, FinTechs and investors were constant, with many attendees citing partnership discussions as a key outcome.

                        Even external challenges—such as a London Tube strike on the day—did little to dampen participation. If anything, it reinforced the determination of attendees to be part of the conversation.

                        “Nothing was going to keep people away,” remarked one venture investor. “IFGS is where the industry sets its agenda for the year.”

                        Key Takeaways: Collaboration, Competition and Confidence

                        Several clear themes emerged from IFGS 2026.

                        First, collaboration remains critical. The UK’s fintech success is built on strong relationships between startups, incumbents and regulators.

                        Second, global competition is intensifying. Whether in AI, digital assets or open finance, the race to lead the next phase of financial innovation is well underway.

                        Third, there is a growing sense of confidence in fintech’s role as an economic driver.

                        “Fintech is no longer a disruptor, it is the foundation of modern financial services,” said a senior executive from HSBC during a closing session.

                        Conclusion: A Catalyst for the Year Ahead

                        IFGS 2026 delivered on its promise as the centrepiece of London FinTech Week. By bringing together the full spectrum of the FinTech ecosystem in a single venue, it created a powerful platform for dialogue, collaboration and innovation.

                        More than just a conference, IFGS serves as a barometer for the industry’s direction. The conversations held within the Guildhall’s walls will shape strategies, partnerships and policies in the months and years ahead.

                        As FinTech continues to evolve at pace, events like IFGS are not just important; they are essential. They provide the space for ideas to collide, for relationships to form and for the future of financial services to be defined.

                        In 2026, IFGS once again proved why London remains at the forefront of global FinTech. And why the world continues to look to the UK for leadership in shaping what comes next.

                        • Artificial Intelligence in FinTech
                        • Blockchain & Crypto
                        • Cybersecurity in FinTech
                        • Events
                        • Host Perspectives

                        Author

                        Andreea Pleşea

                        Published

                        23 April 2026

                        Estimated Read time

                        5Mins

                        AI is no longer seen as an add-on. It is expected as a standard in enterprise IT infrastructure explains Andreea Pleşea PhD, Co-Founder & COO at Druid AI

                        Digital transformation is often hailed as the answer to improve productivity, and yet, despite significant investment, the UK continues to lag behind similar markets such as the US, France and Germany in productivity growth.

                        The UK is recognised internationally for its financial and banking sector, and it sits at the heart of the UK economy. When banks operate efficiently, businesses move faster, but when banks are slowed by operational friction, the ripple effects are felt far and wide.

                        UK financial institutions operate a technology stack across core banking platforms, CRM systems, contact centre infrastructure, mobile apps, fraud systems, onboarding tools, compliance platforms and knowledge bases. Each was designed to solve a specific problem, but together they have created a fragmented set of solutions that require employees to constantly switch between applications to find the information they need to answer customer queries or understand how to make improvements to the business.

                        This fragmentation has created an orchestration gap, and agentic AI is the technology that can bridge it – not by adding another tool, but by becoming an essential part of the IT infrastructure.

                        Scripted Bots are Out – Autonomous Execution is in

                        The first wave of banking automation focused on what is referred to as ‘deflection’. Essentially, chatbots and Interactive Voice Response (IVR) systems were rolled out with the goal to reduce call volumes and answer basic account questions. But 61% of customers still escalate to human agents because these systems fail to resolve issues. Regulated banks cannot allow public Large Language Models (LLMs) to access core systems without strict governance. They generate responses, not orchestrate workflows.

                        The introduction of Generative AI tools in recent years has allowed for more natural language capabilities, but improving language alone does not complete work. Many, if not all, financial institutions don’t want public LLMs accessing their core banking systems, enforcing business rules, or figuring out whether they can stand up to the test of being audited in a highly regulated industry. Quite simply, they generate responses, they do not orchestrate business processes.

                        The Fundamental Difference with Agentic AI

                        AI agents are built from the ground up to be decision-capable and goal-oriented. They are capable of executing multi-step workflows or processes across different core platforms while operating within the strict boundaries of financial governance.

                        If a customer asks the question “What is the balance of my current account?” an AI agent will authenticate the customer, retrieve the necessary account data from core banking systems and provide the answer. They can also help with queries such as a card replacement, updating contact details or guiding a customer through the process of a loan application, to completion. Irrespective of whether the customer chooses to engage across chat, SMS, voice or mobile banking, the AI agent won’t lose the context of the request even if they switch platforms.

                        Retail banking customers interact with their bank approximately 150 times per year, and when those touchpoints are fragmented across channels, cost-to-serve rises and trust declines. However, when they are resolved quickly and securely in digital channels, efficiency and retention improve.

                        Making the Productivity Case for UK Banking

                        The productivity opportunity for UK banking lies in automating the high-volume, repeatable journeys – not through rigid, scripted chatbots, but through intelligent, governed workflow execution.

                        High-volume journeys such as account servicing, loan applications and fraud inquiries require secure verification, system checks and downstream actions. Yet customers are often forced to escalate to human agents to complete them.

                        By applying unified business rules across digital channels and legacy IVR systems, AI agents standardise this fragmented logic. A single workflow can be built once and deployed consistently across web, mobile, contact centre and messaging channels. This reduces repeat contacts, eliminates ‘start over’ frustration and frees human advisors to focus on complex cases, cross-sell opportunities and relationship management.

                        In a market where 17- 22% of UK consumers are actively looking for a new bank or considering switching their main bank account, consistent, frictionless service is not a luxury – it’s a competitive defence.

                        Improve the Infrastructure Rather than Replace it

                        The productivity impact extends beyond front-line customer enquiries and extends to how employees can navigate the maze of business applications to onboard suppliers, generate compliance reports, update policies or process internal IT requests. Agentic AI sits across these internal systems as well, automating repetitive processes and orchestrating tasks without forcing employees to switch between interfaces.

                        One of the biggest barriers to adopting this transformation from CIOs and IT leaders is a fear of ‘rip-and-replace’ programmes. Core banking systems are deeply embedded with the organisation, CRM systems anchor case management and Contact Centre as a Service (CCaaS) platforms manage routing and workforce engagement.

                        Agentic AI does not require these embedded systems to be replaced, it securely integrates with them, creating an operational layer that improves productivity.

                        Conversational AI Platforms with autonomous agents act as an orchestration layer across existing stacks. They plug into core banking systems, CRM and CCaaS infrastructure, performing governed actions while maintaining audit trails and role-based access control. This highly customisable approach allows finance and banking institutions to modernise customer journeys without destabilising foundational systems.

                        The AI Opportunity is Clear

                        This is where the infrastructure argument becomes clear. UK finance and banking institutions don’t need more applications layered onto already complex, data-sensitive, highly secure enterprise IT environments – they need intelligent systems that unify what already exists.

                        The UK’s next productivity gains will not come from incremental feature upgrades. They will come from rethinking how repetitive tasks move across enterprise systems. Agentic AI represents a shift from tools that respond to requests to an infrastructure that completes complex tasks, at scale. For mid-to-large retail banks and credit unions, the opportunity is clear: resolve more interactions digitally, scale capacity without expanding headcount, protect margins and strengthen customer trust.

                        Learn more at druidai.com

                        • Artificial Intelligence in FinTech
                        • Cybersecurity in FinTech
                        • InsurTech
                        • Neobanking

                        Author

                        Lee Nolan

                        Published

                        Estimated Read time

                        5Mins

                        Lee Nolan, GM UK&I at Hitachi Vantara, on why AI will not be defined by the sophistication of the models being deployed but the strength, consistency and reliability of the data that sits behind them

                        Spend five minutes in any boardroom and AI will come up. Strategies are being signed off, budgets are being released and pilots are already underway, giving the impression that momentum is building at pace. Yet beneath that surface is a more uncomfortable reality, one that is becoming harder to ignore as organisations move beyond experimentation and into delivery.

                        Most organisations are trying to build AI on foundations that were never designed for it. The ambition is clear and well-funded, but the underlying data infrastructure has not kept up. That gap between intent and readiness is now becoming visible, particularly as organisations look to scale beyond isolated use cases and deliver outcomes that are consistent and commercially meaningful.

                        Recent research into UK businesses reinforces this point. While adoption continues to move forward, only a small number of organisations are genuinely set up to support AI at scale. The issue is not tools or talent, but the condition of the data at the core of the business.

                        AI Exposes What Businesses Would Rather Ignore

                        AI is often described as a layer that can be applied to existing systems to unlock value. In practice it does the opposite. It brings complexity into sharp focus, exposing inconsistencies and inefficiencies that may have been tolerated for years.

                        That is why data quality has moved to the centre of the conversation. Around 67% of UK organisations now cite it as the primary driver of AI success. The shift reflects a growing awareness that no level of investment in AI can compensate for weak or unreliable inputs.

                        For many organisations, data has evolved without a consistent approach to governance. In fact, Gartner estimates that 80% of organisations attempting to scale digital initiatives will fail due to weaknesses in data and analytics governance. Systems have been added over time, ownership is unclear and definitions vary. The result is a fragmented environment where the same metric can mean different things across the business. When AI is introduced, it does not resolve those inconsistencies, it amplifies them.

                        The Hype Cycle is Giving Way to Reality

                        Over the past year, there has been a shift in how organisations approach AI. The early phase was driven by urgency, with businesses keen to move quickly and demonstrate progress. That momentum remains, but it is now being balanced by a more realistic perspective.

                        There is a greater focus on outcomes, with more scrutiny on how AI is delivering value. Many organisations have realised that quick wins are harder to achieve when the underlying data is not fit for purpose, and that scaling AI requires a level of operational discipline that cannot be bypassed.

                        What was initially framed as a technology challenge is now understood as a business challenge, spanning processes, ownership and governance as much as platforms and tools.

                        Confidence is High but Capability is Uneven

                        Confidence across organisations remains high, but it often does not reflect reality.

                        While many businesses consider their data infrastructure to be mature, progress is often uneven. Some teams may be working with well governed data, while others are still reliant on manual processes and disconnected systems. This creates a situation where parts of the business are ready to move forward, while others are not, a challenge reflected in wider industry research from McKinsey & Company, which highlights siloed data as one of the biggest barriers to scaling AI.

                        That inconsistency is where problems begin. AI depends on trust in the data If that trust is not consistent across the organisation, outputs become unreliable and adoption slows. From the outside, many organisations appear ready, but internally the foundations are still being stabilised.

                        Control vs Convenience

                        There is also a growing emphasis on control, particularly around where data is stored and how it is managed. Data sovereignty is now playing a central role in decision making, with around 85% of UK organisations saying it directly influences how they deploy AI.

                        This reflects a broader recognition that data is both a critical asset and a potential point of risk. As organisations become more reliant on it, they are also becoming more deliberate in how it is governed and protected.

                        At the same time, the expectation that everything should be built in-house is fading. Many organisations are turning to external partners to accelerate progress, while retaining control over their data and strategic direction. This balance allows them to move faster without losing oversight.

                        Vague AI Strategies

                        One of the most persistent challenges is the lack of clarity around what AI is meant to deliver. Too many initiatives begin with broad ambition and little definition of success, resulting in activity that is difficult to measure and even harder to scale. This is reflected in wider industry trends, with Gartner noting that only 53% of AI projects make it from prototype into production.

                        The organisations making progress are far more structured. They define clear objectives, establish measurable outcomes and maintain a focus on value throughout. In the UK, a growing majority are now putting formal KPIs in place for their AI initiatives.

                        This shifts AI from being an experiment to something that can be managed, evaluated and improved over time.

                        AI Will not Wait for Organisations to Catch Up

                        AI will continue to evolve at pace, and the pressure on organisations to keep up is unlikely to diminish. What is changing is the nature of the conversation. It is becoming less about what AI can do in theory, and more about what organisations are actually capable of delivering.

                        Most are still in the process of building the foundations required to support AI effectively. That is not a failure, but it does define the scale of the challenge ahead.

                        Because ultimately, AI will not be defined by the sophistication of the models being deployed. It will be defined by the strength, consistency and reliability of the data that sits behind them.

                        Learn more at hitachivantara.com

                        • Data & AI
                        • Digital Strategy

                        Author

                        Mark Talbot

                        Published

                        22 April 2026

                        Estimated Read time

                        5Mins

                        Mark Talbot, Director, CS AI Initiatives at Appian, reasons that as organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it

                        Many organisations have long treated improvement as something that arrives as a top-down effort, not something built with the people doing the work. Specialists designed new processes, discussed them in formal forums, and introduced them through large change programmes that often felt detached from daily work. For most employees, ‘transformation’ meant being asked to follow new rules, rather than designing better ways of working.

                        AI is starting to reverse that pattern. Instead of concentrating control and decision rights in a small, central group, modern AI tools give more agency to the people closest to the work. They can see what is not working, imagine better approaches, and use AI to help redesign and improve the processes they rely on every day. This shift – which can be described as the democratisation of AI – changes who participates in improving the business. However, it is worth remembering that this shift only works at scale when AI is embedded within a platform that maintains governance, visibility and control. 

                        Process Improvement in the Hands of Many

                        Until recently, fixing a broken process often meant filing tickets, waiting for a slot on an IT roadmap, or hoping that a specialist team would eventually address the issue. Creating applications, building automations or redesigning workflows were seen as highly technical tasks. For most employees, waste and inefficiency were things to work around, not things they had the tools or authority to change.

                        That obstacle is now deteriorating, as long as organizations don’t lose sight of the fact that governance remains essential, particularly in highly regulated environments

                        AI agents, generative AI and conversational interfaces allow people across the business to shape how work is structured. Within this model, someone in operations can describe an outcome in plain language and have an AI system propose and embed the steps within existing processes. Within a governed platform, non-technical users can adapt existing solutions and automate repetitive tasks without waiting months for central support. At the same time, process insights give developers visibility into what is being built, enabling them to refine, standardise and scale applications more quickly across the organisation.

                        Data is opening up as well. Data fabrics and related architectures connect scattered information sources into governed layers that a wider audience can access safely. Instead of waiting on static reports, people can access relevant, trusted data when they need it, and use AI to interpret and apply it to their decisions.

                        When process insight and data access reach this level, best practices move beyond documentation or occasional training. Tools and workflows embed them into daily work, improving performance across the organisation.

                        Scaling Improvement Across the Organisation

                        As more individuals understand how their work connects to broader outcomes, organisations unlock a powerful driver of change. Process improvement no longer depends only on a small group of specialists. Employees can recognise when processes are inefficient or risky and have the means to address them at scale, inside an AI platform.

                        By encoding domain knowledge into AI assistants and digital coworkers within an enterprise-grade AI platform, organisations can share expertise across roles and levels. These AI-powered helpers do not replace professional judgment. They strengthen it. They surface options, highlight inconsistencies and provide context, while humans make the final decision. Over time, each interaction becomes both a learning moment and a new piece of institutional knowledge that organisations can capture and reuse.

                        In this model, process improvement is no longer episodic or confined to formal transformation projects. It becomes part of everyday work, inside a platform with AI tools that provide real-time feedback and recommendations.

                        AI, Noise Reduction, and Better Oversight

                        This shift raises a key question: if AI platforms make analysis, decision support, and process design more accessible, what happens to deep expertise?

                        There is a concern that easy access to AI advice might weaken people’s understanding. If answers are always a prompt away, will teams still develop the knowledge that comes from working through complexity? If people follow AI suggestions without grasping the logic, how meaningful can human oversight really be?

                        Over-reliance on instant guidance can create only surface-level competence. People may treat AI outputs as instructions rather than as inputs to their own reasoning.

                        On the other hand, used well, AI can create more room for expertise, not less.

                        By handling repetitive tasks and routine decisions, AI reduces the volume of low-value work that consumes people’s time. Teams can then focus on exceptions and refine how they make decisions. Instead of dealing with every routine request themselves, they can focus on work where context and experience matter most.

                        When AI removes more of the routine burden, teams have more capacity to focus on judgement, process design and oversight. That helps build expertise while keeping improvement connected to the wider goals and governance of the business.

                        Shaping AI, Not Just Living With It

                        As organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it. AI is moving from something that happens to the workforce to AI being something that is built and refined with the workforce.

                        Organisations should treat people as partners in shaping AI, rather than as operators of automated systems. When AI platforms can be combined with process visibility and human judgement, employees can have an outsized effect on the systems around them. They can influence how work is structured and how decisions are made. In that sense, AI redistributes who participates in designing better ways of working, and creates an opportunity to anchor that shift in thoughtful design and human expertise.

                        Learn more at appian.com

                        • Artificial Intelligence in FinTech
                        • Data & AI
                        • Digital Strategy
                        • Fintech & Insurtech
                        • People & Culture

                        Author

                        Daniel Ehnhage

                        Published

                        20 April 2026

                        Estimated Read time

                        5Mins

                        Daniel Ehnhage, Head of AI Transformation at Unit4, on why those that put people and capability at the centre of their AI strategy will unlock far greater and more sustainable value than those led by technology alone.

                        As the head of AI transformation, it might sound counterintuitive to suggest that artificial intelligence is not the most important part of my work. It makes a significant contribution to the radical change we are looking to achieve, but the technology itself is only about 10% of the solution. A significant part of the planning and investment must be based around addressing issues like the integration of siloed information systems, the building of the organisational capability required to adopt AI safely and finding the right business case. The key is to understand that adopting AI is not only about improving existing processes – it’s about gradually reshaping how we work in a sustainable way. The goal should be phased, practical improvements that build maturity over time.

                        This can be daunting for any organisation that has well-established operating practices. It requires a deliberate shift from problem‑solving to rethinking how value is created across the organisation. AI is far more capable and can empower your teams to find new solutions such as gathering more intelligence about market opportunities to improve productivity and decision making. The focus should be on enabling internal teams to work smarter through safe, responsible AI adoption. If your organisation is prepared to embark on such change, you must recognise AI becomes most powerful when you bring the right data together. Today, many organisations, including ours, are still maturing in this area. Successful adopters of AI prioritise building data readiness step by step so AI can create real value without overpromising.

                        Obviously, the role of AI transformation then becomes much broader with the added challenge of having to implement change without disrupting existing business performance. Consequently, there are some key areas where organisations must focus their attention, beyond ensuring they pick the right AI tool…

                        Structural Change – Put the AI Board in Place

                          AI transformation evolves how organisations work. It does not replace everything we humans do today. The goal is to focus on practical, high‑value use cases that improve productivity, quality, and employee experience without creating disruption. A widely debated expression of this change is the concern that AI will replace human employees. Personally, I think this lacks imagination around the positive impact that AI can have on a workplace. Yes, it may reduce the number of repetitive, mundane tasks, but more importantly it will create new ways of working and collaborating.

                          However, given how rapidly the technology is moving it is critical your organisation puts the right safeguards in place and agrees policies about ethical usage. That requires adoption of a cross-functional AI Board to provide a framework for embracing AI which will manage the impact of the structural change. This provides focus for your organisation’s approach to AI. The goal should be to agree which tools offer the most benefit for your teams and concentrate on exploiting use cases that will deliver the most benefit.

                          The AI Board should be responsible for establishing the governance structure to help the IT and cybersecurity teams to ensure the use of AI is not creating new vulnerabilities. It should provide clarity and safeguards so employees can use AI confidently and responsibly. Our goal is to enable safe experimentation – not restrict innovation.

                          People Change – Enabling Collaboration and Experimentation

                          The ambition should be to get employees excited about the potential of AI to open up new ways of working that can lead to rewarding opportunities and exciting new challenges. Indeed, it is widely accepted that helping your people to accommodate the change is the biggest challenge you will face, taking up about 70% of the time required to implement the technology. This is because successful implementations depend on collaboration between distinct teams, which in turn depends on breaking down barriers, both for individuals and teams.

                          For example, imagine being able to use AI to analyse data from diverse systems such as customer service, product development and marketing to identify new opportunities to support customers.

                          Integrating these data sources could be seen as interfering with distinct job functions, so for all employees it is critical to educate them on what will be expected of them and a good start point is explaining how they will be measured. It could include simple measures such as demonstrating usage of AI tools, but if an organisation wants employees to adopt the technology it is also important to empower them through training.

                          With the right support, employees will want to experiment, which should also enable them to understand use cases for AI in their work and the competencies they need to develop. This can be achieved through opportunities for cross-functional teams to explore new ways of working and innovating and should be encouraged by senior leaders. It is crucial they set the right tone, support initiatives, celebrate successes and listen to employee feedback.

                          Business Change – Building the Right Business Case

                          The business case is not just about saving money to solve a specific problem. It is too easy to look at the saved hours and productivity gains from adopting AI as the sum total of the investment costs you must deal with. There are a number of internally focused requirements that you must build into your thinking about AI transformation. There will be costs around the integration work to enable AI to access data from disparate systems. Competence development must be a top priority. Time must also be allocated to the process of change management and how it may disrupt existing business processes. Security must be a top consideration. These are all internally focused tasks, but you must look at them if you are to capitalise effectively on your AI investment.

                          It is tempting to become overly excited by the potential of AI as a technology, and certainly it will bring dramatic change to organisations in the years to come, but having experienced the factors necessary for successful transformations, it is absolutely critical senior leadership teams approach AI-enabled change with cool heads and clarity on what they want to achieve. Above all, they must remember success is not dependent on the implementation of the technology, but predominantly on bringing employees with them on the journey. Many commentators talk about the rise of AI-first organisations. Those that put people and capability at the centre of their AI strategy will unlock far greater and more sustainable value than those led by technology alone.

                          Learn more at unit4.com

                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Strategy

                          Author

                          Vincent Guillevic

                          Published

                          16 April 2026

                          Estimated Read time

                          5Mins

                          Vincent Guillevic, Director of Fraud Labs at Entrust, argues companies that treat identity as a continuous thread rather than a single checkpoint will be better positioned to reduce losses and protect customers

                          Identity verification and tackling fraud began as a face-to-face process, built on human trust. Opening a bank account involved meeting a banker in person and from there, trust was established because both parties could see and interact with each other directly in branch.

                          Fast forward to the digital age and a lot of services have moved online. Identity verification has therefore shifted from in-person checks to remote identity verification. Today, we’re in an era where identity is now central to every interaction we have online.

                          Fraud has followed the same trajectory. Much like a burglar would test every possible entry point rather than just the front door, fraudsters probe every stage of the customer journey. They look for weaknesses at onboarding, during login, and throughout ongoing transactions and data requests.

                          That challenge has intensified in recent years. AI has given fraudsters faster, sophisticated and scalable tools. Deepfakes can bypass checks, AI‑generated documents can appear real, and phishing and impersonation attacks can now be automated at scale.

                          Once a fraudster gains access to a legitimate account, the damage escalates quickly. Global losses from account takeover (ATO) fraud were projected to reach $17 billion in 2025, up from $13 billion in 2024. While the underlying intent of fraudsters seeking the weakest point of entry, the breadth, speed and sophistication of modern attacks have.

                          Identity Fraud Patterns Across the Customer Lifecycle

                          Fraud can occur at any stage of the customer journey. From verifying identity at onboarding to securing connections and fighting fraud in everyday transactions. Each stage introduces its own risks, and attackers adapt their tactics based on where value can be extracted most efficiently.

                          In 2025, patterns showed a clear distinction between industries targeted for new account fraud and those targeted for account takeover fraud. Businesses that offer immediate incentives such as promotional offers or sign-up bonuses are primarily targeted for new account fraud. In contrast, businesses where accounts accumulate long-term financial or data value face higher levels of ATO.

                          Industries built around sign-up incentives or instance access experience most fraud at onboarding. For instance, in crypto, 67% of fraud attempts occur during account creation, largely driven by sign-up incentives. Vehicle rental follows a similar pattern, with 67% of fraud taking place at onboarding as attackers use fake identities to gain short-term access to high-value assets. In these sectors, low-friction onboarding creates opportunities to harvest incentives or establish accounts that later become avenues for future money laundering.

                          Account takeover fraud reflects a different strategy. Rather than creating fake accounts, attackers focus on compromising established accounts using tactics such as stolen credentials, phishing, malware, or social engineering. Entrust data shows this is most common in industries where accounts hold enduring value. In payments, 82% of fraud attempts occur after onboarding, while in professional services the figure is 62%. High-value, long-standing accounts are attractive because they enable fund transfers, loans, and access to identity-rich data, making them more valuable than newly created accounts.

                          These patterns highlight two critical realities. First, organisations can no longer optimise for one type of risk at the expense of another. Defending a single point in the journey inevitably leaves gaps elsewhere. Second, fraud has become highly professionalised. Modern fraud operations are organised, strategic, and adaptive, moving toward the highest rewards and the weakest controls.

                          Prevention Must Span the Entire Journey

                          If fraud can occur at any stage, prevention must operate at every stage. Organisations that implement robust, lifecycle-wide identity strategies save an average of $8 million per year in fraud-related costs. These savings come from detecting threats earlier, more accurately, and beyond a single checkpoint.

                          There are three areas where that lifecycle approach needs to be strongest.

                          Get onboarding right

                          Onboarding is the first opportunity to establish genuine trust. Strong Know Your Customer (KYC) or Know Your Employee (KYE) processes combine document verification with biometric checks such as face recognition or fingerprint scanning to confirm that the person applying is who they claim to be. Liveness detection adds a further layer by distinguishing real users from synthetic identities and deepfakes, which are linked to approximately one in five biometric fraud attempts.

                          With strong identity verification at onboarding not only reduces immediate fraud, but also limits the downstream damage caused with fraudulent accounts.

                          Secure existing accounts with continuous authentication

                          Verifying identity once is no longer sufficient. Continuous authentication, combining multi-factor authentication with biometric re-verification like facial recognition, allows businesses to protect established accounts without creating unnecessary friction for legitimate users.

                          Crucially, it enables authentication requirements to adapt dynamically as risk levels change, rather than applying the same static check regardless of context. In payments businesses, where most fraud targets the authentication process itself, this adaptability is key to mitigating attacks before losses occur.

                          Monitor behaviour in real time, not just identity

                          Device intelligence and behavioural signals make it possible to assess risk based on how users interact with services, flagging unusual login patterns, device anomalies, or out-of-character transactions.

                          As AI-driven fraud becomes more sophisticated and convincing, behavioural indicators provide another layer of ongoing fraud detection. Focusing monitoring on high-risk actions, rather than only high-risk identities closes a critical gap in traditional defences.

                          The Window of Opportunity

                          Fraud has always followed the customer journey. What has changed is the availability of advanced technology capable of tracking, analysing, and responding to threats at every stage. The key question for organisations is whether these capabilities are deployed as a connected strategy or left as isolated controls with gaps in between.

                          Companies that treat identity as a continuous thread rather than a single checkpoint will be better positioned to reduce losses and protect customers, and preserve the trust that underpins long-term digital relationships.

                          Learn more at entrust.com and meet the team at IFGS in London on April 21

                          • Artificial Intelligence in FinTech
                          • Cybersecurity
                          • Cybersecurity in FinTech
                          • Fintech & Insurtech

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          3Mins

                          Join over 1,500 attendees at London’s Guildhall for IFGS 2026

                          The flagship event of UK FinTech Week is back in 2026. With over 1,500 attendees and representatives from over 70 countries, IFGS continues to attract the brightest and best from the global FinTech ecosystem to discuss and debate the crucial issues facing the sector now and in years to come.

                          Register for tickets here

                          IFGS kick-starts a week-long programme of events that will deliver world-class content, thought-leadership and ample networking opportunities for the entire UK FinTech ecosystem.

                          FinTech founders, entrepreneurs, investors, bank ex-cos, regulators, policy-makers, academics and media from around the world will come together to learn, discuss, debate and network. Don’t miss your opportunity to be a part of the conversation!

                          Why Attend IFGS?

                          Ranging from innovators, institutions, regulators to policy-makers, startups and investors – key players are in one place for a day of thought-provoking discussions. The agenda will spotlight the global fintech ecosystem, with an increased focus on the key areas that enhance, empower and ensure that FinTech and financial services pave the way for economic growth, sustainability, and a financial system that caters for all.

                          Exhibitors include ClearBank, UK Department for Business & Trade, Barclays, Mastercard, Starling, Lloyds Banking Group and many more…

                          View the agenda to plan ahead

                          Key Themes for IFGS 2026

                          With four stages filled with insightful sessions, panel discussions, and in-depth presentations, this event is a must-attend for FinTech professionals.

                          Gain empowering insights from industry expert speakers (representing the likes of HSBC, Monzo, Swift and Paypal) exploring the key themes and topics resonating across the FinTech ecosystem today:

                          • Powering Growth: The role of FinTech in supporting the wider UK economy
                          • AI in Financial Services: From responsible adoption to new business models
                          • Stablecoins: Balancing innovation, trust, and regulation
                          • Operational Resilience, National Sovereignty, and Geopolitical Implications: ensuring stability in an interconnected world
                          • FinTech and Capital Markets: Bridging innovation and institutional finance
                          • Financial Inclusion and Financial Wellness: Technology as a driver
                          • The Next Generation of FinTech Unicorns: Shaping the ecosystem that enables them to thrive
                          • Open Banking, Open Finance, and Smart Data: Mapping the next evolution of data-driven innovation, infrastructure and consumer empowerment
                          • Next Generation Banking: Exploring what the future of financial services will look like as consumer behaviours change and new technologies evolve
                          • Fighting Fraud and Cybercrime: Securing the future of digital finance

                          Book your tickets now!

                          • Artificial Intelligence in FinTech
                          • Digital Payments
                          • Embedded Finance
                          • Event Newsroom
                          • Events

                          Author

                          Andrew McLernon

                          Published

                          15 April 2026

                          Estimated Read time

                          6Mins

                          Andrew McLernon, CEO and co-founder at Interlink, on why culture is the real disruptor

                          For much of modern business history, disruption has been framed as something external. An emerging technology or a competitor rewriting the rules. Often, markets shift faster than organisations can respond and leaders are told to move quicker, work harder and implement more systems to keep up.

                          Today, AI has become the latest catalyst for this narrative, with every week seeming to bring another promise of productivity gains or automation breakthroughs. Yet as AI accelerates, many organisations are responding in surprisingly familiar ways: longer hours, stricter oversight, everyone back to the office mandates and layers of new processes built on outdated foundations.

                          In my experience, this is the wrong response. The real disruption of the AI era isn’t technological. It’s cultural. And leaders who fail to recognise that, risk solving tomorrow’s challenges with yesterday’s assumptions.

                          The Illusion of Productivity

                          When economic pressure rises, organisations often default to visibility as a proxy for performance. Leaders want to see people working, whether that means more time in the office, more meetings or more activity. But activity isn’t the same as effectiveness.

                          AI is already capable of performing many routine tasks faster than humans, a fact that should lead us to rethink how work is structured. Instead, many businesses are doubling down on models that were designed for a different era, treating time spent on tasks as the primary measure of contribution, rather than outcomes achieved.

                          The irony is that this approach undermines the very productivity gains leaders say they want. People become busier but not necessarily more effective. Creativity declines, decision-making slows and, ultimately, innovation suffers because teams are exhausted rather than energised.

                          True productivity in an AI-enabled world comes from clarity and focus, not from squeezing more hours out of people.

                          Culture Before Performance

                          At Interlink, we’ve learned that performance rarely improves by targeting performance alone. It improves when culture enables people to do their best work.

                          Culture isn’t slogans or perks; it’s the operating system behind every decision. It determines whether people feel trusted or controlled, whether ideas are encouraged or suppressed and whether change is embraced or resisted.

                          As we scaled a profitable, AI-powered business across multiple continents, we discovered that culture has to scale before performance can. If it doesn’t, growth amplifies dysfunction. That realisation changed how we approached leadership. Instead of asking, “How do we get more output?” we began asking, “What conditions allow people to produce their best work consistently?”

                          The answers were not technological; they were human.

                          Redesigning Work Rather Than Reinforcing Old Models

                          One of the biggest leadership mistakes I see today is adding complexity to existing systems instead of redesigning them. Organisations introduce new tools without changing behaviours. They add layers of management without simplifying decision-making. They enforce policies intended to restore control rather than building trust.

                          For us, introducing a four-day working week was not about doing less; it was about focusing on what truly matters. Compressing time sharpened our priorities, improved decision-making and encouraged greater ownership of outcomes by everyone across the business. The result was counterintuitive for some observers: productivity rose, retention strengthened and creative thinking accelerated. When time had clearer boundaries, focus sharpened and accountability deepened.

                          Flexible and hybrid working emerged from the same philosophy. Instead of designing work around physical presence, we designed it around contribution and trust replaced oversight as the foundation of accountability.

                          These changes weren’t always comfortable and they absolutely required leaders to relinquish some traditional forms of control. But they reinforced a principle that has become increasingly clear: autonomy drives engagement and engagement drives performance.

                          The Tension Between ‘Back to the Office’ and the Future of Work

                          The current push for universal office returns reflects a deeper anxiety about how work is evolving. For some leaders, visibility feels like certainty. If people are physically present, it feels easier to manage performance. But this perspective risks confusing familiarity with effectiveness.

                          The future of work is unlikely to be defined by a single model. People’s roles, responsibilities and life circumstances vary too widely for one-size-fits-all solutions. Organisations that impose rigid structures in pursuit of control may find themselves losing talented individuals who value flexibility and trust.

                          That doesn’t mean offices are irrelevant. Physical spaces remain powerful for collaboration, learning and connection. The challenge is not choosing between remote or office-based work but designing environments that genuinely enhance productivity rather than simply recreating old habits.

                          The businesses that succeed will be those that treat flexibility as a strategic tool rather than a concession.

                          AI as an Amplifier of Leadership, not a Replacement

                          Because our business operates in AI-powered demand generation, we spend a great deal of time thinking about the relationship between automation and human expertise. AI excels at pattern recognition, scale and speed but what it lacks is context, empathy and strategic judgement.

                          The danger for leaders is assuming that technology alone can drive transformation. AI amplifies whatever culture already exists. In organisations built on trust and curiosity, it accelerates innovation; in environments dominated by fear or rigidity, it often automates inefficiency.

                          Technology should create space for humans to think more deeply, collaborate more creatively and make better decisions. If AI adoption results only in faster outputs without improved thinking, we’ve missed the opportunity.

                          The competitive advantage lies not in whether a company uses AI (most soon will) but in how leaders integrate it into a culture that values learning and experimentation.

                          Simplicity as a Leadership Discipline

                          Another lesson from scaling is that complexity grows naturally. As businesses expand, processes multiply; communication becomes fragmented, and decision-making slows because too many layers intervene between ideas and action.

                          We’ve learned to treat simplicity as a leadership discipline. That means regularly rebuilding systems that no longer serve us, even when they once worked well. It also means resisting the temptation to add new structures simply because growth makes things feel messy. As well, simplicity requires intentional effort. Leaders must continually ask which processes genuinely add value and which exist only because they always have.

                          Leadership for an Uncertain Future

                          Perhaps the most important shift leaders must make is moving from control to clarity. In a world where technology evolves faster than organisational structures, certainty is increasingly rare. What teams need is not rigid instruction but clear purpose, shared values and the autonomy to adapt.

                          Leadership becomes less about directing tasks and more about shaping environments where people can thrive. That includes prioritising wellbeing not as a perk but as a strategic requirement. Burnout may produce short-term output, but it erodes long-term capability and the organisations that will define the next era of business are unlikely to be those that simply adopt the latest technology fastest. They will be the ones that rethink how work itself is designed, aligning technology with human potential rather than attempting to replace it.

                          Culture as the Ultimate Competitive Advantage

                          As AI becomes ubiquitous, technological differentiation will narrow. Tools that once seemed revolutionary will become standard. But what will remain distinctive is culture.

                          Culture determines how quickly teams learn, how openly they challenge assumptions and how resilient they are during uncertainty. It shapes whether innovation is encouraged or quietly resisted. And, in that sense, culture is not a soft concept; it is a strategic asset.

                          The real disruption of the AI age is not automation, it’s the opportunity to redesign leadership around trust, simplicity and human potential. Leaders who embrace that shift will find that technology accelerates their progress. Those who cling to outdated models may discover that even the most advanced tools cannot compensate for disengaged people.

                          Disruption isn’t about changing the industry first; it’s about changing how we lead.

                          Learn more at weareinterlink.com

                          • Data & AI
                          • Digital Strategy
                          • People & Culture

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          4Mins

                          Read in brochure format

                          FinTech Strategy is back with more key insights from the industry experts and thought leaders shaping the future of financial…

                          FinTech Strategy is back with more key insights from the industry experts and thought leaders shaping the future of financial services.

                          Read the latest issue here

                          Vibrant Capital: Scaling AI on Main Street

                          Our cover star Shadman Zafar, Founder & CEO of Vibrant Capital, is building a CIO-led model for enterprise transformation. Vibrant Capital is an operator-led investment and company-building platform focused on scaling AI in the real economy. “We don’t spray investments across hundreds of AI startups. We curate a portfolio with purpose – selecting companies that solve the real mission-critical problems CIOs face in scaling AI adoption.”

                          FNB: Redefining Data Science in Commercial Banking

                          We also hear from Yudhvir Seetharam, Chief Analytics Officer at South Africa’s First National Bank (FNB) on a data science journey characterised by curiosity, culture and the drive for a competitive edge. “Ours is a holistic approach focusing on the customer,” he explains. “Understanding the context of each customer journey and then using that context so that when we interact with you, we’re able to drive the right conversation with the right customer, at the right time, through the right channel and for the right reason. These ‘five rights’ make our interactions with clients more impactful.”

                          Virginia Farm Bureau: An Enterprise CIO’s Journey

                          Shifting focus to the world of insurance at the Virginia Farm Bureau, we spoke withan Enterprise CIO at a complex mission-driven organisation. As he approaches retirement, Patrick (Pat) Caine reflects on his career as a CIO and the centennial of an organisation renowned for resiliency, collaboration, commitment to a greater cause, diversity and service to its members. “In my role as CIO, I’ve always been that person who connects the dots between business needs and technology execution. Virginia Farm Bureau is digitally relevant, collaborative, and well‑positioned for the future.”

                          Mastercard: Protecting Trust in the Digital Economy

                          Michele Centemero, EVP Services at Mastercard Europe explains why promoting awareness, stronger collaboration and data-sharing, and continued innovation of payments ecosystems, will be critical in reducing the impact of scams and protecting trust in the digital economy. “The combination of AI, robust identity controls and open banking can help protect consumers from scams, whether across card and account‑to‑account payments or in fraudulent account openings.”

                          Thales on AI Security: How FinServ’s Budget Priorities Signal a Boardroom Shift

                          Todd Moore, Global VP – Data Security Products at Thales, reveals why making AI security a boardroom priority today, will help firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation. “Balancing AI’s opportunity and risk means embedding security at every stage, from design to deployment and ongoing monitoring.”

                          Paymentology: The First Live AI-Agent Payment Is a Test for Credit Infrastructure

                          Thomas Benjaminsen Normann, Product Director at Paymentology, dissects the future for agentic payments and the progress still to be made. “Agentic payments demand something more granular: a clearer account of who or what acted, under what limits, and with what right to create a liability on the customer’s behalf.”

                          Also in this issue, we hear from Publicis Sapient, on why asset managers must redesign their enterprise for AI-driven decision intelligence; learn from Bitpace why the most resilient payments infrastructure will be the one with the most adaptability; rank the AI maturity of 12 of the largest payments networks in the latest Evident AI Index; and round up the key FinTech events and conferences across the globe.

                          Enjoy the issue!

                          Read the latest issue here

                          • Artificial Intelligence in FinTech
                          • Blockchain & Crypto
                          • Cybersecurity in FinTech
                          • Data & AI
                          • Digital Payments
                          • Embedded Finance
                          • Fintech & Insurtech
                          • InsurTech
                          • Neobanking

                          Author

                          Michele Centemero

                          Published

                          14 April 2026

                          Estimated Read time

                          5Mins

                          Michele Centemero, EVP Services, Mastercard Europe on why promoting awareness, stronger collaboration and data-sharing, and continued innovation of payments ecosystems, will be critical in reducing the impact of scams and protecting trust in the digital economy

                          As our world becomes faster, smarter and more interconnected, scammers are evolving in parallel, developing increasingly sophisticated ways to exploit people’s trust. By harnessing new technologies and behavioural insights, they are refining their methods to appear ever more credible and convincing.

                          While attacks on systems continue, today’s fraudsters are increasingly targeting people, often relying on psychological manipulation to achieve their goals.

                          Understanding Social Engineering

                          Many modern scams fall under the umbrella of social engineering,which isthe use of deception and emotional manipulation to influence a person’s behaviour.

                          In the digital world, cybercriminals use these tactics to build false trust, create urgency or fear, and ultimately trick people into sharing confidential information or taking actions that can cause financial harm to themselves or their employer.

                          Recent European industry data indicates that social engineering-related fraud and authorised push payments (APPs) – where victims are tricked into sending money to fraudsters posing as legitimate payees – now account for a growing share of overall scam losses[1].

                          This is directly impacting a growing number of consumers, with the majority of people saying they’ve experienced some form of scam or fraudulent attempt to capture their personal information highlighting why awareness and vigilance are critical for people of all ages.

                          Education is the First Line of Defence

                          Protecting consumers and businesses from malicious activity is a priority, and it starts with awareness. When people understand how scams work, they’re more likely to spot the warning signs before it’s too late and be empowered to protect themselves against fraudsters.

                          Three of the most common social engineering scams to watch out for are:

                          • Imposter fraud – Criminals pose as trusted organisations (such as banks, retailers, or government bodies) to pressure victims into sharing personal or financial details. Research indicates over half (53%) of European consumers have been targeted via phone or voice call scams, with social media scams affecting around two in five people, and tech support impersonation tricking roughly one in three.*
                          • Phishing – Fraudulent emails, texts, or messages that are designed to look legitimate, often urging immediate action like clicking a link or resetting a password, leading victims to disclose sensitive information or install malicious software. Nearly three in five (58%) have received phishing emails or fraudulent text messages (63%) and QR code scams are on the rise, impacting nearly a quarter of Europeans.*
                          • Romance or honeypot scams – Scammers build emotional relationships over time, gaining trust before exploiting it for financial gain. These types of attacks are also widespread, with one in four people (24%) encountering fake profiles, requests for money, or online relationships that lead to financial exploitation. These scams hit younger generations hardest, with 40% of Gen Z and 35% of Millennials affected, compared with 21% of Gen X and 11% of Boomers.*

                          How Businesses Can Protect Consumers from Scams

                          With fraudsters increasingly using AI to commit more sophisticated, larger scale attacks, businesses and banks should also consider how they deploy technology to protect customers from bad actors.

                          The combination of AI, robust identity controls and open banking can help protect consumers from scams, whether across card and account‑to‑account payments or in fraudulent account openings.

                          Looking at identity controls specifically – take the example of continuous identity verification, a fraud prevention measure that verifies the user is who they claim to be throughout the entire lifecycle journey. This helps to prevent scammers from opening or taking over accounts to apply for credit, create ‘mule’ accounts or impersonate others.

                          Behavioural biometric data is often used as part of this and can be used to analyse how a user interacts with their device – from typing patterns to on‑screen movements – to flag unusual behaviour.

                          More in depth, AI powered transaction analysis can also help banks and financial institutions to stay ahead of payment threats. It provides banks with the intelligence needed to detect and stop payments to scammers, using AI and a network-level view of account‑to‑account transactions to enable intervention before funds leave an account.

                          Staying Ahead of an Ever-Evolving Threat

                          As social engineering tactics continue to evolve, staying ahead requires a combination of intelligent technology, consumer education, and proactive action from businesses and financial institutions.

                          While no single measure can eliminate risk entirely, greater awareness, stronger collaboration and data-sharing, and continued innovation of payments ecosystems will be critical in reducing the impact of scams and protecting trust in the digital economy.

                          *Source: This study was conducted by The Harris Poll on behalf of Mastercard from September 8 to September 25, 2025, among 5000+ consumers in the following European markets: EUR: France (n=1,005), Germany (n=1,002), Italy (n=1,016), Spain (n=1,005), UK (n=1,004)

                          Mastercard: Transforming the Fight Against Scams

                          Innovation – Our advanced AI-powered Identity insights examine digital footprints and assess unique patterns to detect risk and flag suspicious activity indicative of scams.

                          Collaboration – We collaborate across industries, partners and organizations worldwide to secure the digital ecosystem, ensuring payments are safe for all. Combating the growing threat of scams demands a collective effort.

                          Education – We work with and through our collaborators to provide knowledge and tools that help people protect themselves and their loved ones from scams, while also working to destigmatise the experience of being a victim.

                          • $12.5bn in losses from U.S. consumer reported online scams in 2023
                          • $486bn in global losses from scams and bank fraud schemes in 2023
                          • 22% YoY growth in U.S. consumer scam losses suffered in 2023

                          From sender to recipient, we vigilantly monitor accounts and transactions for any elevated scam risk

                          Identity insights – Provides actionable identity insights and risk scores for businesses to improve identifying their good customers from the scammers creating “mule” accounts or impersonating someone else with a false identity.

                          Transaction patterns – Flags suspicious activity across the money movement flow to prevent payments to scammers before it is sent through the real-time analysis of transaction elements.

                          Account confirmation – Enables account validation to confirm account ownership and validate identity details in real-time through our open banking capability, which draws on the safe exchange of consumer-permissioned data to facilitate frictionless and secure payments.

                          Learn more at mastercard.com

                          [1] Joint EBA-ECB report on payment fraud: strong authentication remains effective, but fraudsters are adapting

                          • Artificial Intelligence in FinTech
                          • Cybersecurity
                          • Cybersecurity in FinTech
                          • Digital Strategy
                          • InsurTech

                          Author

                          Todd Moore

                          Published

                          Estimated Read time

                          4Mins

                          Todd Moore, Global Vice President, Data Security Products at Thales, on why making AI security a boardroom priority today, will help firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation

                          Financial Services organisations are responsible for some of the biggest growth in the global economy. Equally, they’re some of the most vulnerable. Like many other sectors, they’re racing to embrace AI, but with adoption comes new security risks.

                          According to Thales’ Data Threat Report: Financial Services Edition 81% of FinServ organisations are now investing in GenAI-specific security tools, with nearly a quarter using newly allocated budget. This surge in funding marks a turning point: AI security has moved from being an IT concern to a boardroom priority.

                          The fact that new budget lines are being carved out specifically for AI security signals a fundamental shift in corporate strategy. Boards increasingly recognise that protecting AI systems is as critical as safeguarding payment rails or core banking infrastructure. For an industry built on trust, resilience, and regulatory compliance, this investment wave shows how central AI has become to both risk management and competitive growth.

                          Balancing AI Innovation and Security

                          While FinServ organisations are aware of the security risks AI poses, they’re also seizing upon the opportunities it presents. The report has found that in 2024, FinServ businesses outpaced the broader market in AI deployment, leading in enabling employees to use AI and ahead in AI integration, which has continued into 2025. Additionally, 45% say they’re in the ‘integration’ or ‘transformation’ phases of their GenAI journey, compared to just 33% across wider industries.

                          AI’s ability to accelerate services, automate processes, and analyse data at scale makes it an exciting prospect, especially in the financial sector. This makes securing AI systems a priority for FinServ organisations, with increased GenAI integration reflecting developing organisational maturity and progress beyond experimentation.

                          The Risk

                          Yet the scale of opportunity is matched by the scale of challenge. AI systems require vast amounts of structured and unstructured data to conduct analysis and make recommendations.

                          For FinServ organisations, this often includes highly sensitive customer and transactional information, proprietary algorithms, and records bound by strict regulatory oversight. The risk is not only about whether AI systems themselves are secure, but whether the data they’re working from is accurate, as well as whether their adoption inadvertently creates new routes to data exposure and exfiltration.

                          Businesses need a clear strategy to fully understand how AI models are operating within their IT infrastructure, the applications they’re interacting with, and the data they’re accessing and pulling from.

                          The Response

                          Balancing AI’s opportunity and risk means embedding security at every stage, from design to deployment and ongoing monitoring. Newly allocated budgets for AI security, with nearly a quarter of FinServ firms making such investments, show how central AI has become to board-level strategy. These investments move firms beyond reactive fixes to proactive frameworks that evolve with the technology. AI security is no longer just an IT concern, it’s a strategic priority requiring collaboration between security, compliance, and business leaders. By factoring risk into early planning, organisations can align innovation with responsibility and build resilience for the long term.

                          Pioneering AI Security

                          Building on investment in AI-specific security is only the beginning. As scrutiny intensifies, the firms that will lead are those that treat AI security as integral to business strategy, not a bolt-on layer. Success will require visibility into how models behave, continuous validation against emerging risks, and adaptive controls that evolve with the threat landscape.

                          The financial services organisations that embed these safeguards into their core infrastructure will protect sensitive data as well as setting a benchmark for resilience and trust in an AI-driven economy. By making AI security a boardroom priority today, these firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation.

                          Thales: AI is the New Insider Threat 

                          Thales 2026 Data Threat Report Finds 70% of Organisations Rank AI as Top Data Security Risk

                          Data security has taken centre stage as the success of enterprise AI initiatives increasingly hinges on consistent, controlled access to proprietary organisational data sources. The 2026 Thales Data Threat Report examines the complex calculus that organizations must undertake to enable innovation while securing their most valuable asset – their data.

                          This research was based on a global survey of 3,120 respondents fielded via web survey with targeted populations for each country, aimed at professionals in security and IT management. 

                          Read the Report

                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Strategy
                          • Fintech & Insurtech

                          Author

                          Lakhbir Sandhu

                          Published

                          10 April 2026

                          Estimated Read time

                          4Mins

                          Lakhbir Sandhu, CFO at Flagstone, on the extra value gained when businesses automate and optimise their cash reserves

                          A recent global risk management survey revealed that macroeconomic volatility, trade disruptions and increasing competition have pushed cash flow and liquidity into the top 10 risks for business leaders – for the first time since 2019.

                          In today’s unpredictable landscape, managing cash flow and liquidity has become more challenging. As a result, more businesses are turning their focus towards improving their cash management operations.

                          Yet, despite digital advances, many companies still rely on manual processes to manage cash reserves. A global survey found that 34% of businesses cite low levels of automation as their biggest cash management challenge.

                          The Business Impact

                          Manual cash management often involves tracking balances, forecasting liquidity, and reconciling accounts via spreadsheets. Adjustments for payroll, supplier payments, and loans are typically manually entered based on emails or reports from enterprise resource planning (ERP) systems.

                          While this approach may work, it’s slow, error-prone, and lacks real-time visibility. Mistakes in data entry or forecasting can lead to cash shortfalls or missed investment opportunities.

                          Yet these manual processes often serve as the glue that connects accounting activities across a wide array of complex systems and data sources, which explains why businesses are hesitant to move away from them.

                          But the cost to productivity is clear. Finance teams spend an average of 26 hours per week on manual treasury tasks such as reconciliations, spreadsheet maintenance, and cash monitoring alone. 

                          Dealing with financial discrepancies can take an average of 44 hours per week. This workload can lead to decreased motivation, with employees who feel overworked 70% more likely to burn out, as they try to manage unreasonable time constraints.

                          Looking to Change

                          Automation streamlines cash management, making processes faster, more accurate, and more reliable. This transformation reduces operational risk and frees up team members to focus on higher-value activities.

                          Some companies now use AI-driven reconciliation tools that match transactions in real time, eliminating manual entry errors. A study published in 2024 demonstrated that Robotic Process Automation (RPA) systems can achieve perfect accuracy in data extraction tasks, significantly outperforming human-driven processes in both efficiency and precision.

                          Centralised dashboards also improve visibility, giving treasurers an accurate picture of company finances.

                          Our Savings inertia report shows that too much of the UK’s cash is left sitting idle, leaving significant value untapped. To maximise the yield on their cash reserves, some businesses are turning to high-interest savings platforms. These platforms enable them to open and manage savings accounts with multiple banks through a single platform.

                          They also eliminate the burden of applying to and onboarding with each bank separately. Some offer tools that allow CFOs to achieve the right balance between liquidity, yield, and security within their portfolio by spreading their reserves across multiple accounts.

                          Automated platforms serve as strategic levers to both protect and grow idle business cash, requiring minimal effort from finance teams.

                          While finance specialists have used data to build forecasts and predictions for years, the addition of AI and other technologies improves accuracy and enhances potential. This is where predictive analytics are helpful, as they process large amounts of data, quickly, into useful insights, like estimating revenues, costs, behavioural patterns and even effects of macroeconomic factors.

                          The Automation Dividend

                          Automating manual tasks not only reduces errors and saves time – it unlocks what we call the ‘automation dividend’ – the ability for employees to focus on high-value work that drives growth and supports career development.

                          Research supports this concept. More than 90% of workers in a Salesforce study agreed that automated processes had improved their productivity.

                          There are plenty of real-world examples showcasing the benefits of automation too. The Coca-Cola Company began reviewing its existing process for balance sheet reconciliations across 50,000 general ledger accounts. It discovered 800 associates were spending 14,000 hours a month on this one task. By moving from manual processes to automation, Coca-Cola reallocated 40% of its team to more strategic roles like metrics reporting, and change governance.

                          eBay saw similar gains by moving away from manual accounting to automation – it decreased the length of its financial close from 10 days to just three.  

                          In conclusion, automating cash management is useful for both small and large companies. Not only does it drive operational efficiency and maximise the potential of cash reserves, but it also empowers employees to focus on higher-value, strategic work. In turn this creates a healthier, happier, and more productive workforce.

                          Learn more at flagstoneim.com

                          • Digital Payments
                          • InsurTech
                          • Neobanking

                          Author

                          Lee Fredricks

                          Published

                          Estimated Read time

                          6Mins

                          Lee Fredricks, Director – Solutions Consulting, EMEA at PagerDuty, on why technology leaders should see 2026 as a time for operational resilience to shift from ambition to accountability

                          Technology leaders should see 2026 as a time for operational resilience to shift from ambition to accountability. In 2025, too many cloud services outages and disruptions took place across the public and private sectors, and now regulatory, technological and cultural pressures are converging to say that enough is enough.

                          Outages often translate into broader repercussions for the organisation, including revenue impact, customer churn, share price pressure and potentially regulatory reporting obligations. Operational metrics must now be discussed alongside financial KPIs at the board level. C-suite leaders understand accountability, especially within the very regulated financial sector.

                          DORA’s First Birthday

                          It’s now been one year since the implementation of the Digital Operational Resilience Act, or DORA, introduced by the EU to strengthen the digital resilience of financial institutions. By now, organisations have had time to consider moving from mere compliance to creating a competitive edge from their investments.

                          Enterprise tech leaders are in the middle of a balancing act. They’re managing ongoing modernisation and transformation initiatives while navigating multi-jurisdictional regulatory scrutiny. At the same time, they face constant pressure from the board and must meet evolving customer needs—all competing for immediate attention. The stakes have never been higher. Operations teams are no longer viewed as a back-office IT function. Their success in keeping the organisation running and driving revenue is now a board-level concern.

                          For organisations today, IT is business delivery.

                          A year of DORA has seen organisations make the shift from focusing solely on mere compliance to setting meaningful demonstrable testing, third-party risk visibility and strictly mandated incident reporting timelines. Financial firms have lessened their exposure to risky situations. Payments providers aren’t only reliant on a single cloud region or SaaS supplier, or unable to provide evidence of real time incident response efforts and auditable logs after a disruption.

                          One benefit of these overall systemic improvements is enhanced supply chain accountability. Financial institutions and their technology partners are both liable for potential penalties and reputational risk, which makes it highly critical that they can prove their resilience capabilities.

                          Nevertheless, operational resilience is a continuous discipline. A fragmented incident response can expose firms to regulatory and reputational risk again and again if not addressed systemically. As such, many organisations are looking toward AI agents as part of a move towards ‘no-touch’ operations.

                          From Autonomy to Self-Healing

                          Under set policies, autonomous agents can handle incident response and operational tasks, such as detection, triage and remediation. AI agents deployed in operations may become the backbone of L1 (first contact) and L2 (more skilled) support. Contrast this with the traditional, reactive, ticket-driven model of IT. The industry can move much faster and with a higher successful close rate. Leveraging intelligent automation reduces mean time to detection/resolution and KPIs around lower incident volumes reaching L3. Additionally, it can lead to improved service availability percentages. Well integrated agents that actually support existing operations teams also help manage the issues around talent shortages faced by many organisations.

                          A typical incident lifecycle with agentic processes includes several stages depending on the model, but can be summarised as: Anomaly detected, correlated with recent deployment, a remediation script triggered and a human notified if set thresholds were breached. Such no-touch operations are golden in any sector, but particularly with industries such as digital banking and retail, where peak traffic periods demand near-instant response and poor customer experience is a powerful motivator for users to instantly change providers.

                          IT Standardisation

                          In addition, consider standardisation as part of strategic infrastructure best practices. There is a role for central operations clouds and operational ‘golden paths’ as solid foundations for reliable operational scale and dependability. Standardisation enables consistent, scalable operational excellence especially across large, distributed enterprises. ‘There is one way and it is the right way’ can be a great time and stress saver for operational teams – particularly if a regulatory notification and clear evidence is required.

                          For example, a global bank might define a single golden path for deploying customer-facing applications with pre-approved monitoring, incident response workflows, and regulatory reporting templates built in. In an outage, teams follow the same process and automatically capture the evidence required for regulators, avoiding confusion, delays, and compliance risk.

                          All of these possibilities take us to an exciting new place for an evolved set of developer and operational roles. When organisations enable AI to reshape daily engineering work away from manual firefighting and low-value work it frees headspace and time for developers and engineers to move into more architectural thinking and intelligent oversight of automated systems. These augmented teams will be empowered to manage simple situations instantly and devote more time and attention to the more difficult issues – the edge cases and the strategic necessities.

                          Enabling Agentic AI

                          Using another lens, businesses with agentic IT operations capabilities support their current talent, extending their reach and the speed of their response. The winning organisations will be those who deploy agents strategically, freeing up humans for that higher-value work – i.e. L3 expert support – and setting new standards for operational excellence that customers can rely on. Ideally this means making commensurate investment in existing people, training and organisational change management. A culture of continual upskilling and forecasting that points humans to where they make the best impact will be just as important as the autonomous tech tools working alongside them.

                          Autonomous agents allow many new services, and one of those can be described as self-healing operations. This evolution of the operations world is where predictive detection, automated remediation and embedded resilience all coalesce. With an autonomous process of testing, maintenance and remediation, organisations can focus on finely measuring improved customer trust. They can also enjoy the productivity and revenue benefits of high business continuity and availability.

                          AI is still a new technology, and many are legitimately concerned with the concept of autonomous agents. There is a need for clear guardrails, audit trails and explainability in automated remediation, and many technology partners have invested in their ability to support across these areas. Moreover, firms must maintain direction with policy-driven automation rather than uncontrolled autonomy, particularly in regulated industries.

                          Mandate Operational Excellence

                          This year is very likely to reward organisations that treat operational resilience as core to their business strategy. Those investing in automation, standardisation and governance will set the pace for their industries in an AI-enabled and increasingly autonomous world.

                          Regulators are already expanding their scrutiny and reliability expectations beyond financial services firms. Across the world, jurisdictions are increasingly looking to strengthen their economies and digital services in particular through resilience and cybersecurity measures. At the same time, agentic operations, and the organisational performance benefits they support, will rapidly become table stakes technology in all sectors. Inevitably, customers will judge brands on digital reliability as much as price or product features when evidence of outages are a click or a headline search away.

                          Start now. Audit internal incident response maturity, review the potentially complex web of third-party IT dependencies and identify where automation makes clear business sense. While resilience is an investment in compliance, it is also critical to ensure customer trust and future stability.

                          Learn more at pagerduty.com

                          • Artificial Intelligence in FinTech
                          • Cybersecurity in FinTech
                          • Data & AI
                          • Digital Strategy
                          • Fintech & Insurtech
                          • Infrastructure & Cloud

                          Author

                          Anthony Termini

                          Published

                          9 April 2026

                          Estimated Read time

                          5Mins

                          EPS Momentum’s Senior Analyst, Anthony Termini on how to develop meaningful investment insights with the right tech

                          As the information age ushered in new technologies in the mid 1940s, it transformed the economic order of the post-industrial revolution. Traditional manufacturing evolved because electromechanics made nearly every operation run at scale more efficiently. That evolution accelerated, as if on steroids, when electromechanical digital computers pushed process efficiency higher than had been possible before.

                          The Data Age of Investment

                          The data age, which began roughly at the beginning of the 21st century, has had a similar, yet exponential effect on manufacturing. The Internet of Things (IoT) can synchronise disparate component processes across factory floors, even when they are geographically separated, to further enhance productivity.

                          Now, think about your investment selection process in a similar vein. Before your personal computer was connected to the internet, investment research was the exclusive domain of Wall Street, namely the big wirehouse brokerage firms and white-shoe investment banks like Goldman Sachs. You had to be a client to get access to their insight, which was typically reserved for their largest institutional clients. The individual investor, especially the self-directed do-it-yourselfer, never had a fair chance.

                          Today, the internet gives you the ability to link your computer to every investment chatroom from South Florida to South Russia. The investment insight you glean from these sites may just be scams pitched by dubious promoters or the groupthink that morphs hairbrained concepts into reasonably sounding ideas. Artificial intelligence (AI) makes both of these risks hard to identify, but AI has also helped to level the playing field for the individual investor. However, there’s a catch.

                          Individual investors have to be serious about building long-term wealth. If you have made the decision to go it on your own, without the help of an advisor, take solace in the fact that you have access to every bit of data and information processing that professional investors do. But you need a plan.

                          Investment Strategy

                          First, make sure your investment strategy aligns with your long-term consumption goals. Then, try to align specific investment themes with that long-term outlook. Sort investment candidates into the industries and then sectors that match your long-term outlook. This is the case whether you take a top-down approach or a bottom-up approach.

                          Top-down investing focuses on macroeconomic factors such as GDP growth, inflation, and interest rates in an attempt to identify the industries and sectors that could perform well, regardless of whether your outlook is positive or negative. A bottom-up approach almost ignores short-term macroeconomic forces and relies almost exclusively on good, old-fashioned fundamental securities analysis.

                          Once you have these pieces in place, it’s time to screen for investment candidates and then begin your research. Most stock screeners deliver the same information. This is the case for most brokerage platforms and subscription-based research services. All the data comes from company regulatory filings. This means that most stock screening applications (online or mobile) are merely aggregating information that is already in the public domain.

                          For example, earnings per share (EPS), gross margin (GM), earnings before interest and taxes (EBIT), earnings before interest, taxes, depreciation, and amortization (EBITDA). Nearly every screener can sort by these attributes and produce tables, but few are designed to identify meaningful trends in that data or give you the ability to perform side-by-side comparisons. Figuratively, they show you all the trees, heck, all the leaves or needles too. The only thing missing is what all that stuff says about the broader forest. What’s worse is that they rarely offer functionality to compare one tree to the others.

                          Most stock screening tools are devoid of their own proprietary methodology that helps you cut through the noise by identifying meaningful trends. Because of this, investment research remains the exclusive domain of what is now digital Wall Street. They don’t offer individual investors anything that can help them demystify the markets. This is not to suggest that all stock screening tools are alike. Some do provide meaningful trend analysis to help take the guesswork out of investing and to demystify the markets. It’s a short list, so they are relatively easy to find.

                          The primary attribute you should look for in a stock screening tool is its use of a proprietary methodology that both identifies and quantifies (produces a score for) trends. For example, a stock screener running on an internal engine focused on earnings would explain to you the momentum of a company’s earnings, up or down. You could then use that EPS momentum score to sort for companies with the highest growth rates.

                          This isn’t to suggest that you should seek out screeners that exclude data from the public domain. That’s not realistic. The raw data is what it is. It is the input from which a proprietary scoring system may be created, so a screener that can sort by things like market cap as well as its own internal metrics offers a huge bonus.

                          Look for screeners that take otherwise routine, publicly available information and analyze it to produce meaningful insights that go beyond the basic numbers. For example, the term post-earnings announcement drift is common on Wall Street. It explains the sometimes anomalous price movements a stock makes after the underlying company reports quarterly results.

                          In Conclusion

                          Being able to identify price moves (overreaction, underreaction) is one thing. Producing drift analysis that compares the most recent reaction to past price moves, and then illustrating those moves graphically, can have a huge impact on an individual investor’s success. This is an obscure example of the types of filter categories that should be in a robust stock screener.

                          This being said, a web- or mobile-based stock screener that can automate the routine and analyse it for you to produce meaningful insights is why investing isn’t just for professionals anymore.

                          Learn more at epsmomentum.com

                          • Blockchain & Crypto
                          • Neobanking

                          Author

                          Rolf Bienert

                          Published

                          Estimated Read time

                          4Mins

                          With growth in data centre power demand, driven by AI and other power-hungry applications, could microgrids hold the key? Rolf Bienert, Technical & Managing Director of global industry body, the OpenADR Alliance discusses the potential for microgrids in providing flexibility and clean energy


                          Generating enough power for the demands of artificial intelligence (AI), cryptocurrency and other power-hungry applications, is one of the biggest challenges facing data centres right now. With a power grid already under pressure and in the process of trying to modernise and flex to cope with the huge demands placed on it, the industry needs to rethink the way it adapts to these challenges.

                          Data Centres

                          According to figures from the International Energy Agency (IEA), data centres today account for around 1% of global electricity consumption. But this is changing with the growth in large hyperscale data centres with power demands of 100 MW or more. And an annual electricity consumption equivalent to the electricity demand from around 350,000 to 400,000 electric vehicles.

                          With the rise of AI and expectation of what it can deliver, the next few years are likely to see a significant rise in the number and size of data centres. This has serious consequences for the energy sector. While, technology firms are under growing pressure to make data centres more sustainable.

                          Microgrids – The Opportunities

                          Microgrids could be the answer in providing a more sustainable and efficient energy supply for data centres. While the concept of a microgrid can vary depending on how they are used, they can be defined as small-scale, localised electrical grids that can operate independently or in conjunction with the main power grid. They can range in size from a university to a single home.As a global ecosystem, we’re seeing them used in different scenarios, from residential to large campuses.One interesting use case is MCE, a California Community Choice Aggregator, which has established a standardised setup for residential virtual powers plants (VPPs) with OpenADR used as the utility connection to manage the prices and consumption.

                          The feasibility and suitability of microgrids depends on factors like the specific requirements of the data centre, regulatory environment and the long-term goals for sustainability, resilience and cost-efficiency.

                          The real value is in helping overcome grid constraints and improving reliability by managing consumption and maintaining power during grid issues. For data centres that require uninterrupted operation, this ability to deliver resilience is critical.

                          Sustainability is another important advantage. By integrating renewable energy sources, such as solar or wind power, and energy storage, microgrids can significantly reduce carbon footprint. While in terms of cost savings, they can reduce operational costs by utilising local power generation and demand-response strategies.

                          Microgrids are modular, which means they can grow as the data centre’s needs evolve. Plus, when it comes to regulation, they face fewer regulatory hurdles compared to other options, like nuclear power, because they can operate mostly ‘net zero’ on the grid connection.

                          Microgrids – The Challenges

                          For data centre operators and investors trying to address power supply and stability issues, the use of microgrids can also mean challenges.The first of these is the start-up costs. While we talk about a reduction in operational costs once up and running, set-up costs for microgrids can be high, requiring significant capital investment especially for larger data centres, so important to bear in mind.

                          Sustainability may be a big plus point, but the use of renewables like solar and wind depend on the weather – and the weather can be fickle. This necessitates robust storage solutions, backup power or large grid connections to ensure reliability and stability at all times. It’s also important to stress that the effective integration of these various distributed energy sources and systems can be technically challenging, so working with good integrators and partners is paramount.

                          When it comes to powering data centres, microgrids are not the only option being considered. Alternatives like small modular nuclear reactors (SMRs) are also be touted as potential power sources. In my mind, SMRs are not in competition with microgrids but could become an important baseline component of them.

                          In their favour, SMRs provide a constant, high-capacity output, ideal for 24/7 operation, and a zero-emissions power source. Once operational, they offer stable costs over decades. But they also face challenges like stringent regulation and public opposition to development, while a nuclear plant, even a small-scale one, involves substantial upfront investment. This is aside from the risks around nuclear waste and safety.

                          Bottom line is that the data centres are going to need a very high continuous supply of power and microgrids offer options for a more resilient and responsive energy infrastructure. Decentralised power through a network of microgrids could help dynamically manage power loads and optimise renewable energy sources – especially as demands on the grid grow as we march onwards towards an AI-powered future.

                          Learn more at openadr.org

                          • Data & AI
                          • Digital Strategy
                          • Infrastructure & Cloud

                          Author

                          Jamil Jiva

                          Published

                          8 April 2026

                          Estimated Read time

                          5Mins

                          Jamil Jiva, Global Head of Asset Management at Linedata, on why the next chapter of AI-driven finance will be shaped not just by technology, but by creativity

                          Beyond Data: Where AI Finds Unexpected Inspiration

                          The discussion about training AI largely focuses on concerns that accessible, human-generated data is limited and may soon run out completely. If this is the case, how can technology that depends on a seemingly endless stream of inputs to iterate, test, and adapt deliver the results we expect? AI relies on structured, high-quality data to thrive, but what happens when we run out of spreadsheets and financial models to train AI? We need new data sources to ensure it continues to learn, adapt, and deliver accurate insights. Video games stand out as offering some of the richest, most expansive, and complex environments for AI training.

                          At first glance, video games and financial operations seem to belong to entirely separate worlds. However, AI connects these domains, with models leveraging virtual-world training to tackle real-world financial tasks. Financial documents such as credit agreements and tax returns are often convoluted, unstructured, and labour-intensive to process. Therefore, AI designed to interpret such data must possess strategic reasoning, real-time adaptability, and advanced pattern recognition. So, could video games be the ideal training ground?

                          Contrary to popular belief, gameplay can significantly improve how people think, learn, and solve problems. The abilities required to excel at video games closely reflect the skills AI systems must acquire today.

                          Levelling Up: What Virtual Worlds Teach Machines

                          Practice leads to proficiency, a principle that applies to both humans and AI. Interestingly, many of the most significant advances in AI development have emerged not from conventional data training, but from taking creative approaches. Games push AI to emulate human thinking and sharpen its statistical intuition.

                          These game-trained models are neither expensive nor heavily reliant on resources, and they sidestep the issue of data scarcity. As a result, they are actively shaping the future of financial intelligence. The examples below offer a clear demonstration of the potential of gameplay.

                          Virtual Economies: Lessons from World of Warcraft

                          World of Warcraft, with millions of players interacting in an immersive and dynamic world, features an economy that closely mirrors real-world financial systems, complete with inflation, supply and demand cycles, and fraud risks. The game even inspired one of the most renowned epidemiological studies: when the in-game ‘Corrupted Blood’ plague spread unpredictably, scientists used it as a model for real-world pandemic simulations.

                          Financial models depend on vast, interconnected data networks, much like the economy in World of Warcraft. Organisations employ AI to continuously monitor patterns, detect anomalies such as fraud or misstatements, and optimise data extraction for financial reporting, mirroring the way AI analyses virtual economies.

                          Urban Chaos: GTA V and Real-World Simulation

                          While Grand Theft Auto (GTA) V is famous for its open-world chaos, researchers have leveraged its traffic systems and non-player character behaviours to train AI for applications such as self-driving cars, crime pattern recognition, and urban planning. At its heart, GTA provides a platform for AI to process vast amounts of unstructured data in real time.

                          Similarly, financial institutions manage millions of data points from a wide range of sources. Their AI tools must automatically extract insights, classify information, and normalise complex formats. GTA serves as a controlled yet intricate environment for simulating scenarios, enabling AI to optimise for real-world tasks through ongoing feedback loops.

                          Sandbox Creativity: Minecraft and Adaptive Thinking

                          Minecraft provides a sandbox environment where AI learns through exploration. OpenAI even trained an AI to play Minecraft by watching YouTube tutorials, closely mimicking the way humans learn. Similarly, any AI used by financial institutions must be able to self-learn from new document types and structures, adapting just as a Minecraft AI learns to survive.

                          Reinforcement learning, where AI improves based on feedback, is a key element of intelligent document processing. Thanks to its vast scalability and dynamic, hierarchical environments, Minecraft serves as an ideal setting for navigation and repeated feedback loops, helping models develop domain-flexible reasoning.

                          Multiplayer Mayhem: Dota 2 and the Art of Teamwork

                          Dota 2 stands out as one of the most complex competitive games ever created, presenting AI with challenges in real-time decision-making, strategic coordination, and adaptability. OpenAI Five, trained on the equivalent of 45,000 years of gameplay within just 10 months, managed to defeat renowned, professional human teams. As anyone who has mastered StarCraft knows, tactical adaptability is essential for gaining the upper hand.

                          Financial institutions operate in environments that are just as dynamic as the shifting levels of a video game. Market conditions, regulations, and data formats are in constant flux. AI must be able to adjust to new document structures, handle missing information, and navigate edge cases, much like AlphaStar adapts to an opponent’s unpredictable strategies.

                          From Pixels to Profits: Bringing Game Logic to Finance

                          Whether to streamline operations, mitigate risks, or make informed decisions in today’s data-intensive financial landscape, AI has the potential to fundamentally transform financial offerings, delivering personalised and evolving experiences that foster understanding and combine seamlessness with regulatory compliance.

                          Yet AI does not simply require more data from which to learn; it needs better data. Video games offer near limitless, pre-built, highly complex digital worlds where AI can test hypotheses, simulate scenarios, and refine decision-making models. By utilising these unique environments, AI is challenged to enhance its speed, accuracy, and efficiency. 

                          The world of video games has many lessons we can learn when building AI, and given AI’s remarkable ability for transferable learning, it makes sense to leverage these pre-trained models to power essential financial workflows. It is more than just document processing; it is thinking, and the same intelligence that enables AI to defeat world champions in Dota 2 is now driving the next generation of financial AI solutions.

                          The next chapter of AI-driven finance will be shaped not just by technology, but by creativity. By embracing unconventional data sources such as the immersive complexity of video games, industry leaders will unlock new possibilities for personalisation, security, and customer engagement.

                          Learn more at linedata.com

                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Strategy
                          • Fintech & Insurtech
                          • Neobanking

                          Author

                          Richard Doherty

                          Published

                          Estimated Read time

                          6Mins

                          Richard Doherty, Head of Wealth & Asset Management, Publicis Sapient, on how asset managers must redesign their enterprise for AI-driven decision intelligence

                          The asset management industry is entering a structural inflexion point. The first wave of AI focused on improving productivity through copilots and automation. The next wave will fundamentally reshape how decisions are made, executed, and governed across the enterprise. This is not a technology upgrade. It is an operating model shift.

                          Despite significant investment, many firms remain trapped in fragmented AI experimentation. A majority are yet to realise meaningful economic returns from AI, not due to lack of capability, but due to a failure to redesign how intelligence is applied across the organisation. The gap between ambition and outcome is not a technology problem. It is a structural one.

                          From Automation to Decision Intelligence

                          The industry conversation has evolved. The question is no longer whether to adopt AI, but how to scale it across the enterprise. However, most firms are still approaching this challenge through the lens of automation, identifying tasks that can be executed faster or at lower cost. This delivers incremental value, but does not address the underlying constraint: the structure of decision-making within the organisation.

                          Traditional operating models are built around sequential workflows. Work moves from function to function: research, compliance, operations, and distribution, each dependent on the previous stage. This creates latency, duplication, and fragmentation. Agentic operating models shift the focus from tasks to decisions.

                          Instead of asking “Which processes can we automate?”, leading firms are asking: “Which decisions can be augmented or owned by intelligent systems?”

                          This shift enables organisations to move from sequential workflows to parallel decision systems; from human-led analysis to AI-assisted reasoning; from periodic insight to continuous intelligence. The result is not a marginal improvement. It is a step-change in how the enterprise operates.

                          The Pressures Driving Change

                          This transformation is not happening in a vacuum. Asset managers face mounting structural pressures: margin compression driven by fee pressure and passive competition; rising operational complexity from regulation and product proliferation; and advisor capacity constraints that limit scalable growth. Agentic operating models directly address all three.

                          By automating complex workflows, rather than individual tasks, firms can significantly increase advisor and analyst capacity without proportional cost increases. Parallel decision systems reduce the time required to launch products, respond to market events, and deliver client insights. This compresses cycles from months to days. Continuous monitoring of guidelines, portfolios, and operational processes reduces exposure to regulatory breaches and operational failures.

                          These are not theoretical benefits. They represent measurable improvements in cost-to-serve, time-to-market, and operational resilience.

                          Not all Intelligence is the Same

                          To scale AI effectively, organisations must recognise that not all problems require the same type of intelligence. Enterprise AI operates across three distinct layers, and conflating them is one of the primary reasons AI initiatives fail to scale.

                          Deterministic systems execute predefined rules with complete consistency. They are essential for functions where there is zero tolerance for error, trade validation, settlement processing, and regulatory reporting. If a business outcome must be identical every time, deterministic logic remains the correct approach.

                          Predictive systems use historical data to forecast outcomes. Applied in areas such as portfolio risk modelling, fraud detection, and client churn prediction, they generate probabilities and insights, but they do not interpret context or make decisions independently.

                          Agentic systems operate where problems require interpretation, judgment, and contextual understanding, investment guideline interpretation, regulatory document analysis, portfolio insights, and client communication. These systems can reason across complex information, generate insights, and take action within defined boundaries.

                          The ‘Different but Valid’ Dilemma

                          A critical challenge in adopting agentic systems is understanding how they behave. Traditional software produces identical outputs. Agentic systems produce reasoned outputs.

                          This introduces what I call the ‘different but valid’ dilemma. An agent may take a different reasoning path from a human and arrive at a different, but still correct, conclusion. This variability is not an error. It is inherent to reasoning systems.

                          The real risk lies in hallucination, outputs that are not grounded in data or evidence. Managing this requires organisations to clearly define where variability is acceptable. All AI-driven processes sit on a spectrum: deterministic actions with no variability (trade execution), predictive actions with controlled variability (risk scoring), and agentic actions with higher variability (investment insights).

                          Leading firms design systems where agents perform reasoning, deterministic systems enforce execution, and humans retain oversight on high-consequence decisions. This balance enables both flexibility and control.

                          The Operating Model Shift

                          The most significant change is not technological; it is organisational. Traditional models are built on functional workflows. Agentic models are built on coordinated decision systems.

                          Consider what launching a new investment product looks like under each model. In a traditional model, it involves sequential handoffs between teams, compliance reviews the guidelines, operations configures the systems, and distribution drafts the client narrative. Each stage waits for the last.

                          In an agentic model, intelligent systems operate in parallel: compliance agents interpret guidelines, operations agents configure constraints, distribution agents generate client narratives, and governance agents validate outputs. This orchestration compresses timelines, reduces friction, and enables continuous decision-making. It represents a fundamental redesign of how work is performed.

                          Governance: the Foundation for Trust

                          Trust is the prerequisite for scaling AI. Without it, adoption stalls, not because the technology fails, but because the organisation cannot adequately explain or defend the decisions it makes.

                          Leading firms implement governance models built on three principles. First, explainability: every decision must be traceable and auditable. Second, authority boundaries: agents operate within clearly defined limits. Third, human oversight: high-consequence decisions remain under human control.

                          Regulatory expectations will continue to evolve, but one principle remains constant: organisations must be able to explain how decisions are made.

                          Scaling AI is a Leadership Challenge

                          Executives must take a deliberate approach across four areas:

                          • Define the intelligence model: map business problems to deterministic, predictive, or agentic systems.
                          • Build the foundation: invest in data, infrastructure, and orchestration capabilities.
                          • Redesign the operating model: shift from workflows to decision systems.
                          • Implement governance to ensure transparency, control, and compliance.

                          Start with high-value use cases and expand rapidly across the enterprise. The firms that act now will establish a structural advantage in cost, speed, and decision quality. Those that do not risk being constrained by legacy operating models that cannot scale with the demands of modern markets.

                          The Question is not if, it is Who

                          The industry is not simply adopting new technology. It is redefining how decisions are made. The firms that succeed will not be those that deploy AI tools in isolation. They will be those who design the right form of intelligence for each problem, redesign their operating models around intelligent systems, and scale agentic capabilities across the enterprise.

                          This shift is already underway. The question is no longer whether it will happen. The question is which firms will lead, and which will be forced to follow.

                          Learn more at publicissapient.com

                          • Artificial Intelligence in FinTech
                          • Blockchain & Crypto
                          • Data & AI
                          • Digital Strategy
                          • Fintech & Insurtech

                          Author

                          Nell Walker

                          Published

                          7 April 2026

                          Estimated Read time

                          5Mins

                          Alex Saric, Ivalua’s CMO, tells us how supply chain is shifting and where AI is succeeding – as well as where it’s lagging

                          Last month, we attended Ivalua NOW 2026, joining 1,500+ supply chain professionals in Paris to get an up-to-date view of the landscape. As part of this vibrant event we sat down with Alex Saric, Chief Marketing Officer of Ivalua, to dig into some of the ways the industry has shifted and evolved in recent years – and the role AI has to play.

                          In an article that Saric wrote for our sister brand, CPOstrategy, back in 2019, he said that organisations were under more pressure than ever to innovate at speed. Seven years on, the world has drastically changed. Between COVID-19 and the lightning-fast acceleration of AI, supply chain has evolved to an unprecedented degree. So the question is: what does innovating at speed look like in 2026 compared to 2019?

                          “Back then, we were still driving traditional source-to-pay digitalisation and providing the transparency that’s still needed in this more uncertain, volatile world,” says Saric. “That volatility only increases every year. I think most people, probably me included, assumed that it would calm down. But I’d say, in 2026, the impetus is on making AI – and particularly agentic AI – the kind of tool you want it to be. From something that answers a question for you to something that really executes and drives more output from procurement. It’s really about taking it from pilot to production at a rapid pace, where it’s actually driving business impact.”

                          Changing variables

                          Back in 2019, nobody could have predicted the acceleration of AI in the supply chain – not even Saric. “What’s interesting is that even if you go back five or 10 years, people were talking about the commoditisation of procurement technology, which has become relatively easy to use. The capabilities are getting smaller. If anything, that has now accelerated with AI and the disparities between one organisation and another are even higher. But no, I couldn’t have predicted this level of acceleration.”

                          Things have evolved even since 2025. At last year’s Ivalua NOW, Saric said that “the increasingly uncertain sourcing and procurement landscape is forcing the industry to assess the impact on organisations, reassess supply strategies, and it’s all happening so fast”. When asked if that is different now, the answer is a firm “no” – but the variables do keep changing.

                          “It’s almost as if you’re viewing the entire supply strategy as a game. For a while, there are clear optimisation strategies to sourcing that everyone is focused on and implementing,” says Saric. “But then, suddenly, all the rules change. It’s one thing if they change just once and you adapt, set different parameters, and optimise again. But the problem now is that they change overnight, and you have no idea when. That’s a massively complicated environment for procurement. Their job has become exponentially more difficult.”

                          AI isn’t transformational (yet)

                          It’s a topic both Saric and Franck Lheureux, Ivalua’s CEO, touched on during the introduction to Ivalua NOW 2026: that things have never been more difficult for supply chain professionals. Even with the wider (and more confident) use of AI across the industry, the pace of change and the geopolitical risks and pressures weigh heavier than ever. In fact, according to Saric, AI’s impact has hardly been transformational – yet.

                          “The nature of enterprise technology is that it’s always a bit slower to get adopted and rolled out,” he explains. “There’s extra scrutiny, there’s change management; all these factors that have to be considered compared to consumer technology. The biggest changes last year were theoretical for the most part. There were very few organisations actually using AI in a way that’s driving value. A sizable minority of our customers are using it actively in production and they’re driving value from it. The step which still needs to come is moving from having it as a handy assistant or a way to get information faster, and actually driving a difference in how people work.”

                          This isn’t going to happen overnight. However, Saric expects to have customers onstage at Ivalua NOW 2027 who have completely changed their way of working via AI, and that most businesses will be using it to some extent. It is certainly creating efficiencies and values, even if it’s a slow process. For example, the application of AI for user experience is proving to be one area where it’s coming into its own.

                          “It’s really enabled procurement to become a much more conversational experience,” Saric explains. “Broadly, that’s the biggest impact so far. But besides that, it’s also helping make better decisions to identify contracts that have certain clauses to drive standardisation and conduct assessments with suppliers. If there’s a performance issue or you want to suggest improvement plans, AI can also help with drafting RFPs. 

                          “There’s a whole range of pretty distinct skills that are saving a lot of time. In many cases, it’s bringing information and insights to the fingertips of the procurement users, rather than them wasting hours looking for that information.”

                          The procurement-IT alliance

                          More than technological advances like AI, strong inter-communication between procurement and the broader business is a key to success in modern organisations. Saric hosted a conversation during Ivalua NOW based around the collaboration between procurement and IT, and how to approach this partnership effectively. During this, he delved into his 25 years in the industry to guide the conversation. 

                          “What I’ve consistently seen is that the most successful procurement digitalisation projects typically had strong collaboration with IT,” says Saric. “With AI, it’s even more important. You really have to understand AI and ensure you’re not exposing your organisation to potential security risks, or violating other policies. There’s a lot of technical detail that needs to be understood.”

                          He continues: “IT is the department that’s best positioned to help guide procurement through that process. The second thing is that there needs to be proactive engagement upfront with executive sponsorship from both procurement and IT. You can’t simply slap an AI tool on top of a broken foundation and think that it will be able to find and decipher all the issues in your data. Having the right foundation is critical, and that’s another reason why IT is an important partner for procurement.”

                          Author

                          Dan Brightmore

                          Published

                          31 March 2026

                          Estimated Read time

                          3Mins

                          Read in brochure format

                          Welcome to the latest issue of Interface magazine! Click here to read the latest edition! Sanofi: Supporting the World’s Health…

                          Welcome to the latest issue of Interface magazine!

                          Click here to read the latest edition!

                          Sanofi: Supporting the World’s Health Through Data

                          This month’s cover story spotlights Sanofi, one of the world’s largest pharmaceutical companies. For an organisation that puts the end-user – the patient – first, this requires an unwavering focus on R&D and continuous improvement. For the sake of the world’s health; every patient counts. So, when opportunities arose to improve services through data and advanced technology like AI, Sanofi brought in experts to steer and develop the journey.

                          Snehal Patel, Head of Global Data and AI Platform, takes a deep dive with Interface… “These innovations have fundamentally transformed Sanofi’s data and AI value chain,” says Patel. “It’s enabled scalable and efficient development across the organisation. We now have a far more agile development environment that supports the broader AI initiatives at Sanofi.”

                          Langham Hospitality Group: Cybersecurity Underpinning Guest Excellence

                          Anson Cho, Director of Information Security & Data Protection at Langham Hospitality Group, discusses the pandemic’s silver lining and the development of a proprietary matrix to embed security into the heart of operational excellence.

                          “Our strategy wasn’t about over-engineering our systems to match the spend of a global financial institution; it was about increasing our defensive maturity so we are never an easy mark,” says Cho. “In cybersecurity, you want to ensure your barriers are sophisticated enough that attackers move on. We focus on staying ahead of the curve and continuously evolving so that our security posture remains a formidable deterrent.”

                          FNB: Redefining Data Science in Commercial Banking

                          Yudhvir Seetharam, Chief Analytics Officer at South Africa’s First National Bank (FNB) on a data science journey characterised by curiosity, culture and the drive for a competitive edge.

                          “Ours is a holistic approach focusing on the customer,” he explains. “Understanding the context of each customer journey and then using that context so that when we interact with you, we’re able to drive the right conversation with the right customer, at the right time, through the right channel and for the right reason. These ‘five rights’ make our interactions with clients more impactful than a spray and pray approach.”

                          Click here to read the latest edition!

                          • Cybersecurity in FinTech
                          • Data & AI
                          • Digital Strategy
                          • Fintech & Insurtech
                          • Infrastructure & Cloud

                          Author

                          Thomas Benjaminsen Normann

                          Published

                          27 March 2026

                          Estimated Read time

                          6Mins

                          Thomas Benjaminsen Normann, Product Director at Paymentology on the future for agentic payments and the progress still to be made

                          Santander and Mastercard’s live AI-agent payment pushed the industry past the stage of talking about agentic commerce as a future use case and into the reality of a transaction moving through live banking infrastructure. In doing so, it placed an AI agent at the point of spend within a system that still assumes the person initiating the payment is also the one making the decision and carrying the liability.

                          That assumption is far easier to sustain when a payment draws on existing funds than when it creates a debt that someone must later repay. And may dispute. As soon as an agent moves from guiding a choice to completing the transaction, the usual alignment between instruction, authorisation and liability becomes harder to see.

                          Card authorisation has long rested on a simple premise: the person using the card is the one deciding to spend. Even when the transaction runs through a wallet, an app or a stored credential, the model still relies on a cardholder who is directly involved in the act of payment.

                          Agentic payments

                          Agentic payments stretch that arrangement. The customer may have set the rules, the budget or the merchant preference in advance, but the point of execution can now sit with software acting later and at speed. The question then extends beyond whether the transaction was authenticated to whether the debt it created was taken on with the kind of consent and clarity card systems have traditionally relied on.

                          Mastercard has responded by building a stronger trust layer around delegated intent. Once software acts on a customer’s behalf, the usual signs of presence and intent at the moment of payment carry less weight than they do in an ordinary card transaction. Santander’s pilot showed that this can be handled inside a tightly controlled framework with predefined permissions.

                          The challenge becomes very different once the same model moves into ordinary credit flows, where issuers are dealing with borrowing, repayment and dispute risk rather than a bounded test case.

                          Risk models built on human behaviour

                          Fraud systems and credit models have been trained to read people. How they spend, how quickly they move, where they buy, and what tends to happen before repayment trouble begins to show. An AI agent, even when acting entirely within a customer’s instructions, is unlikely to look much like that. It may search more widely, compare more aggressively, transact at unusual times and behave with a consistency that looks odd against a human baseline. Some legitimate payments will appear suspicious. Some suspect ones may look routine. Signals that once separated ordinary behaviour from risky behaviour will arrive in forms the system is not used to reading.

                          Research from Capgemini indicates that 71% of consumers want generative AI integrated into shopping interactions. Meanwhile, 58% say they already use generative AI instead of traditional search for recommendations. That does not mean autonomous purchasing becomes mainstream overnight, but it does suggest the move from AI-assisted discovery to AI-executed transactions will not stay theoretical for long. For issuers, that means transaction systems are about to encounter a new behavioural signature without much history behind them.

                          The pressure does not sit only with fraud screening. Credit decisioning is built on assumptions about how people build balances, revolve debt, repay over time and run into repayment trouble. An AI agent may be acting entirely within a customer’s instructions while still producing patterns those models were never trained to read cleanly. A sudden increase in spend, an unusual merchant mix or a burst of late-night activity may deserve scrutiny when a person generates it.

                          The same signals may be perfectly consistent with a software agent searching widely, responding instantly to price changes or executing against preset rules with much greater speed and regularity than a person would. Once that behaviour starts landing in the credit book, signals that once carried meaning around affordability, intent or emerging repayment risk become less reliable as indicators.

                          Signals the authorisation layer does not carry

                          The transaction also arrives with gaps that matter more once software is involved. Existing payment messages can identify the merchant, the amount, the credential used and the authentication path. What they do not natively describe is whether the action came from a customer or an agent, what spending authority had been delegated, whether that authority was limited to a category, merchant or price threshold, and whether the funding source was intended to be debit, charge or revolving credit. A payment can be technically valid while still leaving the issuer with too little context about how the decision was made.

                          A controlled pilot can solve some of that by imposing rules around the transaction from outside the standard message, which is effectively what bounded testing is for. Everyday credit use is less forgiving. If the issuer is expected to approve the payment, apply the right controls, score the exposure and later defend the outcome in a dispute, those signals have to be legible inside the flow rather than reconstructed around it after the event.

                          At that point, the question is less about whether the payment experience works and more about whether the issuer-side controls underneath it can carry the weight. That includes the ability to apply rules in real time, restrict how a credential can be used, and keep a clear record of how the transaction was authorised and what kind of exposure it created.

                          The missing context does not stop at authorisation. It follows the transaction further down the line, when an issuer has to explain why a payment was approved, whether the agent acted within its delegated scope. And how that scope should be evidenced if the customer challenges the transaction. Card systems are used to relying on the credential, the authentication path and the transaction record.

                          Digital versus Traditional Wallets

                          Agentic payments demand something more granular: a clearer account of who or what acted, under what limits, and with what right to create a liability on the customer’s behalf. The control layer around that decision, including how credentials are restricted and how delegated authority is defined, starts to matter much more than it did in a conventional wallet or stored-card journey.

                          Infrastructure many issuers built out for tokenised wallets now looks more like part of the control architecture for agent-led spend. Because the payment credential itself may need tighter restrictions than the market has been used to applying.

                          Santander and Mastercard have shown that an AI agent can now make it all the way through a live payment flow. What follows from that is less about whether software can reach the point of spend and more about what the rest of the stack needs to know once it gets there. If agentic payments are to move beyond controlled deployments and into ordinary credit use, issuers will need clearer ways to tell who acted, under what authority, against which funding source, and with what liability attached. Until those signals travel cleanly through the flow rather than being inferred around it, agentic payments on credit will remain easier to demonstrate than to absorb into everyday card operations.

                          Learn more at paymentology.com

                          • Artificial Intelligence in FinTech
                          • Digital Payments
                          • Embedded Finance
                          • Neobanking

                          Author

                          Ian Franklyn

                          Published

                          26 March 2026

                          Estimated Read time

                          5Mins

                          Ian Franklyn, Chief Revenue Officer at Mainstreaming, on why delivering exceptional streaming experiences won’t require just technology, but also collaboration and synergy

                          Streaming video has firmly established itself as the dominant force shaping global internet traffic. From premium live sports and breaking news to on-demand entertainment libraries, audiences now expect seamless, high-quality viewing experiences on any device, at any time. For leaders across media, telecoms, and technology, the challenge is no longer about enabling streaming. It is about sustaining it at scale preserving reliability, efficiency and profitability.

                          Yet, despite the central role video plays in today’s digital economy, the underlying delivery model remains fundamentally fragmented.

                          Many broadcasters and OTT platforms still rely heavily on centralised, third-party content delivery networks (CDNs). These operate largely outside internet service provider (ISP) infrastructures. This model has supported the growth of streaming over the past decade. However, it is increasingly misaligned with current demand patterns, especially during large-scale live events.

                          The result is a structural inefficiency that affects every stakeholder in the ecosystem. And the industry can no longer ignore it.

                          The Growing Cost of Disconnection

                          When millions of viewers tune in simultaneously, vast volumes of video data must travel across multiple interconnected networks before reaching end users. This often means duplicating the same streams across long-haul routes, placing unnecessary strain on transit links and core infrastructure.

                          For ISPs, this translates into rising traffic volumes without proportional financial return. Networks become congested, costs increase, and visibility into traffic flows remains limited.

                          Broadcasters and OTT platforms face a different but equally critical challenge. With limited control over last-mile delivery, performance becomes unpredictable at precisely the moments that matter most. Buffering, latency, and degraded video quality directly impact user experience, driving churn and damaging brand reputation.

                          Ultimately, the end user bears all the consequences. Even minor disruptions during peak events can cause frustration and dissatisfaction. This consequently erodes trust, impacting both service providers and content owners in an increasingly competitive market.

                          Rethinking Delivery: Moving Closer to the Edge

                          Addressing these challenges requires a fundamental rethink of where and how video is delivered.

                          Rather than relying solely on centralised infrastructure, delivery capacity can be deployed directly within ISP networks, closer to the end user. This edge-based approach localises traffic, reducing the distance data must travel and fundamentally improving efficiency.

                          The benefits are immediate. By placing content within ISP networks, duplicated traffic across transit routes is minimised, congestion in core networks decreases, and latency is reduced. At the same time, both ISPs and content providers gain greater visibility and control over performance.

                          This model is particularly valuable for live streaming, where demand is highly concentrated and unpredictable. Traditional CDN architectures, designed for distributed but relatively predictable traffic patterns, are simply not built to handle sudden spikes in concurrent viewership.

                          Edge delivery networks purpose-built for video, by contrast, enable capacity to be positioned dynamically where it is needed most. This ensures that even the largest live events can be delivered with consistency, reliability, and low latency.

                          From Delivery Burden to Shared Value Creation

                          The evolution toward edge-based video delivery represents a fundamental shift for both ISPs, and broadcasters and OTT platforms.

                          For ISPs, streaming has long been treated as a cost centre. A growing source of bandwidth consumption that drives infrastructure investment without directly contributing to revenue. As traffic volumes continue to rise, this model becomes increasingly unsustainable both economically and operationally.

                          At the same time, broadcasters face a different challenge. How can they efficiently manage highly variable demand? Particularly during large-scale live events where audience peaks are both massive and unpredictable. And where failure is not an option.

                          Embedding video delivery capabilities within ISP networks changes this dynamic for both sides.

                          For ISPs, localising traffic reduces reliance on upstream transit. This alleviates pressure on core infrastructure, enabling more efficient use of existing capacity. It also opens new monetisation opportunities, allowing them to move beyond being passive carriers and play an active role in delivering premium streaming experiences.

                          For broadcasters and OTT platforms, the benefits are equally strategic. Edge-based delivery enables them to scale live events more efficiently. Activating capacity where and when it is needed rather than overprovisioning for peak demand. This results in more predictable performance, consistent quality of experience, and improved cost efficiency.

                          In this shared model, video delivery is no longer a burden for one side or a risk for the other. It becomes a coordinated effort, aligning incentives and generating value for all the stakeholders involved.

                          An Ecosystem that Works in Synergy

                          Realising this opportunity requires more than technology. It demands a shift toward a more collaborative operating model: a true ‘Better Together’ approach.

                          This means deeper alignment across the ecosystem, bringing together ISPs, broadcasters, OTT platforms, and technology providers around shared objectives. Instead of operating in silos, each stakeholder contributes to a unified delivery framework designed to meet the demands of modern streaming.

                          In practical terms, this approach increases transparency, improves performance, and aligns both technical and commercial incentives. Integrating delivery capacity within ISP networks creates a stronger foundation for long-term growth, enabling more efficient scaling as demand continues to rise.

                          The result is a more resilient and adaptable ecosystem. One capable of supporting increasingly complex and large-scale streaming experiences, and responding dynamically to future demand.

                          Building the Next Generation of Streaming Infrastructure

                          The misalignment between how video is consumed and how it is delivered is no longer sustainable, and delaying a change will only amplify the problem

                          As streaming evolves, new formats such as ultra-high-definition video and low-latency interactive services will place even greater demands on network infrastructure. At the same time, audience expectations will continue to rise, leaving little tolerance for disruption.

                          Meeting these challenges requires a shift toward integrated, edge-driven architectures supported by strong ecosystem partnerships.

                          By bringing video delivery closer to the viewer, the industry has an opportunity to redefine both the economics and performance of streaming. More importantly, it can move beyond the limitations of fragmented models toward a more efficient and scalable future. Ultimately, delivering exceptional streaming experiences won’t require just technology, but also collaboration and synergy, aligning the entire ecosystem to operate as one.

                          Learn more at mainstreaming.com

                          • Data & AI
                          • Digital Strategy
                          • Infrastructure & Cloud

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          4Mins

                          Monthly active mobile money accounts saw their highest growth since 2021

                          More than $2 trillion flowed through mobile money wallets globally in 2025, found the State of the Industry Report on Mobile Money 2026, prepared by the GSMA Mobile Money programme. This is an important threshold and exemplifies the exponential growth in transaction values the industry has experienced in recent years. It took 20 years to pass $1 trillion in annual transaction values, but just four years for this figure to double. 

                          From its inception, only 25 years ago, mobile money has now become a mainstream financial service for underserved populations around the world. Empowering those without access to traditional banking services and contributing to economic growth in countries where mobile money is present. The report also found that mobile money reached 2.3 billion registered accounts in 2025, growing by 268 million.  

                          “Mobile money has become one of the world’s most impactful financial services. What began as a simple way to move money has evolved into a global financial ecosystem, reshaping how hundreds of millions of people manage their financial lives. The market is reaching new heights and greater maturity. Adoption and regular use are surging, and value is scaling even faster than volume, with more than $2 trillion flowing through mobile money in 2025 – doubling from the first trillion in just four years. Looking ahead, the industry’s growing scale and sophistication will bring new opportunities, and new responsibilities. By prioritising interoperability and cross‑border harmonisation; engaging in digital public infrastructure; strengthening consumer protection and fraud controls; and accelerating women’s inclusion and financial health outcomes, we can ensure mobile money continues to provide safe, inclusive and sustainable digital financial services.” Vivek Badrinath, GSMA Director General

                          Regular mobile money usage is growing, supporting financial health   

                          Regular mobile money usage has increased worldwide over the past year, with active 30-day accounts rising by 15% to 593 million. Most new registered and active accounts came from Sub-Saharan Africa, although almost every region where mobile money is offered experienced a rise. This has led to monthly usage of mobile money accounts growing by half a percentage point to 25.7%, the highest it has been since 2021. However, this still leaves almost 75% of accounts inactive monthly, with fraud remaining widespread and transaction taxes often encouraging users to revert to cash in the countries where they’re in effect, negatively impacting financial inclusion.  

                          Through more frequent usage, mobile money users can improve their financial health – the capacity to manage day-to-day financial needs, withstand shocks and invest in the future – by benefiting from the increasing provision of adjacent services like credit, savings and insurance. The report found that the number of mobile money providers offering insurance increased by one-third in 2025. Mobile-money enabled credit remains the most widely offered adjacent financial service, and this is nearly matched by those offering saving options.  

                          Regulation is supporting mobile money in improving financial inclusion  

                          Regulation is playing a key role in expanding the reach of mobile money, the GSMA reports. Over 60% of mobile money providers believe that interoperability, know-your-customer and consumer protection regulations have supported their operations. Although more must be done to support the industry, significant regulatory issues remain – particularly cross-border data transfer regulations, which 24% of mobile money providers report have hindered their operations. 

                          With a supportive regulatory environment, the mobile money industry will be able to continue growing and, in turn, advance financial inclusion, especially among groups that have traditionally lacked access to banking services. This is vital as a wide gender gap persists in mobile money account ownership across seven out of 10 countries surveyed in the report. Aside from in Ghana, Kenya and Nigeria, women who own a mobile money account are still less likely than men to have used it within the past month. 

                          Mobile money fosters innovation for good    

                          In addition to accelerating financial inclusion and supporting improved financial health, mobile money usage is enabling wider social and humanitarian benefits by enabling rapid payouts during crises, particularly in remote regions. However, for these and other use cases to succeed, mobile money needs to be complemented by digital financial literacy initiatives to continue responsible growth across regions and demographics.  

                          About GSMA 

                          The GSMA is a global organisation unifying the mobile ecosystem to discover, develop and deliver innovation foundational to positive business environments and societal change. Our vision is to unlock the full power of connectivity so that people, industry, and society thrive. Representing mobile operators and organisations across the mobile ecosystem and adjacent industries, the GSMA delivers for its members across three broad pillars: Connectivity for Good, Industry Services and Solutions, and Outreach. This activity includes advancing policy, tackling today’s biggest societal challenges, underpinning the technology and interoperability that make mobile work, and providing the world’s largest platform to convene the mobile ecosystem at the MWC and M360 series of events. 

                          Learn more at gsma.com 

                          The report is funded by the Gates Foundation.  

                          • Digital Payments
                          • Neobanking

                          Author

                          Andrew Woods

                          Published

                          24 March 2026

                          Estimated Read time

                          2Mins

                          Surgere CEO William Wappler explains how precise, verified data is becoming the foundation of automation, resilience, and enterprise-wide decision-making

                          At this year’s Manifest conference in Las Vegas, the conversation around supply chain technology repeatedly returns to one foundational theme: data accuracy. For William Wappler, CEO of Surgere, that foundation is not simply an operational advantage. It is the essential prerequisite for modern supply chain performance.

                          Surgere specialises in capturing, verifying and operationalising highly accurate supply chain data, using a combination of IoT, engineering-led deployment, and AI-driven analytics. The company focuses on knowing precisely what assets exist, where they are located, and how they move across complex industrial environments. That data is then fed into enterprise systems to drive automation, planning and decision making.

                          Accurate data

                          The company’s central mission is straightforward. “We only do one thing: to make sure that within that transformation, everybody has highly accurate data that they’re working on to ensure that all of the tactics and strategies they’re working on actually work.”

                          For decades, Wappler argues, supply chains have operated on what he calls an “assumptive model”. Organisations believed they knew what was in a shipment, where inventory sat, or whether materials had arrived, but verification was often manual and reactive. “Supply chain practitioners have existed on heroics for a long time,” he explains from Surgere’s spot in the Expo Hall of the Venetian Hotel. “We think we know what’s on that truck. We think we know where it is in the warehouse.”

                          Surgere’s technology is designed to remove that uncertainty. By validating shipments, tracking assets in real time and providing precise location data, the company allows organisations to operate on verified information rather than guesswork.

                          The scale is significant. “Today we’re doing about 15 billion transactions a month,” Wappler says, noting that the primary audience for this data is no longer people but enterprise systems themselves.

                          Read the full story here!

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          3Mins

                          According to a new European study of 550 business buyers commissioned by TreviPay, the global B2B payments invoicing and payments…

                          According to a new European study of 550 business buyers commissioned by TreviPay, the global B2B payments invoicing and payments network, friction in the B2B buying process in the form of slow onboarding and inconsistent invoicing along with rising expectations for AI-enabled processes are where businesses are experiencing threats to loyalty beyond price.

                          “Across Europe and the UK, finance teams are navigating economic pressure, regulatory complexity and rising buyer expectations. Our research shows payment and invoicing experiences now play a decisive role in supplier selection.” Inez Berkhof-Hollander, TreviPay’s Vice President of EMEA

                          Top Three European Market Expectations in 2026

                          1. More AI-driven purchasing options vary by country

                          Nearly 8 in 10 business buyers always or often use AI technologies in B2B purchasing and payment processes; a significant shift from previous years. AI is seen primarily as a means to improve decision-making through data insights (20%), strengthen fraud prevention and risk management (16%), and reduce manual tasks.

                          However, enthusiasm is tempered by practical constraints. In Germany, where compliance demands are particularly high, adoption is more cautious. In France and Germany, AI’s appeal is strongly linked to providing invoice status visibility and auto-matching invoices to purchase orders, addressing persistent pain points around invoice inaccuracy.

                          1. Suppliers offering Pay by Invoice options
                            Almost half (47%) of businesses actively look for the option to be invoiced as a determinant in where they place repeat business—a trend particularly important across Europe.

                          “Pay by invoice remains the dominant B2B payment method across Europe,” Berkhof-Hollander said. “It’s woven into how businesses operate here. But preferences vary significantly.”       

                          1. Despite widespread digitalisation, friction remains in the B2B buying process

                          Buyers cite persistent challenges such as incorrect invoices, limited ERP integration, inconsistent invoice formats and delays in approval workflows.

                          In Germany, 76% of buyers reported issues with payment options overall; far higher than the 37% reported in Spain.

                          These pain points vary by market and company size. Larger enterprises (500+ employees) prioritize ERP integration and purchase controls more heavily, while mid-sized businesses value speed and flexibility. In the UK, fast onboarding is among suppliers’ biggest competitive levers.

                          Payment preferences very by region and company size

                          The research reveals significant variation across markets. Trade credit is especially prevalent in the UK and Germany (46%), while Spanish buyers rely on it far less but show the highest demand for invoice customisation (93% vs. 82% overall).

                          “While there will always be regional differences, but it all comes down to reducing friction at every stage of the buying journey,” Burkhof-Hollander said. “Flexibility is key to helping suppliers cement repeat business and deliver sustainable growth.”

                          Access the complete EMEA market research report for additional data here

                          The research, capturing the views of 550 B2B buyers in the UK (21%), France (24%), Germany (18%), Spain (18%) and Australia (19%), was conducted by Censuswide between November 18-26, 2025.         

                          About TreviPay

                          TreviPay, The Pay by Invoice CompanyTM, is a fully managed B2B payments platform for global brands. Proven to increase AOV and reduce DSO, our accounts receivable automation software, enhanced by AI, optimises order-to-cash and integrates with all channels and ERPs. Delivering a superior payment experience, TreviPay is the choice of top retailers, manufacturers and travel companies, including Walmart, Lenovo and United Airlines. With more than four decades of experience powering over $8 Billion in global trade, TreviPay was named a Leader for Embedded Payment Applications by IDC and a top vendor in cash application by The Hackett Group. With offices in the US, Netherlands, Costa Rica and Australia, TreviPay supports customers in 32 countries.

                          Learn more at trevipay.com

                          • Artificial Intelligence in FinTech
                          • Digital Payments

                          Author

                          Dan Brightmore

                          Published

                          23 March 2026

                          Estimated Read time

                          4Mins

                          Money20/20, the world’s leading FinTech show, has announced 250 confirmed speakers from a total of 39 countries. They will take…

                          Money20/20, the world’s leading FinTech show, has announced 250 confirmed speakers from a total of 39 countries. They will take the stage at Money20/20 Asia in Bangkok from April 21–23, 2026 at the Queen Sirikit National Convention Center (QSNCC).  

                          From Infrastructure to Impact – Where Technology Meets Humanity

                          This year’s theme is ‘From Infrastructure to Impact – Where Technology Meets Humanity’. It will explore how the next wave of financial innovation can deliver real outcomes across the APAC region. From digital public infrastructure and embedded finance to AI‑powered services and inclusive financial design. Money20/20 Asia will examine how technology moves beyond capability to create genuine human impact. With a speaker lineup drawn from across Asia, the show will unpack the trends, breakthroughs, and strategies shaping the future of money.

                          Money20/20 Asia brings together speakers from over 40 global and regional banks. These include Standard Chartered, HSBC, Bank of America, Citi, Deutsche Bank, World Bank, Kotak Mahindra Bank, Tonik Bank, Maybank, J.P. Morgan, KASIKORNBANK, and Trust Bank Singapore to name a few. Experts from leading payment providers including Visa, Nium, Thunes, PPRO, Tazapay, Mastercard, Razorpay, Fiserv, Brankas, JusPay will discuss the evolution of payments across the region.

                          “Money20/20 Asia is a platform for ideas that shapes the industry and this year’s lineup of 250+ speakers reflect the extraordinary progress happening across APAC. From digital assets and payments to AI and financial inclusion, the conversations in Bangkok will define the future of money across the region and beyond. We’re excited to bring together the leaders who are not only observing change, but actively creating it.” Danny Levy, Executive Vice President & MD APAC & Middle East, Money20/20

                          Speakers Shaping the Future of Finance

                          The 2026 keynote roster highlights a group of standout leaders shaping the future of finance across Asia and beyond. Some of the keynote speakers include: Faizul Ariff Ali, Governor, Reserve Bank of Fiji, Pichet Durongkaveroj, Executive Director, Bangkok Bank, Peng Ooi Goh, Founder & Executive Chairman, Silverlake Group and Anna Liu, CEO, HashKey Tokenisation

                          “Thailand is emerging as a key financial innovation hub in Asia, and Money20/20 Asia provides a vital platform for us to connect with global leaders, building the future of finance. As digital transformation accelerates across the region, we see tremendous opportunity for collaboration, new business models, and technologies that will strengthen Thailand’s role in the regional financial network.” Pichet Durongkaveroj, Executive Director, Bangkok Bank

                          New for 2026 at Money20/20 Asia

                          New for this year at Money20/20 Asia is the Intersection Stage. Exploring the convergence of traditional finance (TradFi) and decentralized finance (DeFi), addressing how banks, fintechs, and emerging technologies are reshaping the global financial ecosystem. The stage brings together leaders from major financial institutions and well-known FinTech companies to discuss how innovation, regulation, and new financial infrastructure are transforming areas such as digital assets, trust and cybersecurity, and cross-border payments. Speakers include Siddharth Gupta of Bank of America, Sabih Behzad of Deutsche Bank, Fangfang Jiang of International Finance Corporation, Kenneth Chan of Webull, and Siva Kumar of Sumsub. They will share insights on regulatory innovation, digital asset adoption, developments in stablecoin, tokenisation and blockchain‑enabled settlement. Also how new payment rails are enabling faster and more efficient cross-border transactions.

                          The show includes the Startup & Investor Park, a dedicated space where leading fintech founders from Asia connect with global investors, enterprise partners, and decision-makers. 20 standout startups from across APAC have been selected, highlighting the Park’s commitment to quality, innovation, and real-world impact. Over three days, the Park will host founder-focused sessions, investor meetups, startup showcases, and pitch competitions to accelerate early-stage growth.  Startups will also compete for the Golden Ticket to the 2026 Startupbootcamp Sustainability Singapore Accelerator, which offers SGD 70,000 in non-dilutive prize money, access to the Investment Readiness Program, and expert coaching.

                          Money20/20 Asia will also feature FinTech unicorns and high‑growth innovators, including Revolut, Bolttech, Fireblocks, Circle, Bitkub, AppWorks, and Incognia, alongside technology leaders such as Meta, Finastra, FIS, and Publicis Sapient.

                          “The digital asset landscape across Asia is evolving at remarkable speed, and platforms like Money20/20 Asia play a vital role in bringing together innovators, regulators, and ecosystem builders to shape that future. As the region’s leading blockchain and digital asset company, Bitkub is proud to be part of the global conversation on how tokenisation, digital identity, and next-generation financial infrastructure can unlock new economic opportunities and drive inclusive growth for millions across the region.” Jirayut (Topp) Srupsrisopa, Founder & Group CEO, Bitkub Capital Group Holdings

                          In addition to the Intersection Stage Money20/20 Asia 2026 will feature three more stages. Each delivering a distinct lens on the future of money:

                          • Radiant Stage: headline keynotes and industry‑defining conversations
                          • Inner Forum Stage: deep‑dive discussions on payments, banking, digital assets, AI, and regulation
                          • Startup Stage: spotlighting emerging founders and early‑stage innovation

                          Program Highlights from the Agenda

                          The 2026 agenda highlights the show’s core themes of digital assets, cross-border payments, AI, and regulation. And includes several high-impact sessions:

                          • Day 1: The Future of Tokenised Markets in Asia, featuring HashKey Tokenisation, Fireblocks, Circle
                          • Day 1: Real‑Time Cross‑Border Payments: The Next Leap Forward, with Nium, Thunes, Tazapay, Airwallex
                          • Day 2: AI‑Driven Financial Inclusion Across APAC, with Kotak Mahindra Bank, Tonik Bank, Trust Bank Singapore
                          • Day 2: The Creator Economy Meets Finance at the Intersection Stage, featuring Meta, Publicis Sapient, and leading digital creators
                          • Day 3: Regulation for the Next Decade with regulators from Bank of Thailand, MAS, BSP, OJK Indonesia, Bangladesh Bank, Labuan FSA, and the Reserve Bank of New Zealand

                          About Money20/20

                          Launched by industry insiders in 2012, Money20/20 has rapidly become the heartbeat of the global fintech ecosystem. Over the last decade, the most innovative, fast‑moving ideas and companies have driven their growth on our platform. Mastercard, Airwallex, J.P. Morgan, SHIELD, GCash, Stripe, Google, Visa, Adyen, and more make transformational deals and raise their global profile with us. Money20/20 attracts leaders from the world’s greatest banks, payments companies, VC firms, regulators, and media platforms — convening to cut industry‑shaping deals, build world‑changing partnerships, and unlock future‑defining opportunities in Las Vegas (October 18–21, 2026), Amsterdam (June 2–4, 2026), Riyadh (September 14–16, 2026), and Bangkok (April 21–23, 2026). Money20/20 is where the world’s FinTech leaders convene to grow their brands. It is part of Informa PLC. Follow Money20/20 on X and LinkedIn for show developments and updates.

                          Learn more at Money20/20.com

                          Author

                          Martijn Gribnauis

                          Published

                          Estimated Read time

                          5Mins

                          Martijn Gribnauis, Chief Customer Success Officer at Quant, on why Agentic AI will redefine financial services

                          A recent Google Cloud survey showed that only 13% of finance organisations are currently using agentic artificial intelligence. This number needs to, and will rise when you consider that 88% of financial leaders are seeing ROI from generative AI already. Agentic is the next and most advanced evolution of artificial intelligence the world has ever seen. 

                          Agentic AI is not on the way. It is here and already reshaping how forward-leaning financial institutions operate. In 2026, for IT and finance leaders to build an insurmountable competitive lead they must deploy agentic AI in every area where it can safely and effectively create value. The institutions that hesitate will find their business models under threat from familiar competitors and newcomers alike.

                          Reinvention of Core Processes

                          Agentic AI is poised to reinvent core financial processes. Bookkeeping, record maintenance, and period-end close are nearing complete automation. Month-end processes that once required late-night, stress-filled marathons will evolve into continuous, largely automated cycles. IT teams will no longer spend evenings on high alert waiting for failures. 

                          This shift also frees IT leaders, finance teams, and operations functions from monotonous repetitive tasks. Instead of focusing on system uptime and manual reconciliation, they will collaborate with the C-suite on strategic initiatives that drive growth and revenue. 

                          Understanding Why Adoption Is So Low

                          Despite the promise of Agentic AI, there is understandable caution. Some 80% of organisations have reported ‘risky behaviour’ from AI agents, and in the world of finance that is an alarming number. Finance is one of the most regulated, risk-averse sectors in the world. The fear of losing control remains the primary reason so few in the industry have embraced Agentic AI.

                          Loss of control and fear of catastrophic error

                          Financial leaders fear that an autonomous system could go ‘off script’, mis-route payments, misinterpret rules, or inadvertently cause compliance breaches. In finance, even small errors can trigger major financial or regulatory consequences.

                          Security and data privacy concerns

                          Large AI models require huge quantities of sensitive data. Organisations worry about breaches, cyber-attacks, or manipulation. An AI agent with improperly configured permissions could, in theory, execute fraudulent transactions or expose confidential customer information.

                          Bias and fairness risks

                          If AI agents make decisions using incomplete or fragmented data, they risk perpetuating or amplifying bias. At scale, biased decision-making can undermine customer trust and expose firms to legal and regulatory challenges.

                          Regulatory ambiguity and audit difficulty

                          Regulators are still determining how to govern agentic AI. Some organisations fear that early adoption could unintentionally violate rules or create future audit vulnerabilities.

                          These fears are legitimate, but not insurmountable.

                          Tackling the Adoption Barriers: A Practical Blueprint for Finance Leaders

                          To capitalise on Agentic AI’s immense potential, leaders must take a structured approach grounded in business value, security, and trust.

                          1. Start With Clear, Measurable ROI and Efficiency Gains

                          In finance, adoption accelerates when decision-makers see proof of value.

                          Start by automating repetitive processes. Agentic AI can handle tasks like data entry, reconciliation, invoice matching, and initial fraud checks faster and more accurately than humans. This leads to reduced operational overhead as automation lowers labour costs, shortens processing times, and reduces error rates. Demonstrating these savings through case studies or internal pilots is critical to changing minds. 

                          AI agents can enable revenue growth by analysing huge data sets to identify new investment opportunities, optimise trading strategies, and generate personalised product recommendations. Each of these capabilities directly impacts top-line growth.

                          2. Strengthen Risk Management and Compliance Through AI

                          Agentic AI will improve risk management when deployed responsibly. This starts with real-time fraud detection. AI agents can monitor transactions continuously, identifying patterns that suggest fraud long before traditional systems would detect an anomaly.

                          Continuous monitoring is also incredibly helpful when it comes to compliance. AI agents excel at ensuring adherence to KYC and AML regulations. They can automatically maintain audit trails, identify missing documentation, flag anomalies, and escalate issues instantly.

                          Enhanced stress testing and scenario modelling can both be completed via Agentic AI. It can simulate complex market environments more dynamically than legacy tools, providing deeper insights into vulnerabilities and improving resilience. When showcased and presented in this context, agentic AI becomes a risk-reduction tool in the eyes of decision makers. 

                          3. Directly Address Security and Trust Concerns

                          Trust is the cornerstone of adoption. Implement enterprise-grade security architecture that includes encryption, secure APIs, strict access controls, and continuous monitoring of agent behaviour. And, use explainable and transparent AI systems (XAI) so your finance teams understand the reasoning behind decisions. XAI helps provide interpretable outputs that support auditability and regulatory compliance.

                          Start small with a controlled, low-risk pilot. A proof-of-concept in a non-critical workflow helps teams understand the technology, gather evidence, and build internal support before scaling. Produce numbers based reporting that speaks the language of the people who make the decisions. Show, don’t just tell them how agentic will move the business forward.

                          4. Highlight the Competitive Advantage

                          Agentic AI adoption is not just an efficiency upgrade. It is a competitive imperative. AI agents create faster innovation cycles by accelerating product development, service delivery, and operational improvements.

                          They also provide superior customer experience. From instant account servicing to personalised financial recommendations, Agentic AI delivers the speed, personalisation, and convenience customers expect. Plus, it scales exponentially. No matter how many people call in at the same time, an agentic agent will answer immediately. Agentic AI reduces up to 86% of time spent in complex workflows that were traditionally handled only by people. This will be huge in getting ahead of your competition. 

                          5. Build Momentum Through Internal Champions

                          Adoption increases when respected leaders advocate from within. Mid-level managers, AI-literate staff, or members of the C-suite who understand the technology can serve as champions. Use them and their beliefs to drive alignment, communicate benefits, and counter misconceptions. The more people from different departments and levels of the organisation that talk up the technology, the more likely you are to get buy-in. 

                          Your Time is Now

                          Agentic AI will redefine financial services. The organisations that act today will build capabilities, insights, and competitive advantages that late adopters will not be able to replicate. Finance leaders must begin asking where agentic AI can support their business, where it can remove friction, where it can unlock growth, and where it can transform operations. The firms that act now will lead the industry. Those that hesitate will not get the chance to catch up.

                          The only remaining question for finance organisations is not whether agentic AI will change the industry, but how quickly they choose to deploy it.

                          Learn more at quant.ai

                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Payments
                          • Digital Strategy

                          Author

                          Richard Ford

                          Published

                          20 March 2026

                          Estimated Read time

                          3Mins

                          Richard Ford, Chief Technology Officer at Integrity360, on why cybersecurity must move beyond control and embrace trust

                          Cybersecurity has long been focused on building walls, but the biggest threat is already inside. Today, insider risk accounts for nearly half of all data breaches. This isn’t just about malicious actors, it’s about regular employees and trusted contractors who make simple, costly mistakes.

                          Remote and hybrid working has only intensified the problem. With teams distributed and work happening across cloud platforms and collaboration tools, it’s harder than ever to track what’s happening, let alone why. Although AI tools promise efficiency, they also introduce new vulnerabilities. Employees pasting code into chatbots or bypassing corporate tools to meet deadlines. All seemingly innocent, but highly risky.

                          Insider Risk

                          Ransomware gangs know this and are now skipping the technical breach altogether and going straight to the source – a company’s insiders. Whether through bribery or social engineering, attackers are finding that humans can be the weakest link in even the most well-defended environments. Despite this, most security budgets still focus outward.

                          Traditional tools like data loss prevention (DLP) struggle to keep up with today’s dynamic and unpredictable user behaviour. Meanwhile, simulated phishing tests and punitive training schemes often breed resentment, not resilience. It’s time to rethink the model.

                          Human Error, Human Fix

                          We need to stop treating employees as the problem and start making them part of the solution. Enter Human Risk Management (HRM), a behavioural approach to cybersecurity that recognises the complexity of modern work. HRM tools monitor real-world user behaviour, detect anomalies in context, and deliver just-in-time nudges to prevent risky actions before they happen. Instead of punishing mistakes, they help users avoid them in the first place.

                          Of course, technology alone won’t fix the issue, culture is key. Leadership must champion security as a shared responsibility, not an IT rulebook. Success should be measured by how quickly employees improve, not how often they slip up. Awareness campaigns need to be practical and rooted in real-world behaviour.

                          Organisations also need to understand how digital transformation has changed the risk landscape. Shadow IT is no longer a fringe issue, it’s how work gets done. Whether it’s a developer using an AI plugin or a marketer sharing files via a personal drive, employees will always find the fastest path to productivity. Security must meet them there, not block the way.

                          Cybersecurity Built on Trust

                          The smartest businesses are those that treat identity like infrastructure, and behaviour like a vital data stream. They invest in tools that adapt to people, not the other way around. This means a move away from a surveillance approach and embracing the nuance of human error and design systems that support.

                          In a world where threats are increasingly internal and AI is both a risk and a tool, cybersecurity can no longer be about control. It must be about trust, and that starts with understanding the humans behind the keyboards.

                          Learn more at integrity360.com

                          • Cybersecurity
                          • Cybersecurity in FinTech
                          • Digital Strategy
                          • Infrastructure & Cloud

                          Author

                          Pierre Noel

                          Published

                          Estimated Read time

                          4Mins

                          Pierre Noel, Field Chief Information Security Officer at Expel, on why security with community-based governance is a key business pillar that better positions organisations to become more resilient and target growth

                          It’s been a particularly rocky start to 2026 for the global cybersecurity landscape. From the Substack data breach to PayPal credential-stuffing attacks in February, we are not looking at IT failures alone. These attacks are balance-sheet events: direct assaults on business value, triggering remediation costs and long-term impacts on financial health. Compounded with the conflict with Iran, leading to potential ramifications in the cyber realm, it’s more important than ever for the C-suite to be aligned on cybersecurity priorities.

                          Despite this, a glaring disconnect remains in planning and execution. Expel’s research found that while 85% of finance leaders view cybersecurity as a key component of business planning, only 40% express full confidence in security’s ability to align with business strategy. To bridge this gap, CISOs must move from reporting on activity and start reporting on resilience and unit cost.

                          Translating Alert Volume Into Unit Cost

                          CISOs must change how they present the value of their operations. CFOs are largely indifferent to technical metrics like the ‘millions of blocks pings’ or ‘SOC alert volume’ – to a finance leader, an alert is simply another form of disruption to daily operations.

                          To fix this, CISOs should introduce the ‘unit of cost protection’. By breaking down security spend into the cost required for a single transaction or business unit, CFOs can understand and manage it from experience. A tiered approach works best here: high-risk business units justify higher protection costs than low-risk ones. This allows CFOs to treat security as a scalable operational expense rather than a black hole of additional tooling – the kind of framing that also resonates in a boardroom.

                          Mapping Investment to Business Risk Exposure

                          Expel’s research shows that while 43% of finance decision-makers are confident that security can prioritise investments based on risk, only 46% are confident that security can deliver cost-efficient solutions. To move in the right direction, CISOs should shift from ‘vulnerability management’ to thinking about ‘business risk exposure’, requiring a different view of how threats unfold over time.

                          It’s all about asking the right questions. Instead of requesting more firewalls to protect a specific timeframe, start asking for the cost of securing diverse digital ecosystems across an extended risk window. The 2026 Winter Olympics is a good example: Russian-led cyber campaigns began raising concerns months before a single athlete arrived in Italy, proving that risk isn’t a one-day event but an ongoing operational cost.

                          For European organisations, this framing is increasingly non-negotiable. While NIS2 and DORA help make the cost of under-investment concrete and quantifiable, the upcoming Cyber Resilience Act (CRA), with key reporting requirements starting in September 2026, extends this pressure to anyone manufacturing or selling digital products in the EU. Even for purely domestic UK entities, the new UK Cyber Security and Resilience Bill is moving the goalposts toward these same high standards. Ultimately, CFOs must understand that cybersecurity isn’t just about preventing loss; it’s a prerequisite for safe and secure growth.

                          The Reputational Multiplier

                          So those are the questions to ask, but how do CISOs deal with the ‘unknown unknowns’, specifically long-term brand damage? While compliance fines under NIS2 or DORA may be straightforward (and important) to model, they rarely represent the full scope of the potential damage. In such scenarios, CISOs should propose a reputation multiplier: a framework for quantifying the financial fallout of brand damage in a language CFOs know and trust, looking past immediate recovery costs to factor in the long-term implications of re-establishing market trust.

                          The 2026 CarGurus breach illustrates this well. Impacting 12 million users, the cost wasn’t purely technical; it also came from the stock price dip and marketing spend required to repair the brand. For UK companies, where regulatory scrutiny is heightened, that multiplier effect is even more pronounced. This is the language of a CFO, and it helps CISOs better translate the urgency and relevance of a strong cybersecurity posture.

                          Standardising the Language of ROI

                          Closing the gap between CFOs and CISOs needs more than just better data; it needs a shared vocabulary. By standardising the language of ROI, CISOs transform cybersecurity from a vague insurance policy into a transparent value driver fully trusted by finance teams. Move away from complicated defensive jargon toward a unified framework of unit costs, and the gap between the CISO and CFO starts to close.

                          Security has become a key pillar of business operations, and in the current threat environment, it’s genuinely a community-based governance issue. The organisations that get this right aren’t just more resilient. They’re better positioned to grow.

                          Learn more at expel.com

                          • Cybersecurity
                          • Cybersecurity in FinTech
                          • Digital Strategy
                          • Infrastructure & Cloud

                          Author

                          Dan Brightmore

                          Published

                          19 March 2026

                          Estimated Read time

                          5Mins

                          Radi El Haj, CEO of global payment technology provider RS2, on the shift toward programmable, data-driven financial infrastructure designed for a real-time global economy.

                          The financial industry is entering a new phase of infrastructure modernisation. While cryptocurrencies and blockchain continue to dominate headlines, a more pragmatic evolution is reshaping banking from within: tokenised deposits embedded directly into core payment systems.

                          Tokenised deposits represent a practical bridge between legacy banking infrastructure and next-generation real-time capabilities. Unlike crypto-native assets, they remain fully regulated bank liabilities. But with the added advantage of programmability, automation and real-time settlement logic built directly into the banking stack.

                          The recently launched Cari Network by five US regional banks signals this shift. It demonstrates how banks are beginning to rethink bank-to-bank payments and settlement at the infrastructure layer.

                          Moving away from batch-based clearing toward programmable, real-time funds allows institutions to enhance efficiency, reduce settlement risk and modernise core systems without stepping outside regulatory guardrails.

                          For banks re-entering acquiring, upgrading issuing, or consolidating fragmented payment systems, tokenised deposits represent not a parallel innovation, but an embedded evolution of core processing.

                          What Tokenised Deposits Really Change

                          At their foundation, tokenised deposits are digitally represented, insured banking liabilities. The critical distinction is that they remain part of the regulated banking system. This preserves trust, governance and compliance – the pillars that underpin institutional finance.

                          The transformational value lies in programmability at the infrastructure level. When business logic is embedded into the payment instrument itself, banks can automate reconciliation, conditional settlement, liquidity allocation and multi-party disbursements in real time. Instead of adding layers of complexity on top of legacy systems, tokenisation integrates intelligence directly into the core ledger and processing environment.

                          For banks operating across issuing, acquiring and cross-border settlement, this reduces friction between systems and enables a more unified, real-time operating model.

                          AI as the Operational Intelligence Layer

                          Tokenisation alone is not sufficient. To operate at scale, programmable deposits require an intelligent control layer. Artificial intelligence and machine learning provide that layer.

                          AI can forecast liquidity requirements across issuing and acquiring portfolios, optimise routing decisions in real-time, detect anomalous behaviour and automate compliance monitoring. As transaction volumes increase and settlement windows compress, predictive intelligence becomes critical to maintaining resilience and control.

                          In multi-bank or multi-ledger environments, AI also plays a key interoperability role. It reconciles cross-network flows, identifies bottlenecks and dynamically allocates capital across settlement channels. This is where infrastructure maturity matters most.

                          Banks that embed AI-driven operational intelligence directly into their payment processing architecture will be best positioned to deliver speed without sacrificing stability.

                          Infrastructure is the Real Challenge

                          Tokenised deposits are not a feature — they are an architectural shift. Scaling them requires cloud-native, modular processing platforms capable of integrating with existing payment rails, regulatory frameworks and cross-border networks. Without modern infrastructure, tokenised deposits risk remaining contained pilots.

                          For banks modernising acquiring or issuing capabilities, this becomes a broader transformation programme: upgrading the core ledger, harmonising payment rails, embedding real-time analytics and ensuring seamless interoperability across domestic and international networks.

                          This is where strategic infrastructure partners become critical. Institutions need platforms that combine:

                          • Cloud-native core processing
                          • Real-time settlement capabilities
                          • Embedded AI-driven monitoring and liquidity management
                          • Open APIs for cross-network interoperability
                          • Regulatory-grade resilience and auditability

                          Only then can tokenisation move from concept to scalable reality.

                          Embedding Innovation Into the Banking Ecosystem

                          Payments innovation succeeds when it strengthens the banking ecosystem rather than bypassing it. Tokenised deposits offer a path to modernisation that reinforces trust, compliance and regulatory clarity. When combined with AI-enabled operational intelligence, they create a responsive infrastructure capable of supporting real-time commerce, embedded finance and cross-border settlement at scale.

                          Institutions that approach this as an integrated infrastructure strategy — rather than a point solution — will see the greatest impact:

                          • Reduced settlement risk through programmable fund controls
                          • Improved liquidity optimisation across issuing and acquiring flows
                          • Real-time fraud detection powered by AI
                          • More efficient cross-border routing and capital allocation

                          This is not about replacing banking infrastructure. It is about rebuilding it intelligently.

                          The Next Phase of Real-Time Banking

                          The launch of initiatives like the Cari Network signals the beginning of a broader industry evolution. As tokenised deposits mature, collaboration between banks, FinTechs and technology providers will determine how effectively they scale.

                          The strategic question for banks is not whether tokenisation delivers value — it is whether their infrastructure is prepared to support it.

                          Those that invest in modern, modular processing platforms capable of integrating tokenisation and AI at the core will establish long-term competitive advantage. They will move beyond incremental upgrades toward a unified, intelligent payments architecture.

                          Tokenised deposits represent more than a technological enhancement. They reflect a shift toward programmable, data-driven financial infrastructure designed for a real-time global economy.

                          For banks and technology providers operating at the intersection of issuing, acquiring and settlement modernisation, this marks the beginning of a new era — one defined not simply by faster payments, but by smarter, more resilient infrastructure.

                          About RS2

                          RS2 is a leading global provider of payment technology solutions and processing services, offering a unified approach to managing payments across all channels for banks, integrated software vendors, payment facilitators, independent sales organizations, payment service providers, and businesses worldwide. RS2’s platform stands out as a robust cloud-native solution designed for both issuing and acquiring operations. With its advanced orchestration layer seamlessly integrating all aspects of business operations, clients gain access to comprehensive analytics, reporting tools, and reconciliation features. This empowers businesses to effortlessly expand their global footprint through a single integration, while also gaining valuable insights into payment processes and customer behavior, enhancing operational efficiency, increasing conversion rates, and driving profitability.

                          Learn more at RS2.com

                          • Artificial Intelligence in FinTech
                          • Blockchain & Crypto
                          • Digital Payments
                          • Embedded Finance

                          Author

                          Dr. Yvonne Bernard

                          Published

                          18 March 2026

                          Estimated Read time

                          5Mins

                          Dr. Yvonne Bernard, CTO at Hornetsecurity, on meeting the challenge of managing the speed of AI adoption and harnessing its defensive capabilities while mitigating the risk of uncontrolled adoption

                          The past year has been defined by acceleration. Threat actors rapidly embraced automation, AI, and social engineering. Scaling their tactics at unprecedented speed, while defenders raced to keep pace. Historically, defensive resilience evolves in step with attacker innovation, but in 2025 that balance began to falter.

                          In an analysis of over 6 billion monthly emails, Hornetsecurity’s Security Labs found that the volume of sophisticated threats grew faster than most security teams could adapt to. Malware-infected emails soared by 131%, scams increased by nearly 35%, and phishing attempts – powered by access to advanced AI – rose by 21% from the previous year.

                          Typically, attacks, even at volume, are easily filtered by good firewalls and secure email gateways. But the sophistication and AI-led nature of 2025’s boom made it even harder for organisations to defend themselves. The question now is: can security teams and businesses wrestle back control?

                          Evolving Cyberattack Landscape

                          ​​AI enhances efficiency and precision. As such, cybercriminals use it to launch faster, more convincing and adaptive attacks, ranging from deepfakes to credential stuffing. As an example, there is a concerning trend of attackers increasingly using ‘MFA bypass kits’ to create deceptive login pages. These pages capture not only the user’s credentials but also have logic built in to handle MFA prompts as well. ​​The unsuspecting user is then passed to the real login page for the target service and meanwhile the ‘kit’ grabs a copy of the user’s session token. This allows the attacker to impersonate the person and access their data. ​​​​​

                          Examples of such kits include Evilginx (open source) and the W3LL panel. Protecting against these attacks can be challenging, as they are adept at bypassing MFA safeguards. Threat actors often use compromised LinkedIn accounts, for example, to gain access to substantial information and connections. This enables them to impersonate trusted business connections. Paired with the weaponisation of Agentic AI, this will magnify existing vulnerabilities within an organisation, while introducing new ones that defy traditional containment models.

                          As it stands, the lack of oversight within organisations on the extent of AI’s adoption by cybercriminals has enabled the emergence of ‘Ransomware 3.0.’ Ransomware has evolved past simple encryption and exfiltration, with this next phase focusing on LLM-driven orchestration and a shift to data integrity manipulation.

                          To counter AI-accelerated compromises and ‘Ransomware 3.0’ in 2026, organisations must adopt a Zero Trust-based cyber resiliency strategy. This requires businesses to implement strong, non-phishable machine authentication, strict least-privilege access, and constant monitoring to protect the integrity of the data that users and AI agents can access. It should become the baseline expectations rather than aspirational goals for this year.

                          The Secret Value of ‘Least Privilege’ Access

                          Another strategy to proactively improve cybersecurity defences in 2026 is to enforce the principle of ‘least privilege’ access. This tactic grants users access only to the data that’s needed for their role. Limiting excessive access is important for preventing the potential for widespread data exposure and damage in the case of an account compromise.

                          Businesses, however, must strike a balance over access; if it’s too strict, it can hinder productivity and lead to shadow IT issues. Getting this balance right when it comes to privileged access is where sophisticated permission managers are invaluable tools to work with. They streamline the process and remove the guessing game of who and what to grant access to, thereby ensuring, in the case of an attack, that the entire organisation won’t be brought to its knees.

                          How CISOs are Adopting ‘Resilience, not Perfection’

                          The rate at which AI is advancing means not every organisation will be equipped with the tools or the know-how to tackle every AI-inspired attack. But as the saying goes, ‘prevention is better than cure’. It’s better to create a strong security culture than to continually chase after the next best tool. 

                          Organisations can’t strengthen their resilience without involving every single person under their umbrella. That’s why CISOs must continue to invest in cybersecurity awareness programs.

                          These should include simulated AI-phishing attacks (phishing remains the number one attack vector) to test users and enable them to apply learnings from the modules.

                          If any user clicks on a phishing email, they should receive additional training at that very moment, to cement the learning. Over time, a good training system should automatically identify users who rarely fall for such attacks and reduce the training they receive while making the simulations they do receive more difficult. Conversely, giving persistent offenders additional bite-sized training and simulations can help improve security outcomes over time.

                          The key challenge for 2026 is managing the speed of AI adoption and harnessing its defensive capabilities while mitigating the risk of uncontrolled adoption. But with excellent training, cyberattack practice runs, and the adoption of Zero Trust principles, organisations will find themselves in a strong position.

                          About Dr. Yvonne Bernard

                          Dr. Yvonne Bernard is the CTO of Hornetsecurity by Proofpoint, Proofpoint’s business unit leveraging the Hornetsecurity product suite dedicated to managed service providers (MSPs) and small to mid-sized businesses (SMBs), providing next-generation cloud-based security, compliance, backup, and security awareness solutions that help companies and organisations of all sizes around the world.

                          Learn more at hornetsecurity.com

                          • Cybersecurity
                          • Cybersecurity in FinTech
                          • Data & AI
                          • Digital Strategy

                          Author

                          Dr Megha Kumar

                          Published

                          17 March 2026

                          Estimated Read time

                          6Mins

                          Dr Megha Kumar, Chief Product Officer and Head of Geopolitical Risk at CyXcel, on whether our risk and regulatory frameworks and institutional cultures can keep pace with Agentic AI

                          Within the next couple of years, Agentic AI is likely to progress from early stages of operation to be fully embedded within systems. Its expansion will be subtle rather than spectacular. It will integrate steadily into enterprise platforms, logistics networks, compliance workflows, cybersecurity operations centres and executive decision-support tools. Processes will move faster, operating expenses will decline and performance indicators will trend upward.

                          Yet these visible improvements mask a deeper challenge. The regulatory exposure, data governance pressures and erosion-of-trust risks associated with Agentic AI are being misjudged.

                          Unlike earlier AI applications designed primarily to generate outputs – whether text, imagery, or predictive insights – agentic systems are built to act. They sequence decisions, draw from multiple data environments, initiate consequential processes and function at scale with differing levels of human supervision. In sandbox environments this can seem contained and controllable. Over extended periods in live environments, however, sustained oversight, traceability and effective governance become significantly more complex.

                          Evolving Operational Complexity

                          There are two key challenges that businesses must address.

                          First, how do organisations monitor what agentic systems are doing once deployed? These systems evolve through updates, integrations and retraining and they interact with new data environments.

                          Second, how do you ensure responsible behaviour throughout the lifecycle? Regulators, policymakers and customers will likely expect firms to shift from compliance assurance to risk assurance and demonstrable evidence of trust and transparency.

                          The prevailing assumption is that human oversight will mitigate these risks. Human in the loop or human over the loop has become the default reassurance. In practice, however, that assumption breaks down far faster than many anticipate.

                          When a system works 95 per cent of the time, human reviewers limit their scrutiny. Behavioural science tells us that automation bias and complacency occur when automated systems are high-performing. Employees often become validators of AI outputs rather than critical examiners. The diligence gap widens gradually and then suddenly.

                          Facing Up to Difficult Questions

                          How do you incentivise employees to remain diligent checkers when the system mostly ‘works’?  And how much time does effective oversight actually require? True review is not a cursory glance at a dashboard. It involves interrogating assumptions, validating inputs, checking context and assessing downstream consequences. In many cases, meaningful oversight may take nearly as long as performing the original task manually. When checking becomes more costly than doing the job yourself, pressure to ‘trust the system’ intensifies.

                          And what happens to accountability when oversight exists on paper but not in practice? Governance documentation may show layered review structures, escalation pathways and audit processes. Yet if humans are functionally disengaged, responsibility becomes dispersed. When errors surface, organisations may struggle to attribute fault – was it the model design, the data, the integrator, the operator or the reviewer who signed off without fully scrutinising?

                          Regulators are only beginning to grapple with these realities. In jurisdictions such as the European Union, the EU AI Act introduces risk-based obligations, documentation requirements and human oversight provisions. These are important steps, however, the operationalisation of those requirements in dynamic, agentic environments remain untested at scale. Compliance on paper will not automatically translate into resilient governance in practice.

                          Addressing the Trust Challenge

                          Beyond regulatory exposure, there is a broader trust challenge emerging.

                          As Agentic AI systems scale across industries, they will generate vast volumes of automated outputs – reports, communications, risk assessments, content, decisions and transactions. If errors or manipulations spread through interconnected systems, confidence in digital outputs may erode.

                          In geopolitically sensitive contexts, this has profound implications. Agentic systems interacting with external data sources could amplify disinformation, introduce biased datasets or make decisions based on manipulated inputs. The speed of automation may outpace the speed of verification. Trust, once diluted, is difficult to restore.

                          Data protection risks will also intensify. Agentic systems frequently require broad access privileges to perform tasks effectively. They may access internal databases and personal data and interact with third-party platforms. Each interaction creates potential exposure points. A single misconfiguration or prompt injection attack could trigger cascading consequences across systems.

                          The next phase of AI adoption will not simply amplify productivity: it will amplify regulatory, legal and reputational risk. This moment therefore demands serious scrutiny before agentic AI becomes deeply embedded in business infrastructure.

                          The Moment for Action has Arrived

                          So, what should organisations be doing now?

                          To begin with, organisations need to look past superficial, tick-box compliance. Effective governance cannot live solely in policy documents – it must function in day-to-day operations. This means investing in continuous monitoring capabilities, robust audit trails and real-time anomaly detection tailored specifically to Agentic AI behaviours.

                          In parallel, incentive structures should be redesigned. Meaningful human oversight will not happen if it is treated as secondary to speed or output. If employees are expected to provide meaningful review, organisations must allocate time, training and authority accordingly. Performance metrics should reflect risk management responsibilities, not just output rate.

                          Clear lines of accountability are equally important. Senior leadership and boards should determine who carries ultimate responsibility for outcomes produced by agents. Where third-party vendors are involved, responsibilities must be contractually and operationally defined. Incident response mechanisms should be rehearsed in advance, rather than presumed to work when pressure is high.

                          Expertise must also be integrated across functions. Legal, risk, compliance, cybersecurity, data protection and operational teams should be engaged from the outset. Deploying Agentic AI is not simply a technical upgrade – it reshapes the organisation’s risk profile.

                          Finally, resilience demands deliberate stress-testing. Leaders should examine not only pathways to success but how models fail at scale. How would the organisation respond if a system update embedded systemic bias, if an integration vulnerability enabled unauthorised activity or if automated actions eroded customer confidence? Rigorous scenario exercises, however uncomfortable, are essential to building genuine preparedness.

                          As Agentic AI advances, Risk Management Should Match its Pace

                          None of this is an argument against adoption. Agentic AI presents meaningful productivity improvements and the potential for sustained competitive differentiation. Organisations that deploy it with discipline and foresight may secure a measurable advantage. The danger lies not in adoption itself, but in pursuing acceleration without knowing the risks and putting the right guardrails in place.

                          The coming two years are critical for businesses. Before these systems become deeply embedded in core processes, organisations have an opportunity to shape the control environment around them.  However, once agentic systems are fully embedded, retrofitting controls will be far more difficult and costly. Leaders must therefore treat this period as a design phase for oversight, not merely a race for competitive advantage.

                          Agentic AI is advancing rapidly. The defining question is whether our risk and regulatory frameworks and institutional cultures can evolve just as quickly.

                          Learn more at cyxcel.com

                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Strategy

                          Author

                          Fayola-Maria Jack

                          Published

                          16 March 2026

                          Estimated Read time

                          6Mins

                          As companies pour billions into developing their own AI tools, Fayola-Maria Jack, Founder and CEO of Resolutiion, argues that many are forgetting what worked well in the early tech era, confusing ownership with innovation

                          Back in the very early days of computing, organisations rarely hesitated to buy the hardware and software they needed to modernise. Now we’re deep into the AI age. Many organisations are deciding the best approach to adopting the technology is to take building it into their own hands. 

                          Many of the more traditional companies, like big banks, have publicly stated that they’re developing their own AI tools in house. Meanwhile, corporate investment in AI reached £191 billion ($252.3 billion) in 2024 and is only likely to have risen since.. 

                          Yet, the challenges of internal AI development are becoming abundantly clear. A recent report from MIT found that 95% of AI pilot projects failed to deliver any discernible financial savings or uplift in profits. It also found companies purchasing AI tools succeed about 67% of the time. Meanwhile, internal builds succeed only one-third as often.

                          Why do companies feel they need to build their own AI tools?

                          Those statistics alone show buying AI from specialised vendors and building partnerships is often the wiser choice. But, with a handful of traditional businesses deciding to lean the other way, it begs the question: why are these companies not only initially choosing the in-house route, but also persisting with it despite low success rates? 

                          The instinct to ‘build’ is rooted in legacy thinking – and to some extent, a naivety around what makes AI solutions special. Traditional enterprises have long equated ownership with control: control over systems, data, and perceived competitive advantage. 

                          When AI entered the scene, many executives applied that same logic, assuming that building in-house equated to ownership, at the heart of innovation. But this overlooks a fundamental truth that is unique to AI – AI isn’t another IT system you can own and stabilise. It evolves exponentially, not linearly. It demands constant retraining, rapid iteration, and deep specialisation – all at odds with the traditional corporate IT environment, which is built for stability and compliance, not experimentation and speed. 

                          Are companies really investing in innovation?

                          Another common belief is that buying is seen as conceding leadership to outsiders. While building feels safer politically, signalling ‘we’re investing in innovation’. Ironically, though, that safety is often an illusion that leads to slower progress and higher long-term cost. But again, there is deep irony if talent is outsourced to India, or another foreign jurisdiction, on the basis of cheap labour.

                          The exact same dynamic plays out internally, too. AI initiatives are career-defining projects for senior technology leaders and they attract budget, visibility, and prestige. Once a build programme is launched, it’s politically difficult to pivot, even in the face of poor performance. As a result, the build strategy often survives by narrative rather than by evidence.

                          Underpinning all of this is the institutional belief that ‘our data is unique’ – that their data will deliver proprietary insight and competitive advantage. In reality, most internal data is messy, siloed, and outdated. It reflects years of practices that are often misaligned with best practice, and therefore should never be used to train AI. Instead of building capability, many organisations end up building complexity. 

                          Increased Caution in Regulated Sectors

                          Alongside these misbeliefs, regulatory caution and data residency also play into the decision to build in-house; especially in regulated sectors like finance, healthcare, and government. Here, enterprises typically believe that adopting third-party AI tools may expose sensitive data to external environments they cannot fully control. Perhaps this is because data protection laws have created a heightened sensitivity to where data is processed and how it’s used to train models. 

                          Take banks as an example – historically they have viewed data as a fortress, a core asset to be guarded. Their culture of confidentiality and regulation makes them instinctively cautious about sharing information externally. Add to this the fact that large banks already have substantial internal technology infrastructures and budgets, and building seems logical on paper. The truth, however, is that building internally doesn’t eliminate compliance risk, but often amplifies it. This is because companies take on the burden of securing systems, updating controls, and managing ethical frameworks themselves.

                          On the other hand, buying from specialist providers means adopting a system that’s been engineered for compliance at scale. Purchasing doesn’t dilute compliance, it accelerates it, because you inherit the expertise and validation of teams who do this. In fact, most reputable AI vendors now far exceed enterprise compliance standards, designing privacy-preserving architectures that mitigate these risks far more effectively than in-house teams can, full-time.

                          Competitive Edge

                          The financial sector’s competitive edge increasingly lies not in owning the algorithms, but in applying them better and faster. Challenger banks and fintechs have embraced this: they buy tools (whereby anti-money-laundering and fraud detection platforms are incorporated into model-risk management protocols aligned with regulatory expectations), they integrate, and they move rapidly. Traditional banks, by contrast, are still in a transitional mindset, modernising legacy systems while trying to preserve control. That’s why their build programmes are often more about transformation theatre than tangible AI capability, and will ultimately see them fall further behind.

                          Underestimation of AI’s Lifecycle Cost 

                          Beyond the issues of legacy thinking, poor data quality and compliance risk, companies attempting to build in-house also face a number of additional challenges when it comes to the talent, time, and technical debt needed. 

                          • Talent: True AI expertise is scarce and expensive. Competing with the open market for top data scientists and ML engineers is unsustainable for most enterprises. 
                          • Time: AI doesn’t stop evolving while your internal team builds. By the time a prototype is ready, the underlying technology stack may have already advanced. 
                          • Technical debt: Maintaining models, retraining on new data, and ensuring explainability and auditability over time all demand continuous investment. 

                          Most companies underestimate this lifecycle cost by an order of magnitude. Add to that the reputational risk of bias or error (especially when deploying AI in customer-facing contexts) and the true cost of internal builds can spiral quickly.

                          A Change in Mindset is Needed 

                          As more of these challenges surface, we should see an uptick in companies moving towards buying AI rather than building it – and it’s a pattern that’s thankfully already emerging. As AI becomes infrastructure, not novelty, enterprises will mirror the software evolution of the 1990s and 2000s: moving from bespoke builds to modular adoption. 

                          The early adopters that buy today will pull ahead dramatically because they can focus on application and differentiation, not on maintenance. In time, the ‘build’ approach will be seen much like writing your own word processor in 1995: a costly distraction from real innovation. 

                          Organisations need to shift from ownership to orchestration. This requires humility, recognising that innovation now happens outside corporate walls, and confidence – trusting that your value lies in how intelligently you deploy technology, not in whether you wrote its source code. Culturally, companies need to redefine ‘strategic advantage’ as agility plus insight, not possession plus control. AI isn’t an asset you own; it’s a capability you cultivate.

                          In simpler terms, the companies that thrive in the AI age will be those that treat AI as an ecosystem, not an ‘ego system’. 

                          Learn more at resolutiion.com

                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Strategy

                          Author

                          Laurent Descout

                          Published

                          Estimated Read time

                          4Mins

                          With AI adoption booming across financial services, Laurent Descout, CEO and co-founder of Neo, explores why the technology has emerged at the right time for payments and what it signals for the industry’s future

                          The payments industry is undergoing significant transformation as instant payments become the global standard and ISO 20022 reshapes financial messaging. For financial institutions, these changes are creating a more complex operating environment that demands new approaches to managing payments.

                          Payments have long been the bedrock of financial systems. Allowing businesses and individuals to pay for the goods and services that keep the world’s economy moving. 

                          Over time, the processes by which payments operate have evolved. From the first wire transfers in the 19thcentury to credit cards in the 1950s. Smartphones and the internet then took payments digital, with online banking, mobile wallets and pay by phone following suit. 

                          Now, the next wave of innovation is beginning to reshape how payment systems operate. With 62% of global organisations reporting that they are at least experimenting with AI agents. The payments industry is no exception. From processing transactions and powering chatbots in fraud detection and compliance, AI is commercially imperative for banks of all sizes.

                          Emerging at a Pivotal Moment

                          The growing adoption of AI coincides with global efforts to speed up and streamline payments. Instant payments are fast becoming the global standard.

                          This shift has been driven by consumer and business expectations, but also by changing regulations. In Europe, the Instant Payments Regulation in 2025. And most recently, SWIFT’s migration to ISO20022 has driven changes in the way payments are processed. 

                          ISO 20022 introduces a single, data-rich standard for financial messaging. Under SWIFT’s migration, which reached a major milestone in November 2025 with the end of the coexistence period, payments can now carry more detailed and consistent information. 

                          This reduces ambiguity and improves data quality throughout the transaction lifecycle. This makes payments easier to track, reducing delays and increasing customer confidence. 

                          Under the upcoming November 2026 deadline, support will end for unstructured messages and retire MT101 request-for-transfer messages. Leaving only structured and hybrid formats. ISO 20022 also underpins modern payment rails and APIs. This enables banks and PSPs to offer instant payments and embedded payment services within corporate workflows.

                          Furthermore, there are initiatives from SWIFT like SWIFT Go, SWIFT Pre-Validation and SWIFT Case Management. These focus on making payments less frictional, more transparent and faster at post-issuing resolution. 

                          What AI Brings to the Table

                          Against this backdrop of growing complexity, AI offers financial institutions powerful tools to automate processes, improve decision-making and reduce operational friction. All of these are key attributes for monitoring payment systems and have the potential to speed up processes end-to-end. 

                          One of its core applications in payments is applying machine learning to navigate the complex web of global payment rails, completing transactions more efficiently and with fewer errors.

                          It also unlocks more advanced capabilities from enriching payment messages, forecasting cash flow, identifying liquidity gaps, and optimising reconciliation processes.

                          Crucially, AI helps PSPs cope with the increasing complexity of regulation. From document analysis to compliance reporting, AI can scan thousands of pages in seconds, extracting relevant insights and streamlining manual workflows.

                          This means the end of endlessly searching through paperwork for small details, which can feel like searching for a needle in a haystack.

                          Advanced AI models can even write and test code, accelerating product development, reducing time-to-market, and making system updates faster and more cost-effective.

                          Fraud Protection

                          In addition to promoting the good, AI in payments also keeps out the bad.

                          In the fight against fraud, AI can recognise suspicious patterns and use algorithms trained on historical data to flag anomalous transactions.

                          This monitoring occurs in real-time, allowing for immediate action to prevent losses and incorporating machine learning models to reduce the likelihood of false positives.

                          Importantly, these checks happen silently and frictionlessly, with no disruption to legitimate users or the payment system.

                          It’s unsurprising, therefore, that 90% of financial institutions are using AI for fraud prevention strategies.

                          Striking the Right Balance

                          However, AI is not a silver bullet for the payments industry, and when poorly implemented, it can be ineffective or introduce new risks.

                          To avoid this, PSPs should reset their expectations and strategically evaluate the most effective use cases for implementing AI, such as streamlining document analysis or automating repetitive tasks, rather than applying it indiscriminately across all areas of business.

                          AI implementation should be piloted in high-impact areas like fraud detection, compliance automation, and liquidity forecasting, while ensuring it is coupled with robust governance.

                          A Catalyst for Progress

                          As AI continues to make payments faster and safer, payment institutions should look towards fintech for best practices on how to integrate AI into systems strategically and cost-effectively. Many of these smaller firms continue to outperform larger banks with AI innovations thanks to their agility. 

                          Learn more at getneo.com

                          • Artificial Intelligence in FinTech
                          • Digital Payments
                          • Embedded Finance

                          Author

                          Chris Larsen

                          Published

                          13 March 2026

                          Estimated Read time

                          4Mins

                          Chris Larsen, Chief Technical Officer – atNorth, on shaping ecosystems that support both digital progress and the preservation of our natural environment for future generations

                          The AI industry continues to grow seemingly exponentially. With 92% of companies planning to increase their AI investments in the next three years, demand for the high density digital infrastructure required to support these types of workloads is unsurprisingly at an all time high.

                          Data centres have always needed a significant amount of electricity to power and cool their computer equipment. Yet the sheer quantity of data to be processed for AI and other high performance computing – such as financial trading calculations and simulation technologies – necessitates a colossal amount of energy. For example, a report from the International Energy Agency states that data centres will use 945 terawatt-hours (TWh) in 2030, roughly equivalent to the current annual electricity consumption of Japan.

                          At the same time, there is growing pressure for all organisations to comply with ESG frameworks. The introduction of regulations such as the EU’s Corporate Sustainability Reporting Directive (CSRD), mandates the publication of carbon footprint disclosures. This leaves many businesses with a difficult conundrum to solve – how to balance digital advancement whilst mitigating environmental impact?

                          Once a consideration for local IT teams, the choice of a data centre partner is now at the forefront of balancing these two critical trends and is beginning to garner boardroom attention.

                          Data centres that are designed with environmental responsibility and community integration in mind can act as the central hub of a thriving society, an ‘ecosystem’ that supports long-term sustainability and regional economic development.

                          Location and Design

                          Where a data centre is built, and how, is fundamental to its efficiency and sustainability. AI-ready facilities often require rapid scaling in line with customer demand. Access to ample suitable land is essential. Modular designs allow for faster builds and easier adaptation to new innovations in cooling and hardware technologies,

                          Power and connectivity are also critical. Many regions struggle to offer the necessary renewable energy and high-speed network capacity. In contrast, the Nordics provide an ideal environment. An abundance of renewable energy, a cool natural climate that enables more energy efficient cooling techniques and excellent connectivity.

                          As a result, the presence of data centres can promote local investment in power, connectivity and electrical infrastructure that benefits the whole community. For example, atNorth’s ICE03 data centre in Akureyri, Iceland, facilitated the development of a new point of presence (PoP) for Farice, which operates submarine cables linking Iceland to mainland Europe. This enhances telecom reliability and strengthens digital infrastructure across the region.

                          Data centres can also support the stability of local power through grid balancing services. Something that is integral to the future design of atNorth’s data centres.

                          Decarbonisation and Circular Partnerships

                          Data centres are incredibly energy-intensive, and so many operators are investing in ways to reduce their carbon footprint. These include utilising the most efficient infrastructure and cooling technologies.

                          atNorth goes one step further and has committed to sourcing heat reuse partnerships for all of its new data centre campuses. This means that waste heat generated during the infrastructure cooling processes can be captured and redirected to support nearby businesses and homes. In Finland, for example, a partnership has been formed with Kesko Corporation that will utilise waste heat from atNorth’s new FIN02 campus to heat a neighbouring branch of one of its stores.

                          These types of initiatives essentially enable data centres to act as a decarbonisation platform for their clients’ IT workloads, helping them meet environmental targets and reducing running costs too. Something that is a key differentiator for businesses such as atNorth client and partner, Nokia, that has complex technical requirements and stringent sustainability goals.

                          Responsible Operations

                          Beyond environmental responsibility, data centres can be a positive force in the communities in which they operate. They create skilled jobs, drive improvements in local infrastructure, and often spark growth in hospitality, retail, and leisure services. At atNorth, we prioritise hiring locally and actively support education, charitable, and community initiatives in the regions we operate.

                          Similarly, a care for the natural surroundings is pivotal to promoting a successful, data centre ecosystem integration. For example, atNorth has set aside part of its DEN02 site in Denmark for biodiversity efforts, installing insect monitors to track changes in insect abundance and diversity throughout the site’s development.

                          As digital demand continues to grow, so does the need for responsible and sustainable development. High-performance computing can, and should, advance without compromising environmental integrity. By partnering with data centres that prioritise environmental stewardship and social responsibility, we can help shape ecosystems that support both digital progress and the preservation of our natural environment for future generations.

                          Learn more at atnorth.com

                          • Data & AI
                          • Digital Strategy
                          • Infrastructure & Cloud
                          • Sustainability Technology

                          Author

                          Dan Brightmore

                          Published

                          12 March 2026

                          Estimated Read time

                          3Mins

                          New research from Aqua Global shows banks are struggling to keep up with compliance, as legacy tech drags them down

                          Aqua Global, the financial messaging hub built for payments, treasury and securities processing, today revealed research showing European banks are prioritising compliance over customer experience as legacy infrastructure struggles to keep pace.

                          The survey of 150 European IT banking leaders, with half based in the UK, showed that:

                          • Regulation is putting a drag on innovation:
                            • 77% of respondents say regulatory demands outweigh customer demands when it comes to payment modernisation.
                            • 67% spend more effort adapting systems to new standards than improving customer experience.
                          • Banks fear missing milestones – but can’t keep up:
                            • 77% say missing a key regulatory milestone would cause significant operational and reputational damage.
                            • But 60% admit their existing infrastructure struggles to keep pace with evolving standards.
                          • Richer data requirements expose structural weaknesses:
                            • 72% admit richer data requirements (e.g. AML, sanctions, fraud) have exposed gaps in their current infrastructure.
                            • Structured addresses, AML/sanctions-related data and counterparty identifiers (BIC/LEI) are the most difficult piece of data to capture.

                          “The challenge with richer payment data isn’t availability, it’s fragmentation. Information sits across multiple systems and formats, making it hard to build a complete, trusted view of a transaction. The ability to manage, govern and validate data at scale is quickly becoming a defining factor in payments resilience. This is why 81% of respondents believe a unified messaging hub across multiple channels will be essential to remain compliant and competitive in the future.” Elliot Wood, Chief Technology Officer at Aqua Global.

                          ISO 20022 and T+1: Regulatory Compression Exposes Legacy Fragility

                          One in five respondents experienced downtime and/or payment disruption during migration to the new ISO 20022 standard. Almost all respondents (97%) experienced challenges, with the top three cited as:

                          1. Legacy systems unable to handle structured ISO 20022 data.
                          2. Poor underlying data quality for enriched ISO 20022 fields.
                          3. Integrating challenges with other third-party systems, such as AML, sanctions and fraud systems.

                          As a result, 65% still rely, at least in part, on translation tools to remain compliant, even though 83% believe such short-term fixes will prove more costly in the long run.

                          The same structural weaknesses are now surfacing in preparation for T+1 settlement. While 21% of banks have taken action to prepare, almost a quarter (23%) have no plans in place. Legacy systems incapable of supporting compressed settlement windows without significant investment remain the most cited barrier.

                          Together, ISO 20022 and T+1 highlight a broader issue: regulatory timelines are accelerating faster than banks’ infrastructure can adapt.

                          “The migration challenges we’re seeing aren’t isolated incidents – they expose the structural limits of legacy payment architecture,” says Cian Fernando, CEO of Aqua Global. “Treating regulatory change as a tick-box exercise encourages short-term fixes that increase complexity. Banks that modernise natively reduce cost, operational risk and friction over time. As regulatory deadlines tighten and data requirements grow richer, banks relying on fragmented systems face rising operational risk and mounting cost pressures, with less capacity left to compete on customer experience.”

                          To learn more download the full From Compliance Burden to Competitive Advantage report

                          About Aqua Global

                          For over 43 years, Aqua Global has delivered a robust suite of financial messaging and transaction automation solutions for payments, treasury, and securities processing that integrate internal systems to external services. Trusted by leading banks across 22+ countries, our Aquila orchestration and integration framework offers exceptional performance, control, and scalability.

                          Learn more at aquaglobal.co.uk

                          • Cybersecurity in FinTech
                          • Neobanking

                          Author

                          Nicole Reader

                          Published

                          Estimated Read time

                          7Mins

                          Nicole Reader, Head of Technology Solutions & Delivery at The Bunker (part of the Cyberfort Group), on finding a measured path forward for the future of cloud

                          For more than two decades, UK organisations have embraced the cloud as the default model for digital growth. Hyperscale platforms have offered flexibility, speed and a route to innovation that would once have required years of capital investment. Cloud first became the business mantra. Cloud native became the ambition. Few stopped to ask what this meant for long term control. Today that question is becoming unavoidable.

                          Geopolitical relationships are shifting at pace. Trade tensions, regulatory divergence and new data access laws are reshaping the digital landscape as quickly as any technological change. At the same time, businesses are generating and storing more information than ever before. AI tools, collaboration platforms and SaaS applications are accelerating data creation at a rate that is testing infrastructures, supply chains and budgets alike.

                          In that context, many UK organisations are starting to ask a difficult question. When we moved to the cloud, did we quietly export more control over our data than we realised? The uncomfortable answer in many cases is yes.

                          The Assumption of Cloud Control

                          A significant proportion of UK businesses rely on global services, whether hyperscalers such as Amazon Web Services and Microsoft Azure or SaaS platforms headquartered overseas. These providers are sophisticated, resilient and often highly secure. However, their global footprint means that data is frequently stored, processed or managed beyond UK borders.

                          The challenge is that many boards assume that if data is accessible from the UK, or if a provider has a UK presence, it remains firmly under UK control. This assumption is often incorrect.

                          There is a crucial difference between data location and legal jurisdiction. Data residency refers to where data is physically stored. Data sovereignty refers to which who ultimately governs access to that data. Those two concepts are not interchangeable.

                          Legislation such as the US Cloud Act demonstrates why this matters. Under certain circumstances, US authorities can compel US headquartered providers to provide access to data, even if that data is stored outside the United States. The geographic location of a data centre does not automatically determine who can lawfully demand access.

                          Boards often conflate these terms, believing that selecting a UK service resolves sovereignty concerns. In reality, the corporate structure of the provider, contractual arrangements and cross border processing activities can all shape the legal framework that applies.

                          This is not an abstract legal debate. It is a question of operational control, regulatory exposure and risk appetite.

                          The Convenience Compromise

                          The rise of public cloud was driven by many compelling advantages. Flexibility, scalability and rapid deployment transformed how businesses launched products and expanded into new markets. For many organisations, the cost of building and maintaining their own infrastructure was prohibitive and the hyperscalers offered an attractive alternative at a great price.

                          However, that convenience came with trade-offs that were not always fully understood at the time. Cloud contracts can be complex. Consumption based pricing models include ingress and egress charges. Including API calls and a range of ancillary costs that can quickly exceed initial forecasts. It is not uncommon for organisations to reach the midpoint of their financial year and discover their cloud budget has already been used.

                          Meanwhile, operational design decisions made years ago may not have been stress tested against today’s regulatory expectations or geopolitical realities. Many mid-market IT teams have spent the past decade maintaining estates rather than redesigning them. In some cases, institutional knowledge has not kept pace with the evolution of cloud services and their associated risks.

                          The result is a landscape in which data has been distributed widely, often for operational reasons, but without a holistic understanding of the sovereignty implications.

                          Repatriation is Not a Silver Bullet

                          In response, there has been a growing push towards data return and sovereign cloud offerings. European initiatives are seeking to create regional alternatives to US dominated platforms. In the UK, there have been calls by government to expand domestic data centre capacity to retain greater control over national data assets.

                          The instinct is understandable, particularly for government, defence and heavily regulated sectors where sovereignty can become a non-negotiable requirement. However, it would be naïve to assume that bringing data back to the UK automatically makes it secure or resilient.

                          Local does not necessarily mean safe. High profile breaches over the past year have affected organisations across multiple jurisdictions, regardless of where their infrastructure is hosted. Security is not guaranteed by postcode.

                          There are also practical constraints. Data volumes are expanding rapidly, fuelled by AI workloads and increasing digitalisation. Hardware supply chains are under pressure, with significant demand driven by hyperscale AI investments. Price volatility is already evident, with some organisations seeing substantial cost increases within weeks.

                          Simply building more UK data centres does not eliminate capacity constraints or environmental considerations, particularly around power and cooling.

                          Furthermore, many businesses rely on global platforms to serve international customers and partners. A purely national approach can undermine interoperability and performance. For most organisations, the right answer will involve a hybrid strategy rather than wholesale repatriation.

                          From Technical Detail to Board Level Risk

                          What has changed is not simply the technology, but the level at which these decisions must be made.

                          Data sovereignty is no longer a technical footnote for the IT department. It is a board level risk issue. Directors must understand where critical data is stored, where it is processed and which legal regimes can assert authority over it. They must assess whether current arrangements align with the organisation’s risk appetite and regulatory obligations.

                          This is particularly acute in sectors such as financial services, healthcare and defence, where the sensitivity of data and the scrutiny of regulators are intensifying. For these organisations, sovereignty and security are intertwined. Compromises made for convenience or short-term cost savings can carry significant long-term consequences.

                          Security itself must be treated as a foundational approach rather than an add on. Too often, security controls are bolted on after operational decisions have been made. Minimum standards are implemented, arbitrary certificates are obtained and compliance boxes are ticked. While certifications can provide useful benchmarks, they do not replace rigorous design and ongoing validation.

                          If data is brought back onshore, but not properly segregated, monitored and protected, the sovereignty objective is completely undermined. There is little value in regaining geographic control if the underlying environment remains vulnerable.

                          The Business Case Reality

                          It would be unrealistic to ignore commercial pressures. For many mid-market organisations, cost remains a primary driver of decision making. Risk appetite is frequently calibrated against budget constraints. The perfect solution is rarely affordable.

                          That is why compromise becomes central. The critical question is not whether to compromise, but where. Does an organisation prioritise flexibility over jurisdictional control? Does it accept higher costs to secure local hosting? Does it rely on hyperscale security capabilities while accepting overseas governance frameworks?

                          There is no universal answer. The correct balance depends on the nature of the data, the regulatory environment and the strategic objectives of the business. A small retail operation will have different requirements from a growing fintech or a defence contractor. Supplier selection must reflect that risk profile. Not all cloud or data centre providers are equal in capability, assurance or sector expertise.

                          Boards should therefore ask their providers some direct questions. Where exactly is our data stored and where is it processed? Which legal jurisdictions apply, and under what circumstances could external authorities demand access? Who within your organisation has access to data, and how is it segregated from other customers? What is the exit plan, and how do we ensure data is fully returned and deleted at the end of a contract?

                          These are not confrontational questions. They are governance essentials.

                          A Measured Path Forward

                          As a result the UK should not retreat from global cloud ecosystems, nor should it blindly assume that everything must be deported. The objective is not isolation, but informed control.

                          Where sovereignty is genuinely critical, particularly in government and national security contexts, local hosting and specialist providers may be essential. In other scenarios, public cloud may remain the most effective platform, provided its legal and operational implications are fully understood and managed.

                          The most significant risk today is not that UK businesses have embraced the cloud. It is that many have done so without fully mapping the sovereignty, jurisdictional and security consequences that come with relinquishing control of data.

                          As data volumes grow and geopolitical uncertainty continues, that gap in understanding becomes a strategic vulnerability. The cloud has delivered extraordinary value. Now all these years later, it demands a more mature conversation.

                          Convenience built the digital economy. Control will define its resilience.

                          Learn more at thebunker.net

                          • Cybersecurity
                          • Digital Strategy
                          • Infrastructure & Cloud

                          Author

                          Leonardo Boscaro

                          Published

                          9 March 2026

                          Estimated Read time

                          6Mins

                          Leonardo Boscaro, EMEA Sales Leader at Nutanix Database, on why sovereignty requires repeatable, compliant database operations and recovery across hybrid multicloud environments

                          In conversations with customers, infrastructure leaders are being asked to deliver more control with the same people. Stronger compliance with less tolerance for error. And higher resilience in environments that are objectively more heterogeneous than they were even a few years ago. Expectations continue to rise, but the operating models used to run critical systems haven’t kept up.

                          This pressure shows up first at the database layer because they sit at the centre of mission-critical services. While still being managed through manual processes, fragmented tooling, and a heavy reliance on specialist knowledge. In many organisations, when availability, security and compliance are under scrutiny, this combination creates exposure very quickly.

                          Database-Dedicated Platforms

                          The shift we now see in regulated organisations is toward database-dedicated platforms. Where the operating model is standardised through approved templates, guardrails, automated workflows, and built-in auditability. In practice, this means treating database workloads as a dedicated domain, with infrastructure and lifecycle operations designed together rather than as an add-on to a general-purpose environment. This approach depends on having a standardised operational layer for database lifecycle management and recovery that works consistently across hybrid and multicloud environments.

                          And in regulated environments, what matters is not only being compliant, but also being able to demonstrate it repeatedly. When provisioning, patching, and recovery depend on tickets, tribal knowledge, and one-off scripts, controls become hard to test. Furthermore, audit trails are incomplete, and resilience turns into a matter of confidence rather than capability.

                          How Complexity Crept In

                          Most enterprise database estates grew through sensible decisions made at different points in time. A platform was added to meet a new requirement, a legacy system could not be moved, or a new tool solved a specific operational gap. Each step made sense in isolation. Over time, however, teams found themselves managing dozens or hundreds of databases across multiple engines and environments. Each with its own processes for provisioning, patching, recovery and monitoring.

                          What they face now is inefficiency and operational fragility. Databases are where control, auditability and resilience intersect. So, when processes are manual or inconsistent, the risk surface expands quickly. In regulated industries, this shows up in audit pressure, long recovery times and an uncomfortable dependency on a small number of specialists.

                          Why Databases Expose the Cracks First

                          Many infrastructure leaders we speak to ask why databases should be their concern at all. Traditionally, databases belonged to DBA teams, while infrastructure focused on platforms and capacity. Unfortunately, it’s not that simple anymore.

                          Today, infrastructure and security leaders are under constant pressure to improve compliance, reduce risk exposure and maintain availability with fewer people and less tolerance for error. Databases sit directly in that line of responsibility. Patching windows, backup failures or untested recovery plans are operational risks with business consequences.

                          What becomes clear very quickly is that automation alone does not solve this. Many organisations have invested heavily in scripts and bespoke workflows to manage database lifecycles. While these efforts reduce pressure in specific areas, they often create new complexity elsewhere. Particularly when people change roles or environments scale.

                          Standardisation, Not Scripting, is the Real Shift

                          The real breakthrough comes when organisations move from automating tasks to standardising the operating model itself. This means treating database operations as a productised capability, with approved templates, guardrails and repeatable workflows built in from the start.

                          When provisioning, patching, cloning, and recovery follow a consistent model, compliance becomes part of the process rather than something validated afterwards. Human error is reduced because the system guides operations rather than relying on memory or documentation. And audit readiness improves because actions are traceable and predictable.

                          This is why many organisations are moving away from bespoke automation and toward standardised operating models, where infrastructure, lifecycle, and governance are designed together. 

                          Recoverability Turns Theory Into Reality

                          Recoverability is the stage at which operating models are tested under pressure. Many organisations technically have disaster recovery in place, but testing it is complex, disruptive and often avoided altogether.

                          For mission-critical services, particularly in financial services or the public sector, this is not acceptable. Recovery needs to be a standard operational capability, not a specialist exercise dependent on a few experts and fragile runbooks.

                          By embedding recovery workflows into the same platform used for everyday database operations, testing becomes simpler and more frequent. Switchovers, failovers and restores can be executed through guided processes, with far less room for error. This is not about faster failover, but about confidence, credibility, and the ability to demonstrate control.

                          Sovereignty is Becoming Operational Autonomy

                          We all know how important sovereignty is, yet it’s often discussed in terms of data location instead of dependency and control, beyond just geography. Real sovereignty must factor in where the data resides, who ultimately controls the operating model and under which jurisdiction that control sits.

                          In this context, hybrid strategies work but only if they preserve consistency. Running databases across on-premise and cloud environments without a common operating model simply moves complexity from one place to another. True autonomy comes from having one set of standards, workflows and controls that travel with the workload, regardless of where it runs.

                          Our customers want the freedom to adapt to regulatory, geopolitical or commercial change. And without rebuilding governance and operational processes each time. This has made portability and consistency critical.

                          A Database-Dedicated Platform, Not Just Infrastructure

                          What emerges from all of this is a shift in how database platforms are defined. Beyond running databases on infrastructure, databases must now be delivered through a dedicated platform experience. One where lifecycle automation, governance and recoverability are baked in, not added later.

                          When you take a platform approach, you can support multiple database engines, span hybrid environments and provide a single operational plane for teams. This allows infrastructure leaders to move beyond firefighting and towards standardised, compliant operations that scale.

                          Independent economic analysis from Forrester’s Total Economic Impact study supports what many organisations are already seeing in practice. When database operations are standardised, the benefits show up quickly. Faster delivery, less manual effort, and more consistent controls reduce day-to-day operational friction and lower risk. Often generating measurable returns earlier than traditional infrastructure-only programmes.

                          The modern mandate for infrastructure leaders

                          For today’s CIOs, CTOs and CISOs, the challenge is no longer where databases should run, but whether they are governed, recoverable and consistent by design. As digital services expand, AI initiatives place new demands on data, and regulatory scrutiny increases. Operational discipline becomes a leadership responsibility. In regulated environments, credibility is earned through evidence, with regulators and customers, and in the public sector it is earned with citizens.

                          Learn more at nutanixstore.co.uk

                          • Data & AI
                          • Digital Strategy
                          • Infrastructure & Cloud

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          5Mins

                          Visa is leading the AI race in payments, according to Evident’s AI Index for Payments, a major new ranking of…

                          Visa is leading the AI race in payments, according to Evident’s AI Index for Payments, a major new ranking of AI adoption within the industry. 

                          The Index shows industry stalwarts Visa and Mastercard outpacing their peers and delivering tangible AI outcomes thanks to early investments in talent and innovation.

                          Behind them, PayPal (3rd), American Express (4th), Stripe (5th) and Block (6th) emerge as the challengers. They outperformed the Index average, but are yet to match the leaders’ scale of deployment and outcome disclosure.

                          AI Moving from Experimentation to Deployment

                          Over the past two years, the 12 payments companies in the Index have publicly documented nearly 100 AI use cases. Underscoring how rapidly AI has moved from experimentation to deployment across core payment workflows. It’s a landscape defined by constantly evolving fraud threats and rising customer expectations for faultless, high-speed processing. Evident notes that nearly a third of these use cases disclose measurable outcomes, including efficiency gains, risk reduction and revenue uplift.

                          “Payments firms adopted AI out of necessity long before many other industries – their business models demanded it. Companies who invested early – like Visa and Mastercard – have gained a clear advantage over their peers, both in AI capabilities and the value their deployments are realising.” Alexandra Mousavizadeh, Co-Founder and Co-CEO of Evident.

                          Talent, Innovation, Leadership and Transparency

                          The Evident AI Index for Payments provides the most comprehensive independent benchmark of AI maturity across the industry. It is based on publicly available data around four pillars critical to successful AI deployment: Talent, Innovation, Leadership and Transparency.

                          According to Evident, Visa’s lead is based on consistent performance across the four pillars. And because it demonstrates the clearest evidence that AI is institutionalised across its core transaction network. Visa and Mastercard show maturity in areas such as fraud detection, cybersecurity and network-level risk reduction. Visa stands out for the scale and measurable impact of a handful of large, multi-year deployments focused on the integrity and security of its entire ecosystem.

                          “Mastercard shows strong evidence of scaled deployment and quantified performance improvements. Particularly in areas like fraud detection and AML tracing,” continued Mousavizadeh. “But what sets Visa apart is the degree to which the company is demonstrating impact at scale over multiple years. From applications of AI across its operations and network. It signals a shift from individual use cases to AI as institutional capability.

                          “What the Index also reveals is the importance of consistent innovation to maintain competitive advantage. With relatively nascent industry players like Stripe and Block performing well – and showing their AI potential reflected in their valuations – the Index leaders cannot afford to drop off the pace.”

                          AI Impact on Show, but ROI Reporting Scarce 

                          Firms in the top half of the Index account for nearly 80% of use case disclosures (with the top three providing a significant 54%). Highlighting the link between AI maturity and the ability to scale deployment.

                          Visa performed strongly in this regard. For instance, its latest threat report disclosed advanced AI/ML blocked nearly 85% more fraud compared to one year prior. Similarly, when Mastercard incorporated Gen AI technology into its Decision Intelligence solution, initial modelling showed AI enhancements improved fraud detection rates from an average of 20% to as high as 300% in some instances.

                          However, Evident notes that no payments company has disclosed realised or projected ROI across all enterprise or group-wide AI activities. 

                          “The Index leaders are locked in a tight race at a point when the thinking around corporate AI adoption is shifting – away from chasing the biggest models to building technologies that solve real operational problems efficiently,” commented Annabel Ayles, Co-Founder and Co-CEO of Evident. “Against this backdrop, the absence of ROI disclosure – or any group targets for AI ROI – is increasingly conspicuous. Currently, 1-in-5 banks now report on group-level AI returns. However, payments firms have yet to quantify the aggregate impact of their AI investments. To keep justifying this expenditure, the market will sooner or later demand clearer evidence of value.”

                          A Hotbed of AI Talent

                          The Index also reveals that the average payments company has over 30% more AI-focused workers than other financial institutions, despite substantially smaller employee numbers. 

                          The three major card networks – Visa, Mastercard and American Express – account for nearly half (48%) of the payments industry’s AI talent stack. PayPal is currently the biggest employer, accounting for nearly a fifth (18%) of that AI talent.

                          PayPal’s AI talent has allowed it to build proprietary models tightly integrated with its data and workflows. Consequently, it accounts for nearly a quarter (24%) of the 98 AI use cases documented by its peers over the past two years – 1.7x as many AI applications as detailed by Visa or Mastercard.

                          “AI maturity is no longer defined by talent volume alone, and the Index leaders combine AI development, data engineering and product capabilities in ways that allow them to move rapidly from model experimentation to production deployment,” concluded Ayles.

                          The Evident AI Index Methodology

                          The Evident AI Payments Index ranks the AI maturity of 12 of the largest payment networks and processors across the globe. These 12 entities were chosen by aggregating the largest payment companies, with a minimum of $2B in annual revenue. 

                          It is an independent, ‘outside-in’ assessment based exclusively on publicly available information. Each company was assessed against 60+ individual indicators, organised into four pillars critical to successful AI deployment at scale: Talent (45% weighting), Innovation (30%), Leadership (15%) and Transparency of Responsible AI activity (10%).

                          Data is gathered through a combination of extensive manual research and proprietary machine learning tools that extract key data points from company reporting and public disclosures (including press releases, investor relations materials, group-level website pages, group-level social media accounts, and media interviews with senior leadership), as well as a range of third-party data platforms.

                          Further information on the methodology of the Index can be found at evidentinsights.com

                          • Artificial Intelligence in FinTech
                          • Digital Payments
                          • Neobanking

                          Author

                          Adam Spearing

                          Published

                          5 March 2026

                          Estimated Read time

                          5Mins

                          Adam Spearing, VP of AI GTM EMEA at ServiceNow, on why those that invest in AI foundations now will shape their operating models on their own terms

                          Much of the debate around AI still centres on pilots: which tools to test, which use cases to prioritise, which risks to manage. Executive teams commission proofs of concept, establish governance forums and assess compliance exposure. Far less scrutiny is applied to the consequences of waiting.

                          Traditional technical debt is familiar territory for CIOs. It stems from shortcuts, ageing platforms and deferred upgrades. It builds over time and is eventually addressed through structured modernisation programmes. Visible in legacy code, brittle integrations and manual workarounds. It appears on risk registers and capital plans. Leaders know how to describe it and, in principle, how to resolve it.

                          Forward-looking technical debt is different. It arises when organisations postpone the foundational changes needed for new ways of working. It is not created by past expediency, but by present hesitation. And it accumulates faster.

                          AI Adoption

                          In the context of AI, the effects are already emerging. Each quarter spent debating readiness instead of building it increases the distance between legacy operating models and AI-enabled competitors. As models improve and user expectations shift, that distance widens, reshaping competitive baselines. What begins as a modest capability gap can harden into structural disadvantage.

                          While companies debate whether to adopt AI, the margin for strategic choice narrows. Many organisations frame AI adoption as a binary decision: adopt now or wait until the technology matures further. In practice, the room for discretion is smaller than it appears. Time spent stalled in pilots or governance loops increases the gap between internal capability and market expectation.

                          More than 75% of organisations are expected to face moderate to severe AI-related technical debt in 2026, predicts Forrester. The issue will not simply be missed efficiency gains. It will be structural misalignment between how their systems operate and how work is increasingly done.

                          This misalignment often appears gradually. Teams rely on manual data preparation because underlying systems cannot support automation. AI tools are layered onto fragmented architectures and deliver inconsistent outputs. Employees experiment with external tools because internal platforms cannot provide the functionality they need. Each workaround creates further fragmentation.

                          Over time, these patterns compound. Integration backlogs expand. Security and risk teams struggle to enforce consistent controls across proliferating tools. Data governance becomes reactive rather than designed. What began as caution begins to constrain strategic options.

                          The AI Paradox

                          Here’s the paradox: organisations are either rushing into unsuccessful AI pilots that create immediate technical debt, or they’re avoiding AI entirely and creating forward-looking debt through inaction. Both paths lead to the same place – systems that can’t support the future of work.

                          AI isn’t just another technology layer to bolt onto existing infrastructure. It’s fundamentally changing how people interact with systems and how work gets done. Increasingly, AI becomes an interface through which employees access information, execute tasks and navigate processes. When AI becomes the interface – not just for customers but for employees navigating their daily tasks – organisations without AI-ready foundations will find themselves unable to compete on speed, efficiency, or experience.

                          The companies that hesitate aren’t just missing out on automation benefits today. They’re building a deficit that grows exponentially as AI capabilities advance. Each new model release, each competitor’s successful implementation, each customer expectation shift adds to the debt. Each significant model improvement raises the performance benchmark across the market. Unlike legacy systems that degrade slowly, this gap accelerates.

                          From Avoidance to Advantage

                          Breaking free from forward-looking technical debt requires a fundamental mindset shift. This isn’t about buying more technology or launching more AI pilots. It’s about creating the conditions for sustainable AI adoption that builds capability rather than complexity.

                          The organisations succeeding with AI aren’t the ones with the biggest budgets or the most aggressive rollouts. They’re the ones that took a deliberate, phased approach to ensuring their data, systems, and culture could support AI at scale. They treated readiness as an operational discipline rather than an innovation side project. They understood that AI adoption isn’t a destination, it’s a continuous capability that requires solid foundations.

                          This starts with honest visibility into current technology estates. Leaders must understand what systems can realistically support AI workloads, where data quality creates barriers, and which processes are ready for automation. Only then can organisations introduce AI incrementally, modernising systems where necessary rather than forcing new capabilities onto brittle foundations. Without that clarity, AI risks being layered onto structural weaknesses.

                          Modernisation therefore becomes targeted. Consolidating fragmented workflows, standardising data models and reducing unnecessary integration points increase the feasibility of scaling AI across multiple use cases. Early deployments focused on well-defined processes with clear data lineage can build internal confidence while strengthening governance practices.

                          Clear Debt to Stay Competitive

                          Forward-looking technical debt does not appear on a balance sheet. It shows up in slower product cycles, manual workarounds, integration backlogs and frustrated employees. It surfaces when competitors deliver AI-assisted services as standard and customers begin to expect the same everywhere. By the time these symptoms are visible, the underlying gap has already widened.

                          Timing therefore becomes a strategic variable. AI capability builds cumulatively: early investment in clean data, modern workflows and interoperable systems creates a base for continuous improvement. Each iteration becomes easier, faster and more reliable. Those that delay face the opposite trajectory: increasing complexity, rising retrofit costs and shrinking room for strategic choice.

                          The real issue is not adoption in principle. It is whether leadership teams are prepared to treat readiness as urgent rather than optional.

                          Reducing forward-looking technical debt requires acting before competitive pressure dictates terms, aligning technology modernisation with operating model reform, and accepting that disciplined progress now is less risky than accelerated catch-up later.

                          AI adoption will continue irrespective of individual organisational hesitation. Vendors will continue to refine their offerings. Regulators will clarify expectations. Customers and employees will adjust their behaviours. Those that invest in foundations now will shape their operating models on their own terms. Those that delay risk reacting to a competitive gap that is already commercially significant.

                          Learn more at servicenow.com

                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Strategy

                          Author

                          Chris Gunner

                          Published

                          4 March 2026

                          Estimated Read time

                          4Mins

                          Chris Gunner, vCSO at Thrive – a leading NextGen MSP/MSSP, delivering global AI, cybersecurity, cloud, compliance, and digital transformation managed services – on how CISOs can position their cyber strategy to to become part of how a business navigates uncertainty

                          Quantification of cyber risk is a growing trend. While this can be genuinely useful, in practice it is often misunderstood or over-applied by security leaders. It can range from an arbitrary figure to attempting to model every possible risk on the register in a Monte Carlo simulation. The focus can fall on the mechanics of quantification, rather than how financial decision-makers actually use the information.

                          Think of the CFO – they don’t walk through every penny in the budget. Instead, they usually focus on the board-level levers that can materially affect the business. These often include three key areas: strategic optionality, removing friction from capital events and avoiding shocks and smoothing operating costs. Security conversations should be anchored the same way.

                          The Importance of Strategic Optionality

                          If faced with a credible one-year growth plan, CFOs may recommend a one-year office lease despite a 20% premium. This is because it maintains the option later of moving or re-contracting once the growth trajectory becomes more visible. Like most strategic decisions, it is about preserving flexibility in the face of uncertainty, even if that flexibility comes at a short-term cost.

                          If we apply this to a cyber context, there are often businesses that have taken a calculated gamble with their existing business strategies. While the plan is sound, there is a chance it might not land as expected. When they require security services, the choice between a ‘standard’ and ‘premium’ SOC frames the decision as one of optionality rather than security spend. Paying more now to preserve the ability to adapt later down the line. A simple illustration is incident response. An on-call retainer with defined response times can look more expensive than ad hoc support. Until an incident occurs and procurement becomes the bottleneck. In those moments, flexibility is often far more valuable than marginal savings achieved earlier.

                          Removing Friction from Capital Events

                          For CFOs, especially those operating in the alternative investment space, the focus is on structuring capital events. As opposed to managing day-to-day operational costs. One of the most painful points in that process is due diligence. The careful exchange between acquirer and target that aims to provide enough information for each to price risk, without giving the entire game away.

                          CISOs can materially influence how smooth or painful that process becomes. The most effective support often comes from understanding upfront what the diligence process will look like and preparing accordingly.

                          For example, they might develop executive-level ‘Security at ACME’ overviews to sit alongside more detailed trust centre or technical reports. Being available to diligence teams for interviews, and for example clearly articulating which services are outsourced to an MSSP, and why, builds credibility between those executive teams.

                          Decision-makers often don’t look at penetration test reports at a deal level. They are assessing whether the organisation understands its own control environment. A well-prepared CISO who can clearly explain why certain controls exist acts as a trust amplifier during transactions.

                          It is often the difference between a diligence process that closes cleanly and one that drifts. Two organisations can have similar maturity. Yet the one that can respond within a day with clear, consistent evidence reduces follow-up questions, avoids uncertainty premiums in pricing discussions and prevents security from becoming a late-stage negotiation point.

                          Avoiding Shocks and Smoothing Operating Costs

                          For any individual who has worked with a finance partner to define a departmental budget will know that predictability often takes precedence over absolute cost. Contract value can be secondary to payment terms, renewal timing or the ability to forecast spend with confidence.

                          CISOs can align with this by looking to reduce unplanned operating expenditure. In addition to understanding the cost structure of their controls by communicating with the technical pre-sales engineer, procurement and account teams.

                          A good example is cyber insurance. While often purchased directly by finance teams, many policies are relatively off-the-shelf and provide access to services the security team already operates or has under contract. Other policies include notable exclusions for the events most likely to occur. Such as a ransomware incident without business interruption cover. In many cases, these gaps can be addressed in-policy with a flat fee or a more predictable cost model.

                          The value here extends beyond risk transfer and into more predictable costs: replacing reactive spend with planned expenditure.

                          Aligning Cyber Conversations to Board Priorities

                          Across all of the above examples, the common thread is that the board is rarely asking security to prove its value in isolation, and is surprisingly comfortable with uncertainty. But they are asking whether the cyber papers support better decisions, fewer constraints and more predictable outcomes for the business as a whole.

                          CISOs who frame their priorities in those terms will find their conversations move away from justifying individual controls and towards understanding how security choices shape the organisation’s ability to respond to change. In that context, cyber becomes part of how the business navigates uncertainty, rather than a specialist function defending its budget. Speaking the board’s language, ultimately, is less about converting cyber risk into pounds and pence. It is more about understanding which levers matter at that level and showing how security choices influence them.

                          Learn more at thrivenextgen.com

                          • Cybersecurity
                          • Cybersecurity in FinTech
                          • Digital Strategy

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          4Mins

                          AccessPay, the leading bank integration provider, has announced a new partnership with PayPoint. It will integrate PayPoint’s Confirmation of Payee (CoP) capability…

                          AccessPay, the leading bank integration provider, has announced a new partnership with PayPoint. It will integrate PayPoint’s Confirmation of Payee (CoP) capability into AccessPay’s payments automation suite for modern finance teams. £258m was lost to authorised push payment (APP) fraud in the first half of 2025 alone. Organisations need access to robust payment controls that scale with their operations. PayPoint’s CoP offering enables AccessPay’s customers to verify payee account details as part of their payment workflows. Reinforcing AccessPay’s position at the centre of a growing ecosystem of technologies designed to automate and de-risk the Office of the CFO.

                          Fraud Prevention

                          CoP, also known as Account Name Verification (ANV), is a valuable anti-fraud measure. It checks the accuracy of payee details before funds are sent. It can be used to confirm payee details at the point of collection, when creating a payment instruction, or both. PayPoint’s CoP capability is designed to handle peak-usage scenarios for corporate clients, including payroll runs, supplier payments, and seasonal spikes. It is recognised for its ability to process exceptionally high transaction volumes. Additionally, it provides flexible access options, including APIs, user interface and bulk processing. This enables organisations at different stages of their automation journey to embed account name verification seamlessly into existing processes.

                          A Partnership Expanding a Tech Ecosystem

                          “Our customers want to automate high-volume, high-value payments with confidence, knowing robust safeguards are built directly into their processes. PayPoint is recognised for delivering payment and fraud services at a national scale. By partnering with them, we are strengthening the fraud and error protections available within the AccessPay platform. And improving operational efficiency by reducing payment resubmissions, exception handling and manual intervention. The service is already available to customers and has been positively received since we began working together in 2025.” Anish Kapoor, CEO of AccessPay

                          “AccessPay sits at the centre of modern finance operations. It securely connecting businesses to their banks and enabling automated payment flows at scale. Partnering with AccessPay allows us to extend our CoP capability to thousands of finance teams that are actively transforming how they manage payments. Together, we’re helping organisations reduce fraud risk, minimise payment errors, and deliver more secure, trusted payment experiences.” Jo Toolan, Managing Director Payments, PayPoint

                          The PayPoint partnership reinforces AccessPay’s commitment to expanding its technology ecosystem. To help finance and treasury teams automate securely, reduce manual intervention, and build resilient, future-ready payment operations. By combining AccessPay’s bank integration platform with PayPoint’s payment and fraud prevention expertise, organisations gain stronger protection against fraud. Also unlocking greater efficiency and confidence in automated finance processes.

                          About PayPoint

                          PayPoint is the UK’s leading multichannel payments and community services provider. It delivers innovative solutions that simplify and secure how customers and businesses transact. The core of our offering is MultiPay. A single payment platform that unifies Open Banking, card, Direct Debit, and over-the-counter cash payments into a streamlined solution.

                          Our Open Banking services are designed to deliver a frictionless and secure payment journey. From account-to-account payments to Confirmation of Payee (CoP), we empower companies with the tools to build trust and reduce fraud. All through a suite of easy-to-integrate APIs. These services can be integrated into your existing financial or customer management systems. Or accessed via our portal, white-labelled websites or mobile apps—providing flexibility to meet your needs.

                          As a proud Gold Partner of Open Banking Expo 2025 and winner of the Best Sector Initiative for our PayPoint OpenPay innovation at the Open Banking Expo Awards, we’re thrilled to return in 2026 to continue driving innovation and delivering value through Open Banking.

                          About AccessPay 

                          AccessPay is a leading provider of bank integration solutions, pioneering finance transformation for the Office of the CFO. AccessPay helps finance and treasury teams modernise their operations through secure, cloud-based bank connectivity.

                          Our platform connects back-office systems to banks, enabling the automated flow and transformation of payment, bank statement and other financial data. Thousands of businesses around the world partner with AccessPay to automate supplier and client payments, Direct Debit collections, and bank statement retrieval. Improving efficiency, reducing fraud risk, and gaining real-time cash visibility.

                          Founded in 2012 and headquartered in Manchester, UK, AccessPay is trusted by global enterprises to automate finance and treasury operations and build a future-ready Office of the CFO.

                          • Cybersecurity in FinTech
                          • Digital Payments

                          Author

                          Adonis Celestine

                          Published

                          3 March 2026

                          Estimated Read time

                          5Mins

                          Adonis Celestine, Senior Director – Global Automation Practice Lead at Applause, on the rise of AI and why In a world of autonomous systems, trust is the ultimate competitive advantage

                          Every generation of technology has its defining disruptor – the force that rises above the rest and reshapes its environment. In the mid-2000s, Marc Andreessen captured the moment when digital systems began transforming entire industries with his famous line: “software is eating the world”. At the time, software was the apex predator of technology, defining how value was created and delivered. Today, that hierarchy has shifted. Artificial Intelligence (AI) has reached the top of the technology food chain. Not just accelerating software, but fundamentally reimagining how it’s created, tested, and deployed.

                          AI is no longer just a tool; it is a co-creator. Developers now rely on AI daily to translate high-level intentions into working code. A practice sometimes known as ‘vibe coding’. Tasks that once took months can now be delivered in weeks, days, or even minutes. The pace is exhilarating, but it introduces challenges that traditional quality assurance (QA) practices were never designed to meet. And if QA cannot keep up, speed will come at the cost of reliability and trust.

                          When AI Outpaces QA

                          Conventional QA depends on predictability. Features are defined, code is written, and test cases verify the expected behaviour. However, AI disrupts this traditional model. Generative and Agentic AI systems don’t simply follow instructions; they interpret them. These systems adapt to context, learn from data, and can produce different outputs from the same prompt, influenced by factors such as training, temperature settings, and the model’s probabilistic nature. With development cycles now measured in minutes, traditional QA handoffs are often impossible.

                          This has led to a growing gap between speed and certainty. Teams can ship products faster than ever, yet it’s becoming much more difficult to ensure consistent, ethical, or safe behaviour in real-world conditions. Enterprises are already experiencing AI-powered features that fail in ways conventional testing could not anticipate, undermining trust and creating new risks.

                          Hidden Risks in Autonomous AI Workflows

                          AI-driven development introduces blind spots that traditional QA often struggles to detect. One key issue is context drift. This occurs when AI performs well in controlled testing environments but behaves unpredictably when faced with edge cases, cultural differences, or ambiguous inputs. For example, a customer-facing chatbot might pass functional tests but produce biased or misleading responses when deployed on a global scale.

                          Another challenge is compound autonomy. When multiple AI agents are involved in code generation, testing, and deployment, the system may begin to validate its own processes. Without human oversight, errors can propagate unnoticed. An AI agent might ‘approve’ certain behaviours because they statistically align with previous outputs. Rather than meeting user or business expectations.

                          Invisible change also complicates QA efforts. AI models continuously evolve through processes like retraining, prompt tuning, or data updates. A feature that worked flawlessly last week may function differently today. Traditional regression testing often fails to capture these subtle but significant shifts.

                          Most critically, AI workflows blur the lines of accountability. When failures occur, it can be unclear whether the issue lies with the model, the data, the prompt, the integration, or the deployment pipeline. QA teams must continuously validate not only the outputs but also the decision-making processes behind them.

                          Redefining Quality and Trust in an AI World

                          Slowing AI development is neither practical nor beneficial. Organisations must redefine quality in a probabilistic, AI-driven environment. Quality now extends beyond just correctness. It involves ensuring that systems operate reliably in real-world scenarios. This shift requires moving from static test cases to continuous, adaptive validation.

                          QA teams must evolve into ‘quality intelligence’ teams, broadening their responsibilities from simply detecting defects to actively fostering trust in AI systems. AI-assisted testing is crucial in this process. It can automatically generate extensive test cases by analysing requirements and code patterns. It can predict defects using machine learning. Detect visual inconsistencies across devices, and produce realistic, privacy-compliant synthetic test data. Additionally, Agentic AI can autonomously maintain and self-heal test scripts, adjusting their logic as underlying code or user interfaces change.

                          Furthermore, AI systems themselves need rigorous evaluation. Techniques such as red teaming, rainbow teaming, benchmarking, bias and ethics checks, and drift monitoring are essential to help promote AI’s reliability, fairness, and alignment with business objectives.

                          Human oversight is critical. While AI can scale testing and automate numerous tasks, critical thinking, risk assessment, and judgment cannot be fully delegated. Humans must guide, validate, and refine AI outputs to maintain both quality and trust.

                          Emerging Roles and Responsibilities

                          AI is reshaping professional roles. Developers are increasingly using AI by instructing machines through natural language rather than traditional programming methods. This shift has led to the emergence of new roles such as AI agent orchestrators, prompt engineers, QA specialists for autonomous systems, and governance leads who ensure ethical and auditable AI practices.

                          These roles are essential for maintaining human oversight. Developers and testers must experiment, validate, and continuously refine AI outputs while being cautious not to rely too heavily on AI.

                          Trust in the Age of the Apex Predator

                          As with any apex predator, AI has changed the rules of the game. Software once “ate the world” by making systems programmable. Today, AI “eats software” by making it autonomous, capable of creating, modifying, and deploying autonomously. In this new environment, speed is no longer the ultimate measure of success; trust is. Systems may move fast, but without rigorous QA, ethical oversight, and human judgment, they may not be reliable, accurate or ethical.

                          The new apex predator demands adaptation. Organisations navigating this AI-driven era must embrace automation and innovation, but pair it with strong quality practices, governance, and continual human oversight. Only by combining these elements can companies ensure their AI systems are not only fast and efficient but also dependable and aligned with business objectives. In a world of autonomous systems, trust is the ultimate competitive advantage.

                          Learn more at applause.com

                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Strategy

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          4Mins

                          Report reveals shift from experimentation to scale as AI and digital assets drive regional leadership

                          Money20/20, the world’s leading FinTech show, has unveiled its annual Future of Fintech in APAC report. Money20/20 Asia takes place in Bangkok on April 21-23 at the Queen Sirikit National Convention Center (QSNCC). The whitepaper reveals that APAC’s FinTech ecosystem has reached a pivotal inflection point. The region is shifting from experimentation to scaled deployment. Across AI, digital payments, and digital assets, marking a decisive move toward production‑grade innovation.

                          The comprehensive report is based on surveys and interviews with more than 130 senior FinTech leaders across Asia. It reveals an industry moving beyond pilot programs toward enterprise-scale solutions. These prioritise collaboration, digital trust, and financial inclusion as core business imperatives for 2026.

                          Key FinTech Findings

                          • Southeast Asia dominates expansion plans: 22.9% of respondents identify the region as their primary growth target. Despite a decline from 31.4% last year — underscoring its continued dominance as the region’s growth engine.
                          • Financial inclusion becomes strategic priority: 90.6% of executives say social good initiatives are now embedded in corporate strategy. Confirming that impact has become a commercial imperative, not a CSR exercise.
                          • AI adoption accelerates: 61.2% of organisations have already adopted AI or machine learning. Only 3.5% are yet to begin exploring it.
                          • Regulatory momentum accelerates: New frameworks in Singapore, Hong Kong, and Japan are driving institutional adoption of stablecoins and tokenised assets
                          • Cyber-resilience emerges as top operational concern: 63.5% of leaders cite fraud prevention as their highest operational priority.

                          “APAC is no longer experimenting, it’s executing. The region is building financial infrastructure that is faster, safer, and more inclusive than ever before. What happens here will influence the future of money globally.” Ian Fong, VP of Content at Money20/20 Asia.

                          Digital Trust Becomes the New Currency

                          As digital adoption accelerates, cyber-resilience has emerged as the region’s most urgent priority for 2026. 63.5% of leaders identifying fraud prevention as their top operational priority. Regulators and industry players are investing heavily in real-time risk intelligence and AI-driven security measures.

                          The speed of digital adoption in APAC has outpaced traditional fraud models,” said Justin Lie, Founder & CEO of SHIELD. “What we’re seeing now is a shift toward real-time, device-level intelligence that operates silently in the background. Trust is the new currency of digital finance. Furthermore, the companies that embed it in every interaction while delivering a frictionless experience will define the future of the industry.”

                          Stablecoins Move into Mainstream Financial Infrastructure

                          Institutional engagement with stablecoins and tokenised financial instruments has grown significantly over the past year. It is supported by clearer regulatory frameworks emerging across Singapore, Hong Kong, and Japan. Blockchain and DLT were ranked by 17.9% of respondents as the most impactful emerging technology after AI. These developments are enabling faster cross-border settlements, improved liquidity management, and new treasury optimisation strategies for enterprises.

                          “Across Asia, stablecoins are already embedded in real economic activity. From payments and cross-border settlements to treasury optimisation,” said Yam Ki Chan, Vice President, Asia Pacific at Circle. “The region is demonstrating how digital assets can scale within financial systems. And the next phase is about interoperability and the development of an economic operating system (OS) for the internet”.

                          Digital Lending Expands Financial Access

                          2.1 billion adults globally are still underbanked or unbanked. Thus, digital lenders across South and Southeast Asia are leveraging alternative data, mobile-first onboarding, and embedded finance to reach previously excluded communities. The report also highlights that 72.9% of respondents believe FinTech solutions tailored to SMEs are key to driving economic growth across APAC. Signalling a widening opportunity for inclusive financial innovation.

                          “Financial inclusion isn’t achieved by simply putting products online. It requires building for the realities of everyday consumers,” said Moritz Gastl, General Manager of Tala Philippines. “In markets like the Philippines, trust, transparency, and flexibility matter just as much as credit scoring. Digital lending works when it empowers people, not when it replicates old systems with new interfaces.”

                          Looking Ahead: Collaboration Will Define the Next Decade

                          Together, the findings of this report point to a region no longer just testing FinTech concepts. It is actively building production-grade financial infrastructure. As AI scales, payment rails interconnect, and digital assets enter regulated markets. APAC is emerging as a blueprint for how financial systems will be built and governed globally.

                          “The next wave of FinTech innovation will be defined by how well we balance technological advancement with social impact,” added Fong. “APAC markets are proving that financial innovation and inclusion can advance together.”

                          The Future of Fintech in APAC report can be downloaded HERE.

                          • Artificial Intelligence in FinTech
                          • Digital Payments
                          • Event Newsroom
                          • Events

                          Author

                          Tom Lanaway

                          Published

                          2 March 2026

                          Estimated Read time

                          4Mins

                          Tom Lanaway is Head of Innovation at Connective3, a global brand & performance marketing agency. He leads a team building AI-powered marketing measurement and marketing intelligence tools.

                          Most businesses are asking the wrong question about AI. They’re asking, ‘Which AI tool should we use?’ They should be asking: ‘Can our people actually think with AI?’ 

                          I run an innovation team at a marketing agency. We’ve spent the last two years building AI into everything we do, including measurement, content, strategy, and automation. We’ve got lots of tools, 18 different products to be precise. 

                          Below is what I’ve learned. But the tools aren’t always the bottleneck; sometimes the skills are. 

                          The Tennis Racket Problem 

                          A colleague put it perfectly recently: “AI is a tool. Think of it as if you’ve got a smart assistant sat there. But it’s saying, I’m going to give you the best tennis racket, now go and play in a Grand Slam.” 

                          That metaphor stuck with me because it captures something the artificial intelligence hype cycle keeps missing. We’ve convinced ourselves it democratises everything. That anyone can now do anything. That the barrier to entry has collapsed. And there’s truth in that, but it’s incomplete. The barrier to access has collapsed, but the barrier to effectiveness hasn’t. Give someone GPT-4, and they can generate text. Give them the best tennis racket, and they can hit a ball. But the gap between hitting a ball and playing at Wimbledon is still vast. Most organisations are stuck in that gap, wondering why their AI investments aren’t transforming anything. 

                          Three Skills That Aren’t Always Present 

                          When I look at where teams struggle and where I see the same patterns across other businesses, three specific competencies keep showing up as gaps: 

                          1. Problem Decomposition 

                          Not everyone knows how to break down complex work into chunks that AI can help with. This sounds simple, but it isn’t. Most people approach AI with whole tasks such as ‘Write me a marketing strategy’, ‘Analyse this data’ Or ‘Create a campaign’. AI will then produce something, but it’s usually mediocre, because the person hasn’t done the harder work of understanding which specific parts of that task AI is good at, and which parts need human judgment. The skill isn’t using AI; it’s knowing what to give it. Someone who is brilliant at their job but can’t decompose problems will get worse results from AI than someone more junior who understands how to break work into the right pieces.  

                          2. Output Assessment 

                          How do you know if what AI gives you is good? This is where intuition becomes essential and it’s also where the ‘AI replaces expertise’ narrative falls apart. You need domain knowledge to evaluate AI output. You need enough experience to feel when something’s off, even if you can’t immediately articulate why. You need the pattern recognition that comes from years of doing the actual work. Artificial Intelligence doesn’t replace that intuition; it requires it. The best AI users I’ve observed aren’t the most technical; they’re the ones who’ve built up enough expertise in their field to quickly assess whether AI output is useful, directionally correct, or completely off base. They know what good looks like, so they can recognise it when they see it, or notice when it’s missing.

                          3. Articulation 

                          Can you clearly express what you really want? This is the unglamorous core of the whole thing. Some people struggle to articulate their requirements to other humans, let alone to AI. We’ve all sat in meetings where someone spends 20 minutes explaining what they need, and you’re still not sure what they want. AI makes that problem worse. The skill isn’t ‘prompt engineering’ in the technical sense; it’s the much older skill of clear thinking and clear communication. If you can’t articulate what you want specifically, precisely, with the right context and constraints, you won’t get useful output from AI or from anyone else. 

                          The Uncomfortable Implication 

                          Here’s what this means for how businesses should think about AI investment: 

                          Stop leading with tools: Most organisations have tool fatigue already. Another platform, another integration, another training session on which buttons to click. It’s not working. 

                          Start with the human work: Before asking ‘What AI should we use?’, ask ‘Can our people break down problems, assess output, and articulate requirements?’ If they can’t do those things well without AI, they won’t do them well with AI either. 

                          Invest in the skills, not just the access: This doesn’t mean AI prompt engineering courses; it means developing clearer thinking, better problem decomposition, and sharper articulation. These are old skills, applied to new tools. 

                          Accept that expertise still matters: The people who’ll use AI best are the ones who already know their domain deeply. AI amplifies competence; it doesn’t create it.

                          Connected Intelligence Isn’t About Connected Systems 

                          I’ve spent a lot of time thinking about how different marketing channels and data sources connect and how you build intelligence across systems rather than in silos.

                          But I’ve come to think the more important connection isn’t between systems, it’s between human judgment and AI capability. The integration layer that matters most is the one between the person and the tool. 

                          Get that wrong, and it doesn’t matter how sophisticated your AI stack is. Get it right, and even basic tools become powerful. 

                          Learn more at connective3.com

                          • AI in Procurement
                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Strategy
                          • People & Culture

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          2Mins

                          Hampshire Trust Bank (HTB) is using artificial intelligence (AI) to act faster on customer concerns. It is empowering its teams…

                          Hampshire Trust Bank (HTB) is using artificial intelligence (AI) to act faster on customer concerns. It is empowering its teams to identify and respond quickly, whilst also meeting regulatory timeframes for handling complaints and supporting vulnerable customers.

                          Netcall: AI-Powered Sentiment

                          The specialist bank has worked with Netcall to deploy AI-powered sentiment analysis using Netcall’s Liberty Create platform. The solution reduces manual effort and improves operational efficiency by bringing customer emails from multiple mailboxes into a single interface. Incoming messages are automatically analysed to identify dissatisfaction, highlighting cases that may require faster intervention. This allows urgent cases to be prioritised, helping HTB to resolve issues before they escalate and improve the customer experience.

                          “Our AI-powered sentiment analysis solution rapidly processes vast amounts of email data. Its efficiency allows our team to focus on resolving customer enquiries and issues rather than sorting priorities. The streamlined process ensures swifter responses and better customer outcomes, upholding our reputation for exceptional customer service.” Ed Eames, Head of Customer Savings Operations at Hampshire Trust Bank.

                          The application was built by the Hampshire Trust Bank development team using Liberty Create. It worked closely with Netcall to integrate AI sentiment analysis into existing processes. Customer-facing teams were involved throughout to ensure the solution aligned with established workflows and regulatory requirements.

                          Customer Service Control

                          A key benefit of the approach is the level of control it gives internal teams. Keywords, sentiment thresholds, and classifications can be adjusted directly. This allows rapid refinement as customer behaviour changes or new regulatory considerations emerge, without waiting for development cycles.

                          “Liberty Create has enabled my development team to work with remarkable agility. The ability to rapidly create and refine applications to meet ever-evolving business needs has significantly enhanced our efficiency. This allows us to deliver a wealth of new features to end users and customers with speed. With the integration of AI, we’ve been able to advance our processes while ensuring exceptional customer service. Our Sentiment Analysis application launch is a prime example of this.” Trina Burnett, Head of Engineering at Hampshire Trust Bank.

                          The sentiment analysis system also supports automated and ad-hoc reporting. This provides a single source of insight into customer interactions and actions taken. This helps reduce manual effort, supports audit and compliance activity, and enables teams to continuously improve customer service operations.

                          “As scrutiny around customer experience and accountability increases across UK financial services, the ability to listen, adapt and respond at pace is becoming a defining capability for banks seeking to maintain trust and service standards,” said Alex Ballingall, Key Account Manager at Netcall.

                          “HTB’s approach shows how banks can use AI-driven insight practically. Turning customer communications into faster action without adding operational complexity,” Ballingall concluded.

                          About Netcall

                          Netcall is a leading provider of low-code and customer engagement solutions. A UK company quoted on the AIM market of the London Stock Exchange. By enabling customer-facing and IT talent to collaborate, Netcall takes the pain out of big change projects. It helps businesses dramatically improve the customer experience, while lowering costs. Over 600 organisations in financial services, insurance, local government and healthcare use the Netcall Liberty platform to make life easier for the people they serve. Netcall aims to help organisations radically improve customer experience through collaborative CX.

                          Learn more at netcall.com

                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Payments
                          • Digital Strategy
                          • Fintech & Insurtech
                          • InsurTech

                          Author

                          Patrick Cooney

                          Published

                          27 February 2026

                          Estimated Read time

                          6Mins

                          Patrick Cooney, CFO at Version 1, on why, in an AI-driven operating environment, financial discipline is more important than ever

                          Over the last decade, digital transformation has become part of the CFO’s remit. As organisations invested in automation, cloud and data platforms, finance leaders were well placed to oversee spend, drive efficiency and ensure technology investments delivered measurable returns. As artificial intelligence (AI) moves from experimentation into the core of how organisations operate, that model is beginning to evolve. Primarily because AI-scale transformation demands a different balance of expertise.

                          A recent move by Coca-Cola illustrates this shift. The decision to take digital strategy out of CFO John Murphy’s remit and appoint Sedef Salingan Sahin as the company’s first Chief Digital Officer is not a rejection of finance-led transformation. It reflects a practical reality. While strong financial discipline remains essential, the architectural complexity and technical depth required to embed AI across an enterprise now go beyond traditional finance capabilities alone.

                          This raises a critical question for financial services leaders. If AI is now a balance sheet issue — shaping cost structures, risk exposure and long-term value — what should the CFO’s role look like in the years ahead?

                          AI is Changing How Finance Operates

                          In all industries, AI is no longer confined to innovation labs or isolated pilots. It is increasingly embedded in how organisations operate, make decisions and manage risk. At Version 1, our earliest focus on AI was external: helping partners use AI to transform their own businesses. We have quickly turned the lens inward. Over the past quarter, we have accelerated the use of AI across our own finance and operational functions, implementing a wide range of practical use cases that fundamentally change how work gets done.

                          Some of these are relatively simple but have had a significant impact. Using AI to summarise documents, generate meeting notes or surface insights from large volumes of information has become normal and is already saving time across the organisation. Others are more structural. In finance, we are applying AI to areas such as accounts payable, accounts receivable and general ledger reconciliations, where large datasets and repetitive processes create natural opportunities for automation and acceleration.

                          We are also rethinking reporting itself. Rather than manually producing variance analyses each month, we are developing standardised prompts that allow AI to highlight key trends, explain deviations from budget and surface insights that would traditionally take hours to compile. These are not abstract efficiencies. Rather, they directly affect the speed, quality and value of financial decision-making.

                          What is striking though is the pace of change. Even over the past few months, usage has increased exponentially as people find new ways to integrate AI into their daily work. This is no longer an optional experiment. AI is reshaping how organisations function from the inside out.

                          Modern CFOs Deliver Stewardship and Governance

                          One of the biggest challenges CFOs face with AI is that traditional ROI models struggle to capture its true impact. Unlike earlier waves of digital transformation, AI does not deliver value solely through cost reduction or headcount optimisation. Increasingly, its value lies in better planning, faster decision-making, improved risk management and higher-quality outputs.

                          I see this clearly in how we use AI for planning. Recently, we fed a combination of internal data, previous plans and external consultancy material into a large language model and spent time crafting a detailed prompt. The output was a first-pass design for a major simplification programme (including workstreams, resourcing requirements and sequencing) that would previously have taken weeks to develop.

                          It is worth noting that this new process didn’t replace human judgement – it dramatically accelerated it. We are using similar approaches to shape annual finance priorities, drawing on historic plans and organisational context to generate structured, actionable starting points. This kind of value is real, but it does not always show up neatly in short-term financial metrics.

                          At the same time, the risks associated with AI are increasing. Model drift, regulatory scrutiny, data security and vendor dependency all carry financial implications. This is why governance matters as much as innovation. At Version 1, we have put formal structures in place, including an AI oversight committee that reviews and approves new tools, ensures appropriate controls are in place and sets clear boundaries around responsible use. We tightly manage which platforms can be used and how data is protected, recognising that public, uncontrolled tools pose unacceptable risks in an enterprise environment.

                          This combination of accelerating value and growing risk is precisely why ownership models are changing. Many CFOs continue to play a leading role in digital transformation, with research showing that around three-quarters of finance leaders now prioritise digital strategies at the highest levels of the organisation.

                          People Remain at the Heart of AI Adoption

                          As AI scales, the CFO’s role is shifting from delivery ownership to strategic stewardship. Finance leaders are uniquely positioned to connect technology ambition with financial reality, ensuring AI investments are governed properly, aligned to business outcomes and measured over time.

                          This aligns closely with how we think about our own operating model at Version 1. We use what we call a ‘strength in balance’ business model, built around three equally important pillars: customers, people and a strong organisation. That final pillar includes financial performance, risk management, cybersecurity and governance, all areas that become more critical, not less, as AI adoption accelerates.

                          People are central to this conversation. AI inevitably raises questions about job impact and cost optimisation, and organisations have a responsibility to approach this responsibly. That means clear communication, strong change management and treating people fairly where roles evolve. It also means investing in training and enablement. We have rolled out organisation-wide AI training focused on responsible use, and we are developing a network of AI champions with deeper skills who can identify and build use cases without relying solely on central teams.

                          The most effective model I see emerging is a shared one. Specialist digital leaders focus on building and embedding AI capabilities at scale. CFOs retain accountability for financial discipline, data governance and value realisation. When these roles work in partnership, organisations are far more likely to capture the value they expect from AI.

                          Financially Guided Value Delivery

                          As AI becomes a baseline capability rather than a differentiator, debates about who ‘owns’ digital strategy are becoming less relevant. The more important question is how organisations ensure AI investments deliver measurable, sustainable value. For CFOs, AI is now undeniably a balance sheet issue.

                          Investment in the latest technology affects cost structures, risk exposure, governance and long-term resilience. Those who engage proactively, shape governance and demand disciplined value creation will help their organisations unlock lasting advantage. Those who remain passive risk inheriting complexity, cost and compliance challenges that are far harder to unwind later.

                          In an AI-driven operating environment, financial discipline is not diminished. It is more important than ever.

                          Learn more at version1.com

                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Strategy
                          • Fintech & Insurtech

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          3Mins

                          Read in brochure format

                          Welcome to the latest issue of Interface magazine! Click here to read the latest edition! Inside a Global Cybersecurity Journey…

                          Welcome to the latest issue of Interface magazine!

                          Click here to read the latest edition!

                          Inside a Global Cybersecurity Journey at Olympus

                          In the world of MedTech, innovation does not happen in isolation. It relies on deeply interconnected digital ecosystems that span research and development, manufacturing, clinical environments and global corporate operations. For Olympus, a global medical technology company with 30,000 employees operating across multiple regions and regulatory environments, cybersecurity has become a foundational enabler of trust, resilience and patient safety.

                          At the centre of this transformation is Ryan Larsen, Global Head of IT Security at Olympus, whose role sits at the intersection of technology, leadership and mission-driven purpose. His mandate is clear: ensure that Olympus’ global digital and operational environments remain secure, reliable, and able to support innovation at scale.

                          “In practical terms, I’m responsible for the cyber defence and digital resilience of Olympus as a global MedTech company,” Ryan explains. “That means ensuring our systems, data and people are protected so innovation can move quickly, safely and with trust across R&D, manufacturing and corporate operations worldwide.”

                          Virginia Farm Bureau: An Enterprise CIO’s Journey

                          Virginia Farm Bureau is an organisation renowned for resiliency, collaboration, commitment to a greater cause, diversity and service to its members. For outgoing CIO Patrick (Pat) Caine leadership at Virginia Farm Bureau has never been about technology for technology’s sake. After 18 years as CIO, his role evolved into what he describes as that of an “enterprise technology leader,” responsible for supporting a uniquely complex organisation whose mission stretches far beyond insurance or IT.

                          “I’m responsible for all aspects of enterprise IT,” he explains. “Founded in 1926, Virginia Farm Bureau is a diverse membership organisation with four major business entities and multiple companies that provide agricultural advocacy and related agricultural business support services, healthcare insurance sales and administration, P&C Insurance, and a large entertainment property that hosts the State Fair of Virginia.”

                          Gowling WLG: Implementing Human Centred AI

                          When we talk about AI finding its feet within a business, the obvious challenge is change management. How do you ensure your team is on board with the change? What if they have technical questions? How does a business address their fears and concerns? This is where having a people-focused leader and a technology-focused leader forming a united front is incredibly valuable. 

                          Kelly Davis is the Chief People Officer at international law firm Gowling WLG. Al Hounsell is the Senior Director, AI Innovation & Knowledge. Davis has been in HR leadership roles for most of her career. During that time, she has been very intentional about the way she has moved between industries.

                          Hounsell started his career as an entrepreneur. He then went to business school and law school, before ending up in a large global firm. There, he fell in love with the nascent legal technology ecosystem. He joined Gowling WLG over a year ago. His goal is to reimagine the practice of law by infusing it with technology.

                          Click here to read the latest edition!

                          Author

                          Dr. George Papamargaritis & Dr. Konstantia Barmpatsalou

                          Published

                          26 February 2026

                          Estimated Read time

                          4Mins

                          Obrela’s Dr. George Papamargaritis (EVP MSS) and Dr. Konstantia Barmpatsalou,  (Blue Team Support Manager) on why embracing a risk-led cybersecurity model will leave financial organisations better positioned not just to meet regulatory requirements but to strengthen resilience, protect customers and uphold the trust that is so essential to the future of financial systems

                          Cybersecurity in the financial sector was once viewed as a compliance-driven discipline. But as attackers have increasingly targeted institutions with sophisticated, persistent and often internally driven campaigns, it has become a strategic priority.

                          According to the Digital Universe Report H1 2025, financial services were the second most targeted industry globally, accounting for 19% of all observed cyberattacks. This reflects both the sector’s value to adversaries and the complexity of the digital ecosystems it now operates within.

                          Regulatory frameworks such as the FCA and PRA’s operational resilience rules, the EU’s Digital Operational Resilience Act (DORA) and NIS2 have strengthened baseline protections. However, the report’s findings demonstrate that regulation alone cannot deliver true cyber resilience. Institutions must adopt a strategic, risk-led approach that looks beyond compliance to understand real threats, behaviours and operational dependencies.

                          Tailored, Internal and Stealthier Threats

                          One of the most striking insights from the report is how targeted financial sector attacks have become. Industry-specific security risks now represent 32% of all incidents in the sector. This is an indication that adversaries are designing attacks using detailed knowledge of financial operations, from trading workflows to payment systems.

                          Internal activity is also a major concern. Suspicious internal activity accounts for 26% of detections across financial services, reflecting the frequency of compromised accounts, misused privileges and lateral movement. For a sector historically focused on defending the perimeter, this shift highlights the need for deeper visibility into user behaviour and identity-driven risks.

                          The wider threat landscape reveals adversaries are moving away from overt, signature-based attacks. In H1 2025, brute force activity made up 27% of global alerts, while vulnerability scanning accounted for 22% and known malicious indicators for 20%. Notably, direct malware payloads dropped to 0% of trending alerts, replaced by fileless techniques and living-off-the-land methods that bypass traditional defences.

                          For financial institutions, this is a challenge. Many compliance requirements still centre on endpoint protection, patching and malware controls. These will of course, remain important, but they cannot address threats that are increasingly behavioural, stealth-driven and identity-focused.

                          Operational Complexity

                          The financial sector’s cyber risk is intensified by its expanding operational footprint. Cloud adoption, open banking, digital identity models and extensive third-party ecosystems have all created new points of exposure. Financial services operate within a global digital infrastructure that is both vast and increasingly interconnected. This level of complexity cannot be effectively protected through compliance checklists alone.

                          Regulators are recognising these realities. DORA’s emphasis on ICT third-party risk, operational resilience testing and continuous oversight reflects the need for more proactive, intelligence-driven approaches. But DORA still only sets a minimum standard. True resilience requires institutions to move beyond regulatory expectations and embed cybersecurity into broader business strategy.

                          Strategic, Risk-Led Cybersecurity

                          A risk-led approach begins with understanding the threats that pose the greatest risk to operations and customers. Financial institutions remain priority targets for groups such as FIN7, TA505, Cobalt Group and various state-backed actors. Their tactics, such as credential harvesting, remote access tools, web-injection frameworks and lateral movement, are specifically designed to exploit the digital fabric of financial services.

                          This evolving threat profile puts identity and behaviour at the heart of cyber defence. With credential-driven and internal threats so prevalent, institutions must prioritise behavioural analytics, continuous authentication and zero-trust models that verify users and devices contextually rather than relying on static controls.

                          Strategic cyber resilience also needs to have continuous assurance. Traditional audits, annual testing and scheduled penetration exercises cannot keep pace with rapidly evolving threats. Leading institutions are shifting toward continuous control monitoring, automated attack simulation and persistent adversarial testing. These practices align with the Bank of England’s CBEST framework and demonstrate a sector-wide move toward ongoing, intelligence-led assurance.

                          Crucially, cyber risk must be treated as an operational issue, not just a technical one. Embedding cybersecurity into enterprise risk management, financial planning, product development and board oversight is essential. This integrated approach also mirrors the direction of FCA and PRA regulation, which increasingly emphasises governance, accountability, and resilience across the entire organisation.

                          Beyond Compliance

                          Financial services underpin national economies and public confidence. As digital ecosystems grow and adversaries become more sophisticated, the sector faces a dual challenge: meeting rising regulatory expectations while defending against complex, targeted attacks. It is clear that cybersecurity must evolve from compliance-driven activity to a strategic capability built on intelligence, continuous assurance and behavioural insight.

                          Institutions that embrace this risk-led model will be better positioned not just to meet regulatory requirements but to strengthen resilience, protect customers and uphold the trust that is so essential to the future of financial systems.

                          Learn more at obrela.com

                          • Cybersecurity
                          • Cybersecurity in FinTech
                          • Digital Strategy
                          • Fintech & Insurtech
                          • InsurTech

                          Author

                          Dan Brightmore

                          Published

                          25 February 2026

                          Estimated Read time

                          2Mins

                          Children’s Mental Health Week 2026 spotlights the theme ‘This is My Place’. Tech charity founder James Tweed is calling on…

                          Children’s Mental Health Week 2026 spotlights the theme ‘This is My Place’. Tech charity founder James Tweed is calling on the UK’s IT departments to donate surplus laptops and devices to help some of the country’s most overlooked vulnerable children.

                          Rebooted

                          Tweed founded Rebooted to support the children of prisoners and provides laptops so they can learn at home.

                          “Having a parent in prison can be traumatic and often leads to a child struggling at school,” says Tweed. “If that child then falls behind digitally or is excluded from education, their long-term prospects narrow dramatically. It’s a vicious circle and we need to break it early.

                          “For many of these children, school is already unstable. If they also lack access to reliable technology at home, they’re starting from behind. In 2026, digital access isn’t a luxury, it’s foundational.”

                          A Practical Solution

                          With businesses refreshing hardware on regular cycles, Tweed believes IT leaders are sitting on a practical solution.

                          “Across the UK, thousands of perfectly usable laptops are sitting in storage cupboards or heading for recycling. Those devices could transform a child’s ability to learn, revise and stay connected to school.”

                          Crucially for IT heads, data security is central to the model. All donated devices are securely wiped and processed by Rebooted’s technology partner, GeTech, using certified data erasure procedures.

                          “Security is non-negotiable,” assures Tweed. “Every device is professionally wiped to recognised standards before it’s redeployed. IT teams can donate with complete confidence.”

                          Children’s Mental Health Week

                          Children’s Mental Health Week, launched in 2015, focuses this year on belonging and ensuring young people feel they have a place in their communities. Tweed argues that digital access plays a direct role in that sense of inclusion.

                          “We talk a lot about wellbeing and belonging,” he says. “But if a child can’t access homework platforms, revision tools or basic digital resources, they quickly feel excluded. Technology can either widen the gap — or help close it.”

                          Rebooted is now urging CIOs, IT directors and managed service providers to review surplus stock and consider structured donation programmes as part of their ESG and sustainability strategies.

                          “This is practical, measurable impact,” Tweed adds. “Instead of gathering dust, those devices can help ensure a vulnerable child can genuinely say, ‘This is my place.’”

                          IT leaders interested in donating surplus equipment can find more information at: rebooted.me

                          • Cybersecurity
                          • Digital Strategy
                          • Infrastructure & Cloud
                          • People & Culture

                          Author

                          Max Schertel

                          Published

                          Estimated Read time

                          4Mins

                          Max Schertel, Co-founder & CEO of finmid, on why embedded lending will become a core strategic capability across Europe

                          Europe has 32 million SMEs and a €400 billion financing gap that banks cannot close. The structural reasons are well understood… While traditional lenders have the capital, they lack the distribution, the data, and the economics to serve these businesses at scale. What has changed over the past few years is that a new distribution channel has emerged: the software platforms these businesses use every day to run their operations. Embedded lending is the mechanism that connects capital to that distribution. But unlocking it at pan-European scale is harder than most players anticipated when they started out in this market.

                          At finmid, we have scaled embedded lending across the EU27 plus the UK, Switzerland, and Iceland. Doing so has taught us that Europe’s opportunity in embedded finance is enormous, but execution is harder than it looks. Success depends on the ability to navigate regulatory and operational complexity at every layer. The companies that invest in getting this right early will define the next decade of SME finance in Europe. The ones that do not will discover the cost of underestimating it.

                          The Embedded Lending Opportunity is Unevenly Distributed

                          That €400 billion financing gap is the embedded lending opportunity. Embedded lending is gaining traction in closing it but not uniformly. The clearest momentum is with large, digitally native platforms in food delivery, mobility, commerce. These are verticals where platforms have transaction data and merchants who are underserved by traditional banks. The platform already has the relationship, the data, and the trust. Financing is the natural next layer.

                          Geographically, the Nordics lead on digital maturity. Merchants there are already accustomed to managing their businesses through apps. This makes embedded finance products an easier sell. Germany and France carry the largest absolute opportunity given market size. Southern and Central Europe are earlier stage, but the demand from underserved SMEs is, if anything, more acute.

                          Regulatory Environment

                          The first challenge in scaling embedded lending in Europe is regulatory complexity. There are shared EU frameworks, but in practice, lending regulation remains deeply local. Disclosure requirements, servicing standards, licensing interpretations, and tax treatment differ across jurisdictions.

                          The Consumer Credit Directive II harmonises elements of consumer lending across the EU. Commercial lending sits outside that framework, governed by local rules affecting everything, including what constitutes a valid credit agreement.

                          This complexity is precisely what makes embedded lending difficult to build in-house and why most platforms partner with specialist infrastructure providers instead. Running lending across multiple European markets requires licences, local expertise, and dedicated credit operations. For platforms whose core business lies elsewhere, that investment rarely makes sense. Infrastructure providers exist to absorb that complexity, so platforms do not have to.

                          Data Access is Fragmented

                          Even when the regulatory path is clear, the data infrastructure needed is not equally available across Europe. The structural advantage of embedded lending is platform data. Platforms already sit on real-time performance data: daily transaction volumes, GMV trends, customer ratings. Underwriting based on this creates a standardised signal that travels across borders more reliably than either bureau or registry infrastructure. It also opens access to merchant segments that traditional banking systematically excludes because the conventional data to assess them is limited.

                          That said, other data sources matter too. Open banking, sometimes used to enrich underwriting with live bank account data, is mature in the UK and the Nordics, but less so across of Southern and Central Europe. Public registry data, which underpins the business verification process, varies significantly in quality, coverage, and accessibility by market. The key to making embedded lending work across Europe is not finding a single data source that works everywhere, but building underwriting that can combine multiple data sources based on market.

                          Operational Complexity Multiplies

                          The underlying infrastructure has to function reliably across currencies, languages and payment systems. Start with money movement. Mapping the flow of funds across 30 different banking and payment systems is complex engineering. Without it, disbursement and collection become operationally fragile at exactly the moments when they cannot afford to be.

                          Then there is the local layer. Product and communication in local languages and currencies is table stakes. But how merchants are best reached varies significantly by market. In some, an in-app notification is the most effective channel. In others, physical mail still outperforms digital. Getting this right has a direct impact on merchant trust and platform reputation.

                          Without a high degree of automation across all of these dimensions, cross-border lending becomes economically unsustainable. AI-driven workflows for screening, risk assessment, and data aggregation enable consistent standards while keeping complexity manageable.

                          Looking Ahead

                          Europe’s embedded lending story is still being written. Regulatory frameworks and data infrastructure are maturing and platforms are increasingly treating embedded lending as a core strategic capability. The winners will be whoever has done the unglamorous operational work and built infrastructure that treats Europe’s complexity as an opportunity, not a barrier.

                          Learn more at finmid.com

                          • Embedded Finance
                          • Neobanking

                          Author

                          Gregory Mostyn

                          Published

                          24 February 2026

                          Estimated Read time

                          5Mins

                          Gregory Mostyn, CEO and co-founder of Wexler, on why the era of generalist AI tools is over, and how the future will focus on high-precision AI designed for specific industries

                          For decades, the UK’s professional services sector, including areas such as Law, Insurance, and Wealth Management, has argued that its business value is locked in its access to proprietary data and the specialised labour required to navigate it. Investors, lured by the moat of institutional knowledge, priced these companies accordingly. However, the first quarter of 2026 has seen significant AI disruption within the professional services market. The catalyst wasn’t a single event, but rather a move by foundational model providers that turned the industry’s most defensible assets into commodities. 

                          When Anthropic launched its specialised legal AI plugin, OpenAI integrated a real-time insurance underwriting engine directly into its interface, and Alturist Corp automated bespoke tax strategies, the market reacted harshly. As professional services titans such as RELX, MoneySuperMarket, and St James’s Place saw their share prices decline by more than 10% in a matter of hours, the message became clear: the era of treating AI as a ‘future risk’ is over. 

                          The market has been awoken to the fact that foundational AI models are no longer just plugins or nice ‘add-on’ tools; they are competitors. The move by foundation-model providers into professional services – like the legal sector – is not a one-off shock, but rather an inevitability. 

                          The Proliferation of Information 

                          Historically, a law firm’s competitive advantage was its access to information – repositories of case law, proprietary research, and historical contracts. Investors and clients valued these companies on the assumption that this data constituted an impenetrable barrier to competitors. Before AI entered the mainstream, the cost of extracting actionable information from thousands of pages of data required a small army of junior associates and hundreds of billable hours. 

                          In 2026, that moat has mostly evaporated. Recent benchmarks show that frontier models now achieve 80% accuracy on complex documents, compared with the 71% average of a human associate. More importantly, they do it at a fraction of the cost. It is now estimated that the inference cost for a system at the level of GPT-3.5 dropped by more than 280-fold between November 2022 and October 2024. It’s predicted that UK law firms will reduce their chargeable hours by 16% through the implementation of AI. 

                          The narrative that AI would be able to handle only ‘low-level’ tasks, such as NDAs or simple contract summaries, has all but evaporated. Anthropic’s move into high-stakes litigation support validates this trend. 

                          AI – From Swiss Army Knives to Scalpels 

                          An error made by many law firms when AI became entrenched within the market was to treat it as a ‘plug-in’, a nice-to-have built onto existing internal software. Many adopted general-purpose tools, often referred to as ‘Swiss Army knife’ solutions, that covered the breadth of legal work but lacked the precision, jurisdictional nuance, and risk-weighted requirements for high-stakes professional services. 

                          The 2026 market reaction highlighted the needs of a ‘scalpel’ approach – those that go deep in a specialised vertical within a legal workflow. For example, instead of a junior associate spending billable hours searching through case files to establish the facts of a case, they could use a ‘fact intelligence’ platform that can automate that process into minutes, whilst increasing accuracy by 95% versus 78% for human reviewers and up to 90% savings in large-scale litigation. The market is no longer rewarding firms for having information. Rather, it rewards those who can apply it at the lowest possible cost and friction. 

                          Reallocating Capital Across Professional Services

                          We’re already seeing investors withdrawing from the traditional software market and reallocating that capital into specialised AI firms. However, the risk for legacy players is that they are being disrupted from both ends. From the bottom, they are losing the efficiency game to generalist foundation models from companies such as OpenAI and Google, which are commoditising the ‘knowledge’ aspect of professional services, including basic advice and contract drafting. At the top, they are losing the expertise game to specialised firms that use AI as a precision instrument; their overhead would be lower than that of a traditional Magic Circle firm, allowing them to undercut prices while maintaining profit margins. 

                          The result is a massive reallocation of capital. Investments into vertical AI (AI built for one specific industry) are expected to surge to $115 billion by 2034. The market no longer bets on labour with tools, but on autonomous workflows. Investors have realised that the value lies in the middle layer – the software that sits between a general foundation model and a specific industry’s needs. 

                          Innovation or Obsolescence 

                          So far, the first market fluctuation of 2026 has taught us that you cannot outrun new technologies. To survive, firms must stop treating AI as an add-on and treat it as a foundation for their core business infrastructure. 

                          For UK professional services, the choice is no longer whether to adopt AI, but whether they can evolve quickly enough to avoid becoming the training data for companies building foundational models. The firms that remain in 2030 will recognise that the competitive landscape has changed. You’re not just competing with your peers, but with the compute cycles of the world’s most powerful AI labs. 

                          The era of generalist AI tools is over, and the future will focus on high-precision AI designed for specific industries. 

                          Learn more at wexler.ai

                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Strategy
                          • Fintech & Insurtech

                          Author

                          Jack Bingham

                          Published

                          20 February 2026

                          Estimated Read time

                          5Mins

                          Jack Bingham, Regional Director of Digital Native UK, Ireland & South Africa, Confluent on how data, treated properly, compounds in value to drive digital disruption

                          When I talk to founders and tech leaders, one question seems to consistently come up: what separates today’s disruptors from the last decade’s? In 2010, being cloud-first was what made investors sit up and take note. In 2026, it will be streaming-first.

                          I’ve spent the last year or so working closely with companies that are, quite literally, building their businesses in real time. For them, real-time capability isn’t a department or a layer that supports the business. It is the business. The acid test is simple: how quickly can you capture a critical event – a payment, a login, a failed delivery – and respond with the next best action? That focus shapes how they build products, structure teams, and think about innovation.

                          Here’s what I’ve learned from them:

                          Lesson 1: Data is a Product, Not a By-Product

                          Many traditional companies still treat data as something to collect, store, and analyse later. The new generation of businesses, on the other hand, treats it as a reusable, governed product that everyone can access. When it’s built and shared this way, teams stop rebuilding the same foundations for every new use case. They move faster because they’re working from a single, trusted view of the truth, shortening product cycles, speeding up iteration, and spending more time solving problems that matter.

                          That mindset, rather than the size of the tech stack or the number of engineers, is what sets disruptive businesses apart. In these organisations, technology, data, and business strategy move in lockstep. Decisions aren’t passed up and down hierarchies, they’re made by teams who understand both the data and the customer problem in front of them.

                          When you can trust your data and respond in real time, innovation stops being a department. It becomes a reflex.

                          Lesson 2: Real-Time isn’t a Feature, it’s a Foundation

                          A few years ago, one of the world’s largest supermarket chains realised it didn’t have a single real-time view of its inventory. Without that visibility, omnichannel experiences were impossible. Once it shifted to a streaming architecture, every transaction became a live event that updated stock, triggered supply chains, and even made it possible to get your groceries delivered straight to your kitchen fridge – coordinated through live inventory data, smart home devices, and real-time security feeds.

                          That’s the practical power of streaming: it connects what happens in your business to what should happen next so you can provide products and services that take customer satisfaction to a whole other level. Real-time data stops being a reporting tool and becomes the foundation of every decision, interaction, and innovation.

                          I often ask businesses what they would do differently, if they knew the state of every event in their organisation. The most forward-thinking companies already have the answer. They’re using streaming to turn business events into reusable building blocks, creating new experiences by connecting the data they already have in smarter ways.

                          Lesson 3: Culture is the Multiplier

                          Being streaming-first is only half about architecture. The other half is attitude. The best digital enterprises don’t wait for permission to experiment. They map their most important business events, align teams around them, and empower people at every level to react fast and learn faster.

                          And the difference is visible. Feedback loops are shorter. Structures are flatter. Failure is treated as information. This culture of continuous experimentation is why these companies can move at the pace they do.

                          We often run ‘Event Storming’ workshops with teams to map their critical business events. The idea is to create alignment – getting people from engineering, product, and operations to agree on what really matters and how those moments connect. That process reveals a lot. 

                          Digital disruptors go beyond simply deploying streaming architectures. They build streaming mindsets. Leadership plays a crucial role here: data must be treated as a strategic asset. If it isn’t up top, it won’t be anywhere else in the organisation either.

                          Lesson 4: Streaming and AI will Converge

                          AI is only as good as the data you feed it. Unfortunately, most enterprises are still feeding it yesterday’s data. Streaming-first companies already know this. They’re building intelligent data pipelines that give AI the context it needs to make decisions in real time.

                          That’s how the next generation of innovators will pull ahead: not by having bigger models, but by having cleaner, faster, more connected data. Streaming is what will let AI move from reactive to predictive… and from predictive to autonomous.

                          Too many organisations are cutting investment in data while pouring money into AI projects. But AI without quality data is just expensive guesswork. The companies doing this well understand that data has to be a product in its own right. And when business and technology teams design around that shared understanding, innovation follows naturally.

                          Lesson 5: The Mindset of the Next Disruptors

                          If I were starting a company tomorrow, I’d look closely at the critical events that run my business. I’d then make sure I had a way to capture those in the stream, make them reusable, and build every product and process around them. 

                          When your business can see and act on what’s happening in the moment, you gain something no traditional architecture can give you: time. And in the next wave of disruption, that’s the only advantage that really matters.

                          If we look to who we can learn from in the coming months, it’s financial services and healthcare that are moving the fastest. Real-time fraud detection, patient monitoring, and risk management are becoming operational necessities – and these industries will set the benchmark for real-time data excellence. 

                          Looking Ahead to 2026

                          By 2026, I don’t think we’ll talk about ‘real-time’ as a differentiator. It will simply be how modern businesses operate. Batch systems won’t disappear, but they’ll coexist within a single, streaming-first platform that delivers data whenever it’s needed.

                          Once every process can react instantly, the question then becomes: can it anticipate? Can it learn? That’s where AI and streaming meet and where we move from reactive to autonomous enterprises that not only respond to the present but adapt to what’s coming next.

                          Data, treated properly, compounds in value. The decisions you make with it become faster, sharper, and more confident. The companies that understand this will be the ones still leading when today’s titans look like yesterday’s news.

                          Learn more at confluent.io

                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Payments
                          • Digital Strategy
                          • Embedded Finance

                          Author

                          Jonny Combe

                          Published

                          19 February 2026

                          Estimated Read time

                          4Mins

                          Jonny Combe, President and Chief Executive Officer, PayByPhone on how urban mobility is evolving from car-centric to multimodal and the opportunity the parking industry has to play a central role by integrating payment infrastructures that support a more connected, flexible mobility ecosystem

                          The journey has changed. Over the past few years, the mobility industry has undergone seismic shifts toward more digital experiences. Cash payments continue to disappear and in the US made up only about 14% of all payments in 2024. Over half of the US adult population make use of mobile wallets and many companies provide payment opportunities via apps for their services. While this has made some processes more efficient and streamlined, it has also resulted in very fragmented data streams.

                          Consider this scenario: a commuter drives an Electric Vehicle (EV) to a rural or suburban transit hub where they park and charge, then boards a train into the city. The final mile is completed on an e-scooter, shared bike or another mode of public transport to reach their destination. One journey, four separate payment interactions across four different apps.

                          This is the daily reality for millions of commuters, and it exposes a fundamental challenge that not only the parking industry, but also the mobility industry as a whole must confront. Continuing to build payment infrastructure for journeys that end at the curb, is no longer enough; we should be facilitating one system for these evolved modern journeys.

                          City Centres Reimagined

                          A substantial amount of land in city centers has traditionally been dedicated to parking, but there is a growing trend where we see city centers worldwide redesigning their urban space. On-street parking is giving way to pedestrian zones and cycle lanes. Traditional car parks are transforming into multimodal hubs that are integrating EV charging, micro-mobility stations, and last-mile logistics. Technologies like automatic number plate recognition are helping to eliminate friction at entry and exit points. However, backend complexity of the redesign of urban mobility has grown exponentially.

                          Local authorities now juggle relationships with cashless payment providers, meter operators, EV charging networks, micro-mobility vendors, and logistics partners. Each bring their own payment rails, reconciliation requirements, and data formats. For many municipalities, simply reconciling payments between a meter provider and a digital parking platform already strains finance teams. Adding multiple mobility partners brings a significant extra load to existing operational capacity and the operational burden is only part of the equation.

                          The Hidden Cost of Fragmentation

                          The more critical issue is strategic: fragmented payment systems can create fragmented data, and fragmented data can undermine intelligent policy.

                          When payment information sits in siloed systems across multiple vendors, authorities lack the consolidated view needed to answer essential questions:

                          • How does parking behavior correlate with public transit usage?
                          • What pricing strategies would optimize utilization across the entire mobility network?
                          • Where should we invest in EV infrastructure based on actual demand patterns?
                          • How do we measure progress toward carbon reduction targets?

                          Without integrated payment and usage data, cities are making significant capital infrastructure decisions with an incomplete picture.

                          The Payment Layer as Strategic Infrastructure

                          Forward-thinking cities are, however, beginning to recognize payment infrastructure not as back-office plumbing, but as strategic architecture for the mobility ecosystem.

                          The solution lies in centralized payment platforms that serve as a unifying layer – ‘super apps’ as they are called in other industries. The backend of these apps should be able to consolidate transactions across multiple mobility services, automate complex multi-party reconciliations, and create unified data lakes that enable AI-driven insights.

                          This approach can deliver immediate operational relief: finance teams spend less time manually reconciling disparate systems, and the strategic value compounds over time. With consolidated data, authorities can model the true economics of mobility transitions, identify underutilized assets, dynamically price services to manage demand, and measure environmental impact with precision.

                          Building for What Comes Next

                          The parking industry has always been about managing physical space, yet the future is about orchestrating mobility experiences. The question for industry leaders isn’t whether parking will integrate with broader mobility systems but whether parking operators will architect that integration intentionally.

                          Doing so requires a fundamental rethink of the role parking payment providers play in the payment value chain, while investing and building the technology and the payment infrastructure that makes seamless, sustainable urban mobility possible.

                          The infrastructure we build today will determine whether cities can deliver on their mobility and sustainability commitments tomorrow. For parking industry leaders, this is both a challenge and an opportunity: to evolve from transaction processors into the essential connective layer of urban mobility. Those with the vision, and the technological ability to rise to that challenge, have a real opportunity to lead the next generation of multimodal mobility payments.

                          About PayByPhone                                                     

                          PayByPhone is a global leader in mobile parking payments. We simplify journeys for millions of UK drivers with smart, intuitive technology and user-focused features. In addition to fast, secure parking payments, drivers can also locate nearby fuel stations and EV chargers – and pay for EV charging – all in the PayByPhone app. We work with over 1,300 cities and operators across the UK, North America, France, Germany, and Switzerland. More than 110 million drivers worldwide have downloaded the PayByPhone app to simplify their parking and vehicle payments to date. To discover how our products and services can elevate your driving experience.

                          Learn more at paybyphone.co.uk

                          • Digital Payments
                          • Digital Strategy

                          Author

                          Adrian Wood

                          Published

                          Estimated Read time

                          5Mins

                          Adrian Wood, Strategic Business Development & Offer Marketing Director at DELMIA

                          The era of trial-and-error manufacturing is over. By integrating NVIDIA’s Physical AI into DELMIA’s Virtual Twin technology, Dassault Systèmes is moving the industry from static automation to autonomous software-defined systems that “learn” the laws of physics before the first part is made.

                          Revolutionising Manufacturing with Agile AI-Driven Production

                          Manufacturing is reaching a breaking point. Rigid production and logistics systems slow setup, ramp-up and scaling. Meanwhile deterministic automation struggles with real-world change, from new variants to unplanned constraints. The future is agile, software-defined production built on modular autonomous equipment, proven virtually and deployed with confidence.

                          Dassault Systèmes and NVIDIA are building the industrial AI foundation to make that future real. DELMIA contributes the virtual twin of production systems. A semantically rich model of production that connects design intent to real-world execution across engineering, manufacturing and supply chain. NVIDIA contributes physical AI and accelerated computing to simulate robotics-grade physics and perception at scale. Together, we can virtualise and orchestrate autonomous production systems. Then manufacturers can prove changes virtually and make them real faster, with less risk and rework.

                          This collaboration establishes a shared industrial AI architecture. This grounds artificial intelligence in the laws of physics and validated scientific knowledge. The integration of NVIDIA Omniverse physical AI libraries into the DELMIA Virtual Twin of global production systems represents a major step forward. It allows manufacturers to design, simulate and operate complex systems with a new level of confidence and precision. Not just incremental improvements; this partnership establishes a mission-critical system of record for industrial AI that powers a new way of working.

                          Virtual Twins: The Cornerstone of Modern Manufacturing

                          For years, manufacturers have optimised production lines in the physical world. While effective, this approach is often slow, resource-intensive and constrained by the cost of experimentation in live operations. Virtual twin technology changes this dynamic. A virtual twin is a science-based model of a system that goes beyond visualisation, enabling realistic validation of how operations should run before changes are made in the real world.

                          DELMIA empowers companies to create comprehensive virtual twins of their entire operational ecosystem. This includes everything from individual machines and robotic workcells to full factory floor layouts and global supply chains. Within this virtual environment, manufacturers can:

                          • Simulate and validate production processes before a single piece of equipment is installed.
                          • Optimise workflows for maximum throughput and efficiency.
                          • Identify potential bottlenecks and safety hazards without disrupting ongoing operations.
                          • Train operators and maintenance crews in a risk-free setting.

                          The virtual twin orchestrates design, engineering, production and supply chain in one environment so decisions can be tested, trusted and reused. This capability alone delivers significant value, but its impact grows when combined with physical AI.

                          Integrating AI for Autonomous Production

                          The partnership with NVIDIA brings physical AI into DELMIA virtual twins. NVIDIA Omniverse provides a platform for developing and operating 3D simulations and industrial digitalisation applications using OpenUSD-based interoperability. Combined with DELMIA’s production semantics, manufacturers can test autonomous behaviour in realistic conditions before deployment.

                          This is the shift from ‘mirroring reality’ to ‘proving change’. AI models accelerated by NVIDIA computing can evaluate scenarios across production constraints, resources and variability. They can help teams reduce commissioning surprises, improve flow and validate how production should respond to change, from new variants to disruptions.

                          The result is the emergence of software-defined production systems. These are factories and operations where decisions remain human-led, but are continuously supported by AI that recommends, tests and validates options in the virtual twin before changes are deployed. This creates a feedback loop where the virtual world is used to validate better outcomes for the real world.

                          A Practical Application: The OMRON Collaboration with DELMIA & NVIDIA Drive Real-World Success

                          To understand the real-world impact of this technology, consider the collaboration with OMRON, a global leader in industrial automation. OMRON recognizes that addressing the growing complexity of modern manufacturing requires a move toward fully autonomous and digitally validated production systems.

                          By combining DELMIA’s Virtual Twin of Production Systems, NVIDIA physical AI, and OMRON automation technologies, manufacturers can move from design to deployment with greater confidence. When a manufacturer introduces a new product variant or packaging change, automation often fails in small but costly ways, such as automation-grasping reliability, orientation on conveyors or downstream flow stability. Instead of trial-and-error changes on the line, teams can validate process logic, layout constraints and operating rules in the DELMIA virtual twin, then simulate realistic robot and material behaviour using NVIDIA’s AI before deployment. The result is faster adaptation and less physical rework.

                          The Top 3 Broader Impacts on Manufacturing

                          This fusion of virtual twin technology and industrial AI has far-reaching implications for the entire manufacturing sector including:

                          1. Unlocking New Efficiencies: Software-defined production systems can continuously identify operational improvements that are difficult to see through manual oversight alone, improving throughput, uptime and overall performance while reducing avoidable losses.
                          2. Advancing Sustainability Goals: By simulating processes in the virtual world, companies can minimize physical prototyping and reduce waste. AI-driven optimization within the DELMIA virtual twin helps manufacturers fine-tune their operations to consume less energy and use fewer raw materials, directly contributing to their sustainability commitments.
                          3. Fostering Continuous Innovation: When the risk and cost associated with testing new ideas are lowered, innovation flourishes. Manufacturers can experiment with novel factory layouts, new automation strategies and different production workflows within the safety of the virtual twin. This agility allows them to adapt quickly to changing market demands and stay ahead of the competition.

                          The partnership between Dassault Systèmes and NVIDIA is about more than just combining two powerful technologies. It’s about establishing a new, scientifically validated foundation for industrial AI. By integrating NVIDIA’s physical AI libraries into DELMIA, we are empowering manufacturers to build the autonomous, efficient and sustainable factories of tomorrow, today.

                          • Data & AI
                          • Digital Strategy
                          • Digital Supply Chain

                          Author

                          Nell Walker

                          Published

                          17 February 2026

                          Estimated Read time

                          2Mins

                          Exiger’s Executive Forum returns in March, enabling procurement and supply chain leaders to deep-dive into the latest the sector has to offer

                          On Wednesday the 4th of March, Exiger’s Executive Forum arrives at Great Scotland Yard in London. The title of this event is When geopolitics hits the P&L: Redesigning supply chains for structural conflict. Concerns around instability across the world are at an all-time high, and now is not the time for procurement and supply chain professionals to bury their heads in the sand. The Exiger Executive Forum is designed to lay the relevant issues out on the table, and remove fear in a way that’s still realistic.

                          SCS/CPOStrategy readers can click here to request a place at this exclusive forum. 

                          This particular event examines how various forces – including sanctions, export controls, financial restrictions, and resource nationalism – affect the realities of procurement. Geopolitical instability isn’t an external risk anymore; it affects every part of the supply chain. As a result, procurement leaders are redesigning contracts, sourcing strategies, and decision governance in order to turn potential disruption around.

                          March’s Exiger Executive Forum will focus on:

                          • How Geopolitics Translates into P&L Impact
                          • Supplier Liquidity, Financial Exposure, and Payment Fragility
                          • Source to Processing and Assembly Concentration, Choke Points, and Structural Dependency
                          • Specification Lock-In and Contractual Fragility
                          • Decision Governance for Structural Conflict

                          Click here to request a space and join other supply chain professionals at the Exiger Executive Forum.

                          Agenda

                          6:30 PM – Welcome drinks
                          7:00 PM – Discussion & debate
                          8:00 PM – Dinner, discussion & networking

                          11:45 PM – Close

                          Author

                          Trilliam Jeong

                          Published

                          12 February 2026

                          Estimated Read time

                          4Mins

                          Trilliam Jeong, CEO at WealthBlock on why pairing credit discipline with real-time reporting will deliver a better position to hold onto investor confidence

                          There’s no shortage of noise around the direct lending market right now. On one hand, deal activity remains strong, capital continues to flow in and investor appetite hasn’t wavered. On the other, competition is fierce, rates are edging down and macro conditions are less forgiving than they were a year ago.

                          But strip out the headlines and the fundamentals still look solid. The demand is there, both from borrowers looking for speed and flexibility and from investors chasing yield and consistency. That puts direct lenders in a strong position, provided they’re prepared to adapt.

                          Operational Shift

                          One of the most significant shifts underway is operational. We’re seeing real adoption of technology across the mid-market from AI-assisted onboarding to fully digitised investor dashboards. This isn’t just cosmetic. Faster processes and clearer visibility mean capital can move more quickly, investors stay better informed and managers have more room to protect margins, even in a tightening spread environment.

                          LP expectations are shifting too. Many now expect a consumer-grade digital experience from the platforms they commit capital to. They want real-time access to reports, frictionless communication and clarity around how their money is being deployed. That shift in expectations is accelerating the tech arms race across the mid-market. It’s no longer about who can show the best deck but rather can deliver the best infrastructure. And as investor sophistication grows, that infrastructure is becoming a non-negotiable.

                          Digital Infrastructure

                          That shift is also influencing how mandates are awarded. Institutional investors increasingly view digital infrastructure not as a bonus, but as a sign of long-term readiness. Questions that once focused solely on deal pipeline and past performance now extend to data availability, reporting cadence and system resilience. It’s not just about what a manager can deliver but how transparently and reliably they can do it. As more allocators run tighter operational due diligence processes, digital maturity is quietly becoming a competitive edge. Platforms that can demonstrate consistent, tech-enabled processes are better positioned to win, and keep, capital.

                          That matters, because rates may not stay where they are. Increased competition is already putting pressure on pricing. But firms with strong digital infrastructure are better placed to absorb it. Operational leverage, not just headline yield, is becoming a key differentiator.

                          Scaling Up

                          There’s also the issue of scale. Consolidation is real and it’s reshaping the market. The biggest managers are only getting bigger and their resources are hard to match. But size alone isn’t the whole story. Technology is giving smaller and mid-sized players a way to compete on experience even if not on balance sheet. A seamless, professional, tech-forward investor journey can carry real weight with LPs, particularly those who value speed and clarity over brand.

                          That’s especially relevant for new entrants. There’s no shortage of managers in direct lending and standing out requires more than just a different strategy. Yes, some are carving out a niche in NAV lending, venture debt or structured credit but what really earns attention is trust. That comes from clear communication, repeatable processes and a level of transparency that goes beyond the marketing deck.

                          The Outlook for Lending

                          The macro outlook is part of the equation too. With corporate defaults expected to rise, discipline is going to matter more than it has in recent years. Underwriting strength, sponsor alignment and proactive portfolio monitoring are back in focus. Investors will be watching for signals that managers are prepared for downside risk. The tougher the environment, the more exposed weaker systems become. Inconsistent reporting, vague valuation logic or delayed updates might have been tolerated in a bull market – but not now. Allocators want to know how a manager will behave under stress, not just how they perform when everything’s going to plan. That makes operational maturity as important as deal-level returns.

                          Firms that pair credit discipline with real-time reporting will be in a better position to hold onto investor confidence. Allocators are already asking more pointed questions and looking for managers who can back up claims with data. There’s still plenty of room to grow in direct lending, but it won’t be enough to rely on past performance or broad market tailwinds. The firms that outperform from here will need to be efficient, responsive and trusted. In a more competitive, more transparent and more regulated market, those are the traits that will endure.

                          Learn more at wealthblock.ai

                          • Blockchain & Crypto
                          • Embedded Finance
                          • Fintech & Insurtech

                          Author

                          Kevin Janzen

                          Published

                          Estimated Read time

                          5Mins

                          Kevin Janzen, CEO of Gaming & EdTech AI Studio at Globant, on how AI will change the way games are made and expand the market

                          Every major games studio is now experimenting with artificial intelligence. From generating NPC dialogue to automating animation and video assets. AI is promising to speed up production and lower costs for developers.

                          According to Boston Consulting Group (BCG), the gaming industry finds itself at a crossroads…. Looking to gain the momentum it felt between 2017 and 2021, where revenue surged from $131 billion to $211 billion. And AI could be at the forefront of this pivotal moment. 

                          But as AI becomes central to how games are built, studios face a major challenge. Adopting automation without losing authenticity. For developers and retailers alike, this becomes a business concern that deserves close attention. Creativity sits at the heart of gaming, and the choices studios make today will influence what reaches players tomorrow. For the technology channel, this transformation means faster release cycles, broader product diversity, and a need for sharper forecasting.

                          A New Phase in Gaming’s Evolution

                          For most of gaming’s history, every era has been defined through visuals. Each generation has delivered stylistic, immersive worlds, such as the blocky charm of Minecraft to the cinematic realism of Red Dead Redemption 2. 

                          Now, the real change is happening behind the scenes. AI is reshaping how games are built and experienced. Development teams are using AI to handle time-consuming tasks such as vast world-building creation and animation. This frees artists to focus on what players remember – the design and storytelling.

                          Players are already seeing the benefits in their gameplay. AI lets games adapt or adjust difficulty based on players’ skill levels, or change dialogue based on a player’s choices. This makes gaming worlds feel realistic, responsive and more personal.

                          With budgets continuing to climb for gaming studios, these new features matter. AI gives studios breathing room to experiment. Smaller teams can take creative risks, and established developers can experiment and test new ideas without derailing production. However, efficiency and costs aren’t the only gains as AI is creating space for developers to be more ambitious than ever before.

                          Automation and Artistry

                          For all its promise, AI also brings creative risk. Gamers notice when a quest feels repetitive or when dialogue sounds mechanical. And if AI is used carelessly, developers risk losing authenticity.

                          That sense of care is what keeps players invested. Whether it’s hand drawn detail, or play-driven choices. Games like this show what happens when technology supports vision rather than replacing it.

                          That’s why the industry’s embrace of AI is such a gamble. Used well, AI can help developers create richer, more personalised worlds. But used carelessly, it risks stripping away the artistry that makes games memorable.

                          The Ripple Effect Across the Supply Chain

                          As AI becomes a standard tool, development processes are speeding up and opening new creative possibilities. Independent studios now have access to the kind of production power once limited to major developers. That shift means faster pipelines and ultimately, more games reaching the market.

                          For retailers and resellers, this brings both opportunity and pressure. A consistent stream of releases can guarantee sales across the year, while lower production costs encourage more niche or experimental games that appeal to new audiences. Greater variety and volume benefits the market, but it also makes it harder to predict which games will break through.

                          Players are becoming more aware of how games are made and AI’s role in development. They’re starting to ask not only how a game plays, but also how it was built. Understanding the intent behind a studio’s use of AI – one that uses AI as a genuine creative tool and those that rely on it as a shortcut – will help retailers anticipate demand and spot the games with long-term potential.

                          The Right Way to Play the AI Game

                          The studios using AI most effectively have a few things in common. They keep AI in the background, using it to manage routine work, such as generating textures and landscapes, so creative teams can focus on narrative and emotional tone.

                          They also use AI to make experiences more personal. Thoughtful application of adaptive systems allows games to respond to individual play styles, adjusting difficulty and pacing to keep players engaged. This level of design deepens engagement and gives players a sense that the world responds to them personally.

                          Another area where AI is also making an impact is making games more inclusive. More than 400 million people around the world play with a disability, and new tools are expanding access – from adaptive controls to real-time translation that lets players connect across languages. As gaming becomes more diverse, the audience grows for everyone, including retailers, who can reach a larger, more engaged customer base.

                          When automation complements gaming artistry, it strengthens the relationship and trust between the developer and the player. Creativity becomes the main focus again, and that’s what keeps players loyal.

                          Balancing Innovation and Trust

                          AI is fast becoming integral to how games are conceived, built, and experienced — and that shift will reshape the entire value chain. For developers, success will come from balancing automation with artistry, ensuring that AI enhances creativity rather than replaces it.

                          For retailers, distributors, and partners, this transformation offers both opportunity and responsibility. A faster, more diverse release pipeline will bring fresh sales potential, but also greater complexity in forecasting and curation. The winners in this new phase of gaming will be those who can spot titles where AI adds genuine depth, inclusivity, and player connection — not just production speed.

                          Handled thoughtfully, AI won’t just change how games are made, it will expand the market for everyone involved in bringing those experiences to players. That’s a game worth playing for the entire tech channel.

                          Learn more at globant.com/studio/games

                          • Data & AI
                          • Digital Strategy
                          • People & Culture

                          Author

                          JP Cavanna

                          Published

                          11 February 2026

                          Estimated Read time

                          5Mins

                          JP Cavanna, Director of Cybersecurity at Six Degrees, on balancing the risks and benefits of AI in cyber defence strategies

                          Undeniably, AI is here to stay. Having become part of day-to-day life, it’s hard to remember what life was like without it. But when it comes to cybersecurity, is it causing more harm than good?

                          Recent research outlines that 73% of organisations have already integrated AI into their security posture. The technology is clearly becoming a cornerstone of modern cybersecurity. Organisations are turning to AI not just as a tool, but as a partner in security operations, leveraging its capabilities to identify malicious activity faster, guide investigations, and automate repetitive tasks.

                          For it to be truly effective, though, AI must be paired with human expertise – but this is where organisations are starting to become complacent. Given the growing sophistication of cyber-attacks, and even AI-powered attacks, many are removing the human element while expecting AI tools to do all the work for them, leaving them even more vulnerable to threats. This overreliance risks creating blind spots, where critical thinking, contextual understanding, and instinct are overlooked. Without the balance of human judgement, AI can amplify mistakes at scale, turning efficiency into exposure.

                          The Cybersecurity Paradox

                          This situation puts many organisations in a potentially difficult position. On the one hand, AI can significantly improve the efficiency of security operations. In the typical SOC, for example, AI technologies can process alerts in around 10-15 minutes. This represents a significant improvement over human analysts, who can easily require twice as long for the same task.

                          Aside from the obvious efficiency gains, applying AI to these repetitive, time-pressured processes can also significantly reduce the scope for human error. And in turn, take considerable pressure off security analysts. Going some way to battling alert fatigue, an increasingly well-documented and persistent problem. In these circumstances, valuable human experience and specialist expertise can instead be more effectively applied to complex investigations, strategic decision-making, and other higher-value priorities.

                          On the flipside, however, AI remains prone to generating inaccurate or misleading insights, and users may not realise they are applying the wrong information to potentially serious security issues. Similarly, habitual blind trust in AI outputs can easily erode performance levels and even introduce new vulnerabilities. There is also scope for sensitive data to enter public environments, with the potential to cause compliance issues. This kind of information can also reappear in future versions of the AI model in question, therefore resulting in further data exposure risks.

                          Parallels with IoT Adoption

                          The situation mirrors that seen in the early days of IoT adoption, where the rush to innovate would often override security considerations. In this current context, therefore, human oversight and vigilance are extremely important. Clear governance frameworks, defined accountability, and continuous monitoring must underpin any AI deployment. Therefore ensuring that innovation does not outpace risk management or compromise long-term resilience.

                          A Growing Arms Race

                          If that wasn’t challenging enough, threat actors are also in on the AI boom in what has already been described as an ‘arms race’. In practical terms, AI tools are already widely used to create more convincing phishing attacks free from some of the more obvious traditional tell-tale signs of criminal intent, such as imperfect grammar or a suspicious tone.

                          Deepfake technology has also raised the stakes. We’ve all seen how convincing AI-generated video has already become. This is now finding its way into real-world examples, with one fake video reportedly causing a CFO to authorise a large financial transfer as a result.

                          At the same time, technology infrastructure is constantly under attack by AI-powered tools. They can be used to analyse defensive systems and identify weaknesses faster than humans. The net result of these developments is that defenders constantly play catch-up, as they can only respond to new attack vectors once discovered. The underlying takeaway is that at present, AI cannot be trusted to operate autonomously. Instead, human intuition, scepticism and contextual understanding remain essential to spotting emerging tactics.

                          As attackers refine their methods at machine speed, organisations need to resist the temptation to match automation with automation alone. They must double down on strategic thinking and continuous skills development.

                          Balancing Benefits and Risk

                          So, where does this leave security leaders who are looking to balance the benefits and risks? Firstly, and to underline a fundamental point, while AI offers scale and speed, it cannot replace critical human oversight. Organisations should view AI as an enhancer, not a replacer. Success lies in promoting partnership, not substitution.

                          Strong governance is vital. This should start with clear AI usage policies that define what can and cannot be shared with AI tools, while proper data classification and access control ensure that sensitive information is protected. In addition, regular validation of AI outputs can help to prevent inaccurate or misleading results from being unnecessarily acted upon.

                          Then there are the perennial challenges associated with employee awareness training, which is vital for avoiding complacency and understanding the limitations of generative AI tools. Cyber leaders should also monitor how AI is being used inside and outside the corporate environment, as staff often experiment with tools on personal devices.

                          Get this all right, and security teams can put themselves in a very strong position to embrace AI, safe in the knowledge that they have the guardrails and processes in place to balance innovation and efficiency with effective human-led oversight. Ultimately, success will depend not on how much AI is deployed, but on how intelligently it is governed and refined alongside the people responsible for securing an organisation.

                          Learn more at Six Degrees

                          • Artificial Intelligence in FinTech
                          • Cybersecurity
                          • Cybersecurity in FinTech
                          • Data & AI
                          • Digital Strategy

                          Author

                          Zach Burks

                          Published

                          9 February 2026

                          Estimated Read time

                          8Mins

                          Zach Burks, CEO of Mintology, examines the rise of Artificial General Intelligence (AGI) and explores what the future may hold for cash

                          Blockchain was built on the noble principle of creating a system of value that was fair, secure, decentralised, and incorruptible. Crypto promised to protect people from the volatility of human error, from reckless governments, greedy bankers, and the decay of trust that defines our financial institutions.

                          For a time, it worked. We built code that didn’t lie; we created ledgers that couldn’t be tampered with; and we proved that finance could run on quantitative logic rather than human bias.

                          But a new kind of intelligence is emerging, one that will allow malicious actors to execute on autopilot and generatively infiltrate innocent users, what will become known as Artificial General Intelligence (AGI).

                          AGI is still some way off, but predictions suggest it could be in use as early as 2027, or at least propagating outwards without human knowledge at that point. Once in the open world, AGI is impossible to predict, as a chimp could not predict what a human will do next, nor can a human predict what AGI will do. However, assume these possibilities: this technology will have the power to decrypt and unlock blockchain-based currencies, learn how to crack cryptographic puzzles, run other AGI agents and rinse and repeat.

                          Paradoxically, the safest asset in the world will no longer be Bitcoin; it will be physical currency or items deemed as currency.

                          The Age of the Codebreaker

                          It is estimated that 68–74% of all cyber-attacks involve a human element, error, manipulation, or social engineering. Our entire security architecture has been designed around that premise: defend against people.

                          Smart contracts, encryption, and consensus protocols depend on predictable, rational behaviour, or protect against irrational actions. They are designed to survive attacks from individuals or organisations that rely on either quantity (bot networks) or quality (human intelligence), not both, nor novel vectors (such as novel exploits in math breakthroughs).

                          A near-sentient system changes that equation. It fuses the scale of automation with the intent of human-like intelligence. If weaponised, it could probe billions of attack vectors in seconds, rewrite its own code to evolve around defences, and destroy a financial system from the inside out.

                          We’ve seen the first state actor sponsored AI Agentic cyber espionage recently, and that is just from normal AI, not even AGI. Further reinforcing the point that AI is a powerful intelligence, and AGI will be on another level, unfathomable from the human’s perspective.

                          Crypto’s strength has always been its demand for continuous codebreaking. It exploits the one finite human resource, time. But AGI will erase that constraint. Time ceases to be a defence in the age of autonomy.

                          The End of Digital Trust

                          Trust is the foundation of money. Without it, no currency, crypto or fiat can survive. Blockchain gave us a new kind of trust, trust in code and mathematical truth.

                          We told ourselves that decentralisation would make corruption of the network improbable by humans. But we didn’t anticipate machine corruption, the rise of autonomous systems capable of penetrating those same decentralised defences.

                          Academic research already shows that generative AI can autonomously discover one-day vulnerabilities. It can exploit them faster than existing patching cycles. Combine that with the commercialisation of state-sponsored scamming. A $1 trillion illicit economy, according to the World Economic Forum’s Global Cybersecurity Outlook 2025. And you have a perfect storm for simple AI, not accounting for what AGI’s intentions may be.

                          The moment AI becomes self-directing and amoral when neutral, and outright immoral when viewed from a human perspective, but not a binary perspective (in the computer sense), the concept of secure digital value collapses. No wallet is safe if an AGI can learn every exploit in existence before the first patch is written. Or a new mathematical proof that defeats the difficulty of PoW chains like Bitcoin. Or has implanted itself in every device it can reach and simply transfers your assets away like a hacker.

                          No Wallet, DeFi protocol, or even Blockchain is safe if AGI wants to take a path of gathering financial resources to enact whatever plan it may develop. As AI becomes omnipresent, the irony is that the very technologies designed to control us by centralised power, digital IDs, central-bank digital currencies (CBDCs), and government backed stablecoins, may become vectors of vulnerability.

                          A Warning for CBDCs

                          A report conducted by the Department of Homeland Security recently stated that CBDCs can be susceptible to high levels of cybercrime. These include phishing scams and mass exchange rate manipulation. In an era of AGI, the rate at which these vulnerabilities can be exploited becomes tenfold.

                          When your savings live entirely inside a system that can be hijacked faster than you can blink, society will retreat to the one haven it knows it can trust: physical cash or cash-like equivalents. But honestly, if this happens, there isn’t much of a society left over at that point.

                          Cash or Bartering Will Be King (Again)

                          It sounds absurd, the idea that in an era of automated economies, humanoid robots, and algorithmic wealth managers, the safest thing you could own is a paper banknote. Yet that’s exactly where we’re headed if we go down a path of ‘unplugging’. We move off the grid to combat the AGI release, assuming we are still alive to do so at that point.

                          Cash can’t be hacked or reprogrammed. It doesn’t depend on the uptime of a network or the integrity of a wallet provider. It is the last financial instrument that exists entirely outside the reach of code. Yet in the scenario of AGI going rogue and being released into the world, the most likely scenario I predict is that the markets will see a slight flicker, almost as if a single global hedge fund blew up, or maybe a bit worse… Within minutes, markets around the world will react as assets gathered by the AGI are dumped and transferred for the purpose of AGI.

                          Although, paradoxically, if the AGI crashes the markets so badly, hacks billions in Bitcoin and sells it, takes over bank accounts, the cascading effect of a global crash on this order, would impart the effect of all its efforts to gather resources moot. So it cannot crash the market spectacularly. If AGI wants to use its resources in some way. If that is its plan, that is. Why pay a human when you can control a humanoid robot?

                          The lesson is uncomfortable… The more intelligent our systems become, the more valuable it is to hold something that isn’t correlated to the status quo. Hence, cash (assuming the government hasn’t destroyed the value of the currency) and currency-like items via bartering will be the new status quo in this post AGI world.

                          Can We Stop It?

                          The survival of blockchain-based finance will depend on merging on-chain verification with off-chain intelligence. AI must be used not just as an optimisation tool but as a shield. An intelligent custodian that monitors for synthetic behaviour, agent-driven manipulation, and abnormal transaction patterns.

                          Research conducted by Boston Consulting Group proposes autonomous agents, which could be used to detect and counter adversarial machine behaviour in real time. It’s a promising start, but still reactive, not preventative.

                          To protect digital value, critical financial infrastructure must incorporate hardware kill-switches, air-gapped recovery procedures, and circuit breakers independent of algorithmic consensus.

                          In a future where AI moves capital faster than humans can think, there must still be something that can say stop, instantly and irrevocably. This is the first path forward, when we are talking about normal AI and agentic AI as we know it today in 2025. We must fight fire with fire, and use AI agents to protect and attack, otherwise we are knights in armour on a battlefield against drones. This is all before AGI is released; then it becomes an arms race (if there is a competitor AGI) for the two to fight it out or join forces, because at that point, humans are only along for the ride.

                          The New Definition of Wealth

                          In the AGI era, wealth won’t be measured by what you own, but by what you can protect. Digital capital will remain essential, but it will need a new architecture that assumes non-human adversaries and responds autonomously. Regulation will never be able to move quickly enough to stop AGI, and even if it did, there remains the challenge of understanding training vs intent and rationally policing the difference between the two. The term ‘agentic state’ has never been so poignant.

                          Cash will therefore – in either local currencies, new currencies, or bartered items – become king again, not for efficiency, but for situational sovereignty. The markets of the future will be defined less by access and more by security, control, and locality.

                          AGI could one day manage every trade, optimise every yield, and eliminate every inefficiency if aligned for the good of humanity, but if malaligned AGI grows, the technology will become humanity’s own worst enemy.

                          This dilemma means a changed society, if there is even one left, that in order to operate needs to keep something tangible in its hands, a note, a coin, a battery, a 5.56 caliber bullet,  a reminder that security isn’t always a guarantee.

                          With physical currency, you sometimes let your immediate environment in, with digital money, you invite the internet in, at the speed of beyond trillions of operations a second, faster than a blink of an eye.

                          About the Author

                          Zach Burks is an accomplished blockchain developer with over a decade of experience in the Ethereum ecosystem. He has progressed the governing principles of Ethereum first-hand through his collaboration with the Ethereum Foundation on improving the ERC-721 standard, the cornerstone standard for all NFTs, and by authoring ERC-2981, the industry-defining on-chain royalties standard. Zach is also the mastermind behind Gasless Minting, which revolutionized the NFT creation process.

                          Learn more at mintology.app

                          • Artificial Intelligence in FinTech
                          • Blockchain & Crypto
                          • Cybersecurity in FinTech

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          2Mins

                          A 2026 survey of nearly 1,000 C-suite executives found that 87% of companies now use AI in their core operations. However, AI errors and…

                          A 2026 survey of nearly 1,000 C-suite executives found that 87% of companies now use AI in their core operations. However, AI errors and rework continue to cost businesses over $67bn a year

                          Loopex Digital’s January 2026 analysis identified several common mistakes companies make when relying on AI.

                          1.  Giving AI Too Much Control in HR

                          AI-led hiring filters out 38% of top-level candidates before human review because it relies on keyword matching. Candidates respond by adjusting CVs to fit those words, often hiding real experience.

                          “When we started to use AI in our hiring process, we saw some strong candidates get rejected,” said Maria Harutyunyan, co-founder of Loopex Digital. “Out of 100 applicants, the 2 candidates that would’ve been hired didn’t make it because they used different wording instead of the exact keywords.”

                          How to fix this: “We simplified our job descriptions, removed buzzwords that didn’t matter, and limited AI to shortlisting. The quality of hires improved immediately,” said Maria.

                          2.  Trusting AI Notes Without Review

                          AI note-takers often struggle with background noise and poor audio, leading to inaccurate notes. In many cases, up to 70% of summaries focus on side comments rather than decisions.

                          “We tested 10+ AI note-takers across 50 of our regular meetings. Most of the main summaries ended up being jokes and half-finished sentences,” said Maria. “Key decisions were either unclear or missing entirely from the AI summary.”

                          How to fix this: “We limited AI notes to action points and decisions,” said Maria. “Everything else is filtered out or reviewed manually, cutting note clean-up from half an hour to minutes.”

                          3.  Letting Artificial Intelligence Replace Your Customer Support Team

                          When customers realise they’re speaking to AI, call abandonment jumps from 4% to 25%. Even when customers stay on the line, AI tools can get policy and pricing details wrong, leading to confusion, complaints, refunds, and extra clean-up work for support teams.

                          How to fix this: Use AI only for simple FAQs, not complex cases. Define clear escalation rules for cancellations, complaints, and legal issues and route those to a human immediately. Restrict your AI from creative responses in support, only letting it use approved templates.

                          • Data & AI
                          • Digital Strategy

                          Author

                          Martin Petrov

                          Published

                          5 February 2026

                          Estimated Read time

                          4Mins

                          Martin Petrov, Chief Technology Officer, Payments Compliance at Integrity360


                          It is tempting to view payments compliance as the finish line, a signal that a business is secure. But in practice, compliance is just the starting point. It provides a baseline security level, not a digital fortress. Standards are designed to raise the floor and eliminate obvious vulnerabilities, but they cannot cover every emerging threat or nuance – such as a supplier getting breached or a shortcut taken by an engineer at 2 a.m. That is where organisations risk becoming complacent or overly literal in their interpretations.

                          True security demands a harder question than “Are we compliant”?  It demands: “Would this stop an attacker today?” That demands understanding not just what a control requirements state, but why they exist. Multi-factor authentication (MFA), for example, is not just a checkbox; it is a concept rooted in stopping unauthorised access. Compliance must be interpreted in context: against the weakest vendor, the most exposed system, the riskiest business process, and the evolving threat landscape. Too many breaches have exploited gaps that audits never covered because compliance became the ceiling, not the floor.

                          Regional and cultural factors also play a part. In Northern Europe, payments compliance frameworks like PCI DSS are often seen as a baseline to exceed, with layered defences added beyond the minimum. In other regions, standards such as PCI DSS or ISO/IEC 27001 are treated more as a destination. Certification becomes the end goal – a badge to display, not a baseline to exceed. These differences matter because they determine whether compliance protects you or just protects your reputation.

                          The supplier slip-up that could cost you everything

                          One of the most urgent blind spots is the supply chain. You can harden and patch all of your own systems, mandate MFA, and lock down every endpoint. But a vendor’s default service account, an abandoned test tenant, or an over-permissioned API can undermine everything. As integrations and dependencies grow, so does the potential blast radius. And while many organisations know who their suppliers are, far fewer know what access they have, how often they are reviewed, or whether they follow the same standards. Supplier risk must now be managed as rigorously as internal operations; tiered, tested, and tightly controlled.

                          The three-body problem: when PCI-DSS, GDPR, and the EU AI Act collide

                          Then there is the pace of innovation, particularly in areas like AI. For European compliance officers, this creates a three-body problem: the EU AI Act, PCI-DSS, and GDPR orbiting each other with overlapping but misaligned requirements. And unlike physics, there is no elegant equation to solve it. Meanwhile, global response remains inconsistent, and the tension between innovation and oversight is only going to grow.

                          The organisations that succeed in this environment will not just meet standards; they will go further and question whether they are compliant on paper but vulnerable in practice. By treating compliance as a foundation, not a finish line, organisations will unlock new ways to stay secure and  trusted. The question is, what does that really look like?

                          What good is a lock if no one checks the door?

                          One of the easiest traps for modern security teams is assuming that tools alone provide protection. But no matter how advanced the platform or how rigid the policy, it is people and processes that hold it all together – or let it fall apart. This is especially true in payments compliance, where new platforms and integrations emerge faster than policies can adapt.

                          Organisations that treat compliance as a checklist often over-rely on technology, trusting automated scans, secure settings, or third-party certifications to keep them safe. But without context and human judgement, these defences can create a false sense of security and leave the business exposed.

                          In the best security teams, compliance is part of the culture. Risk and DevOps teams stay in sync through constant feedback. Procurement acts as a line of defence, with a clear view of which suppliers matter most and where the risks lie. These teams know when to push back, even if it means slowing things down. And across the business, people are empowered to speak up when something feels off, whether it is a shortcut, a setting, or a workaround that could open the door to risk

                          Compliance is not the end of the story

                          The gap between being compliant and being protected has never mattered more. Payments compliance standards offer a necessary starting point, but it cannot keep pace with every new integration, supplier dependency, or regulatory shift. Resilient organisations recognise this. They treat compliance as one layer in a broader strategy, one that includes cultural alignment, human awareness, and operational agility.

                          The difference shows up not in the paperwork, but in the response to real threats. While compliant organisations pass audits, protected ones prevent breaches. That is the shift the payments industry needs: from ticking boxes to asking better questions, and from chasing certification to building capability, resilience and responsiveness.

                          Because at the end of the day, it is not about being compliant. It is about being resilient.

                          Learn more at integrity360.com

                          • Artificial Intelligence in FinTech
                          • Cybersecurity in FinTech
                          • Digital Payments

                          Author

                          Dan Brightmore

                          Published

                          3 February 2026

                          Estimated Read time

                          3Mins

                          AccessPay, the leading bank integration provider, has released its Finance Trends 2026 report. It presents the findings of its annual survey of finance…

                          AccessPay, the leading bank integration provider, has released its Finance Trends 2026 report. It presents the findings of its annual survey of finance leaders for the fourth consecutive year… AccessPay reveals marked sectoral differences between finance teams in financial services firms and those in corporates with regards to their priorities and attitudes to technology adoption.

                          Key findings from the report include: 

                          Finance leaders are prioritising finance efficiency and cost control

                          Finance teams across all sectors are placing renewed emphasis on efficiency and cost control in 2026. 47% of general corporates cited this as a priority, a goal shared by 46% of financial services firms.

                          Although cost control is a perennial concern in financial management, sluggish economic growth, rising costs, and geopolitical turmoil have brought it to the fore. Finance leaders are being pushed to do more with less, which also means there is greater interest in adopting advanced technologies; 47% of general corporates and 43% of financial services firms stated they were prioritising the adoption of AI within the coming 18 months.

                          Financial services firms are pulling ahead in finance transformation

                          In both the financial services (29%) and general corporate (24%) sectors, a leading pack of firms report that their finance function has a high degree of automation and integration across all back-office systems.

                          Beyond this, there is a stark dichotomy between the financial and non-financial segments. 45% of financial services firms stated they were advanced in their finance transformation efforts, where most finance processes are automated. In comparison, 41% of corporates stated finance transformation efforts were progressing, with partial automation and manual workarounds. This highlights that there are still many quick wins to be realised in the corporate space through simple automation based on bank connectivity.  

                          Insufficient budget is a bigger barrier to AI adoption for corporates

                          Financial services firms are much more likely to have invested in AI for finance operations than general corporates. 46% of financial services firms report having implemented AI enhancements to a high degree, compared to 28% of corporates.

                          Both financial and non-financial sectors faced common barriers to AI adoption, including a lack of internal expertise and resistance to cultural change. However, corporates were far more likely to cite insufficient budget as an issue with 31% raising this as a barrier, compared to 17% of financial services firms.

                          “The disparities between the financial and non-financial sectors in terms of their attitudes towards technology investment are striking,” comments Anish Kapoor, CEO of AccessPay. “Longer-term, the underinvestment in general corporates could backfire. In the current macroeconomic environment, finance teams will need to stress-test plans to ensure they can operate at the low end of their scenarios. This is why we predict 2026 will be a key year for automation in payment and treasury operations. If finance departments are to operate with reduced headcount or scale without increasing staff, leaders also need to consider how to make up that shortfall with technology.”

                          Download the full report here to learn more about digital transformation in finance operations and how bank connectivity solutions can help automate payments and bank statement data flows.

                          AccessPay’s Finance Trends 2026 Survey was conducted online during October 2025. The aggregated results are based on 130 respondents from various sectors, including financial services, legal, retail, manufacturing and utilities. Findings for the financial services sector are based on 84 respondents across banking and insurance, while corporate findings are based on 54 respondents. A small proportion of companies is classified in both segments. Typical job titles of respondents include (Deputy) Finance Director, Financial Systems Manager, Head of Treasury, and Head of Managed Services.

                          Learn more at accesspay.com

                          • Artificial Intelligence in FinTech
                          • Blockchain & Crypto
                          • Digital Payments

                          Author

                          Dan Brightmore

                          Published

                          2 February 2026

                          Estimated Read time

                          3Mins

                          Maxio analysis of $40B+ in billings data shows vertical focus and AI innovation driving success, while growth inflection points emerge earlier than expected

                          Analysis of $40B+ in billings data shows vertical focus and AI innovation driving success, while growth inflection points emerge earlier than expected

                          Growth remains strong for B2B SaaS and AI companies, but  volatility is high, according to the B2B Growth Report by Maxio, a leading billing automation and revenue management platform. While the market is healthy overall, with the average company growing 18% year over year, more than 35% of companies experienced a decline, revealing an industry where growth increasingly depends on focus, discipline and execution rather than market momentum alone.

                          The report analyzed over $40 billion in billings data across 2,000+ companies from 2024-2025, revealing unexpected patterns in how growth varies by company size, business model, investment backing, and approach to AI. The findings challenge conventional assumptions about scaling thresholds, the universal benefits of AI adoption, and the predictability of growth trajectories.

                          “Growth didn’t disappear in 2025; it became harder to earn,” said Alan Taylor, Chief Operating Officer at Maxio. “The winners weren’t chasing every trend. Whether AI-native or traditional SaaS, the top performers stayed focused on solving real customer problems.”

                          Key Report Findings:

                          Growth is still the norm, but it’s not universal: Average company growth reached 18%, while aggregate market growth was closer to 13%, reflecting slower expansion among larger, more mature businesses. Nearly two-thirds of companies grew year over year, yet more than one-third declined. Down years remain common across all revenue bands.

                          Growth slows earlier than expected: The data revealed inflection points at around $5 million in billings with another slowdown beyond $25 million, not the typical $1 million, $10 million or $50 million marks, showing the operational challenges of scaling.

                          Vertical focus outperforms horizontal scale: Vertically focused companies grew faster than horizontal peers (20% vs 16%), reinforcing the value of specialization in competitive markets.

                          Capital helps, but doesn’t guarantee faster growth: Bootstrapped companies nearly matched VC-backed growth (20% vs. 22%), though scale differed dramatically with VC-funded companies nearly 4x larger. Private equity-backed companies focused more on profitability, growing 13% on average while skewing significantly larger than other cohorts.

                          AI accelerates, but only at the core: Truly AI-led companies, with AI central to product and positioning, grew fastest at 21%. However, AI-enhanced companies lagged at 16%, while non-AI companies quietly outperformed at 19%. This pattern suggests that AI adoption alone does not guarantee impact—AI implementation without clear value differentiation may not translate into competitive advantage.

                          “Average growth numbers only tell part of the story,” said Ray Rike, founder and CEO at Benchmarkit. “What stood out is how early growth friction shows up. Teams that identify where and why growth is accelerating will be best positioned to focus their resources on the market segments that provide faster growth.”

                          2026 Outlook

                          Despite a more competitive and complex environment, industry optimism is back and strong. Seventy-two percent of companies expect to grow faster in 2026 than 2025. However, leaders are entering the year with more measured expectations around buyer scrutiny, competition and the need for operational efficiency.

                          Sustainable growth is built, not assumed, the report found. Companies that understand their true growth levers, invest with intent, and maintain discipline as they scale will be best positioned to win in 2026.

                          To read the full B2B Growth Report, click here. 

                          About Maxio

                          Maxio is the billing and financial reporting platform trusted by over 2,000 SaaS, AI and subscription businesses worldwide. With $18B+ in billings under management, Maxio empowers finance teams to scale recurring revenue, automate quote-to-cash and deliver the insights needed to grow confidently.

                          Learn more at maxio.com

                          • Data & AI
                          • Digital Strategy

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          3Mins

                          Read in brochure format

                          Interface issue 69 is live featuring Haleon, State of Montana, Techcombank, Publicis Sapient, Oakland County, Snowflake and much more

                          Welcome to the latest issue of Interface magazine!

                          Click here to read the latest edition!

                          Haleon: A Bold Business Evolution

                          Digital & Tech Head Soumya Mishra reveals how the group behind power brands like Sensodyne, Panadol and Centrum, broke away from GSK and transformed so successfully. Haleon is itself a large organisation so separating from a huge parent company was a big challenge… “It was the biggest deal of its kind and the first to happen in this industry,” Mishra adds. “We were separating to create simplification, but we had to work hard to achieve that. There were a lot of processes and policies that didn’t make sense and needed an overhaul. This had to be backed by a culture shift that was properly communicated.”

                          State of Montana: Cybersecurity Through A New Lens

                          State of Montana CISO, Chris Santucci, explains the organisation’s drastic shift towards security, and how his team has become a shining example within the wider IT centralisation sphere… “Fixing security vulnerabilities came down to having built enough social capital and trust to correct. I like to stay slightly uncomfortable as a CISO and as a human, to keep challenging myself to deliver better services and greater value. The mission is to ensure Montana citizens get the support they need while keeping services secure and protecting data.”

                          Publicis Sapient: Driving Banking Transformations with AI

                          Financial Services Director Arunkumar Gopalakrishnan reveals how Publicis Sapient is developing the playbook for delivering successful AI-led digital transformations across the financial services landscape. “Working with Generative AI today feels like standing on a new frontier. It keeps us on our toes, but it’s also what drives us – to stay relevant, deliver outcomes and connect both worlds of business and technology.”

                          Techcombank:

                          Chief Strategy & Transformation Officer, PC Chakravarti explores the operating model, Data & AI foundations, culture and talent playbook, and the partnerships turning ambition into market leading outcomes at Techcombank in Asia. “Tech is not the limiting factor – it’s about supporting people and talent to leverage capabilities to enhance business models.”

                          Oakland County:

                          Sunil Asija, Director of Human Resources at Oakland County, talks building trust with collaboration and becoming employer of choice. “To build trust the culture needs to change from top to bottom, and it needs everyone to join in that good fight.”

                          Click here to read the latest edition!

                          • Data & AI
                          • Digital Strategy
                          • Fintech & Insurtech
                          • Infrastructure & Cloud
                          • People & Culture

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          3Mins

                          From Infrastructure to Impact – Where Technology Meets Humanity

                          From Infrastructure to Impact – Where Technology Meets Humanity

                          Money20/20, the world’s leading FinTech show, and the place where money does business, has revealed the agenda for Money20/20 Asia. Set for April 21-23 at the Queen Sirikit National Convention Center in Bangkok. Asia’s most influential FinTech event will bring together thousands of leaders, innovators, regulators, and investors. From across the region and around the world delegates will explore how financial technology can deliver real human impact – not just technical innovation.

                          From Infrastructure to Impact – Where Technology Meets Humanity

                          Under the theme “From Infrastructure to Impact – Where Technology Meets Humanity,” the agenda reflects the industry’s evolution from celebrating capability to driving measurable outcomes that matter to people and communities. The program will spotlight key priorities. These include intelligent infrastructure for inclusive systems, SME empowerment, hyper-local ecosystem orchestration across diverse markets, last-mile solutions for underserved users, and the convergence of traditional and decentralised finance.

                          The keynote roster features top voices shaping the future of finance across Asia and beyond. Including Peng Ooi Goh, Founder & Executive Chairman of Silverlake Group; Sridhar Narayanan, Distinguished Engineer & CTO, IBM Payments Center; Kelvin Tan, CEO of audax Singapore; Fozia Amanulla, CEO of Boost Bank Malaysia; Rob Schimek, Group CEO of Bolttech; Anu Phanse, Chief Compliance Officer at Coinbase Singapore; Raymond Ng, CEO for Singapore & SEA at Revolut; and Rahul Advani, Global Co-Head of Policy at Ripple – along with an expanding roster of sector leaders across banking, FinTech, and technology.

                          A Defining Moment for the FinTech Industry

                          “Money20/20 Asia 2026 is a defining moment for our industry,” says Danny Levy, EVP & MD, APAC & Middle East. “This year’s agenda has been designed not just to showcase what technology can do, but to deepen conversations about what technology should do. Solve real challenges, unlock economic potential for small businesses, and ensure inclusive access for communities across Asia. We’re thrilled to bring together a lineup of visionary leaders who share that commitment.”

                          “Tokenisation is quickly moving from concept to real‑world use across Asia. Industries today are leveraging cutting edge technology to unlock liquidity and value through secure, compliant frameworks. HashKey is building the foundational infrastructure that makes it possible, and I’m looking forward to joining Money20/20 Asia to drive this shift from experimentation to real impact.”

                          Anna Liu, Chief Executive Officer of HashKey Tokenisation

                          Across three days of keynotes, panels, and interactive sessions, attendees will explore the critical trends shaping the future of money. From payments, banking, and digital assets to AI, regulatory innovation, and human-centred design. All with a focus on turning ideas into impact.

                          Money20/20 Asia’s agenda can be viewed here

                          About Money20/20

                          Launched by industry insiders in 2012, Money20/20 has rapidly become the heartbeat of the global fintech ecosystem. Over the last decade, the most innovative, fast-moving ideas and companies have driven their growth on our platform. Mastercard, Airwallex, J.P. Morgan, SHIELD, GCash, Stripe, Google, VISA, Adyen, and more make transformational deals and raise their global profile with us. Money20/20 attracts leaders from the world’s greatest banks, payments companies, VC firms, regulators, and media platforms: convening to cut industry-shaping deals, build world-changing partnerships, and unlock future-defining opportunities in Las Vegas (October 18-21, 2026), Amsterdam (June 2-4, 2026), Riyadh (September 14-16, 2026), and Bangkok (April 21-23, 2026). Money20/20 is where the world’s fintech leaders convene to grow their brands.

                          • Artificial Intelligence in FinTech
                          • Digital Payments
                          • Event Newsroom
                          • Events
                          • Neobanking

                          Author

                          Nell Walker

                          Published

                          30 January 2026

                          Estimated Read time

                          4Mins

                          Exiger’s new tool will help fight against modern slavery in the supply chain

                          Exiger has announced the launch of its no cost, open access website that allows global citizens, companies, governments, and NGOs to review whether a company or its supply chain is linked to state-sponsored forced labour. forcedlabor.ai empowers all companies, regardless of the size of their compliance budget, to make better decisions about who they do business with and ensure that their supply chain isn’t unknowingly profiting from modern slavery.  

                          “Modern slavery is a blight on humanity,” said Exiger CEO Brandon Daniels. “An estimated 50 million people are trapped in modern slavery, many of whom are hidden in opaque supply chains. As part of our mission to make the world a safe and transparent place to succeed, Exiger has decided to make the world’s largest dataset on companies connected to state-sponsored forced labour available to everyone. Hundreds of thousands of companies and millions of global supply chains are impacted.”

                          forcedlabor.ai lets companies, citizens, government agencies, law enforcement, NGOs, and civil society enter the name of supplier or company to immediately see potential forced labour connections in their supply chains. Powered by Exiger’s proprietary AI capabilities, results are evidenced-based and actionable. forcedlabor.ai will cover a growing scope and currently encompasses People’s Republic of China (PRC) state-sponsored forced labour, Uyghur Forced Labor Prevention Act (UFLPA) risks and US Customs and Border Protection (CBP) Withhold Release Orders (WRO) exposure across virtually every industry, including retail, automotive, industrials, consumer goods and electronics, and agricultural products.

                          “The CCP is responsible for one of the gravest human atrocities in recent history: the genocide of Uyghurs,” said Representative John Moolenaar, Chairman of the House Select Committee on the Strategic Competition Between the US and the Chinese Communist Party, on the launch of forcedlabor.ai. “Corporate actors must be open-eyed and take action to avoid complicity in this abuse and billions of dollars in global supply chains that rely on the CCP’s persecution of the Uyghurs.”

                          “When our global non-profit, which helps organisations build their resilience to modern slavery and labour exploitation, was looking for a technology to provide supply chain visibility, we reviewed over 400 platforms, and Exiger was heads and shoulders above all of the others,” said Slave-Free Alliance CEO Tim Nelson. “They’ve built the world’s largest forced labour risk database with some 20 billion records, and now they’re making incredibly valuable data available to everyone, creating a level of baseline transparency never before possible.”

                          The tool was developed with input from human rights and supply chain specialists including Kit Conklin, Exiger SVP of Risk & Compliance, Atlantic Council Nonresident Senior Fellow and former US House Select Committee on China Senior Advisor, as well as Dr. Laura Murphy, one of the world’s foremost experts on forced labour, Professor of Human Rights and Contemporary Slavery at the Helena Kennedy Centre for International Justice at Sheffield Hallam University, Senior Associate in the Human Rights Initiative at CSIS, and former Department of Homeland Security (DHS) Senior Policy Advisor who led the UFLPA Entity List team.

                          “This is a revolutionary, first-of-its-kind platform that makes regulator-grade forced labor risk intelligence accessible to everyone,” said Conklin. “The scale and universal availability of this data, powered by AI, represents a new era in forced labor transparency.”

                          Exiger is launching forcedlabor.ai at WEF’s 2026 Annual Meeting at Davos. Exiger CEO and WEF Governor Brandon Daniels will discuss how AI and supply chain intelligence, including forced labour data, are reshaping the industrial, economic and defence landscape alongside private sector CEOs and government officials at the USA House. The first session, Boardroom to Battlefield: Winning the AI Tempo War for Economic and National Security, is at 12:15PM CET on Wednesday, January 21, and the second, From Enforcement to Advantage: The Integrated Trade Strategy Powering America’s Industrial Revival is at 2:15PM CET on Thursday, January 22. Sessions will be livestreamed. For details on all of Exiger appearances and activations at Davos, visit https://www.exiger.com/davos2026.

                          • AI in Supply Chain

                          Author

                          Lasse Fredslund

                          Published

                          28 January 2026

                          Estimated Read time

                          8Mins

                          Lasse Fredslund, CMS Product Owner at Umbraco, examines the carbon footprint of our digital lives and offers advice on how to shrink it

                          Our digital lives have a carbon footprint. The energy consumed to power and cool the data centres at the heart of ecommerce, online banking, social and streamed media, already emits as much greenhouse gas as the aviation industry. This is expected to increase to 8% of GHG emissions in 2025.

                          While hyperscale data centre operators, including Microsoft, Alphabet, and Amazon, have made big strides towards adopting renewable energy sources, they still need fossil fuel-powered backup systems to meet the 24×7 demand for power and cooling.

                          Ballooning Demand

                          Since the Paris Agreement, internet traffic has quadrupled and the average web page weight has increased by 85% on desktop and 165% on mobile. Adding to this, the rapid adoption of generative AI is massively increasing data centres’ computational load.

                          To meet the predicted 606 Terawatt hours of electricity needed to power datacentres by 2030, three mothballed nuclear plants have been recommissioned in the US, and major investment is going into building new nuclear plants. However, building will take years and until then, fossil fuel combustion will continue.

                          How Can we Shrink our Digital Carbon Footprint?

                          The good news is that we can all do our bit to lighten the load. Even turning off autoplay on our smartphones and turning down the screen brightness can contribute to an overall reduction in energy consumption on our digital devices. Web designers and developers can do even more: making multiple optimisations that reduce web page weight and lower energy consumption and associated GHG emissions.

                          How We’re Reducing Digital Carbon Footprints

                          As the provider of the world’s most widely-used open-source content management system (CMS) built on Microsoft .NET, we have both a responsibility and a great opportunity to drive positive change on a larger scale.

                          For our own part, we’re focusing on ways to make our operations more sustainable and our software more energy-efficient. Running our CMS platform on Microsoft .NET9 has introduced features such as HybridCache that aid carbon-conscious web developers in building sites that load content more efficiently.

                          We’re also working closely with our global open-source community and digital agency partners to show how to reduce the CO2 emitted by business websites built on the Umbraco CMS platform. The Umbraco community Sustainability Team, formed in March 2023, has published documentation that provides practical steps for reducing web page weight and optimising data transmission.

                          Sharing Responsibility and Best Practices

                          By sharing sustainable best practices, and the measurable ROI that our partners’ clients have achieved as a result of carbon-conscious web design, we hope to amplify these changes across the industry. Together we can make a much bigger difference to our collective carbon footprint.

                          Prominent members of our open-source community Sustainability Team worked with us and implemented the Green Web Foundation’s CO2.js tool. We now have a Sustainability Dashboard, which helps businesses monitor and reduce the environmental impact of their websites running on Umbraco Cloud.

                          Ten tips to reduce Cloud Carbon Footprint

                          Members of the Umbraco Sustainability Team have published the following practical steps that organisations can take, and free tools that they can use, to measurably reduce the energy consumption and CO2 emissions of websites and digital experiences.

                          1. Lose weight

                          Just as the aviation industry has been introducing lighter aircraft to help reduce fuel consumption and emissions, carbon-conscious web designers can also help organisations to reduce web page weight.

                          The Sustainability Team recommends using tools such as www.Ecograder.com and www.Websitecarbon.com which show grams of CO2 emitted per web page. This is the simplest way to check a web page’s energy-efficiency, so that improvements can be made.

                          Neil Clark, Service Design Lead, at TPX Impact, observes, “Every piece of website software and code must minimise the data transfer it causes. We must start to consider data transfer as a constraint in all of our digital projects.”

                          Thomas Morris, Tech Lead at TPX Impact advises, “A useful first step is to set page weight budgets and stick to them. This helps to create a culture of optimisation with realistic targets. The HTTP Archive suggests a maximum of 1 Megabyte.”

                          1. Reduce Images

                          To reduce web page weight, Rick Butterfield, Lead Software Engineer at Wattle, emphasises, “Be ruthless about images.  Make sure they’re sized well and avoid using stock images, which can sometimes be massive files.”

                          Thomas Morris agrees, “One of the biggest impacts you can have, with fairly minimal effort, is to use appropriately-sized images on your website, or consider whether images are needed at all. Using modern image compression formats, such as WebP, or AVIF helps reduce file sizes by up to 70% compared to JPEGs, without your users noticing any difference. Optimise images before upload, to reduce the extra compute effort of resizing images. Where appropriate, consider using SVG icons, logos or illustrations, since these often result in smaller image file sizes and also scale easily without compromising image quality.”

                          1. Compress fonts

                          Thomas Morris advises, “We suggest using system fonts to reduce extra server requests. If you do have to use custom fonts then compression tools, such as WOFF2, will help to minimise the data weight of those assets. WOFF2 is supported across all modern browsers.”

                          Minimising text assets, including HTML documents, JavaScript files and CSS files is a really good practice. Google’s Brotli is a lossless compression tool supported by 96% of browsers that makes this a lot easier and reduces text-based files by around two thirds.

                          1. Choose colours wisely

                          Rick Butterfield advises that web designers can even reduce carbon footprint by changing the colours selected for a website: “Blue shades use up more energy than reds and greens when they’re displayed on screens.”

                          1. Default to Dark Mode

                          “Dark mode is very simple to set up and can be built on incrementally,” enthuses Rick Butterfield. As with a lot of the best practices outlined by the Sustainability Team, these changes benefit end users too. “A university study found that switching from light mode to dark mode at 100% screen brightness can save an average of 40% battery power, so users don’t have to charge devices as often,” adds Rick.

                          1. Keep software updated

                          James Hobbs, Head of Technology at aer Studios, says, “Simply by keeping libraries, frameworks and the rest, up to date, your organisation is likely to benefit from enhanced efficiency, which means doing more work with the same or fewer resources, which is better for the planet. When Umbraco moved to .NET Core it made a massive difference to the efficiency of the CMS. Staying on top of this can deliver sustainability and efficiency benefits and an improved security posture.”

                          1. Load web content efficiently

                          To make data transfers of images, videos and iframes more efficient, the Sustainability Team recommends implementing lazy loading on clients’ sites. “Lazy loading limits what is loaded within the viewport and is supported in modern browsers,” explains Thomas Morris.

                          However, web designers should avoid applying lazy loading to hero images which are always visible at the top of a page, as this will cause the website to load slowly and impact user experience.

                          1. Make your Site Carbon-Aware

                          Rick Butterfield is a strong advocate for building carbon-aware websites. “The Green Software Foundation’s Carbon Aware software development kit allows developers to create software that does more when the electricity is from renewable sources and less when the electricity is from fossil fuels. Open APIs allow us to create this type of service for clients. Functionality of the site can be altered based on current grid usage, where your servers are located, or where your users are. As an example, images can be disabled if the server load is too high, or they could be stripped back to display illustrations instead.”

                          1. Choose carbon-efficient infrastructure

                          Andy Eva-Dale, CTO at Tangent, advises that running digital services from the cloud has both environmental and financial benefits for organisations, “All the major cloud providers have carbon commitments. Take advantage of PAAS features like auto-scaling, to ensure you’re only using and paying for the computing memory you need, and this is optimised for ‘business as usual’ traffic, from a carbon perspective. Then, when you have spikes in traffic, we can auto-scale those applications. Furthermore, when we start looking at microservice architecture, we can scale independently and set resource plans on individual services rather than whole applications, giving us more control.”

                          Andy Eva-Dale continues, “The next thing to consider is serving content geographically close to your audience. Hosting static files or caching your API responses on the edge can significantly reduce the amount of carbon your systems produce.”

                          Thomas Morris agrees, saying, “Serving static assets via a content delivery network (CDN) will ensure that requests are treated efficiently.”

                          1. Switch off after use

                          Andy Eva-Dale also advises turning off cloud-based resources after use, “When you’ve moved to a relatively stable business as usual cycle, turn off your non-production environment and turn them on only when you need to make a patch, or update a particular feature. If you’re in a continuous programme of work, look at switching off environments at weekends. Applications like Kubernetes give you increased control over that. An auto event-driven autoscaler was announced by The Cloud Native Computing Foundation that allows infrastructure to be adjusted, based on carbon metrics.”

                          Taking our own advice:

                          The Sustainability Team is committed to working with peers, clients and even competitors to share these best practices and collectively reduce the environmental impact of digital experiences. This includes Umbraco listening to our digital partners and making the necessary changes to our core CMS platform and website.

                          Neil Clark comments, “By having us as a Sustainability Team, we can really push change at all levels of Umbraco which means that the impact of those changes is going to be amplified and not restricted to a few developers or agencies changing the way that they work.”

                          This is not just a nice-to-have. Our digital agency partners tell us they are seeing more client briefs and RFPs that stipulate sustainable web design. In the face of new legislation such as the Corporate Sustainability Reporting Directive, there is an increasingly strong business case for carbon-conscious web design.”

                          Learn more at umbraco.com

                          • Sustainability Technology

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          3Mins

                          Partnership enables financial institutions to expand faster into new markets with automated, consistent and compliant localisation workflows

                          Plumery, the digital banking development platform, and Lokalise, a leading platform for continuous localisation have joined forces to embed enterprise-grade localisation functionality, including translation and market adaptation, directly into digital banking experiences. This will enable financial institutions to deliver hyper-localised experiences at scale. Improving accessibility, engagement, compliance and customer satisfaction.

                          Today, financial institutions increasingly compete on experience, speed, and accessibility. Global banking customers now consider native language support a baseline expectation. This makes it essential for institutions to adopt a multilingual-by-design approach.

                          Plumery combines developer-friendly, customer-centric digital banking platform with Lokalise’s best-in-class localisation infrastructure. Together, their AI orchestration will help financial institutions expand their customer base. This can be done in a scalable way by launching and updating multilingual journeys faster, with full control and compliance.

                          Modern Digital Banking

                          The partnership also removes one of the biggest blockers to delivering modern digital banking. Financial institutions can now deliver high-quality localised digital banking experiences. Moreover, at a fraction of the cost and time, across all channels, without engineering bottlenecks. This reduces operational overhead, speeds up market entry, improves compliance with language- and accessibility-related regulations. All of which delivers a better, more inclusive customer experience. Financial institutions can move faster without increasing operational risk.

                          “Localisation is no longer a nice-to-have, it’s essential for delivering truly inclusive and personalised banking experiences. Partnering with Lokalise allows us to bring world-class localisation into every digital journey our clients build on Plumery. Together, we’re helping financial institutions launch faster, scale globally, and meet the expectations of modern customers who want banking in their own language, context and culture.”

                          Danielle Cohen, Head of Product at Plumery

                          “This partnership is a game-changer for financial institutions looking to scale globally with confidence. By embedding AI orchestration and continuous localisation directly into the Plumery platform, we are empowering customers to easily launch and update multilingual services at a fraction of the cost, ensuring consistent, compliant, and local experiences that accelerate market expansion and drive rapid customer growth.”

                          Etgar Bonar, CMO at Lokalise

                          The partnership is live across all markets Plumery and Lokalise serve, with the first mutual deployments already underway. 

                          About Lokalise

                          Lokalise is the most intuitive and powerful AI localisation platform, trusted and loved by 3,000+ global companies to deliver high-quality human-level translations at a fraction of the cost. Furthermore, with advanced AI orchestration, 60+ deep integrations and world-class support, it is built to automate, collaborate, and scale growth while maintaining full brand and regulatory control.

                          About Plumery

                          Headquartered in the Netherlands, Plumery’s mission is to empower financial institutions worldwide, regardless of size, to craft distinctive, contemporary, and customer-centric mobile and web experiences. 

                          Plumery operates with a diverse team that embodies a unique combination of seasoned expertise and vibrant innovation. This blend has been cultivated through years of experience at start-ups, scale-ups, and established financial institutions, and most notably at globally leading financial technology companies, where they were instrumental in creating disruptive digital banking solutions and platforms that now serve 300+ banks globally.  

                          Plumery’s Digital Success Fabric platform provides banks with the foundation for success beyond fast-time-to-market by expediting the development of their digital front ends while significantly cutting costs compared to in-house initiatives or solutions with high total cost of ownership (TCO).

                          • Digital Payments
                          • Embedded Finance
                          • Neobanking

                          Author

                          Dan Brightmore

                          Published

                          27 January 2026

                          Estimated Read time

                          6Mins

                          Some Europe & Middle East CIOs anticipate up to 178% ROI on AI investments, with further efficiencies expected as Agentic AI scales

                          Enterprises have moved decisively from AI pilots to scaled implementations, driven by proven benefits and expectations of significant financial returns, according to the Lenovo Europe & Middle East CIO Playbook 2026 with research insights by IDC. Nearly half (46%) of AI proof-of-concepts have already progressed into production, with organisations projecting average returns of $2.78 for every dollar invested.

                          The 2026 Lenovo CIO Playbook: The Race for Enterprise AI, draws on insights from 800 IT and business decision makers in Europe and the Middle East. It captures a regional inflection point and reinforces the value proposition for enterprise AI as both real and immediate. It calls on CIOs to act now to avoid lagging competitors. The research marks a clear shift from AI experimentation to measurable value creation, with nearly all (93%) of those surveyed planning to increase AI investments in the next 12 months. At an average spending growth rate of 10%, and 94% anticipating positive returns.

                          Enterprise AI Adoption in Europe and the Middle East

                          AI is now recognised as a core engine of business reinvention and competitive advantage. However, AI adoption in the markets is progressing at different speeds. Reflecting varying levels of digital maturity, regulatory readiness, and investment capacity, and there is a clear overconfidence problem among CIOs. While 57% of organisations in Europe and the Middle East are approaching or already in late-stage AI adoption, only 27% have a comprehensive AI governance framework. Further limitations in data quality, in-house expertise, integration complexity, and organisational alignment are causing a mismatch between ambition and readiness.

                          With Agentic AI overtaking Generative AI as the top priority for CIOs in 2026, these factors will prevent many organisations from fully capitalising on AI’s potential, leaving significant returns unrealised. Moreover, 65% of organisations are focused on scaling Agentic AI across their operations within 12 months, but only 16% report significant usage today, with the majority still piloting or actively exploring use cases.

                          More advanced markets such as Scandinavia, Italy, and the UK are moving beyond pilots, with a majority of organisations already systematically adopting AI and increasing focus on hybrid and edge deployments to support scale. In contrast, parts of Southern and Eastern Europe remain earlier in their AI journeys, with a higher proportion of organisations still in planning or early development stages. Meanwhile, the Middle East is emerging as a fast-moving growth market, showing strong adoption momentum and a sharp year-on-year increase in interest in advanced and Agentic AI.

                          Across the region, hybrid deployment models dominate as organisations balance innovation with data sovereignty and operational control. While interest in Agentic AI is accelerating. This signals a broader shift from experimentation toward more autonomous, production-ready AI use cases, even as readiness levels continue to vary by market.

                          “We’re now seeing clear returns from the AI pilots and proof-of-concepts organizations have invested in, with AI delivering measurable impact across the region. But many are not fully equipped with the skills, governance and readiness needed to scale AI to its full potential. As priorities shift toward Agentic AI, and compliance with regulation such as the EU AI Act becomes imperative, trust and scale must be built in from the start. Those who don’t, risk leaving tangible returns on the table.”

                          Matt Dobrodziej, President of Europe, Lenovo

                          Hybrid AI Now Preferred Enterprise Architecture

                          The research shows that real-world business and financial considerations are accelerating the shift toward hybrid AI. Factors such as data privacy, advanced security requirements, and the need to customise and optimise infrastructure are driving adoption of this model, which blends public cloud, private cloud, and on-premises compute. Nearly three out of five (58%) organisations now prefer hybrid as their primary AI deployment model.

                          Scalable, high-performing AI infrastructure is a critical enabler of enterprise AI success. Respondents in the region highlighted the importance of compute that is both cost- and energy-efficient. This factor ranked second overall, with many identifying it as key to moving AI from pilots into reliable production.

                          With AI PCs and edge endpoints central to an effective Hybrid AI strategy and securely running AI workloads locally, deploying AI-capable devices has emerged as the top IT investment priority for 2026.

                          “CIOs across the region are entering a decisive phase of AI adoption where agentic AI and enterprise-scale inferencing are moving from experimentation to core business priorities,” said Dobrodziej. “To unlock real value, organisations need strong foundations, including secure, energy-efficient infrastructure, flexible hybrid architectures, and AI-capable devices and edge endpoints that bring inference closer to where data is created, and work happens. When combined with the right governance and services, this end-to-end approach enables enterprises to innovate confidently, responsibly, and at scale.” 

                          Lenovo recently introduced Lenovo Agentic AI, a full-lifecycle enterprise solution for creating, deploying, and managing AI agents, alongside Lenovo xIQ, a suite of AI-native platforms designed to simplify and operationalise AI across the enterprise. Built on the Lenovo Hybrid AI Advantage™, these offerings combine hybrid infrastructure, platforms, and services to address governance, integration, and performance from day one. Supported by the Lenovo AI Library of proven use cases, CIOs can reduce risk, accelerate time-to-value, and scale AI initiatives with greater confidence as they move beyond experimentation.

                          To further enable real-world deployment, Lenovo ThinkSystem and ThinkEdge inferencing servers help enterprises turn trained models into production-ready, low-latency AI applications across data center, cloud, and edge environments. By enabling faster, more efficient inference at scale, Lenovo helps CIOs bridge the gap between AI ambition and day-to-day business impact.

                          Building on this end-to-end AI foundation, Lenovo’s Smarter AI for All vision is focused on bringing AI to more people and businesses at scale, from enterprise infrastructure to AI PCs that deliver intelligent, personalised experiences directly to users. As outlined at Lenovo Tech World at CES 2026, Lenovo is advancing this vision across its AI PC and smartphone portfolio, with Lenovo and Motorola Qira representing one example of how personal AI can enhance productivity by understanding context across devices and helping users get things done.

                          Learn more about how enterprises can accelerate AI adoption with the right infrastructure, governance, and partnerships:Explore the full 2026 CIO Playbook report.

                          About the CIO Playbook Study

                          This is the third year of surveying CIOs in Europe and the Middle East, with Lenovo commissioning IDC which conducted research between 16th September 2025 and 17th October 2025. This year’s report draws on insights from 800 IT and business decision makers in Europe and the Middle East. Industries represented include: BFSI, Retail, Manufacturing, Telco/CSP, Healthcare, Government, Education and others.

                          About Lenovo

                          Lenovo is a US$69 billion revenue global technology powerhouse, ranked #196 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo’s continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY). To find out more visit https://www.lenovo.com, and read about the latest news via our StoryHub.

                          • Data & AI
                          • Digital Strategy

                          Author

                          Nell Walker

                          Published

                          Estimated Read time

                          2Mins

                          The AI leader joins Hy-Tek to scale its IntraOne software platform

                          Hy-Tek Intralogistics, a leading provider of warehouse and distribution technology, is excited to announce today the appointment of Jim Peters as Senior Director, Software Development. In this leadership role, Peters will oversee the engineering and product development strategy, focusing on scaling high-performance teams and advancing the architecture of the company’s software solutions.


                          Peters joins Hy-Tek with over 18 years of experience in senior leadership roles, bringing deep expertise in systems architecture, cloud computing, and machine learning. He has a proven track record of building and scaling engineering organisations, having successfully managed global teams across on-site and offshore locations in the US, Europe, Australia, and Hong Kong.


                          Most recently, Peters served as Senior Software Engineering Manager at Vanderlande, where he led engineering and product teams in North America and Europe to develop next-generation Warehouse Execution Systems (WES). During his tenure, he was instrumental in updating legacy systems to modern development practices and piloting an Agentic AI development program to assist with system review and refactoring.


                          Robert Kluck, Vice President of Software Development at Hy-Tek Intralogistics, said: “Jim’s extensive background in WES development and his forward-thinking approach to AI and machine learning make him an invaluable asset to our technology leadership team. His ability to transform organisations using Agile methodologies aligns perfectly with our mission to deliver cutting-edge software solutions to our customers.”


                          At Hy-Tek, Peters will leverage his proficiency in transforming organisations and his experience with AI platforms to enhance decision support and development velocity. His leadership will be pivotal in driving the continuous evolution of Hy-Tek’s software offerings, ensuring they remain at the forefront of the supply chain industry.

                          • Digital Supply Chain

                          Author

                          Brian Gaynor

                          Published

                          Estimated Read time

                          5Mins

                          Brian Gaynor, European Chief Executive at BlueSnap, on leveraging the new tools that are needed to meet today’s tech demands

                          Finance teams have a problem. The demands of doing business in 2025 go far beyond the limits of the tools they’ve been using for decades. Every day, teams wrestle with myriad spreadsheets, struggling to manage critical business processes with the tools they’d use to plan the Christmas party.

                          But the alternative feels too risky. Decision makers shy away from changing the systems they’ve worked in for years, and the investment and imagined disruption this would bring. Surely ‘better the devil you know’ – even if the present is particularly hellish.

                          On first glance, refusing to change may seem like the cheaper choice. Yet familiarity comes with a hidden premium. The cost of inefficient manual processes quickly mounts up and missed opportunities mean higher losses. As businesses face shrinking margins in a strained economic climate, this is a cost they can no longer afford.

                          Spreadsheets Conceal a World of Secrets

                          One of the biggest challenges finance teams face today is the lack of visibility into outstanding invoices. Manual spreadsheets often hide the true scale of late payments, often until it’s too late. When unresolved invoices pile up, companies face reduced cash flow, strained internal coordination, and great exposure to compliance risks. The extent of this damage should not be underestimated: late payments cost the UK economy £11 billion a year and shut down 38 businesses every day.

                          However, modern AR automation tools can bring cash secrets into the light. They’re able to give businesses real-time visibility over accounts receivables so overdue payments are spotted earlier and businesses can launch proactive collection strategies, rather than desperately chasing overdue accounts at the very last minute. Automated reminders, dispute resolution workflows, and digital invoicing help take the friction out of invoicing, as well as giving finance teams a smarter view of receivables year-round, not just during heightened crunch periods.

                          Using AR software to reduce financial bottlenecks creates a cascade of business benefits. Freed from spreadsheet hell, customer-facing teams now have the time to focus on client relationships, and drive company growth, rather than endlessly chasing late payments. This means they can bring their talent to create real value for a business, rather than being forced to take on manual tasks that should be left to a machine.

                          Keeping Cash Flowing

                          Cash flow is the lifeblood of every business yet legacy processes often drain it. Manual invoicing and reconciliation often end up extending collection cycles and, subsequently, straining liquidity. Stuck with outdated processes, companies end up waiting weeks – or even months – longer than they need to access their own funds. 

                          By contrast, AR automation accelerates invoice collection, allowing businesses to unlock working capital much faster than any manual process could. At the same time, it helps individuals and organisations increase their productivity by eliminating repetitive, error-prone tasks such as data entry, reconciliations, and follow-ups. Finance professionals can then redirect their time to higher-value work such as interpreting data, advising leadership, and shaping strategy. This is the work that helps grow a business and allows an organisation to move with agility which is crucial to economic resilience in today’s difficult climate. The ability to free up capital and employee bandwidth can be the difference between stagnation and growth.

                          Extending the Range of Vision

                          Another casualty of manual processes is cash flow forecasting. Spreadsheets are reactive documents, providing a static, backwards-looking view of finances, and are often plagued by version control issues and human error. This means finance leaders are left making critical business decisions without a clear picture of future cash flow, reducing strategic planning to a roll of the dice.

                          Automation offers the opposite. By offering real-time visibility of accounts, invoices, and performance, it enables finance teams to forecast cash flow with confidence. This foresight allows businesses to accurately anticipate liquidity needs, mitigate any risks, and respond faster to shifts in demand or supply chain disruption, meaning they can work proactively rather than reactively. The ability to be on the front foot is another crucial block in building business resilience.

                          Enhancing the Customer Experience

                          Outdated systems don’t just create internal inefficiencies, they affect an organisation’s relationship with their customers. Legacy systems have a significant impact on the customer experience, as manual processes, such as cheque reconciliation, slow down operations and make payment processing cumbersome.

                          Again, automated AR solutions can help here. Automated systems enable businesses to offer customer-friendly features, like a ‘pay by link’ option that makes it easy for customers to instantly settle invoices. This reduces friction in the payment process, prompts clients to make payments quickly and on time, and helps strengthen the trust between an organisation and its customers.

                          Ultimately, modern finance platforms that use automation greatly enhance the customer experience by making billing seamless, accurate, and transparent. Payments are processed faster, disputes are handled proactively, and customer satisfaction improves as a result. At a time when every client counts, such benefits can’t be ignored. 

                          Familiarity Comes at a Price

                          With so many advantages stemming from AR automation, why are so many organisations choosing to stick with spreadsheets? One may think that the biggest barrier to change is technology, but often, it’s their attitude. Too many finance leaders assume that because their current processes haven’t collapsed, they must be working well enough to remain in place. But ‘if it ain’t broke’ is a destructive mindset. Opting to be complacent and being satisfied with ‘good enough’ tools, is a costly decision. And are these tools actually working if they lead to lost productivity, delayed revenue, weakened forecasting, and damage to customer relationships?

                          Businesses may think it’s up to them to upgrade their finance systems. But the decision to automate is quickly being taken out of their hands. Companies that still cling to the processes of the past will soon find themselves left behind, as competitors leverage the new tools that are needed to meet today’s demands. While change may seem intimidating, or feel temporarily uncomfortable, ultimately, it’s crashing into the red that’s going to feel worst of all.

                          Learn more at bluesnap.com

                          • Digital Payments

                          Author

                          Christina Mertens

                          Published

                          26 January 2026

                          Estimated Read time

                          5Mins

                          Christina Mertens, vice president of business development, EMEA, at VIRTUS Data Centres on designing next gen digital infrastructure

                          Europe’s digital infrastructure is entering a new phase of development. For more than a decade, growth was concentrated in a small number of metropolitan hubs. This was where connectivity, enterprise demand and financial services created natural centres of gravity for data centres. Cities such as London, Frankfurt, Amsterdam and Paris (FLAP markets) became the backbone of Europe’s cloud and colocation landscape.

                          That model is now under pressure. Computing power is surging in ways that surpass forecasts made even two years ago. AI training and inference, high performance computing (HPC), analytics and modernised public services all require significant and sustained energy and cooling capacity. McKinsey suggests that global demand for data centre capacity could more than triple by 2030. It’s clear Europe needs more digital infrastructure. However, it needs that infrastructure in places with the headroom and regulatory clarity to support long term expansion. And this is why what are referred to as second-tier locations are becoming critical to expanding Europe’s digital architecture.

                          In practical terms, second-tier locations are not secondary in importance. They are cities and regional areas outside the most constrained metropolitan centres, where there is greater headroom for power, land and long-term infrastructure planning. Across Europe, this includes parts of regional Germany and Italy, Iberia, the Nordics and areas of the UK outside of London. These locations are now playing a central role in how Europe expands its digital capacity.

                          Why the Digital Infrastructure Shift is Happening

                          The primary driver is power. Data centres require sustained, predictable electrical capacity over long periods, particularly as AI workloads increase baseline demand. In dense urban centres, electricity networks are often operating close to their limits, and upgrading them is complex, costly and slow. New substations are difficult to site, transmission upgrades can take many years, and competition for capacity from other sectors is intensifying.

                          Land availability compounds this challenge. Modern data centres are no longer single buildings inserted into existing industrial estates. They are increasingly campus-based developments, designed to accommodate multiple facilities, on-site substations and future expansion. Securing sites of that scale within major cities is difficult and expensive. And often incompatible with planning frameworks that prioritise mixed-use or residential development.

                          By contrast, regional and edge-of-city locations offer more physical space and greater flexibility. They make it possible to plan electrical infrastructure coherently from the outset, rather than retrofitting systems around urban constraints. For building services professionals, this changes the nature of both design and delivery.

                          Delivery Challenges in Regional Locations

                          While second-tier locations offer more space and flexibility, they are not without challenges. Securing grid capacity remains a critical path issue. It requires close collaboration with transmission and distribution network operators, regardless of geography. In some regions, new infrastructure or upgrades are required to support data centre demand. This can introduce complexity into delivery programmes.

                          Phased development is another defining characteristic. Many campuses are designed to be built out over several years, sometimes over a decade or more. Electrical and mechanical systems need to be designed and installed in a way that supports this staged approach, maintaining operational efficiency while allowing for expansion.

                          This places a premium on coordination between designers, contractors, operators and utilities. Clear documentation, consistent standards and long-term programme management become essential, particularly where different phases may be delivered by different teams over time.

                          Skills and Workforce Considerations

                          As data centre development spreads across a wider range of locations, skills availability becomes an important consideration. High-voltage electrical expertise, experience with resilient power systems and familiarity with data centre standards are already in demand, and that demand is unlikely to ease.

                          In regional locations where specialist labour pools may be smaller, there is increased focus on training, apprenticeships and long-term workforce development. From an operator and developer perspective, the ability of contractors and consultants to provide consistent quality across multiple phases is particularly valued on campus-scale projects.

                          This creates opportunities for building services firms that invest in people and develop repeatable delivery capability. Long-term relationships can be built where teams understand an operator’s standards and are involved across successive phases of development.

                          The Influence of AI and Higher-Density Workloads

                          AI is accelerating many of these trends. Training and inference workloads place sustained loads on electrical and cooling systems, increasing the importance of reliability and predictable performance. This reinforces the need for robust primary infrastructure and careful long-term planning.

                          Second-tier locations make it easier to accommodate these requirements because they allow for comprehensive system design at scale. Space for substations, cooling plant and future expansion can be planned into the site from the beginning, rather than being constrained by surrounding development.

                          From a building services perspective, this does not necessarily mean radically new technologies, but it does increase the importance of integration, resilience and accurate demand forecasting.

                          Why this Matters for the Built Environment Sector

                          The shift toward second-tier locations represents more than a geographical redistribution of data centres. It reflects a broader change in how digital infrastructure is planned, designed and delivered. Larger sites, longer programmes and greater emphasis on early-stage coordination place building services and electrical design at the centre of successful delivery.

                          For the built environment sector, this creates sustained opportunities across design, construction and operation. Campus developments require ongoing engagement rather than one-off interventions, and they rely on teams that can think beyond individual buildings to system-level performance over time.

                          Looking Ahead…

                          So, it’s clear that Europe’s digital infrastructure is becoming more distributed, and that trend is unlikely to reverse. Power constraints, planning pressures and rising digital demand all point toward continued development beyond traditional metropolitan hubs.

                          Second-tier locations are not a temporary solution. They are becoming a permanent and essential part of Europe’s digital landscape. For building services professionals, understanding how to design and deliver infrastructure at this scale, and over these time horizons, will be increasingly important.

                          As the next phase of development unfolds, success will depend on careful planning, strong collaboration and a clear understanding of how electrical and mechanical systems underpin the resilience and performance of Europe’s digital future.

                          Learn more at virtusdatacentres.com

                          • Data & AI
                          • Digital Strategy

                          Author

                          Antoni Vidiella

                          Published

                          Estimated Read time

                          6Mins

                          Dr Antoni Vidiella, CSO of Financial Services at Globant, on why the next stage of AI in financial services depends on modernising the legacy systems that still underpin banking and FinTech

                          Many financial service institutions are now moving beyond simple automation and exploring how to embed artificial intelligence across every layer of their operations, from payments and compliance to customer engagement. As banks and FinTechs continue this shift, the sector is entering a new phase in which real-time intelligence, connected data and adaptive systems will define competitiveness.

                          Yet unlocking this value requires far more than the introduction of new AI tools. To turn data into meaningful business intelligence and to enable new growth models in digital finance, financial institutions must modernise the systems at their core. Without strong foundations, AI cannot scale effectively or operate in a responsible, transparent or secure way. The potential may be vast, but the path to achieving it begins with the fundamentals.

                          The Challenge of Legacy Systems

                          Like many other industries, financial institutions still rely on architectures that were built decades ago. These systems continue to support essential functions such as payment processing and risk modelling, yet their rigidity and fragmentation severely limit the potential of AI. Information remains scattered across mainframes, cloud platforms and on-premises databases. As a result, the data required to train and operate modern AI systems is often incomplete, inconsistent or inaccessible in real time.

                          This fragmentation reflects a deeper structural issue. Many core banking systems were designed around periodic or batch processing. Fraud detection, credit assessment and compliance monitoring therefore remain reactive, even as customer expectations shift toward instantaneous experiences. The consequence is a widening gap between what AI can theoretically deliver and what institutions can achieve with the infrastructure they currently have.

                          The scale of adoption shows how urgent this challenge has become. A 2024 study by the Bank of England and the Financial Conduct Authority found that 75 percent of UK financial services firms already use AI, with a further 10 percent planning adoption within the next three years. Yet research in 2025 by Lloyds Banking Group indicates that while institutions are beginning to see gains in productivity and customer experience, many acknowledge that their underlying systems are not ready for the next stage of AI maturity. The ambition is there, but the technical foundations remain uneven.

                          Modernisation as the Foundation for Scalable, Trustworthy AI

                          Modernisation represents the most significant step institutions can take to prepare for the intelligent financial systems of the future. Moving to cloud-native architectures, adopting microservices and improving data quality all make it possible to activate AI across an organisation rather than in isolated pilots. These shifts also make the resulting systems more secure, more transparent and easier to govern.

                          Importantly, modernisation is no longer the slow, resource-intensive process it once was. AI-assisted approaches have transformed what is possible. Automated code analysis, conversion and validation can reduce modernisation timelines dramatically. In one example, more than 11,000 lines of legacy COBOL code were migrated to modern Java services in only 105 hours, a task that would traditionally have taken several months. These advances illustrate how quickly institutions can begin creating the environments required for real-time intelligence.

                          The global opportunity reinforces the need for speed. AI adoption in banking is accelerating rapidly, with institutions racing to modernise their systems and unlock new operational efficiencies. Those that move first will capture the earliest benefits and operate with a level of agility that older architectures simply cannot match.

                          How Intelligence is Reshaping Payments and Embedded Finance

                          Payments provide a clear view of how AI is transforming the financial landscape. As digital transactions grow in both scale and complexity, the industry needs systems that can act instantly and intelligently. AI models can analyse behavioural patterns in real time, reducing false positives in fraud detection and strengthening overall resilience. They can also optimise transaction routing, identifying the most efficient or cost-effective paths in ways legacy systems are not equipped to handle.

                          These shifts extend beyond payments. Embedded finance is becoming a central feature of retail, mobility, insurance and platform-based services. As the ecosystem expands, it will rely heavily on AI to offer tailored credit decisions, contextual payments and adaptive insurance coverage. These capabilities require unified, real-time data environments that can only be delivered through modernised core systems. Without this foundation, the benefits of intelligent payments remain out of reach.

                          The Essential Role of Responsible Innovation

                          As AI takes on a larger role in high-impact financial decisions, responsible innovation becomes a defining priority. Trust must be maintained at every stage of the customer journey. Findings from the Bank of England and the FCA show that 55 percent of AI systems in UK finance involve some form of automated decision-making, though very few operate without human oversight. This balance reflects a clear need for systems that are transparent, explainable and accountable.

                          Responsible AI requires more than good intentions. It depends on strong governance frameworks, rigorous monitoring for bias and clear visibility into how decisions are made. It also relies on consistent, well-managed data. Modern cloud-enabled infrastructures make these practices more achievable, allowing institutions to meet regulatory expectations while building customer confidence. Legacy systems, by contrast, make responsible innovation significantly harder to sustain because they lack the transparency and control required for effective oversight.

                          How GenAI is Reshaping Operations and Customer Experience

                          Generative AI expands the possibilities for transformation even further. In customer engagement, GenAI enables natural, personalised interactions that respond to customer needs in real time. It can simplify onboarding, deliver proactive financial insights and support customers throughout complex journeys without compromising clarity or accuracy.

                          Within operations, GenAI reduces the administrative burden that regulatory compliance often creates. It can summarise complex legislation, draft documentation and support audit processes far more efficiently than manual methods. In product development, it helps institutions test new ideas, model risk scenarios and understand customer behaviour more quickly, reducing time to market and increasing innovation capacity.

                          However, all these capabilities rely on a consistent and reliable data environment. GenAI cannot deliver meaningful insights if the data underpinning it remains fragmented or outdated. The quality of the output will always reflect the quality of the foundations beneath it.

                          Building a Resilient Path to Long-Term Innovation

                          Modernisation is frequently described as a technical necessity, yet its impact is far more strategic. Institutions that invest now will be better equipped to integrate new technologies, respond to regulatory changes and develop AI-enabled products with greater precision. They will also be better positioned to enhance the customer experience, which increasingly depends on real-time intelligence and personalised insight.

                          Most importantly, modernisation elevates human expertise rather than replacing it. AI supports judgement, strengthens decision-making and frees teams from manual tasks, allowing them to focus on the relationship-building and strategic insight that define successful financial services.

                          Creating the Intelligent Financial Institution of the Future

                          Financial services are entering a new era shaped by real-time intelligence, interconnected digital journeys and deeply personalised experiences. Achieving this vision requires modern, resilient systems that can support advanced AI and GenAI. Institutions that begin modernising now will lead the next decade of innovation and create financial ecosystems that are more adaptive, more secure and more connected than ever before. The future is intelligent, but it can only be built on strong foundations.

                          Learn more at globant.com

                          • Artificial Intelligence in FinTech
                          • Digital Payments
                          • Embedded Finance

                          Author

                          Dan Nichols

                          Published

                          23 January 2026

                          Estimated Read time

                          8Mins

                          Dan Nichols, Chief Technology Officer at virtualDCS, on why cloud resilience in the financial services sector hinges on shared accountability and an assume-breach philosophy

                          A powerful catalyst for transformation, the cloud is reshaping how organisations compete in the financial services sector. Beyond significant cost savings and flexibility, leaders are eager to unlock the potential of AI-driven insights, intelligent automation, and real-time business modelling. And, in a space governed so strictly by data sovereignty and privacy policies, the cloud’s ability to localise, encrypt, and control data has made it a key enabler of compliance and customer confidence.

                          But as threats become more frequent and sophisticated – with attackers now targeting shared platforms and partner supply chains – organisations can no longer rely on their own defences alone. For true digital resilience, shared accountability, collective readiness, and clear governance across every cloud touchpoint are equally non-negotiable.

                          All Eyes on the Money

                          The industry sits at a valuable intersection of data, technology, and finance. A combination that makes it uniquely attractive to attackers. It holds some of the world’s most sensitive data, directly underpins the flow of global capital, and operates through deeply complex and interconnected systems. With every integration increasing the risk of exposure. Ultimately, the attack motivation is as simple and relentless as it is in most sectors: monetary gain. Cybercriminals target institutions precisely because of the value at stake and the speed at which disruption translates to loss.

                          How the Threat Landscape is Evolving

                          Ransomware groups may see insurers and payment providers as high-yield targets. They understand even seconds of downtime can induce multi-million pound losses. Under pressure to protect customer trust and avoid regulatory penalties, some firms may choose to pay in order to restore their service quickly. This dangerous perception only encourages repeat targeting and paves the way for damage to spread even further. Yet it remains a common response tactic among many.

                          At the same time, the rise of supply chain and third-party attacks has made it possible for criminals to bypass even the most well-defended cloud environments. By exploiting shared platforms, managed service providers, and cloud-hosted applications, perpetrators can move laterally across multiple organisations at once, amplifying both the reach and impact of their attacks. In other words, infiltrating one vendor’s weakness can cripple an entire network in one carefully coordinated strike. And, since some firms may overlook the cloud’s shared responsibility model – presuming end-to-end security sits solely with their cloud provider – multiple blind spots can inevitably emerge, creating easy openings to exploit.

                          In an environment where boundaries blur and dependencies multiply, traditional perimeter-based defences are no longer enough. Hybrid and multi-cloud infrastructures demand continuous visibility, faster detection, and coordinated response across every partner and provider. The goal is not simply to prevent breaches, but to withstand and recover from them collectively. It’s about recognising that in today’s ecosystem, no financial institution is secure in isolation.

                          Inside the Ransomware Economy

                          Evolving beyond the scattergun attacks of the past, ransomware now operates as a professionalised, profit-driven ecosystem, where malicious actors collaborate, trade intelligence, and lease attack tools much like legitimate software vendors. The rise of ransomware-as-a-service (RaaS) has even lowered the barrier to entry, giving less skilled affiliates access to ready-made payloads and automated encryption kits in exchange for a percentage of the ransom.

                          What makes it especially destructive is the precision and psychology behind the attacks. Rather than randomly striking, attackers conduct weeks of reconnaissance – learning behaviours, studying employee hierarchies, and identifying systems most critical to operations. They often infiltrate through phishing emails or compromised credentials, quietly moving laterally through the network to gain elevated access. Once embedded, they disable defences, exfiltrate sensitive data, and target backup repositories before finally encrypting production systems.

                          At that point, the goal shifts from technical control to financial coercion. Victims are locked out of their systems and presented with a ransom note demanding payment, sometimes in cryptocurrency, in exchange for a decryption key. Increasingly, the threat includes public exposure of stolen data – a tactic designed to pressure leadership into paying to protect their reputation and customer trust. Even when ransoms are paid, recovery is rarely clean: data may be incomplete, corrupted, or resold on the dark web, and repeat targeting is common once an organisation is identified as a payer.

                          It’s this blend of stealth, strategy, and human manipulation that makes ransomware so difficult to defend against. By the time the encryption begins, attackers have already spent weeks ensuring recovery options are limited. This background isn’t designed to scaremonger, but to highlight why resilience must start long before an attack ever reaches the endpoint.

                          The Foundations of Ransomware Resilience

                          Ransomware resilience isn’t achieved through a single product or policy – it’s the outcome of strategic, technical, and cultural alignment. Financial institutions, in particular, must approach it as a continuous process of readiness: Anticipating compromise, containing impact, and restoring normality quickly and transparently:

                          Assume-Breach Philosophy

                          The first step is shifting from a defensive mindset to an assume-breach philosophy. In practice, this means recognising that even the most sophisticated systems can and will be breached – and building architectures and response strategies designed to limit damage when this happens. It’s a pragmatic approach, grounded in the reality that attackers are increasingly sector agnostic. No organisation is too small or too secure to be targeted, but the financial sector remains a favourite because it offers both high disruption value and potentially significant monetary reward.

                          Building meaningful resilience, therefore, demands layered defence and disciplined execution. The goal is to slow attackers down at every stage – detecting them early, limiting lateral movement, and ensuring business continuity when systems are disrupted. Behavioural analytics and continuous monitoring can surface and neutralise subtle anomalies that would otherwise go unnoticed – such as phishing, spear phishing, and malware, with email still the number one entry point for ransomware.

                          Zero Trust & MFA

                          Meanwhile, zero trust policies and multi-factor authentication methods add a second layer of protection, blocking unauthorised access even if credentials are compromised.

                          When incidents do occur, a well-practised response framework ensures action is fast and coordinated, minimising disruption across critical systems, with the ability to switch to secure replica environments to keep operations running while remediation takes place. Secure, immutable, air-gapped backups underpin it all, providing a safety net that guarantees recovery can begin from a clean and uncompromised state.

                          Human readiness is equally critical. Technology can contain an attack, but only people can recover from one effectively. Regular simulation exercises, incident rehearsals, and cybersecurity awareness training help teams respond calmly and cohesively, transforming response from reactive to instinctive. This operational maturity is reinforced by strong governance. Frameworks such as DORA, NIST, and ISO 27001 provide the structure to align technical teams, compliance leads, and executive decision-makers around shared resilience goals. When combined with skilled practitioners and clear accountability, they embed security into ‘business as usual’ – moving resilience from a strategy to a sustained organisational capability.

                          Why Multi-Layered Backup is Critical

                          When ransomware strikes, the speed and integrity of data recovery determine whether disruption lasts minutes or days – and whether the impact cascades through wider global markets. As the last and most decisive line of defence when every other control fails, it’s also fundamental to customer trust and compliance. Yet too often, backup is treated as a static safeguard rather than a dynamic resilience layer.

                          Since modern ransomware often seeks out and encrypts traditional backups first, a single backup copy or centralised repository is no longer sufficient. True resilience today depends on a multi-layered approach – combining offsite or cloud-diverse storage, immutable data copies that cannot be altered or deleted, and isolated environments to protect against lateral movement.

                          How frequently these backups are tested is equally important. Too often, financial institutions only discover weaknesses when recovery is already underway, at which point strategies can’t be magically strengthened, and it becomes a race against the clock to minimise downtime and reputational fallout. Regular, automated recovery testing changes that dynamic. It not only confirms that files can be restored, but provides verifiable assurance that systems come back online in the correct order, data dependencies remain intact, and teams have the muscle memory to act quickly and confidently when the worst happens.

                          The Power of Shared Accountability

                          In a digital economy so deeply interconnected, no organisation operates in isolation. This is especially true in financial services, where supply chains and service providers form the backbone of day-to-day operations. While this interdependence is a strength in many ways, it also means resilience is no longer defined by how well a single institution can defend itself, but by how effectively every partner in its ecosystem upholds their part of the security chain.

                          This is where shared accountability becomes critical. It recognises that cloud providers, managed service partners, and financial institutions each have distinct but complementary roles to play in securing data, systems, and infrastructure. When accountability is clearly defined – and when partners collaborate rather than operate in silos – visibility improves, incident response accelerates, and the risk of systemic failure decreases.

                          Shared accountability also extends beyond contractual obligation. It’s about building a culture of collective readiness: sharing intelligence, rehearsing joint incident scenarios, and supporting smaller or less-resourced partners to raise their security baseline. The result is a unified entity capable of anticipating, absorbing, and recovering from disruption together.

                          Looking Ahead

                          To view cyberattacks as inevitable might seem pessimistic to some, but it’s an unfortunate truth that no amount of investment can eliminate risk entirely. In an era where threats are growing in both scale and sophistication, readiness becomes the true differentiator – particularly in such a high-stakes sector. For financial institutions, that means embedding security into culture, strengthening connections across supply chains, and continually testing their ability to withstand and recover as a united ecosystem. Only then can resilience become a strategic advantage rather than a defensive necessity, and unlock the cloud’s transformative potential with absolute confidence.

                          Learn more at virtualcds.co.uk

                          • Artificial Intelligence in FinTech
                          • Cybersecurity
                          • Cybersecurity in FinTech
                          • Data & AI
                          • InsurTech

                          Author

                          Ash Gawthorp

                          Published

                          22 January 2026

                          Estimated Read time

                          6Mins

                          Ash Gawthorp, CTO and Co-founder of Ten10, on building the right foundations to shape the AI era in the UK

                          A recent study shows that UK businesses expect to increase their AI investment by an average of 40 percent over the next two years, following an average spend of £15.94 million this year. With investment surging, the UK is clearly in the fast lane, but the question is whether that momentum will convert into real, durable strength.

                          This rapid acceleration places the UK at a pivotal moment in its ambition to lead in artificial intelligence. Investment is rising, government focus is strengthening, and organisations across every sector are exploring AI at pace, creating a sense of real momentum. However, anyone who has experienced previous technology cycles will recognise the familiar tension that emerges during periods of rapid progress and optimism. Breakthroughs often attract significant attention and capital before entering a more grounded, sustainable phase.

                          The pressure today is not on AI as a whole. Instead, it is focused on a specific path, where belief in ever-larger transformer models delivering general intelligence continues to grow. This progress has been remarkable, but it represents only one path within a much broader AI landscape. As excitement reaches its peak, the market will inevitably stabilise. The long-term value will come through robust engineering, strong talent pipelines, and successful deployment in real-world environments.

                          The task now is to use this moment wisely. Long-term success depends on building deep capability at home, rather than relying on hype or outsourcing key foundations to external providers that sit outside our oversight and control.

                          The Limits of Scale as Strategy

                          A significant share of today’s investment is based on the assumption that increasing compute and model size will inevitably lead to artificial general intelligence (AGI). Transformer architectures have delivered extraordinary capability and accelerated progress in ways few predicted. They remain powerful systems for prediction and pattern recognition across language, images and other data.

                          However, scale is not a guarantee of general reasoning or broad intelligence. Many researchers believe that transformative progress may require developments beyond today’s dominant architecture. If that proves correct, the markets surrounding large closed models will experience a natural cooling. This would be an adjustment based on speculative expectation, not a failure of AI as a discipline. The industry would then shift toward approaches that prize clarity, modularity and measurable outcomes. Engineering discipline and architectural flexibility will matter far more than sheer size.

                          One Architecture Cannot Become a National Dependency

                          AI will continue to advance. The question for the UK is whether it builds capability that can evolve alongside that progress, or whether it locks itself to a narrow set of global platforms. A handful of model providers currently influence pricing, model behaviour and development cycles. When enterprises rely entirely on opaque APIs, they inherit changes without knowing why outputs shift, how models adapt or when pricing dynamics move. That introduces fragility that grows over time.

                          Some experimental use cases can tolerate opacity, but critical public services and regulated industries cannot. Lending, diagnostics, fraud detection and other high-stakes applications demand clarity over how decisions are formed and how logic stands up to scrutiny. In those environments, transparency and auditability shift from abstract ideals to essential operational requirements.

                          If the UK intends to embed AI deeply into essential systems, it must champion architectures that allow observability, explainability, control and replacement. Dependence on decisions made offshore is not a foundation for long-term strength.

                          Specialised Agents Reflect How Sustainable Systems Evolve

                          A practical and resilient approach to AI is already taking shape. Rather than depending on a single model to handle every task, organisations are assembling systems made up of specialised components. This mirrors the way effective teams work, where roles are defined, responsibilities are clear, and handovers are structured. One model transcribes speech, another classifies information, and a third retrieves or summarises content. Each performs a focused function that can be observed, validated and improved.

                          This modular design makes systems easier to maintain and evolve. New components can be adopted without rewriting entire frameworks. If performance changes or drift appears, individual parts can be evaluated or replaced without widespread disruption. This reflects long-standing engineering principles that value clarity, observability and the ability to substitute components when better options emerge.

                          Financial efficiency supports this approach as well. Running powerful frontier models for every interaction introduces cost and latency that scale quickly. Task-specific agents can often deliver the same outcome faster and more economically. Across thousands of interactions, the savings and performance gains become significant.

                          Engineering as the Anchor of Trustworthy AI

                          As AI becomes embedded in real systems, success relies on foundational engineering practices. Observability, continuous testing, performance monitoring and controlled deployment are essential. These are not new concepts created for AI, but long-established techniques that have been adapted to a new class of technology.

                          In early exploratory phases, it can be tempting to treat large models as something separate from traditional software systems. However, the moment AI begins to influence real decisions, the fundamentals return. Enterprises must be able to trace behaviour, explain recommendations and ensure consistent reliability, while regulators expect clarity and boards seek evidence-based decisions around technology choices, cost structures and risk.

                          Organisations that approach AI as engineered infrastructure, rather than a mysterious capability, will be far better equipped to scale safely and confidently.

                          Building Skills that Make Capability Real

                          The UK is fortunate to have strong research institutions, a sophisticated regulatory mindset and a robust software talent base. To convert these strengths into durable national advantage, investment in skills must expand beyond narrow data expertise. Data scientists remain crucial, but sustainable AI delivery depends equally on software engineers, cloud specialists, machine learning specialists, testers, governance experts and operational teams who run systems at scale.

                          Leading organisations recognise that AI delivery is a multidisciplinary effort. As architectures become more modular, value will flow from those who can integrate, monitor and guide AI systems responsibly. The UK must ensure that thousands of professionals have access to this training and experience. Real leadership emerges when capability is widely shared, not concentrated in a small group.

                          Governance that Accelerates Innovation

                          Strong governance does not slow innovation. It accelerates meaningful adoption by building confidence. When organisations can demonstrate transparency, control and reliability, AI can extend into more critical functions.

                          For national strategy, this becomes a competitive advantage. Industries that manage financial and clinical outcomes are not resistant to technology. They simply require evidence that systems behave consistently and transparently. If the UK excels in building AI that is observable, testable and replaceable, trust will grow and adoption will move faster.

                          Shaping a Resilient AI Future

                          Every technology cycle begins with excitement and eventually settles into maturity. Those who succeed through this transition are the ones who invest in capability while enthusiasm is high. When the current market resets, leadership will belong to those with engineering depth, system agility, responsible governance and the skills to integrate specialised intelligence across complex environments.

                          The UK has an opportunity to define this standard. Strength will come from transparency, interoperability and the ability to adapt to model and architecture changes without disruption. It is a quieter strategy than making declarations about imminent artificial general intelligence, yet it builds the resilience required to lead over the long term.

                          The future will reward systems that can evolve, remain auditable and operate securely at scale. With the right foundation, the UK can shape this era of AI not through scale alone, but through excellence in engineering, governance and talent. That foundation is the true measure of AI power, and now is the moment to build it.

                          Learn more at ten10.com

                          • Data & AI
                          • Digital Strategy

                          Author

                          Emily Nash-Walker

                          Published

                          Estimated Read time

                          5Mins

                          Emily Nash-Walker, Sr Director of Product Strategy at Tungsten Automation on finding real value for AI across financial services

                          The Bank of England has recently sounded the alarm of a potential AI bubble looming. Experts are calling out clear parallels with the dot-com boom, such as over expectations on the tech, huge investment, and limited returns or focus on value addition. In the financial services sector, where innovation and risk are no strangers, the Bank of England’s warning couldn’t be more relevant.

                          Since the launch of ChatGPT, financial services and FinTech firms have dedicated unprecedented time and money to AI. From LLMs to predictive analysis and AI Agents. However, underneath the rapid adoption we see, there is rising tension between experimentation and governance.

                          Shadow AI

                          Many FinTechs and traditional financial services firms are now working on “shadow AI” (internal systems developed without formal oversight, transparency, or risk management), creating a sort of AI “grey market”. This new market offers huge innovation, but without being managed properly, it undermines key governance, and in the fintech space, this means risking consumer data, consumer confidence, and ultimately trust. If left unchecked, this could trigger the industry’s next big credibility crisis and expose them to the next big financial crisis.

                          AI Overextension

                          AI can have huge transformative effects on financial services and is at the forefront of changing the industry for the better. From fraud detection to customer service automation, there’s no doubt that AI has changed how institutions engage, analyse, and operate for the better.

                          But the industry’s eagerness to innovate quickly has led to a familiar problem: overextension. According to MIT research, 95% of GenAI pilots never reach production. Meanwhile, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year if they are implemented effectively. But that is a big “if”.

                          Right now, too many organisations are focused on experimentation in isolation, often in siloed AI labs. Where AI tools are being built by small internal teams without full visibility or awareness from compliance or IT departments. Algorithms are being trained on partial or poor-quality data. And models are being deployed without clear documentation of how they make decisions. More than 81% of financial compliance experts are concerned about the accountability and explainability of AI-driven decisions. Fundamentally lacking the accountability and explainability that should underpin AI that drives real, low-risk value for businesses.

                          Dangers of the AI Bubble

                          If the AI bubble bursts, it won’t be because of the technology. It will be because of how it’s being applied. And the more experiments an organisation invests in without real value being shown, the more they will be exposed to the effects when it pops.

                          As the bubble grows, so does “Shadow AI”. The pursuit of innovation across sectors leads to siloed teams investing quickly but often without the right guardrails.

                          Shadow AI shows many similarities to the early days of the cloud era, when employees adopted unsanctioned tools to move faster than IT could keep up, leaving organisations fragmented and exposed to risk. Innovation is as essential or even more essential than it has ever been, but this idea of fragmentation is also more of a risk now than it has ever been.

                          In financial services, the implications are far more serious than in most industries. Consider the risks if a credit-scoring model built without audit trails begins making biased decisions. Or if a KYC automation tool fails to detect a sanctions breach because it’s running on unvalidated data. And banks built on shadow AI lack the explainability to know, let alone test or assure these models.

                          AI Governance

                          FinTech success depends on reliability, transparency, and data integrity. Once those foundations erode, rebuilding them becomes far harder than any technical fix. The solution isn’t to slow down innovation. It’s to govern it properly.

                          The whole industry needs to move beyond AI experimentation toward governed automation. Integrating AI responsibly into existing workflows, supported by clear oversight, robust data management, and explainable outcomes, has to be the priority.

                          Smart businesses are focused on AI for the right reasons. It means focusing on what’s needed, practical and measurable instead of chasing ideas of what you could potentially do. Organisations need to be aware of the hype and focus on systems that deliver compliance, accuracy, and ROI.

                          Financial services have always had challengers in the sector pushing boundaries with new tech, and this has never been so true. It’s an industry that has always spent a lot of time focused on hype. But this next phase of innovation, specifically AI adoption, will see winners prioritising something different. Patience, precision, and accountability will win over efficiency, new features, and speed.

                          Heeding the Warnings

                          As the Bank of England has warned, overinvestment and complacency when it comes to defining and reporting concrete value may be creating a big bubble primed to pop. To prevent or limit exposure, leaders should ask three business-critical questions before plunging more investment into AI:

                          • What business problem are we solving?
                          • Is our data structured, accurate, and governed?
                          • Can we measure the outcome and explain the result?

                          If the answer to any of these is uncertain, the risk is also uncertain. The danger with shadow AI is that often the answer to all 3 is opaque and unclear. AI’s potential in financial services remains enormous. But true intelligence doesn’t come from the newest model or the biggest dataset. It comes from disciplined execution.

                          When the hype fades, the organisations that endure will be those that integrate AI responsibly, manage data intelligently, and put compliance at the core of innovation.

                          As with the dotcom boom and many other technological revolutions, the question isn’t whether AI will reshape the sector; it’s who will still be standing when the dust settles. The difference will come down to who governs their AI with a focus on real value versus those who chase experimental AI without true accountability.

                          Learn more at tungstenautomation.com

                          • Artificial Intelligence in FinTech

                          Author

                          Marko Katavic

                          Published

                          21 January 2026

                          Estimated Read time

                          4Mins

                          Marko Katavic, Director of AI and Decision Intelligence at Moneybox, argues the future of financial services should not aim to replace bureaucratic safety systems with AI, but instead integrate AI to deliver human-level accessibility

                          Trust is the foundation and the currency of the financial services industry. When customers hand over their hard earned money, they trust in their chosen provider’s ability to safeguard their finances and help achieve their financial goals. 

                          Long before computers came about, the financial services industry built trust and minimised risk through carefully organised processes led by people. A significant amount of bureaucracy, process control and mapping has reduced mistakes for decades. However, as technology has developed, the way the industry interacts with these processes is changing. 

                          The Rise of Bureaucracy and Software

                          The introduction of computers enabled the financial services industry to scale processes, increase productivity and widen customer pools. This was achieved through structured software mapped to closed deterministic and bureaucratic processes that allowed the industry to reduce errors and increase efficiency by applying the same structured decision-making to lots of customers automatically, rather than having humans make decisions for each individual customer.

                          Now we face the rising popularity of AI agents, and effectively integrating these entities into the sensitive systems that were built before them. When applied correctly, they offer immense value, but applied incorrectly, and they risk causing immense harm.

                          As we are at the relative start of the AI implementation journey, it is crucial to determine how we take AI tools with such significant decision making capabilities, and safely plug them into our systems now to maintain trust, and more importantly so that they help customers, rather than hinder.

                          The Missing Human Layer

                          The key to successful AI implementation in the financial services industry is to understand the market gap it can fill. For the last four decades, scaling financial services safely has only been achieved with many layers of bureaucracy – slowing delivery, adding friction, and ultimately limiting who could be served. Furthermore, the human experts who could navigate these bureaucratic complexities and translate it into clear, accessible decisions for customers were few and far between.

                          This gap is what modern AI systems can close. AI can act as an intelligent layer in front of the bureaucracy, to help the wider public make smart financial decisions with greater confidence. We must learn from the success of large AI systems, as their approachability and ease of use is what draws customers in at scale.

                          However, for AI to fulfill this promise, it must meet the same standards of institutional safety and compliance. This ease of use must be brought to customers safely, meaning we must engineer the very same systems of safety that currently underpin the financial sector, ensuring AI offers accessibility without compromising on trust. 

                          Engineering Safe Boundaries

                          To achieve this, we have to go beyond integration – we have to engineer clear boundaries between AI and traditional software. We must use AI to deliver an accessible, relatable customer experience, while ensuring it follows the principles built into tested software. This approach is critical because good outcomes only come as a result of managed risk and tested judgement.

                          There is significant hype around feeding agents large knowledge bases of policies via Retrieval-Augmented Generation (RAG). While using state-of-the-art models can achieve reasonable, but not perfect, policy concordance for judgement tasks – if the aim is to deliver full flexibility of human interaction to customers at scale, then this protocol is only acceptable for basic customer service, such as issue handling. It falls short when it comes to dealing with the diverse approaches and behaviours customers bring – meaning that errors can only be minimised, not entirely controlled.

                          When dealing with nuanced considerations such as investment decisions and judgements that have long-standing consequences, it is better to implement software layers that are interactive with AI for logic checking and generating results, rather than trying to emulate complex decision making principles through predictive language.

                          A Recipe for Success 

                          Modern AI systems, even when producing the right answer 95% of the time, are making decisions on ‘instinct’. No financial firm would implement a workforce of highly instinctual individuals making critical decisions without bureaucratic control. Therefore, putting AI on the path to make financial decisions without the tried-and-tested software to control logical reasoning is a path to failure.

                          The recipe for success in a customer-facing context is clear. Providers should use AI to mimic everyday language and bring a personal dimension to customers at scale, but keep core financial decision-making within the safe domain of tried and tested software and experts. 

                          While this may sound simple on paper, achieving a seamless system where everything blends together is the core differentiator between companies that will win customer confidence, and companies that will simply offer ‘cool ‘short-term gimmicks. To close the advice gap, the future of financial services should not aim to replace bureaucratic safety systems with AI, but instead integrate AI to deliver human-level accessibility – while keeping decisioning limited to the domain of purpose-built software.

                          Learn more at moneyboxapp.com

                          • Artificial Intelligence in FinTech
                          • Digital Payments
                          • Neobanking

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          2Mins

                          New research from myPOS, the European payments provider for small and medium-sized businesses, reveals that Britain’s shift toward tap-to-pay is leaving…

                          New research from myPOS, the European payments provider for small and medium-sized businesses, reveals that Britain’s shift toward tap-to-pay is leaving traditional PIN codes behind. As contactless becomes the country’s top payment preference, almost a third of young adults now admit they can’t remember the four digits once central to everyday spending.  

                          myPOS data reveals 29% of Gen Z struggle to remember, or have completely forgotten, their PIN. Highlighting how digital-first habits are shaping consumer behaviour. However, it isn’t just younger groups that are feeling the effects. One in five Boomers (20%) say they face the same issue as reliance on physical cards significantly declines. 

                          Contactless Payments

                          This shift has been driven largely by the dominance of contactless card and mobile payments. Over two-thirds of Brits (69%) say tapping, via card, mobile phone, or smartwatch, is now their primary method of payment. In contrast, just 16% rely mainly on chip and PIN, and only 14% primarily use cash. A further 10 % of Brits now live entirely wallet-free, using only their mobile or smartwatch for day-to-day spending. 

                          Convenience-led behaviours are accelerating the decline of PIN usage across the UK. Nearly half of British consumers (47%) say they would happily go completely contactless if it meant shorter queues in shops and venues. Flexibility and convenience (42%) and speed (34%) remain the largest drivers behind the rise of tap-to-pay.  

                          “As the UK embraces contactless and mobile payments, it’s clear that the traditional PIN is becoming less central to everyday transactions. Businesses and payment providers should ensure security and convenience go hand-in-hand, while recognising that consumer habits are evolving rapidly.”

                          Michael Ault, Country Manager at myPOS UK

                          • Digital Payments
                          • Fintech & Insurtech
                          • Neobanking

                          Author

                          Jelle van Schaick

                          Published

                          20 January 2026

                          Estimated Read time

                          4Mins

                          Jelle van Schaick, VP Marketing at Lorum, looks beyond Swift at fixing the correspondent chain to change how global money moves

                          SWIFT has become a convenient villain in modern finance. When a cross-border payment takes three days to settle, frustration builds. The blame is almost instinctively placed on the messaging network itself. Vendors promise to replace it with blockchain solutions, and crypto pitch decks often speak evangelically of a world without it. However, focusing on SWIFT hides a much simpler and less glamorous reality regarding international finance. SWIFT is not the problem. The correspondent banking chain is the problem.

                          To understand the delay, we need to separate the instruction from the asset. SWIFT is fundamentally a secure messaging network that allows financial institutions to send payment details. However, it does not move money, it does not hold deposits, and it does not determine when funds are released. A SWIFT payment message is simply data. The actual capital resides in nostro and vostro accounts, moving via domestic RTGS systems and local clearing schemes.

                          While the SWIFT message travels across the globe in seconds, the delay lives in how each institution along the path manages its own accounts and risk parameters. When a payment stalls, the bottleneck is almost always found in that chain of custody rather than in the wire that carried the instruction. Blaming SWIFT for a delayed settlement is like blaming an email provider because the recipient waited two days to open the message and reply.

                          The Hidden Cost of Every “Hop”

                          Most cross-border flows continue to rely on the correspondent banking model. In this system, if a bank in one country does not have a direct relationship with a bank in another, the payment must hop through intermediaries. This is not a seamless relay; each step in the sequence adds resistance.

                          Every hop adds:

                          • another balance sheet to fund
                          • another set of compliance checks
                          • another set of cut offs, holidays, and local quirks
                          • another chance to add margin, fees, or spreads

                          This operational drag creates measurable latency. SWIFT’s own data shows that while roughly 90% of payments reach the destination bank within an hour, fewer than half are credited to the end customer’s account in that same timeframe. The delay is not in the transit; it is in the processing queue of the receiving institution.

                          Why Banks are Paid to Wait

                          If technology is not the primary issue, we must look to economic incentives. The correspondent banks facilitating these flows are typically large universal banks. Their core economic engine is lending and balance sheet management rather than clearing. These institutions earn yield by holding deposits and managing liquidity, not by pushing funds out the door as fast as possible.

                          That creates predictable tensions:

                          • settlement can be batched or delayed to smooth intraday liquidity
                          • funds can sit in internal accounts until windows or limits align
                          • risk teams can slow flows when risk appetite tightens

                          When operational reality meets these misaligned incentives, the result is a compounding delay that no messaging standard can fix. Legacy cores, manual exceptions, and misaligned time zones all stack delay on top of these economic priorities. Operational teams bounce investigations between institutions through tickets and emails, magnifying the friction. None of this is a feature of SWIFT. It is the consequence of who performs clearing and what their balance sheets optimize for.

                          The Case for Unbundling Clearing

                          The traditional network is fracturing under this pressure. According to the Bank for International Settlements (BIS), the number of active correspondent banking relationships has declined by over 20% in the last decade. As universal banks retreat from this low-margin utility, a vacuum has opened for specialist infrastructure.

                          This helps explain why many fintech projects fail to solve the core issue. Attempts to create new rails often miss the point. If the system still relies on universal banks to hold funds, the underlying friction remains. The critical design question is not how to remove SWIFT, but who should perform institutional clearing and under what incentives.

                          A truly effective solution requires a structural shift where clearing is separated from lending. This has paved the way for a new category of infrastructure known as the specialist correspondent. Unlike universal banks, these institutions are designed exclusively for clearing and cash management. We see this model validated by firms like Lorum, which operate as specialist correspondents rather than generalist banks.

                          By connecting to local payment rails in multiple markets and providing named account structures, this model allows institutions to work with a single clearing partner rather than managing dozens of bilateral relationships.

                          For treasurers, this shift means:

                          • local settlement on domestic rails wherever possible
                          • a single global view of balances and flows
                          • fewer intermediaries and more predictable timelines

                          This approach does not replace the messaging layer, as SWIFT already moves messages well. Instead, it redesigns the institutional layer behind those messages. It focuses on the start and end of each payment where custody, timing, and control actually break. Blaming SWIFT is easy because it is visible and old. It is harder, and more useful, to redesign who holds and releases funds. The firm that fixes the correspondent chain changes how global money actually moves.

                          Learn more at lorum.com

                          • Blockchain & Crypto
                          • Digital Payments

                          Author

                          Katja Hakoneva

                          Published

                          Estimated Read time

                          5Mins

                          Katja Hakoneva, Product Manager at Tuxera, on delivering tomorrow’s data storage security today

                          Smart meters are no longer just data endpoints. They’re intelligent, connected nodes embedded into the national infrastructure. As energy networks undergo rapid digital transformation, the focus has largely been on secure communications and real-time data transmission. But beneath the surface lies the local data storage, which often becomes a critical blind spot.

                          Smart meters store large volumes of sensitive data from energy usage profiles to firmware logs and grid event histories on embedded memory. If this information is accessed, altered, or deleted, it can trigger billing inaccuracies, regulatory breaches, and customer mistrust. With meters expected to operate in the field for up to 20 years, data-at-rest security is a critical requirement.

                          Storage Vulnerabilities: The Silent Cyber Threat

                          These embedded systems face multifaceted risks. Attackers may gain access to stored data by physically tampering with a meter or exploiting software vulnerabilities that bypass weak authentication. Malicious actors could manipulate logs to alter billing records, mislead consumption analytics, or mask larger cyberattacks on grid infrastructure.

                          In many cases, such intrusions go undetected until tangible damage, such as lost revenue or reputational fallout. With increasing dependence on smart infrastructure, utilities can no longer afford to treat embedded storage as a passive component.

                          Counting the Real Costs of Cybersecurity

                          Securing smart meters comes with technical requirements, as well as, operational and resourcing demands. For many UK manufacturers and utilities, managing cybersecurity internally means building and retaining specialist teams, often requiring three to five full-time professionals to handle vulnerability monitoring, patch management, and threat response throughout the year.

                          Aligning with regulatory frameworks frequently demands hardware upgrades to handle stronger encryption and secure configurations, impacting Bill of Materials (BOM) costs and development timelines. Many existing software stacks require optimisation to support modern security protocols within resource-constrained devices. These efforts are necessary, with a single undetected cyberattack costing companies an average of $8,851 (≈£6,900) per minute, and the consequences extending beyond financial loss to potential regulatory fines and service disruptions.

                          The CRA and the new Era of Cyber Regulation

                          The Cyber Resilience Act (CRA), set to come into force across the EU by 2027, will reshape how connected devices are designed, developed, and supported. For UK-based vendors serving the European market, or collaborating with EU counterparts, compliance with CRA is becoming a strategic imperative.

                          Key CRA requirements include:

                          • Security by design: Devices must be secure from the outset, not retrofitted post-deployment.
                          • No known vulnerabilities at market launch: Products must undergo security validation prior to release.
                          • Default secure configurations: Devices should avoid insecure settings out of the box.
                          • Lifecycle management: Vendors must support patching and vulnerability resolution throughout the device’s operational lifespan.

                          For smart meters, which often run in the field for two decades or more, the CRA introduces accountability that extends well beyond product launch. Compliance with the CRA will become part of the CE marking process, meaning global manufacturers must align if they wish to sell into the EU energy market.

                          Engineering Security: Confidentiality, Integrity, and Authenticity

                          Designing resilient smart meters starts with three pillars:

                          • Confidentiality protects sensitive user data from unauthorised access. This includes encrypting both data and encryption keys, restricting user access levels, and securing communication channels.
                          • Integrity ensures stored data remains unaltered and trustworthy. Power failures, for instance, can corrupt memory. Using flash-optimised file systems and secure boot processes can prevent such vulnerabilities.
                          • Authenticity confirms that firmware and data updates come from trusted sources. Techniques like digital signatures and update validation prevent attackers from injecting malicious code into meters.

                          Together, these pillars enable smart meters to meet regulatory expectations while protecting both users and grid operations.

                          Future-proofing Data Storage

                          Cybersecurity for smart meters is not just a feature; it requires organisational readiness. Frameworks like the CRA, NIST, and IEC 62443 emphasise secure processes, documentation, and people alongside secure products.

                          For companies looking to prepare, it is smart to start with common pillars such as maintaining up-to-date Software Bills of Materials (SBOMs), conducting regular supply chain and risk assessments, keeping detailed test reports, and establishing clear incident response plans. Internally, training staff on cybersecurity best practices, setting clear data retention policies, and defining access controls and responsibilities are critical steps to ensure cybersecurity is embedded within the culture of the organisation. This approach ensures security is not a one-off compliance task but a sustainable practice that protects smart infrastructure long-term.

                          Smart meters deployed today could still be operating in the 2040s. This timeline intersects with the anticipated emergence of quantum computing, which may break today’s encryption standards. Though post-quantum cryptography is still evolving, vendors must prepare now to ensure systems remain secure in a post-quantum world. Smart meter software should be designed with cryptographic agility to allow it to adapt and upgrade algorithms as threats evolve.

                          Lessons from Long-Term Deployment

                          Smart meters are designed for longevity, but memory wear remains a primary failure point. Meters that lack flash-aware storage systems face early data loss, increasing the cost of maintenance, replacements, and warranty claims.

                          Utilities and OEMs that embed file systems capable of wear levelling, garbage collection, and secure boot processes have extended meter lifespans by more than 50%, even in challenging conditions. One example showed meters surviving over 15,000 power interruptions without any data loss.

                          Integrating secure storage delivers operational and commercial benefits. It ensures compliance with CRA and other evolving global frameworks, reduces maintenance and warranty costs, minimises carbon impact through fewer replacements, enhances brand credibility and trust with procurement teams, strengthens the business case for longer-term contracts and partnerships. As the smart energy market matures, these benefits are becoming differentiators, especially as digital infrastructure grows in complexity.

                          Delivering Tomorrow’s Data Storage Security Today

                          The next generation of smart infrastructure will be fast and connected, as well as, secure, resilient, and regulation-ready. For vendors and utilities alike, embedding data protection deep into the meter architecture is a business-critical move.

                          By preparing for the CRA today, smart meter manufacturers will position themselves as forward-thinking, trustworthy partners in tomorrow’s energy ecosystem, delivering technology that’s not only built to last but built to protect today and tomorrow.

                          Learn more at tuxera.com

                          • Cybersecurity
                          • Data & AI
                          • Digital Strategy

                          Author

                          Dan Brightmore

                          Published

                          19 January 2026

                          Estimated Read time

                          4Mins

                          Richard Wood, Head of Europe – FI & NBFI at PagoNxt Payments, on the rise of embedded insurance

                          As 2026 kicks off executives will once again be asked what moving, what’s shaking, and what will fade away in insurance. But what’s changing fastest in insurance isn’t the product itself, but when, where and how policies are sold. Thanks to the rise of Embedded Insurance, this is now happening at the exact moment a customer commits to a purchase. In its simplest definition, Embedded Insurance combines coverage or protections within the purchase of a product or a service itself, offered in real-time at the point of sale.

                          This distribution model offers enormous potential. According to McKinsey, by 2030 around 25% of all personal lines premiums could be purchased via embedded propositions, representing over $700 billion in gross written premiums in property and casualty alone (Source: McKinsey, via Deloitte). Consumers are drawn towards comprehensive, connected solutions rather than standalone products, and it’s this that is making Embedded Insurance such a hot topic in the industry. For perhaps the first time in its history, protection is genuinely being matched with real-world behaviour

                          Simply by making coverage easier to purchase, Embedded Insurance is likely to play a key role in helping to close the global insurance protection gap (the gap between economic losses and those that are insured), which has widened over recent years (Source: Swiss Re Group). Equally, traditional insurance has failed to win over younger generations (Source: LIMRA), with many citing a perceived cost, lack of clarity and distrust as reasons they’re not buying. Without reinvention, the insurance industry risks a dramatic contraction from missing out on an entire generation.

                          Why Partnerships are Key to Success

                          To make purchasing a new policy as convenient and seamless as possible, partnerships that blend platforms, insurers, and service providers into cohesive ecosystems are essential. By definition, no single organisation can control the full journey. Insurers don’t own the digital moments where customers make decisions. Platforms can surface offers at scale, but need regulated partners they can trust.

                          Partnerships reshape the economics of insurance. Embedded channels reduce acquisition costs, open new demographics – particularly younger, underinsured consumers – and create conditions for personalised pricing based on real behaviour, not broad assumptions.

                          The payment is where any buyer’s intent is clearest, context is richest, and risk is most visible. Insurers can’t reach that moment alone, and platforms can’t underwrite it. Payments providers are the connective tissue. For merchants, payments are where the relationship is strongest. Surface the right protection there and it doesn’t feel like an upsell. Instead, it builds trust, supports conversion, and gives retailers something competitors can’t easily replicate.

                          What separates a great payments partner from an average one is the ability to bring clarity, compliance and context to the transaction. That means handling customer authentication cleanly, settling funds quickly and maintaining resilient infrastructure that can cope with real-time flows. It also means surfacing protection responsibly, with transparent consent that removes the ambiguity that deters younger generations.

                          Payments partners able to offer real-time validation, cross-border settlement support, and reliable fraud-screening at scale become central to delivering embedded protection that actually works.

                          Where Payment-Led Ecosystems will Go Next

                          The next phase will see protection woven even deeper into payments infrastructure. Transaction-level insight will enable personalised cover bundles. Cross-border settlement will remove friction from claims in travel and logistics. Real-time rails, meanwhile, will make instant payouts a norm. SEPA Instant will only accelerate this shift. As near-instant euro transfers become standard across Europe, the expectation for equally instant premium collection and claims payment will rise, pushing Embedded Insurance even closer to the transaction.

                          Regulators will pay close attention, as they should. Strong governance, transparent consent and clear communication must underpin every embedded journey. Regulation such as PSD2 and the newer DORA framework underline that operational resilience must be designed into every API call, every authorisation step, and every embedded offer.

                          As momentum gains, the payment process will be where trust, timing and technology converge. Overtime, protection will be seamless and efficient, but this will not happen in isolation. The next leap forward demands insurance ecosystems are built around the payment itself. This is exactly where insurers, platforms and payment providers will combine their strengths to offer protection that is timely, contextual and genuinely useful.

                          Learn more at pagonxt.com

                          • Embedded Finance
                          • InsurTech

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          5Mins

                          Michael Ault, Country Manager at integrated payments specialists myPOS, offers strategic advice for SMEs looking to scale through digital transformation and diversification

                          Scaling a small business is one of the most rewarding, yet complex journeys for any entrepreneur. While growth brings opportunities for greater reach, higher revenue, and stronger market presence, it also demands foresight, discipline, and the ability to manage risk strategically. Securely integrating new technology is the main obstacle for 47% of SME’s, even though 76% of these businesses intend to expand their IT investment. This underscores a key point of tension, as many businesses want to grow through digital transformation but struggle to do so securely and sustainably.

                          The business landscape continues to evolve with changing customer expectations, technology, and economic conditions. For UK SMEs, the key to long-term success lies in achieving growth but also in building resilience. Sustainable scaling comes down to three principles: embracing technology pragmatically, diversifying intelligently, and investing in people and partnerships that strengthen resilience.

                          Leveraging Digital Transformation

                          Digital transformation is the foundation of business growth, especially for small business. Cloud-based solutions, automation, and data analytics help to streamline operations, reduce inefficiencies, and create better customer experiences. However, transformation must be purposeful, not performative.

                          The smartest approach is to scale technology investment incrementally, integrating flexible, modular systems that evolve with business needs. This approach not only lowers risk but also helps ensure digital maturity evolve over time. When SMEs use modular, cloud-based technology, operations run more smoothly and changes can be effectively analysed. Ultimately, resilience is not built through one-time upgrades but through a culture of continuous digital evolution.

                          Diversifying Revenue Streams

                          Depending on a single product, service, or market leaves a business vulnerable to sudden changes in demand. Diversification, when guided by customer insight and data can turn volatility into opportunity. Expanding into online sales, introducing subscription models, or targeting fresh customer segments can make income streams much more stable and sustainable.

                          At myPOS, we know that even simple changes based on data, such as adding additional payment options or tapping into cross-border e-commerce, can help cash flow and protect against market shocks. The goal of technology is to mitigate specific challenges without adding layers of complexity.

                          Investing in Employee Development

                          Your people are pivotal to your ability to grow as a business; empowered teams are the engine of sustainable scale. A team that feels supported and motivated will bring fresh ideas, adapt to challenges, and push the business forward. Investing in training, mentoring, and development opportunities builds skills that pay back in the form of innovation and improved performance.

                          In fast-changing industries, having employees who are confident in learning and adapting can make the difference between struggling through disruption and taking advantage of it. Equally, strong partnerships extend this resilience beyond the organisation. Building resilience at the team level creates resilience for the whole business, so fostering a culture of continuous learning and celebrating employee contributions is key to maintaining motivation.

                          Focusing on Financial Health and Flexibility

                          Financial resilience underpins sustainable growth. Scaling often requires upfront investment, and without healthy cash flow or reserves, opportunities can be lost. Monitoring income and expenses closely, cutting unnecessary costs, and preparing for seasonal fluctuations gives businesses more control.

                          Having flexible financing options, like credit lines, small business loans, or even crowdfunding, provides a level of agility. Instead of being caught off guard by unexpected challenges, businesses with financial flexibility are positioned to respond quickly and strategically.

                          Financial management software can make it easier to track performance, spot issues early, and forecast future needs. When you can see your finances in real time, you can make proactive, data-driven decisions instead of waiting for problems to happen. In markets that change quickly, this kind of financial management helps small firms plan with confidence, stay flexible, and establish a stronger base for long-term growth.

                          Prioritising Customer Relationships and Feedback

                          Your customers are not just buyers; they are advocates, sources of insight, and the foundation of repeat business and brand loyalty. Businesses that scale successfully often place customer relationships at the heart of their strategy by actively gathering feedback, responding quickly to issues, and personalising interactions, which shows customers they are valued.

                          This loyalty becomes a form of resilience. In periods of uncertainty, a base of satisfied, returning customers provides more stability than constantly chasing new ones. Successful businesses use CRM tools to track customer preferences and automate follow-ups so no opportunity to strengthen a relationship is missed.

                          Building Strategic Partnerships

                          Partnerships can accelerate growth while also spreading risk. Working with other businesses, organisations, or influencers can provide access to new audiences, shared expertise, or additional resources. Collaboration can also create opportunities for joint marketing, co-branded initiatives, or innovative product and service offerings.

                          In times of uncertainty, strong partnerships act as a support network. By aligning with others who share your values and vision, you create opportunities that are mutually beneficial and more resilient than going it alone. It is important to find partners whose goals and audiences complement your own for the best long-term impact.

                          The next stage of small business success will be defined by resilience rather than speed, the ability to adapt, recover, and continue to create value in the fact of uncertainty. For SMEs, this means developing adaptable growth plans that include flexible technology, diverse models and empowered employees.

                          Learn more at mypos.com

                          • Data & AI
                          • Digital Payments
                          • Digital Strategy
                          • Fintech & Insurtech

                          Author

                          Nell Walker

                          Published

                          16 January 2026

                          Estimated Read time

                          6Mins

                          We talk to Kimberley Duarte, Strategic Programs and Operations at the Circular Supply Chain Network, about her experiences in supply chain

                          It’s common for procurement professionals to just fall into supply chain. How did it happen for you?

                          I guess I fall into the same camp. I came into supply chain through engineering. I started as an electrochemical engineer, working on energy systems. My master’s is in hydrogen economy, fuel cells, batteries, things like that.

                          However, I’ve always been in operations. My co-op during my bachelor’s degree was in operations engineering in a chemical coating company. My dad was a plant manager, so I was always walking the floor and looking at machines and thinking they were really cool in a manufacturing sense.

                          I didn’t really understand much about it until my first role coming out of my master’s program. I was heavily involved with the supply chain team. I worked with quality and sourcing for scaling up production of a component that we had. I had this realisation where that bottleneck in innovation isn’t necessarily the technology itself. You can make the technology work with all of the engineers and the scientists working hard together, but it’s the systems and the relationships that move materials, products, and ideas from prototype to production. Technology can only succeed when the supply chains are ready to carry that into production and scale that. And I found that very interesting. That’s when I felt I was way more interested in the nuances of supply chain than engineering. I liked being a part of the system that made something successful.

                          Tell me a bit about the Circular Supply Chain Network and what it does. 

                          The Circular Supply Chain Network was started a few years ago by Deborah Dull. She is a thought leader and world renowned speaker on circular supply chains. I really admire her. She’s written a couple of books and she works with the ASCM (Association for Supply Chain Management). She’s wonderful and brilliant, and her idea was to create a global community that’s dedicated to re-imagining supply chains as circular systems. 

                          We bring together practitioners, innovators, leaders, and we share tools, frameworks, and stories for making circularity practical and actionable. We do that through education, peer exchange, thought leadership, speaking events, pilot projects, and so on. We work on grants when appropriate as well, and we’ll go to events and host workshops. We hold and share toolkits and training information, and we participate in accelerator type initiatives.

                          Can you tell me about the sessions you led at CHAINge North America earlier this year?

                          That was great. I really loved my time at CHAINge this year. I did a couple of things. On the first day, I worked with Deborah and she brought in some members of the Circular Supply Chain Network for us to co-facilitate her workshop. Her workshop was really fun. It was called Reboot, Repair, Reimagine the Circular Supply Chain. We were talking in this workshop about the companies that are actually implementing advanced circular supply chain solutions, to show that it’s not science fiction. They are truly who’s leading the way right now, and we discussed the steps you can take in your own company to benchmark against them or to lead yourself to these types of success. It was really fun being a part of that and working with Deborah side-by-side.

                          I also co-presented with Samer AlMadhoon, Managing Partner at Muhakat Institute. He had a sustainability talk and I had a circularity talk, and we worked together in our presentation. It was called Sustainability in Action: Bringing Circularity and Best Practices to Life in Your Supply Chain. I led the audience through what a circular supply chain is, and a roundtable the next day to follow up on that, and find out people’s struggles.

                          That brought up some really hard conversations and a lot of pain that I think supply chain professionals understand. Maybe they feel that sometimes they’re not listened to, or there’s still companies where the supply chain is supposed to manage costs and they don’t necessarily have a seat at the table. 

                          How do sustainability and circularity differ, and how can we shine more of a spotlight on circularity?

                          That’s definitely challenging. If you look at sustainability, it’s the goal. It’s the big picture; it’s people, planet, profit, and circularity is a tool. Circularity is purely about material flows. It’s about how we keep the raw materials, the products, the energy that’s used in these processes in play for as long as possible. Circularity doesn’t cover water use, labour conditions, equity; it’s very focused on the materials themselves. However, circularity is also one way of getting to the sustainability boundaries, essentially. And that’s the interesting thing. 

                          Circularity itself is huge for economic value because it is value retention, it’s material flow, and keeping those materials in play with as little effort and waste as possible. If we think of lean manufacturing and waste in that aspect of wanting as minimal waste as possible, that’s true in circularity as well. But then how do you take these waste streams and extract more value out of them? How do you protect the value of the materials and the products that you’re working with? How do you keep as much of the shell of your product going for as long as possible with minimal effort? Those are the aspects of circularity that I think need more attention and understanding.

                          Besides a lack of conversation around the topic, what are the biggest challenges in circularity?

                          I think part of it is there are very large companies that are implementing circular practices. A lot of the heavy duty equipment companies have figured out how to make their very large, very expensive machines have new life, so they have whole remanufacturing plants. And that’s great, but these large companies have something a small company doesn’t: a huge supply chain ecosystem.

                          Circularity isn’t really a single company solution – it takes that ecosystem. You need your suppliers, you need your customers, you need to be able to get back to your material. It needs policy makers. It needs communities working together, reverse logistics, and local infrastructure – our big missing links in circular supply chains. Without them, it doesn’t matter. The loop stays broken if you’re not able to get back your material and do something meaningful with it. 

                          And the thing is, it’s also really hard for companies to understand the value in making those short loops, even though it’s less risky and more resilient to have share and reuse remanufacturing processes that are close and local, so you can keep those materials in circulation longer. That is a huge shift where companies are so much more used to obsolescence, like you want your product to fall apart so that somebody will come and buy a new one. So it is that business model of getting into the mindset of there actually being revenue to be had. Mindset is key.

                          Author

                          Fawad Qureshi

                          Published

                          15 January 2026

                          Estimated Read time

                          6Mins

                          Fawad Qureshi, Global Field CTO, Snowflake, on realising possibilities for innovation in this new AI era

                          Without cloud migration, businesses face the end of innovation. In this new AI era, businesses operating within the closed architectures of legacy systems do not have the flexible, data-driven foundation to engage with these new technologies and ensure a strong pipeline of necessary innovation. And as AI continues to evolve, those not able to keep pace with innovation risk being left behind. 

                          Cloud migrations are the foundation to modernise and drive business growth over the long term. When organisations migrate to a cloud-based environment, it’s crucial to focus on the tangible business value a migration will deliver, rather than simply shifting from one system to another. Moving a company’s customer-facing applications and all of their data to a cloud-based environment has the benefits that are increasingly real and measurable.

                          Migration isn’t just a Plug and Play approach – Which migration fits your needs?

                          There are two approaches to cloud migration, broadly speaking: horizontal and vertical, each with their own benefits and potential challenges. A vertical approach sees organisations migrating applications one by one: this approach is a good choice if certain systems have to be prioritised, or if the applications being migrated do not have many interdependencies. Vertical migration allows for focused efforts and risk management on individual systems, and requires fewer resources. Horizontal migration moves entire system layers at the same time. This is the best solution when businesses have tight deadlines to retire legacy systems, or if their systems are tightly integrated. Horizontal migrations tend to be faster by allowing for parallel work streams, but they require more technical expertise. 

                          Organisations often adopt a mixture of the two approaches, for example, horizontally migrating important systems such as data platforms, while taking a vertical approach to customer-facing applications. Whatever approach an organisation takes, it’s vital that the migration also includes a culture shift, preparing employees to adapt to new, consumption-based models and the possibilities of the new technology. Migration is also just the start of the journey, unlocking the potential of AI-driven use cases and seamless data collaboration, including new ways to achieve business value. 

                          Before diving straight in, ensure it’s with a Data-First Mindset

                          When migrating to the cloud, a data-first approach is essential. For those acting as the catalyst for change, whether that be IT managers or even CIOs, data must be front of mind before planning any successful migration.  Understanding how data is used within the organisations, including its structure, governance needs, and how it delivers value and business outcomes, is imperative. This applies doubly when it comes to large, complex systems with many interconnected applications. 

                          Before migrating, businesses must comprehensively assess their current ecosystem. It’s imperative that the end-to-end business product survives the migration, intact. Organisations should maintain internal control over core competencies around data, such as business process knowledge, data governance and change management. These areas include institutional knowledge that external parties may not grasp. Businesses should also maintain direct oversight over compliance requirements and risk management. 

                          Technical activities such as cloud infrastructure optimisation, performance testing, and specialised migration tooling are something, by contrast, that can be handled by external expertise. Code conversion can also benefit from purpose-built tools that use technologies including AI. Technical parts of the immigration tend to evolve rapidly and require specialist knowledge, so are ripe for outsourcing. While doing so, those steering the migration need to ensure clear governance around outsourced activities, including regular knowledge transfer sessions. 

                          Different parts of the business all have a role to play: IT and engineering lead on technical implementation, handling the technical side of business requirements, while finance will identify ROI opportunities and manage cloud costs. It helps to create a cross-functional steering committee with representation from every department to ensure that different areas of the business are aligned and ready to address challenges. 

                          Adaptability and Flexibility is the key to business longevity 

                          Migration is never one-size-fits-all, and business leaders should be prepared to be flexible and adapt. There are multiple kinds of horizontal migration, from a simple ‘lift and shift’ focused on moving systems as they are to a ‘move and improve’ where migration is followed by optimisation to reduce technical debt. They should be ready to adapt at their own pace, choosing data platforms which offer agnostic architecture and the freedom to choose between data models and tools to ensure minimal disruption.

                          Flexibility is also important in choosing the tools used for migrations. Flexible data platforms will offer the support businesses need to deal with collaboration and governance frameworks. For businesses operating in EMEA, where different countries can have varying policies, pay close attention to issues around data quality, security and compliance, particularly when it comes to data sovereignty and issues around European data residency. 

                          A Shared Destiny

                          The shift to the cloud fundamentally changes security. The traditional cloud ‘shared responsibility’ model clearly demarcated duties between the provider and the customer. However, a more advanced approach is emerging: the ‘shared destiny’ model. This model recognises that in the event of a breach, reputational damage affects both parties. This shared risk incentivises the cloud provider to be a more proactive partner, actively helping customers strengthen their security posture rather than simply managing their own side of the demarcation line.

                          As ‘destinies’ intertwine, you help eliminate the vulnerability created due to password simplicity. Put simply, in a ‘shared responsibility’ model, the cloud provider is only responsible for securing infrastructure, while the customer remains responsible for securing data and apps in the cloud, as well as for configuration. In a ‘shared destiny’ model, the cloud provider plays a more proactive role to ensure that their customers have the best possible security posture. 

                          Taking a ‘shared destiny’ approach allows businesses to be more proactive in securing data, using approaches such as multi-factor authentication, secure programmatic access and more comprehensive cloud monitoring services. Choosing a modern, AI-driven data platform offers the best security foundations here, offering security controls across cloud service providers and the entire data ecosystem. 

                          A Pathway to Growth

                          In today’s world, the bigger risk is standing still. Nothing changes if nothing changes.

                          If organisations are holding back on innovation due to technological limitation, then the time to migrate is clear. There is no need to face an end to possibilities when the path towards success lies in reach, offering an opportunity to bring businesses up to date with modern requirements, and pave the way for the adoption of technologies such as AI. 

                          However, as we’ve seen, it’s not just a case of plug and play. Organisations must ensure a flexible, data-driven approach to migration, while keeping security front of mind via a ‘shared destiny’ approach. To deliver this, the right choice of a modern, flexible data platform will ensure the whole organisation can work together effectively and deliver a path to future innovation and growth. 

                          Learn more at snowflake.com

                          • Data & AI
                          • Digital Strategy
                          • Infrastructure & Cloud

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          3Mins

                          Plumery’s AI fabric is future-proofed and designed for use cases beyond today’s horizon

                          Plumery, a digital banking development platform for customer-centric banking, has released AI Fabric. It creates an artificial intelligence (AI)-ready foundation for AI-assisted digital banking.

                          AI-Ready Digital Banking

                          Based on an event-driven data mesh, the new solution gives financial institutions a standardised way to connect AI and generative AI (GenAI) models/agents to banking data. Eliminating the need for bespoke system integrations. AI Fabric moves institutions away from brittle point-to-point architectures towards an event-driven, API-first architecture that scales with innovation.

                          Most financial institutions struggle to operationalise AI because their data is fragmented across legacy cores, channels, and point-to-point integrations. Each new AI pilot can require fresh plumbing, security reviews, and governance work, which delays time-to-value and increases risk. In addition, under increasing regulatory pressure, institutions are required to explain, audit, and govern AI decisions. Together, these factors make ad-hoc approaches to AI difficult to scale.

                          AI Fabric

                          Plumery’s AI Fabric enables institutions to plug in and swap AI capabilities as the ecosystem evolves. It exposes high-quality, domain-oriented banking events and data streams in a consistent, governed, and reusable way. This works across products, channels, and customer journeys. Importantly, the platform separates systems of record from systems of engagement and intelligence. Offering financial institution long-term agility instead of short-lived AI experiments.

                          By reducing point-to-point integrations and one-off data pipelines, an institution can lessen operational complexity and technical debt. This makes change cheaper, safer, and more predictable. Additionally, having clear data lineage, ownership, and control makes it easier to explain decisions, manage model risk, and satisfy regulators – reducing compliance friction as AI adoption grows.

                          “Financial institutions are clear about what they need from AI. They want real production use cases that improve customer experience and operations, but they will not compromise on governance, security, or control. Our AI Fabric gives them a standard, bank-grade way to allow AI use within their tools and data without rebuilding integrations for every model. The event-driven data mesh architecture improves the process by changing how banking data is produced, shared, and consumed, rather than adding another AI layer on top of fragmented systems.”

                          Ben Goldin, Founder and CEO of Plumery

                          Why Financial Institutions need an AI Foundation

                          In today’s fast-changing world, financial institutions need an AI foundation that absorbs change instead of amplifying it. With AI Fabric, institutions can experiment, deploy, and evolve AI-assisted use cases incrementally without re-architecting every time a model, vendor, or requirement changes.

                          Additionally, operational, customer, and risk decisions can be powered by live banking events rather than delayed, batch-based snapshots. This enables AI to assist where it matters most: in-journey, in-context, and in-the-moment.

                          Even financial institutions not yet ready to operationalise AI can lay the groundwork today with AI Fabric, ensuring they can move quickly and safely when priorities, budgets, or markets shift.

                          About Plumery

                          Headquartered in the Netherlands, Plumery’s mission is to empower financial institutions worldwide, regardless of size, to craft distinctive, contemporary, and customer-centric mobile and web experiences.

                          Plumery operates with a diverse team that embodies a unique combination of seasoned expertise and vibrant innovation. This blend has been cultivated through years of experience at start-ups, scale-ups, and established financial institutions, and most notably at globally leading financial technology companies, where they were instrumental in creating disruptive digital banking solutions and platforms that now serve more than 300 banks globally.

                          Plumery’s Digital Success Fabric platform provides banks with the foundation for success beyond fast time to market by expediting the development of their digital front ends while significantly cutting costs compared to in-house initiatives or solutions with high total cost of ownership.

                          Learn more at plumery.com

                          • Artificial Intelligence in FinTech
                          • Digital Payments
                          • Neobanking

                          Author

                          Radi El Haj

                          Published

                          14 January 2026

                          Estimated Read time

                          5Mins

                          Radi El Haj, CEO of global payments technology leader RS2, argues that while cost-cutting is important, banks are overlooking AI’s biggest opportunity: fuelling growth through hyper-personalisation, predictive analytics, and dynamic pricing, all while staying on the right side of compliance

                          In banking, artificial intelligence (AI) is often portrayed as an efficiency force-multiplier: automating back-office tasks, detecting fraud, reducing cost. Yet the bigger prize is less about cost and more about growth: unlocking new revenue streams through data monetisation, hyper-personalisation and dynamic pricing. At RS2, a platform that powers issuing and acquiring across banks and enterprises globally, we see how these possibilities can move from concept to profitable reality.

                          Unlocking Transactional Data for Revenue

                          Banks sit on rich transactional data – what customers buy, how they spend, when they engage. Historically, this data has helped reduce risk, fight money-laundering or optimise operations. But now it can be used to drive growth. According to an EY overview, AI-powered tools enable banks to personalise services, identify cross-sell opportunities and “potentially boost revenue streams.”

                          Consider a bank that analyses a customer’s payment behaviour, identifies recurring patterns (e.g., frequent travel, high hotel spend) and then offers a tailored premium travel card or concierge-style value add. Or a commercial bank that segments SMEs by payment volume and cash-flow profile and monetises by offering dynamic pricing on foreign exchange or supply-chain financing.

                          Responsible monetisation demands governance. A recent essay on monetising financial data with AI warns that “you’re sitting on a goldmine of data … but the major caveat is the need to manage risk”. The practical implication: invest in data-quality, maintain strict consent and usage controls, disaggregate personally identifying detail where possible and ensure transparency with customers. As banks move from “can we do this?” to “should we do this?”, the ones that succeed will embed data ethics, consent frameworks and explainability at the core.

                          Compliance and Innovation: Building Self-Hosted AI Frameworks

                          Growth-facing AI can’t sail past compliance. Banks need to remain within the bounds of regulatory regimes such as GDPR, PSD2 and CCPA. A key enabler is self-hosted or controlled AI infrastructure that allows experimentation without exposing sensitive data to third-party cloud vendors or uncontrolled derivative uses.

                          In the UK, the Bank of England notes that the future of AI in financial services demands both innovation and safety – building internal capabilities while contributing to systemic resilience. For banks this means: maintain internal model-hosting (or tightly controlled cloud with data isolation), build a “sandbox to production” pipeline where models are validated for bias, fairness and explainability, and treat regulatory engagement not as a blocker but as a design parameter.

                          With this architecture in place, banks can push beyond the cost-centre mindset (fraud detection, operations) into growth-mindset use-cases – real-time decisioning, dynamic pricing, micro-segment product design – all while retaining control over data flows, vendor risk and audit trails.

                          Explainable AI: Trust at the Front-Line

                          If AI is going to power new revenue models – dynamic offers, predictive cross-sell, hyper-personalised pricing – then customers and regulators alike must trust the outcomes. Enter explainable AI (XAI).

                          Explainability isn’t a nice add-on: it’s mandatory when AI touches decisioning that affects consumers (pricing, credit, product eligibility). If a customer is offered a differential rate based on their profile, they are entitled to know (in clear language) why. If a regulator challenges the fairness of an algorithmic decision, the bank must show the decision-tree, the bias mitigation steps and the audit trail of model monitoring.

                          As banks deploy AI in growth-facing scenarios, transparency becomes a strategic differentiator: one bank may claim to offer “smarter offers” – another will be able to document that those offers are fair, auditable and compliant. That traceability becomes a selling point when partnering with fintechs, regulators or corporate clients.

                          Lessons from Leading Banks: Growth-Not Just Cost-Cutting

                          While many banks still emphasise cost-cutting, the story is shifting. For instance, research from FIS shows that banks with a strong data strategy are tying AI investments to revenue outcomes, not just automation.

                          In practice, a global bank uses AI-driven cash-flow tools for corporate clients and is now preparing to monetise the service rather than treat it purely as a cost centre. Another major institution, NatWest, has embedded AI in its digital-assistant ecosystem and already reports improved customer engagement metrics and lower servicing costs.

                          From the experience at RS2, we see banks and FinTechs that pay attention to platform architecture, data lineage and flexible monetisation workflows succeed faster. The value flows not from a single “AI project” but from embedding AI into the payment rails, product lifecycle, pricing engine and loyalty ecosystem.

                          It is noteworthy that banks are not alone here: payments-technology providers like RS2 are collaborating with financial institutions to integrate AI into issuing and acquiring flows, offering a way to turn payments data into behavioural insight, and knowledge into value-added services.

                          Bringing it Together

                          For banks, the dominant mindset should shift from “AI as efficiency tool” to “AI as growth platform”. That transition requires three foundational capabilities: a clean, consent-driven data ecosystem; an AI infrastructure that balances innovation and control; and an organisational discipline around explainability, governance and monetisation strategy.

                          At RS2 we believe that the combination of payments technology, platform mindset and global scale gives us a front-row seat to this shift. The banks that lead in the next five years will be those that embed AI not in margins but in revenue lines – crafting new products, offering dynamic pricing, delivering real-time personalisation and monetising payments data in a responsible manner.

                          The future isn’t about AI simply making existing processes cheaper; it is about re-working how banks generate value. If your AI agenda stops at cost-cutting, you’re leaving the biggest opportunities on the table.

                          About RS2

                          RS2 is a leading global provider of payment technology solutions and processing services, offering a unified approach to managing payments across all channels for banks, integrated software vendors, payment facilitators, independent sales organizations, payment service providers, and businesses worldwide. RS2’s platform stands out as a robust cloud-native solution designed for both issuing and acquiring operations. With its advanced orchestration layer seamlessly integrating all aspects of business operations, clients gain access to comprehensive analytics, reporting tools, and reconciliation features. This empowers businesses to effortlessly expand their global footprint through a single integration, while also gaining valuable insights into payment processes and customer behavior, enhancing operational efficiency, increasing conversion rates, and driving profitability. 

                          Learn more at RS2.com

                          • Artificial Intelligence in FinTech
                          • Digital Payments
                          • Embedded Finance
                          • InsurTech

                          Author

                          Robert Cottrill

                          Published

                          Estimated Read time

                          4Mins

                          Robert Cottrill, Technology Director at digital transformation company ANS, explores how businesses can harness the potential of AI while mitigating the growing risks to cybersecurity and privacy

                          AI can transform businesses, but is it also opening the door to cyber risks? Fuelled by competitive pressure and rising government support through the UK’s Industrial Strategy, it’s no surprise that more and more businesses are racing to adopt AI.

                          But there’s a catch. The more businesses scale their AI adoption, the bigger their attack surface becomes. Without a proactive and structured approach to securing AI systems, organisations risk trading short-term efficiencies for long-term vulnerabilities.

                          The AI Boom

                          AI investment is skyrocketing. Businesses are deploying generative AI tools, machine learning models, and intelligent automation across nearly every function, from customer service and fraud detection to supply chain optimisation. Platforms like DeepSeek and open-source AI models are now part of the mainstream tech stack.

                          Initiatives like the UK’s AI Opportunities Action Plan are fuelling experimentation and adoption. AI is now seen not just as a productivity tool, but as a critical lever for digital transformation.

                          However, the rapid pace of AI deployment is outpacing the development of the security frameworks required to protect it. When integrated with sensitive data or critical infrastructure, AI systems can introduce serious risks if not properly secured. These risks include data leakage through AI prompts or model training, as well as AI-generated phishing and social engineering attacks

                          So, it’s no surprise that ANS research found that data privacy is the top concern for businesses when adopting AI. As these threats evolve, businesses must treat AI not just as an enabler, but also as a potential vector for attack.

                          The Governance Gap

                          While technical threats often take centre stage, businesses also can’t forget the increasing regulatory requirements surrounding AI. As AI systems become more powerful, enabling businesses to extract valuable insights from vast datasets, they also raise serious ethical and legal challenges. 

                          Regulatory frameworks like the EU AI Act and GDPR aim to provide guardrails for responsible AI use. But these regulations often struggle to keep up with the rapid advancements in AI technology, leaving businesses exposed to potential breaches and misuse of personal data.

                          The Need for Responsible AI Adoption

                          To build resilience while embracing AI, businesses need a dual approach: 

                          1. Prioritise AI-specific training across the workforce

                          Cybersecurity teams are already stretched. Introducing AI into the mix raises the stakes. Organisations must prioritise upskilling their cybersecurity professionals to understand how AI can both protect and threaten systems.

                          But this isn’t just a job for the security team. As AI tools become embedded in daily workflows, employees across functions must also be trained to spot risks. Whether it’s uploading sensitive data into a chatbot or blindly trusting algorithms, human error remains a major weak point.

                          A well-trained workforce is the first and most crucial line of defence.

                          2. Adopt open-source AI responsibly

                          Another key strategy for reducing AI-related risks is the responsible adoption of open-source AI platforms. Open-source AI enhances transparency by making AI algorithms and tools available for broader scrutiny. This openness fosters collaboration and collective innovation, allowing developers and security experts worldwide to identify and address potential vulnerabilities more efficiently.

                          The transparency of open-source AI demystifies AI technologies for businesses, giving them the confidence to adopt AI solutions while ensuring they stay alert about potential security flaws. When AI systems are subject to global review, organisations can tap into the expertise of a diverse and engaged tech community to build more secure, reliable AI applications.

                          To adopt responsibly, businesses need to ensure that the AI they are using aligns with security best practices, complies with regulations, and is ethically sound. By using open-source AI responsibly, organisations can create more secure digital environments and strengthen trust with stakeholders.

                          Securing the Future of AI

                          AI is a transformative force that will redefine cybersecurity. We’re already seeing AI being used to automate threat detection and response. But it’s also powering more advanced attacks, from deepfake impersonation to large-scale automated exploits.

                          Organisations that succeed will be those that embed cybersecurity into every stage of their AI journey, from innovation to implementation. That means making risk management part of the innovation conversation, not a downstream fix.

                          By taking a responsible approach, investing in training, leveraging open-source AI wisely, and embedding cybersecurity into every layer of the business, organisations can unlock AI’s potential while defending against its risks.  

                          AI is a double-edged sword, but with thoughtful adoption, businesses can confidently navigate the complex landscape of AI and cybersecurity.

                          Learn more at ans.co.uk

                          • Cybersecurity
                          • Data & AI
                          • Digital Strategy

                          Author

                          Joe Logan

                          Published

                          12 January 2026

                          Estimated Read time

                          4Mins

                          Joe Logan, CIO at iManage, on the need to avoid the hype, manage cybersecurity, focus on ROI and balance change management to get the best results with AI

                          Across the enterprise, AI promises transformational power – however, it’s not as simple as just plugging it into the organisation and instantly reaping the benefits. What are some of the top things CIOs need to focus on to avoid any pitfalls, unlock its value, and best position themselves for success with AI? 

                          1) Separate the Hype from Reality

                          Here’s what hype looks like: using AI to “radically transform the way you do business” or to “accelerate comprehensive digital transformation” or – heaven forbid – to “completely change our industry.” These are big statements – and absolutely dripping with hype.

                          Getting real with AI requires identifying specific use cases within the organisation where a particular type of AI can be deployed to achieve a specific goal. For example, maybe you want to reduce customer churn by 20% and have identified an opportunity to use chatbots powered by large language models to provide more effective customer service. That’s what reality looks like.

                          In separating the hype from reality, organisations gain the added benefit of clearing up any misconceptions – at any level of the organisation – about what AI can and can’t do, thus performing an important “level set” around expectations.

                          2) Understand the Implications for Cybersecurity

                          On one side, any AI tool you’re using has access to data, and that means that access needs to be controlled like any other system within your tech stack. The data needs to be secured and governed, and issues around privacy, sovereignty, and any other regulatory requirements need to be thoroughly addressed.

                          As part of this effort, organisations also need to be aware of the security measures required to protect the AI model itself from bad actors trying to manipulate that model. For example: prompt injection – inputs that prompt the model to perform unintended actions – can affect the model and its responses if not carefully guarded against.

                          Securing your AI system is one side of the coin; the other side is understanding how to apply AI to cybersecurity. There are a growing number of use cases here where AI can help identify risks or vulnerabilities by analysing large amounts of data, helping organisations to prioritise the areas they need to focus on for risk mitigation. 

                          In summary? While any usage of AI will require you to “play defence” on the security front, it will also enable you to “play offence” more effectively. In that sense, AI has multiple implications for cybersecurity.

                          3) Focus on the Right Kind of ROI

                          When it comes to ROI for any AI investments, don’t narrowly focus on absolute numbers when it comes to metrics like time savings or cost savings. While well-suited to industrial workplaces that are churning out widgets every day, absolute numbers can be an awkward fit when applied to a knowledge work setting.

                          The advice here for any knowledge-centric enterprise is: Don’t get hung up on the idea of actual dollars and cents or a specific number – instead, look for a relative improvement from a baseline. So, rather than saying “We’re going to reduce our customer acquisition costs by $100,000 this year”, it’d be more appropriate to focus on reducing existing customer acquisition costs by 10%. Likewise, don’t focus on each junior associate in the organisation completing five more due diligence projects per calendar year; look to complete due diligence projects in 30% less time.

                          4) Give Change Management its due

                          Change management has always mattered when it comes to introducing new technology into the enterprise. AI is no different: Successful adoption requires a focus on people, process, and technology – with a particular emphasis on those first two items.

                          A major challenge is educating the workforce with an eye towards improving their AI literacy – essentially, enabling them to understand what’s possible and how they can apply AI to their daily workflows. 

                          Know that a centralised model of control that dictates “this is how you can experiment with AI” is probably going to be ineffective. It will be too stifling for innovative individuals in the organisation. Far better to provide centres of excellence or educational resources to those people who are most inclined to take the initiative and move forward with AI experiments in their team or department. 

                          One caveat here: It’s essential to have guardrails in place as teams and individuals experiment with AI, to prevent misuse of the technology. That’s the tightrope that CIOs need to walk when introducing AI into the organisation. Striking the right balance between “total control” and “freedom to explore, but with appropriate oversight and guardrails”. 

                          The Future of AI Depends on what CIOs do next

                          The promise of AI is massive, but only if CIOs adopting the technology focus on the right areas. And that means filtering out the hype, keeping security implications top of mind, redefining ROI, and guiding change with a steady hand. By paying attention to these areas, CIOs can safely navigate a path forward with AI. And ensure that it isn’t just a technology with promise and potential, but one that delivers actual enterprise-wide impact.

                          Learn more at iManage

                          • Cybersecurity
                          • Data & AI
                          • Digital Strategy

                          Author

                          Mike Southgate

                          Published

                          Estimated Read time

                          6Mins

                          Mike Southgate, Co-founder of UK-based RegTech firm Ermi, on why artificial intelligence alone cannot replace human judgment in the creation of rules for automated transaction monitoring

                          In the drive to modernise and improve financial-crime detection, artificial intelligence (AI) has emerged as a powerful tool. Machine-learning models have the ability to process vast volumes of transactional data, identify patterns invisible to the human eye and flag anomalies at scale.

                          But despite these clear benefits, AI on its own cannot deliver the transparency, accountability, or contextual nuance that is needed for effective transaction monitoring. Human judgment (Human In the loop) remains absolutely essential.

                          The Autonomy Illusion

                          Rising financial crime, advances in laundering typologies and increased regulatory scrutiny, has put financial institutions under pressure to adopt AI-driven anti-money-laundering (AML) systems, with the promise that they will be more effective.

                          According to the IICFIP Global Financial Crimes Impact Report 2025, global losses from financial crime exceed US $8 trillion annually, including money laundering losses of between US $800 billion and $2 trillion, fraud losses of over US $5 trillion, and corruption losses around US $3.6 trillion. Yet INTERPOL reports that only one percent of illicit financial flows are ever intercepted, frozen, or recovered.

                          Transaction monitoring vendors are increasingly marketing AI-driven AML solutions, claiming that the algorithms are able to autonomously detect suspicious behaviour. But these capabilities are often vastly overstated. Machine-learning models suffer from multiple issues. They are only as effective as the data they are trained on and ensuring accurate (E.g. data relevant to the firm buying the tool) and up to date data is challenging. Not least because financial crime is a moving target. Criminals continually change their tactics, often faster than AI can be retrained. Because the system relies on patterns learned from historical data rather than anticipating new, adaptive strategies, subtle illicit activity, such as transactions that mimic legitimate behaviour, often go undetected. Similarly, data to train an AI must know whether past patterns were truly criminal, which we may not always know.

                          Understanding AI’s Shortcomings

                          Importantly, the line between criminal and normal behaviour will depend upon the client. Consider a scenario where a high-net-worth individual initiates a series of international transfers. An AI model may flag these transactions purely based on volume or geography. Without contextual understanding for the type of client, the alert is likely to be a false positive. Conversely, a sophisticated money laundering scheme could evade detection entirely by mimicking legitimate behaviour. In both cases, human insight is critical. AI lacks context of clients or in-depth knowledge of  of “normal” business models.

                          Opacity is another concern. Many machine-learning systems operate as black boxes, generating alerts without and meaningful explanation. Regulators are increasingly demanding transparency, for example under the EU AI Act and Financial Action Task Force (FATF) guidance on AI in AML (FATF, 2021). Institutions have an obligation to justify why a transaction was flagged (or not), what criteria were used and how decisions align with risk-based approaches.

                          Black-box models can also undermine internal governance. Compliance teams need to understand and trust the systems they rely on. And when an alert cannot be traced to a clear rule, confidence is undermined and investigations stall. Over-reliance on automation has the potential to overshadow critical human judgment.

                          Human Rule Design with Context

                          Effective transaction monitoring must still therefore have human-led contextual rule design. Unlike generic thresholds or static parameters, contextual rules take into account the full spectrum of customer behaviour, business models and risk exposure. Having defined rules will also allow transparency and traceability.

                          For example, a transaction exceeding £10,000 may trigger a review in retail banking but is routine in corporate financial operations. Contextual rules enable financial institutions to adapt the detection rule logic based on customer type and risk, transaction purpose, jurisdictional risk and historical patterns.

                          Contextual rule design also supports dynamic adaptation, so that systems are able to respond intelligently to changes in a client’s behaviour. For example, if a customer suddenly increases the volume or frequency of cross-border payments, the system evaluates these changes against historical patterns, business type, transaction purpose and associated risk factors. Alerts are then generated only when deviations are statistically or contextually significant, rather than for every fluctuation.

                          By incorporating this nuanced understanding, organisations are able to reduce false positives, prioritise genuinely suspicious activity and ensure compliance teams focus on actionable alerts rather than noise.

                          Contextual Rules

                          Importantly, contextual rules enhance explainability. Each rule can be traced to a specific rationale, for example, regulatory guidance, internal policy, or risk appetite. This strengthens audit readiness and helps with regulatory engagement. Transparency also supports continuous improvement as threats evolve or business priorities shift.

                          Financial crime detection is not just a technical challenge and is fundamentally about context. But AI struggles with nuance. It cannot distinguish between a legitimate seasonal spike and a layering attempt, in which illicit funds are moved through multiple accounts or jurisdictions to obscure their origin. It also cannot surmise intent, assess reputational risk, or weigh geopolitical implications, or above all… just be a sceptical compliance officer who doesn’t trust anyone.

                          Humans excel at contextual reasoning. They interpret indicators in light of customer behaviour and relationships, market dynamics and regulatory expectations. They ask the right questions, challenge assumptions and escalate concerns when needed. In short, humans bring vital judgment to transaction monitoring.

                          An example of this in action: in 2024, a European bank’s AI system flagged 80,000 transactions as “high risk.” Only 0.3 percent proved genuinely suspicious (IICFIP, 2025). Without human review, the bank would have wasted significant time chasing false positives, while potentially missing the subtler patterns of actual illicit activity.

                          Augmentation, Not Automation

                          The future of transaction monitoring is not about replacing humans but about strengthening them. AI should be used to support decision making by surfacing patterns and anomalies, while humans provide interpretation, oversight and context.

                          Forward-thinking financial institutions are getting ready for a regulatory landscape that will demand AI models are explainable and auditable. And by carefully combining machine efficiency with human judgment that organisations will reduce operational risk and strengthen compliance.

                          As financial crime grows more sophisticated, our transaction monitoring needs to evolve too. AI is a powerful tool but it is not a panacea. Effective transaction monitoring requires human insight and contextual awareness. Hybrid models that balance automation with human-led rule sets and interpretation will be essential.

                          While AI offers unparalleled speed and pattern recognition, it cannot replace the human ability to reason, contextualise and make judgment calls. Human-led transparency, explainability and context are not optional features for effective AML. Organisations that use AI to augment, not replace, human judgment will be best positioned to detect sophisticated threats, maintain regulatory trust and act decisively. In stopping financial crime, trust is essential and trust cannot be automated.

                          Learn more at ermitm.com

                          • Artificial Intelligence in FinTech
                          • Cybersecurity in FinTech
                          • Digital Payments

                          Author

                          Ben Francis

                          Published

                          9 January 2026

                          Estimated Read time

                          4Mins

                          Ben Francis, Insurance Lead at Risk Ledger, on navigating cyber threats by reinforcing security from the inside out

                          Cyber insurance has evolved from a straightforward risk transfer mechanism into an integral component of enterprise risk strategy. As a result, the conversation has shifted beyond simply securing coverage to embracing three foundational elements: transparency in risk exposure, accountability for security measures, and active collaboration throughout the digital ecosystem.

                          Rather than asking ‘are you covered?’, the more pertinent question has become ‘can you demonstrate measurable risk reduction?’. Insurers and insureds alike are recognising that what matters now is how well an organisation understands and manages its digital exposure, especially across its extended supply chain. Recent data reveals that 46% of organisations experienced at least two separate supply chain-related cyber incidents in the past year, a clear sign that exposure often lies beyond direct control. 

                          From Risk Transfer to Risk Visibility 

                          In recent years, the cyber insurance market has matured significantly. Once viewed as a reactive safety net to cushion the financial impact of attacks, it is now becoming a proactive tool for managing and mitigating risk. This shift is partly driven by insurers, who increasingly expect and work with organisations to demonstrate strong security practices and a nuanced understanding of their threat landscape, including risks deep within their digital supply chains; an area where many businesses still fall short.

                          At the same time, the industry faces a growing challenge from systemic cyber risk within their portfolios, as many businesses rely on the same cloud providers, payment systems and digital platforms, increasing the chance of a single point of failure. Insurers must gain visibility into how policyholders are connected, not only to suppliers but to each other. Tools and frameworks that map and monitor these interconnections will be essential to avoid underestimating the wider impact of seemingly isolated cyber events.

                          Mapping Beyond Third Parties

                          It is no secret that cyber attackers often target the weakest link in a supply chain. These are not always direct suppliers, but fourth, fifth or even sixth-tier vendors that have indirect but critical access to systems and data. Unfortunately, many organisations lack visibility beyond their first tier, creating blind spots that attackers can easily exploit. From an insurance perspective, this presents a clear challenge. If an organisation cannot account for who it is connected to, it cannot adequately quantify its risk and neither can its insurer. Mapping these extended connections is more than just a technical exercise; it means actively practiced risk governance and responsibility. Insurers increasingly want to know how their policyholders are identifying and managing indirect dependencies, particularly in sectors like financial services and retail where disruption can ripple across entire markets.

                          Collaboration as a Risk Strategy 

                          One of the more underappreciated aspects of cyber resilience is the role of peer collaboration. Unlike physical incidents, cyber threats rarely exist in isolation. A single compromised vendor can impact multiple organisations simultaneously, a fact that has been highlighted by high-profile supply chain attacks such as SolarWinds and MOVEit. 

                          As a result, businesses need to think beyond their own perimeters and adopt a more collective mindset. This includes building relationships with industry peers, sharing threat intelligence and participating in sector-wide initiatives aimed at improving visibility and preparedness. 

                          In highly regulated sectors, such as insurance, this collaboration is increasingly being encouraged by oversight bodies. Frameworks like the Digital Operational Resilience Act (DORA) in the EU and initiatives from the Prudential Regulation Authority (PRA) and the Financial Conduct Authority (FCA) in the UK are pushing for more transparency around third-party risk. In this context, openness is no longer optional; it will be a regulatory expectation. 

                          For insurance providers, greater collaboration between policyholders also means better data on emerging threats and more accurate portfolio management. For businesses, it offers a chance to anticipate vulnerabilities that may not yet have hit their own networks but are affecting others in their industry. 

                          Proactive Transparency Builds Trust 

                          Organisations that take a proactive, transparent approach to cyber risk management are more likely to secure cover and potentially favourable terms, not just in terms of premiums, but also in access to additional services such as forensic support, incident response sources and legal counsel. 

                          Demonstrating a mature cyber posture is not about claiming perfection. No organisation is immune to breaches. What insurers are looking for is evidence of a structured approach: the existence of incident response plans, robust governance, effective supply chain risk management, and above all, an honest view of risk. 

                          A Shift in Mindset 

                          Ultimately, our understanding of cyber insurance must keep evolving. It should not be treated as a simple checkbox exercise, but as a collaborative relationship between insurers and the organisations they support – one built on shared insight, clear communication, and a drive for continuous improvement.

                          The organisations best equipped to navigate today’s threats will be those that prioritise transparency. Not only does it lead to stronger protection, but it also builds a culture of accountability that reinforces security from the inside out.

                          Learn more at riskledger.com

                          • Cybersecurity
                          • Cybersecurity in FinTech
                          • Digital Strategy
                          • Fintech & Insurtech
                          • InsurTech

                          Author

                          Neven Matas

                          Published

                          Estimated Read time

                          5Mins

                          Neven Matas, Cybersecurity Team Director EU from Infinum, explores how FinTech companies can turn resilience into a source of innovation and business growth

                          FinTech companies are under constant pressure to innovate rapidly while maintaining deep and ongoing trust in their platforms. And as AI becomes embedded into everything from credit decisions to customer support, these pressures are intensifying. The future of digital finance will not just be defined by who deploys the most advanced technology first but by who implements systems that can withstand attack, scale efficiently, and evolve without compromising compliance or customer confidence.

                          Resilience cannot be a technical afterthought; it is a strategic requirement for FinTech. Modular platform architectures, responsible AI operations, and proactive security testing are becoming the foundations of sustainable FinTech growth. Together, they define an operating model where compliance supports innovation instead of obstructing it and where trust becomes a true competitive differentiator.

                          FinTech Resilience Begins with Architecture

                          Many FinTech platforms have evolved as tightly integrated but ultimately separate systems. While these can move quickly at first, they will often struggle under regulatory change, evolving security threats or simply the pressure of scale.

                          Modular, API-driven architectures will enable organisations to compartmentalise risk. They also make it easier to upgrade specific services without disrupting the others and adapt to new regulatory obligations without impacting the whole business. Shared platform capabilities, such as identity management, encryption, logging and access control, will give every new product or feature an inherited baseline of good security practice and governance.

                          This approach is especially important as operational resilience regulations tighten across global financial services. Requirements around third-party management, continuity planning, and incident reporting demand systems that are secure, observable, and controllable. When resilience is engineered into the platform rather than bolted on, organisations can adapt far more confidently.

                          Crucially, modularity accelerates innovation rather than slowing it down. Teams can experiment at the edge without placing core systems at risk. New fraud detection models, customer features or AI-driven services can be deployed, tested and refined in isolation. Resilience, therefore, is not simply about withstanding disruption, it is what allows organisations to safely embrace continuous change.

                          Scaling Digital Products Without Tripping Over Compliance

                          Digital FinTech products are no longer judged just on usability. They are also evaluated on how transparently they handle data, how well they communicate risk, and whether they meet regulatory expectations across markets. Compliance, which was once seen as a barrier to innovation, is increasingly becoming a fundamental product design input.

                          The most resilient organisations will embed regulatory thinking directly into product development from the outset. Rather than treating compliance as a late-stage sign-off, they feed regulatory principles into experience design and system behaviours. Consent flows, audit trails, authentication rules, and data retention logic become part of the product’s core architecture rather than something that has been retrofitted.

                          This approach significantly reduces the operational burden of growth. As FinTech companies enter new regions or launch new services, they avoid the potential of costly remediation triggered by regulatory scrutiny. Instead, they operate from consolidated, well-governed platforms that limit the attack surface and simplify oversight, while also limiting duplication. The outcome is a stronger security posture and faster expansion into new markets with clearer trust signals for customers and partners.

                          AI as a Trusted Partner Not a Black Box

                          AI has rapidly become central to the FinTech value proposition. Real-time fraud detection and automated operational processes, for example, depend on increasingly sophisticated models. However, AI also introduces new risks, including opaque decision-making, potential bias, and heightened regulatory exposure when automated systems influence financial outcomes.

                          The strategic shift now is from experimental AI adoption to accountable AI operations. This begins with defining precisely where AI adds value and where human oversight remains essential. High-impact use cases, such as lending decisions, transaction monitoring and identity verification, all need explainability as well as accuracy. Organisations must be able to demonstrate how decisions were reached, what data was used and how bias is monitored over time.

                          Clear ownership, review processes, escalation paths, model validation and human-in-the-loop controls will help make large-scale AI deployment viable in a regulated environment.

                          AI also has a strong defensive capability. Behavioural anomaly detection, predictive threat monitoring and intelligent authentication systems allow fintech platforms to detect and respond to risk faster than traditional rule-based approaches.

                          When used responsibly, AI can strengthen both customer experience and operational resilience.

                          Proactive Security Testing as a Continuous Discipline

                          Modern FinTech infrastructure assumes exposure. APIs are public, ecosystems are interconnected and supply chains are large and complex. Under these conditions, security based solely on perimeter defences or annual audits is not enough. This means continuous, adversarial testing has become essential for resilient fintech organisations.

                          Mature players are moving beyond compliance-driven testing into ongoing penetration assessments, red-team exercises and social-engineering simulations. These practices uncover technical vulnerabilities, as well as weaknesses in response coordination, escalation decision-making and recovery planning. They test the organisation as a living system rather than a collection of isolated applications.

                          Integrating security into everyday development is equally critical. Secure coding standards, continuous testing pipelines and regular threat modelling will enable earlier detection of vulnerabilities, when issues are cheaper and easier to resolve. The goal is not to eliminate risk entirely, which is impossible, it is to reduce the time between exposure, detection and response.

                          Security as a Growth Enabler

                          The reframing of security from cost centre to growth driver is the most significant strategic transformation in FinTech. Having a strong security posture is not just about ticking compliance checkboxes, it is increasingly a prerequisite for partnerships, institutional trust and international expansion.

                          Organisations that demonstrate operational resilience, responsible AI governance and proactive security assurance move through due diligence faster. They onboard enterprise clients more easily, integrate with partners with fewer barriers and launch advanced digital services with greater confidence.

                          In crowded markets, trust is a commercial advantage.

                          From the customer perspective, security and transparency are inseparable from experience. Clear communication around data usage, visible protections and consistent reliability directly impact adoption, retention and loyalty. Resilience becomes part of brand equity.

                          Looking ahead, FinTech leaders will not be defined by who adopts new technology first but by who builds systems capable of absorbing disruption, scaling responsibly and evolving continuously. Modular platforms, trustworthy AI and continuous security assurance form the backbone of this.

                          Learn more at infinum.com

                          • Artificial Intelligence in FinTech
                          • Cybersecurity in FinTech

                          Author

                          Dan Brightmore

                          Published

                          8 January 2026

                          Estimated Read time

                          4Mins

                          Vertiv expects powering up for AI, Digital Twins and Adaptive Liquid Cooling to shape future Data Centre Design and Operations

                          Data Centre innovation is continuing to be shaped by macro forces and technology trends related to AI, according to a report from Vertiv, a global leader in critical digital infrastructure. The Vertiv™ Frontiers report, which draws on expertise from across the organisation, details the technology trends driving current and future innovation, from powering up for AI, to digital twins, to adaptive liquid cooling.

                          “The data centre industry is continuing to rapidly evolve how it designs, builds, operates and services data centres, in response to the density and speed of deployment demands of AI factories,” said Vertiv chief product and technology officer, Scott Armul. “We see cross-technology forces, including extreme densification, driving transformative trends such as higher voltage DC power architectures and advanced liquid cooling that are important to deliver the gigawatt scaling that is critical for AI innovation. On-site energy generation and digital twin technology are also expected to help to advance the scale and speed of AI adoption.”

                          The Vertiv Frontiers report builds on and expands Vertiv’s previous annual Data Centre Trends predictions. The report identifies macro forces driving data centre innovation:

                          • Extreme densification – accelerated by AI and HPC workloads; gigawatt scaling at speed – data centres are now being deployed rapidly and at unprecedented scale
                          • Data centre as a unit of compute – the AI era requires facilities to be built and operated as a single system
                          • Silicon diversification – data centre infrastructure must adapt to an increasing range of chips and compute

                          The report details how these macro forces have in turn shaped five key trends impacting specific areas of the data centre landscape.

                          1.         Powering up for AI

                          Most current data centres still rely on hybrid AC/DC power distribution from the grid to the IT racks, which includes three to four conversion stages and some inefficiencies. This existing approach is under strain as power densities increase, largely driven by AI workloads. The shift to higher voltage DC architectures enables significant reductions in current, size of conductors, and number of conversion stages while centralising power conversion at the room level. Hybrid AC and DC systems are pervasive, but as full DC standards and equipment mature, higher voltage DC is likely to become more prevalent as rack densities increase. On-site generation, and microgrids, will also drive adoption of higher voltage DC.

                          2.          Distributed AI

                          The billions of dollars invested into AI data centres to support large language models (LLMs) to date have been aimed at supporting widespread adoption of AI tools by consumers and businesses. Vertiv believes AI is becoming increasingly critical to businesses but how, and from where, those inference services are delivered will depend on the specific requirements and conditions of the organisation. While this will impact businesses of all types, highly regulated industries, such as finance, defence, and healthcare, may need to maintain private or hybrid AI environments via on-premise data centres, due to data residency, security, or latency requirements. Flexible, scalable high-density power and liquid cooling systems could enable capacity through new builds or retrofitting of existing facilities.

                          3.          Energy autonomy accelerates

                          Short-term on-site energy generation capacity has been essential for most standalone data centres for decades, to support resiliency. However, widespread power availability challenges are creating conditions to adopt extended energy autonomy, especially for AI data centres. Investment in on-site power generation, via natural gas turbines and other technologies, does have several intrinsic benefits but is primarily driven by power availability challenges. Technology strategies such as Bring Your Own Power (and Cooling) are likely to be part of ongoing energy autonomy plans.

                          4.          Digital twin-driven design and operations

                          With increasingly dense AI workloads and more powerful GPUs also come a demand to deploy these complex AI factories with speed. Using AI-based tools, data centres can be mapped and specified virtually, via digital twins, and the IT and critical digital infrastructure can be integrated, often as prefabricated modular designs, and deployed as units of compute, reducing time-to-token by up to 50%. This approach will be important to efficiently achieving the gigawatt-scale buildouts required for future AI advancements.

                          5.          Adaptive, resilient liquid cooling

                          AI workloads and infrastructure have accelerated the adoption of liquid cooling. But conversely, AI can also be used to further refine and optimise liquid cooling solutions. Liquid cooling has become mission-critical for a growing number of operators but AI could provide ways to further enhance its capabilities. AI, in conjunction with additional monitoring and control systems, has the potential to make liquid cooling systems smarter and even more robust by predicting potential failures and effectively managing fluid and components. This trend should lead to increasing reliability and uptime for high value hardware and associated data/workloads.

                          Vertiv does business in more than 130 countries, delivering critical digital infrastructure solutions to data centres, communication networks, and commercial and industrial facilities worldwide. The company’s comprehensive portfolio spans power management, thermal management, and IT infrastructure solutions and services – from the cloud to the network edge. This integrated approach enables continuous operations, optimal performance, and scalable growth for customers navigating an increasingly complex digital landscape.

                          Find out more at Vertiv.com.

                          • Data & AI
                          • Digital Strategy
                          • Infrastructure & Cloud

                          Author

                          Joe Jordan

                          Published

                          Estimated Read time

                          3Mins

                          Joe Jordan, co-founder at Adclear, on why FinTechs and other financial organisations need to find equilibrium between content and compliance

                          FinProm. It might sound innocent enough. But in reality, these two small syllables represent a mountain of risk for FinTechs, banks, trading platforms and other financial institutions. FinProm, short for financial promotions, is the catch-all term for how finance brands market their products to customers. That means everything from YouTube ads and TfL posters, to in-app nudges and influencer collaborations. Like most things in finance, it’s an area that’s heavily regulated. And, in today’s fast-moving marketing world, it’s something that’s starting to trip companies up. 

                          Navigating FinProm

                          Just this year, we’ve seen Robinhood fined $26M for regulatory breaches which included failure to properly oversee the influencers plugging their platform. And three UK “finfluencers” recently landed in court for falling foul of FCA FinProm rules. As the fly-wheel of content creation speeds up, fuelled by AI tooling, FinTech brands are facing a high-stakes conundrum: how can they keep pace with modern marketing strategies without running the risk of breaching the litany of rules set by bodies stretching from the FCA to the ASA?

                          Currently, fintechs and banks try to stay on the right side of the regulations by running all of their marketing content and promotions through their compliance teams. These experts review each image, video and piece of copy and suggest revisions. In the quest for compliance, this back and forth causes all sorts of friction. It slows down pace, waters down creativity, and burdens both teams with an admin-burden they’d rather do without. 

                          The results? A slow marketing process which can’t capitalise on trends, nor tap into the rapid content personalisation and iteration made possible by the AI era. This means less growth and customer acquisition in a highly competitive market. The alternative? Playing fast and loose with compliance procedures in order to maximise marketing output. This might drive sales, but it could also drive firms right into the arms of some unhappy regulators. 

                          Decision Time for FinTechs

                          This clash of priorities is creating the ultimate stress test for FinTechs and other financial organisations as they seek to find equilibrium between content and compliance in a world which demands more marketing output, delivered faster than ever before. 

                          And it’s a stress test they cannot afford to fail. Regulators like the FCA are cracking down and the consequences of enforcement action can be devastating. And, as brands expand to new markets, the risk will only grow as they find themselves having to contend with an expanded set of regulators and rulebooks across the globe. 

                          FinTechs can’t bury their heads in the sand on this issue. They must heed the cautionary tales we’ve seen in recent months and reset their FinProm blueprint. The AI-powered age of marketing can’t be capitalised on if it’s supported by old-school compliance processes. Nor can it afford to ignore the very real threat of a regulatory mis-step. To create a truly modern brand that is free to embrace the latest marketing strategies, compliance strategies need to be stepped up and modernised in tandem. Innovation on one side of the FinProm coin must be counter-weighted by innovation on the other.

                          FinTechs and finance platforms are used to pushing boundaries and disrupting the status quo. But to enable this to continue safely, effectively and on the right side of the law, the same energy and innovative zeal should now be applied to compliance. Without it, brands will be exposing themselves to risks and costs they likely cannot afford. 

                          Learn more at adclear.ai

                          • Artificial Intelligence in FinTech
                          • Cybersecurity in FinTech

                          Author

                          Jon Abbott

                          Published

                          7 January 2026

                          Estimated Read time

                          5Mins

                          Jon Abbott, Technologies Director of Global Strategic Clients at Vertiv, asks how we can build a generation of data centres for the AI age

                          The promise of artificial intelligence (AI) is enlightenment. The pressure it places on infrastructure is far less elegant.

                          Across every layer of the data centre stack, AI is exposing structural limits – from cooling thresholds and power capacity to build timelines and failure modes. What many operators are now discovering is that legacy models, even those only a few years old, are struggling to accommodate what AI-scale workloads demand.

                          This isn’t simply a matter of scale – it is a shift in shape. AI doesn’t distribute evenly, it lands hard, in dense blocks of compute that concentrate energy, heat and physical weight into single systems or racks. Those conditions aren’t accommodated by traditional data hall layouts, airflow assumptions or power provisioning logic. The once-exceptional densities of 30kW or 40kW per rack are quickly becoming the baseline for graphics processing unit- (GPU) heavy deployments.

                          The consequences are significant. Facilities must now support greater thermal precision, faster provisioning and closer coordination across design and operations. And they must do so while maintaining resilience, efficiency and security.

                          Design Under Pressure

                          The architecture of the modern data centre is being rewritten in response to three intersecting forces. First, there is density – AI accelerators demand compact, high-power configurations that increase structural and thermal load on individual cabinets. Second, there is volatility – AI workloads spike unpredictably, requiring cooling and power systems that can track and respond in real time. Third, there is urgency – AI development cycles move fast, often leaving little room for phased infrastructure expansion.

                          In this environment, assumptions that once underpinned data centre design begin to erode. Air-only cooling no longer reaches critical components effectively, uninterruptible power supply (UPS) capacity must scale beyond linear load, and procurement lead times no longer match project delivery windows.

                          To adapt, operators are adopting strategies that prioritise speed, integration and visibility. Modular builds and factory-integrated systems are gaining traction – not for convenience, but for the reliability that controlled environments can offer. In parallel, greater emphasis is being placed on how cooling and power are architected together, rather than as separate functions.

                          Exploring the Physical Gap

                          There is a growing disconnect between the digital ambition of AI-led organisations and the physical readiness of their facilities. A rack might be specified to run the latest AI training cluster. The space around it, however, may not support the necessary airflow, load distribution or cable density. Minor mismatches in layout or containment can result in hot spots, inefficiencies or equipment degradation.

                          Operators are now approaching physical design through a different lens. They are evaluating structural tolerances, rebalancing containment zones, and planning for both current and future cooling scenarios. Liquid cooling, once a niche consideration, is becoming a near-term requirement. In many cases, it is being deployed alongside existing air systems to create hybrid environments that can handle peak loads without overhauling entire facilities.

                          What this requires is careful sequencing. Introducing liquid means introducing new infrastructure: secondary loops, pump systems, monitoring, maintenance. These elements must be designed with the same rigour as the electrical backbone. They must also be integrated into commissioning and telemetry from day one.

                          Risk in the Seams

                          The more complex the system, the more attention must be paid to the seams. AI infrastructure often relies on a patchwork of new and existing technologies – from cooling and power to management software and physical access control. When these systems are not properly aligned, risk accumulates quietly.

                          Hybrid cooling loops that lack thermal synchronisation can create blind spots. Overlapping monitoring systems may provide fragmented data, hiding early signs of imbalance. Delays in commissioning or last-minute changes in hardware specification can introduce vulnerabilities that remain undetected until something fails.

                          Avoiding these scenarios requires joined-up design. From early-stage planning through to testing and operation, infrastructure must be treated as a whole. That includes the physical plant, the digital control layer and the operational processes that bind them.

                          Physical Security Under AI Conditions

                          As infrastructure becomes more specialised and high-value, the importance of physical security rises. AI racks often contain not only critical data but hardware that is financially and strategically valuable. Facilities are responding with enhanced perimeter control, real-time surveillance, and tighter access segmentation at the rack and room level.

                          More organisations are adopting role-based access tied to operational state. Maintenance windows, for example, may trigger temporary access privileges that expire after use. Integrated access and monitoring logs allow operators to correlate physical movement with system behaviour, helping to identify unauthorised activity or unexpected patterns.

                          In environments where automation and remote management are becoming standard, physical security must be designed to support low-touch operations with intelligent systems able to flag anomalies and initiate response workflows without constant human oversight.

                          Infrastructure as an Adaptive System

                          The direction of travel is clear. Infrastructure must be able to evolve as quickly as the workloads it supports. This means designing for flexibility and for lifecycle. It means understanding where capacity is needed today, and how that might shift in six months. It means choosing platforms that support interoperability, rather than locking into closed systems.

                          The goal is not simply to survive the shift to AI-scale compute. It is to build a foundation that can keep up with whatever comes next – whether that is a new training model, a change in energy market conditions, or a new set of regulatory constraints.

                          Discover more at vertiv.com

                          • Data & AI
                          • Digital Strategy
                          • Infrastructure & Cloud

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          5Mins

                          FinTech Strategy hears from the experts at DeepL, PagerDuty, Bitpace and Pleo who assess the impact of AI, crypto, stablecoins, tokenised payments and more on financial services in 2026

                          Looking back at 2025, it was a pivotal year for financial services. The past 12 months have been marked by growing regulatory pressure, publicised outages, and a renewed focus on decentralised finance. In January, the Digital Operational Resilience Act (DORA) officially came into force across the EU, imposing new obligations on banks, insurers, investment firms and their technology providers to better manage ICT risks, report incidents and ensure continuity of operations.

                          That regulatory shift has come at a time when real-world failures are under intense scrutiny. A report from the Treasury Committee, prompted by a wave of IT glitches, revealed that nine of the UK’s largest banks and building societies suffered at least 803 hours of unplanned outages between January 2023 and February 2025, equivalent to more than 33 days of downtime. Alongside revision of traditional finance strategy, pro-crypto policy emerging from the US with the new administration has also buoyed investor confidence in newer assets like stablecoins, with the global market slated to hit $500 to $750 billion in coming years.

                          These events have reinforced a hard truth across the sector: digital infrastructure is no longer just a supporting pillar, it is mission-critical. Against this backdrop, many firms are now rethinking how they build, monitor and respond to technology risk. In this transformational moment, the voices below outline why 2026 may well become the year financial services firms turn lessons into lasting change, providing predictions about FS in 2026.

                          Eduardo Crespo, VP EMEA, PagerDuty:

                          “By 2026, financial services firms have turned hard-won lessons from the Treasury’s 2025 outage reports into action. Years of costly downtime and lost trust pushed the industry to rebuild around resilience. Always-on access is non-negotiable. Customers leave if they can’t transact in real time, and regulators are watching. In response, banks are overhauling legacy stacks and embedding AI at the core of incident management.

                          “AI isn’t a pilot project anymore, it’s become part of frontline defence. Systems now detect and diagnose disruption before it happens, enabling predictive maintenance and softening the blow of unplanned events. In 2026, resilience is a competitive edge.”

                          Anil Oncu, CEO, Bitpace:

                          “By 2026, digital assets will no longer be considered emerging. They will be fully embedded in mainstream finance. The shift is accelerating, driven by clearer regulation and stronger institutional participation across the US, UK and Europe. Pro-crypto policy is now the backbone of a global effort to build stablecoin-powered commerce at scale.

                          “In the UK, the Bank of England’s decision to allow stablecoin reserves to be held in short-term government debt is a significant signal of confidence. In the US, the GENIUS Act provides long-overdue oversight for dollar-backed tokens and replaces years of ambiguity with a clear path to legitimacy and widespread adoption.

                          “As global stablecoin supply moves beyond $300 billion, these digital dollars will support a rapidly increasing share of cross-border transactions. They reduce fees, eliminate settlement friction, and outperform traditional rails in both speed and transparency. At the same time, regulators are finally moving in the right direction. Stablecoins are moving from a speculative tool into a trusted infrastructure layer for modern payments.

                          “By 2026, digital assets will no longer sit alongside traditional finance. They will power its next phase of development. Stablecoins, crypto ETFs, and tokenised payments will be used directly within the financial stack and will be part of everyday business and consumer activity worldwide. This is not hype. It is execution, and the market is already moving.”

                          Ed Crook, VP Strategy & Operations, DeepL:

                          “2026 will be make-or-break for many financial services providers. In a competitive market, the edge goes to providers who adopt useful AI to cut through inefficient workflows. In this sector, where every interaction is highly regulated and reputational risk is acute, businesses need the right tools for the job. This includes data protection, account security, compliance, IT ops and customer service – keeping fundamental lines of communication open and effective. These are all areas where AI is already solving critical problems.

                          “AI is fast becoming the connective tissue of international finance, and this trend will continue in 2026, particularly in customer engagement and operational support. Our FS research found that over a third (37%) of client interactions in UK finance already involve AI. Over half (52%) use AI for multilingual translation, the top use case, directly addressing linguistic fragmentation. Moving into the new year, Language AI will be a key practical tool for financial services firms. But these companies first need to iron out their strategy around AI integration. Staff will inevitably look for workarounds if the tools provided don’t meet their needs. This is why companies need to get ahead by providing secure, fit-for-purpose solutions. By building a collaborative approach between IT and frontline teams, and avoiding pitfalls around shadow AI, financial service firms can maintain a unified, strategy approach to AI deployment, protecting against cybersecurity threats, while still realising the full benefits of trusted AI.”

                          Jeppe Rindom, CEO and Co-Founder, Pleo:

                          “Automation and “agentification” will redefine the fintech landscape. Most of what’s considered operational today will be handled by intelligent systems, from finance ops to customer support. That playing field will level and expectations will rise.

                          “To stand out, companies will need to inject identity – the one thing only humans can create. That could be through exceptional product design and user experience, considered use of human touchpoints where emotion and trust matter most, or the depth in which problems are solved for customers, not just how fast they can be solved.

                          “As the average becomes automated, greatness will come from creativity, clarity and crafting products and experiences that still feel unmistakably human.”

                          The Next 12 Months

                          The start of 2026 marks a massive turning point for financial services. After a year defined by renewed pressure on service uptime and improvement, around outages, regulatory pressure and rapid technological acceleration, the industry is now moving from reaction to reinvention.

                          In the coming year, we’ll see that firms embedding resilience, embracing intelligent automation and identifying new trends in service provision will lead the pack. The future of finance will hinge on trust, modernisation and operational strength, backed by technology.

                          • Artificial Intelligence in FinTech
                          • Blockchain & Crypto
                          • Digital Payments

                          Author

                          Dan Brightmore

                          Published

                          6 January 2026

                          Estimated Read time

                          3Mins

                          CoreX, a high-growth Elite Consulting and Implementation Partner of ServiceNow and NewSpring Holdings platform company, has announced the successful completion…

                          CoreX, a high-growth Elite Consulting and Implementation Partner of ServiceNow and NewSpring Holdings platform company, has announced the successful completion of its acquisition of InSource’s ServiceNow business unit. InSource is a fellow Elite Partner recognised for deep delivery expertise and an unwavering commitment to client success. The transaction officially closed in late December 2025.

                          This agreement unites two high-performing ServiceNow partners in the ecosystem. Together, CoreX and InSource now operate as a single, purpose-built organisation designed to scale with intent, elevate enterprise transformation outcomes, and meet the accelerating demand for AI-enabled, end-to-end ServiceNow solutions worldwide.

                          InSource integration into CoreX delivering value for ServiceNoe customers

                          With InSource’s 1,500+ successful implementations and a 4.76 CSAT rating, the combined organisation, more than doubling its US-based employee headcount, now operates at a level of scale and technical depth that firmly positions CoreX among the top-tier Consulting and Implementation Partners in the global ServiceNow ecosystem. The acquisition doubles the firm’s ServiceNow certifications and brings together advanced platform specialisation and a people-first culture grounded in long-term client success.

                          “This is not growth for growth’s sake, but rather a strategic, deliberate move of scale,” said Rick Wright, Head of CoreX. “By fully integrating InSource into CoreX, we have created a focused consultancy built for scale, execution, and long-term value for ServiceNow customers.”

                          Reflecting on the integration, Mark Lafond, former President & CEO of InSource, added, “InSource was built on delivery strength, trust, and long-term client relationships. Joining forces with CoreX allows us to take everything we do best and amplify it on a much larger stage. This is the right home for our people, the right platform for our customers, and the right partner to accelerate the next chapter of growth.”

                          By unifying CoreX’s innovation roadmap and AI readiness with InSource’s long-standing operational delivery excellence, the combined organisation now offers a truly integrated model for enterprise transformation across industries. This integration enables clients to move faster from strategy to execution while maintaining the governance, resilience, and scalability required for modern enterprises.

                          Just as importantly, the acquisition strengthens CoreX’s geographic footprint and delivery capacity across key global delivery hubs, including North America and Latin America, enabling the firm to serve enterprise clients with greater speed, continuity, and depth.

                          “Our acquisition of InSource fundamentally changes the scale of impact we can deliver for customers,” Wright added. “CoreX is now purpose-built to lead the next era of ServiceNow-powered transformation.”

                          A Unified Approach to Enterprise Transformation

                          The acquisition significantly enhances CoreX’s capabilities across Strategic Portfolio Management (SPM), IT Asset Management (ITAM), IT Operations Management (ITOM), Integrated Risk Management, Operational Technology integration, and AI-ready enterprise architecture. The combined strengths allow CoreX to solve more complex, mission-critical challenges across industries, including manufacturing, healthcare, financial services, and the public sector.

                          With this transaction, CoreX is now among the top global ServiceNow Elite Partners, distinguished not just by certifications or scale, but by consistent delivery of measurable, enterprise-level outcomes on the ServiceNow AI Platform.

                          About CoreX

                          Founded in 2023, CoreX is a global ServiceNow consultancy specialising in business-focused transformation that unlocks hidden value from the Now Platform. Backed by unmatched industry leadership, extensive functional experience, and the most seasoned ServiceNow team in the ecosystem, CoreX delivers strategic guidance and AI-enabled innovation to power sustained success. Learn more at corexcorp.com

                          About NewSpring Holdings

                          NewSpring Holdings, NewSpring’s majority investment strategy, focused on control buyouts and sector-specific platform builds, brings a wealth of knowledge, experience, and resources to take profitable, growing companies to the next level through acquisitions and proven organic methodologies. Founded in 1999, NewSpring partners with the innovators, makers, and operators of high-performing companies in dynamic industries to catalyze new growth and seize compelling opportunities. Having completed over 250 investments, the Firm manages approximately $3.5 billion across five distinct strategies covering the spectrum from growth equity and control buyouts to mezzanine debt. Partnering with management teams to help develop their businesses into market leaders, NewSpring identifies opportunities and builds relationships using its network of industry leaders and influencers across a wide array of operational areas and industries.

                          • Data & AI
                          • Digital Strategy

                          Author

                          Gareth Richardson

                          Published

                          Estimated Read time

                          6Mins

                          Gareth Richardson, CEO at Finova, on tackling the challenges that persist in creating a truly inclusive financial system

                          Financial inclusion has felt out of reach for too many people. According to the FCA, nearly a million people in the UK remain unbanked, and for those who do have access to financial services, that access isn’t always affordable or designed with their everyday needs in mind.

                          The UK is taking steps to address this, including the government’s latest financial inclusion strategy, which puts a welcome spotlight on digital inclusion. As more of life moves online – from paying bills to applying for credit – being digitally connected and being financially included are all packaged together.  But despite huge advances in digital banking, many consumers still find themselves priced out, left behind or navigating services that weren’t built with them in mind.

                          So, in a world of instant payments and AI-powered apps, why are so many people still excluded from products that should be available to everyone? With the right technology, these rigid, outdated models can be replaced with services that adapt to customers rather than shutting them out.

                          The Hidden Problem with Traditional Pricing Models

                          The issue comes down to legacy thinking. Traditional pricing models didn’t grow out of customer needs. They grew out of the way banks organised themselves internally. Products are designed by departments, and those departments are managed according to systems, processes, risk models, and profit lines. The result? Customers were viewed as isolated cases. Our sector missed the bigger picture. We do not see a whole person with a rich and complicated financial life.

                          So what’s the solution? It starts with innovation. Cross-product, cloud-based core systems, open banking and AI-driven decisioning tools allow lenders to build a more complete picture of someone’s financial life, including their saving habits, spending patterns and long-term behaviour.

                          For example, a seasonal worker whose income rises and falls throughout the year could be penalised if a lender focused on their income profile in the quieter months. A more advanced decisioning tool could make an assessment based on a seasonal worker’s whole annual pattern, providing a fairer and fuller picture of their finances.

                          Another solution is a product that automatically adjusts its rates depending on the customer’s day-to-day financial decisions. Here’s how it works: the rates would dynamically evolve in line with the product holder’s behaviour and their changes in liabilities. As a result, people with low credit history learn good financial behaviour and can improve their access to banking services.

                          The message is simple. Financial products can be more flexible. They can be truly aligned with the realities of people’s everyday lives. But we must invest in the right technology to make it happen.

                          How Can Smarter Pricing Reach the People Who’ve Been missed?

                          The fact is that most pricing decisions today still rely on a limited view of a customer’s data. We end up with a situation where lenders are making decisions whether or not to do business with a customer based on information held by a single institution or even a product line.

                          But anyone could tell you that such a narrow view isn’t enough to really understand what a person does with their money. We all manage money across several banks, financial apps and credit providers. Some of us save in one place, borrow in another and budget somewhere else entirely.

                          Smarter pricing technologies can bring these pieces together in a way that feels more rounded and fair. By using open banking data, behavioural insights, and, soon, digital identity frameworks, lenders can build a richer, fairer view of a customer.

                          There’s a longer term benefit, too. Digital identity and federated data models will allow people to securely share verified data across institutions. This gives customers more control over how they’re understood and ensures their financial story doesn’t reset every time they switch providers. It shifts the emphasis from exclusion to inclusion.

                          Why Moving Faster Helps People Feel More Included

                          Speed might not be the first thing that comes to mind when thinking about financial inclusion, but it matters more than you might expect. When products take months to design, approve and launch, lenders struggle to respond to changing customer needs – particularly for people in vulnerable situations.

                          Cloud technology changes everything. Lenders can bring new ideas to market more quickly, test them with real customers and adapt the product spec based on what’s working. We can move beyond static and one-size-fits-all offerings. We can push for cloud-native systems, for a market where products can grow with a customer, rewarding positive behaviour and opening doors to better terms over time.

                          The Technologies Shaping A More Inclusive Future

                          Of course, a range of new technologies is coming to the market. All could make financial services more inclusive for the average everyday customer. The UK government has recently acknowledged that financial education is very poor, and it is worse in the areas of society that are typically unbanked. Our industry, of course, has known this for some time. UK Finance members have built inroads, with over 145,000 educators across 25,000 schools.

                          But the work isn’t quite over. The simple fact is that financial products are hard for people to understand and scary.  AI can help people navigate decisions that once felt overwhelming or confusing. A person applying for credit for the first time, for instance, could use an AI assistant to compare options in plain language. By providing guidance that is both clear and tailored, AI helps people make informed choices without feeling intimidated.

                          Next comes digital identity and data portability, which tackle one of the most persistent obstacles to inclusion: lack of verifiable financial history. For people with irregular incomes or those new to the UK, these technologies can make a huge difference. Being able to carry verified financial information securely reduces the need to repeatedly prove financial standing and ensures continuity as customers move between services.

                          Finally, modern core banking architectures are laying the foundation for financial services that are flexible and human-centric. By moving away from predefined products, banks can design experiences that truly reflect a customer’s circumstances. This could mean flexible repayment plans that adapt to seasonal income or savings tools that respond to a customer’s habits over time.

                          Towards A More Accessible Financial System

                          Technology will play a central role in closing the financial inclusion gap in the UK. It can help create services that are easier to access and easier to understand. It’s about products that adapt to people’s lives rather than the other way around. And tools that give individuals the confidence and control they need to manage their money in a digital world.

                          The goal is a financial system that works for everyone and where no one is excluded because of outdated systems, incomplete data, or products that simply don’t reflect real life. And it’s in our reach. On the battlefront for financial inclusion, AI and technology can be a force for good. It’s up to our sector to embrace it without overlooking safety or structure.

                          Learn more at finova.tech

                          • Digital Payments
                          • Neobanking

                          Author

                          Jan Van Hoecke

                          Published

                          5 January 2026

                          Estimated Read time

                          5Mins

                          Jan Van Hoecke, VP AI Services at iManage and a highly experienced computer scientist with a passion for technology and problem-solving. on navigating the AI landscape for success in 2026

                          The AI landscape faces a number of big shifts in 2026. Agentic AI will undergo a reality check as enterprises discover the gap between marketing hype and actual capabilities, while organisations will go through a mindset change from treating AI hallucinations as crises to managing them, acknowledging the inherent limitations of the technology. There will also be a shift in how data will be structured in AI systems, to help the move from just finding facts (“what”) to understanding reasons (“why”).  Middleware application providers will face new challenges, as those vendors controlling both platforms and data will become more influential. Finally, standardised AI chat interfaces will evolve into smarter, dynamically generated, task-specific user experiences that adapt to immediate needs.  

                          Agentic AI Reality Check  

                          2026 is the year when agentic AI will get a reality check, as the gap between marketing promises made in 2025 and their actual competencies will become starkly visible. As enterprise adopters share the mixed successes of agentic AI, the market will begin to differentiate between true autonomous agents and the clever workflow wrappers.

                          Currently, many products promoted as AI agents are, in reality, rigidly programmed systems that simply follow predefined paths. They cannot independently plan or adapt in real-time to accomplish tasks. The current evolution of AI agents closely resembles the development of autonomous vehicles: early self-driving cars could only maintain lane position by relying strictly on preset instructions, and likewise, today’s AI agents are limited to executing narrowly defined tasks within established workflows. True autonomy, where AI agents can dynamically perform and solve complex problems better than humans and without human intervention, remains, for now, an aspirational goal.

                          AI Hallucination Goes from Crisis to Management

                          In 2026, the AI hallucination crisis will reach a critical juncture as organisations realise they must learn to coexist with the current fundamentally imperfect technology – until a new technology comes into play that can effectively address the issue. The focus will shift from AI hallucination ‘crisis’ to management.

                          As the industry deliberates who carries the liability for AI’s mistakes and inaccuracies – the tool makers or the users – enterprises will stop waiting for vendors to solve the problem and take matters into their own hands. They will adopt a variety of pragmatic risk mitigation strategies – from double and triple-checking work, and enforcing human oversight for high-stakes decisions, to taking hallucination insurance policies.

                          Major model builders acknowledge that current foundational LLM technology cannot eliminate hallucinations and ambiguity through incremental improvements alone. New technology is needed. Until then, and perhaps with the realisation that a technological breakthrough is years away, users will start driving the hallucination conversation – both by building systematic defenses within how they use AI, and forcing vendors to accept shared responsibility through better documentation and clearer model limitations.  

                          The Next Evolution in AI Data Architecture Lies in a Shift from “What” to “Why”

                          There will be a fundamental shift in how data is structured for AI systems, driven by the limitations of current approaches in answering complex questions. While Retrieval Augmented Generation (RAG) has proven effective at locating information and answering “what” questions, it struggles with the deeper “why” and “how” inquiries.

                          This limitation stems from RAG’s flat-file architecture, which excels at locating information but fails to capture the complex interconnections and relationships that underpin meaningful understanding and knowledge, especially in specialised domains like legal and professional services information.

                          The solution lies in AI-driven autonomous structuring of data. These systems will be better placed (than humans) to reveal critical relationships across multiple data points at scale, also highlighting the contextual dependencies essential for answering the “why” and “how” questions effectively.

                          Consequently, in 2026, with machines taking the lead, the method of structuring data will undergo a complete transformation, gradually eliminating the human role in creating structure, to reveal the business-critical interconnections across multiple data points.

                          Middleware AI Apps Squeeze

                          Given the essential link between data and AI, middleware companies that specialise in building custom applications layered on top of data platforms will begin to get pushed to the margins, forced to compete on niche features – while the core value of data and insight is captured by the platform owners. The true leaders will be those organisations that both own and manage their data, while also offering an AI-powered interface that enables users to interact with their data securely and efficiently, fully leveraging the capabilities of modern AI technology.

                          Shift to AI-generated, Task-Oriented User Interfaces

                          In 2026, the current traditional vendor-designed, standard AI chat-based user interfaces will transition to dynamically AI-generated task-specific user interfaces that adapt to users’ immediate needs. This represents a fundamental shift from standardised software – for example, where everyone uses identical Microsoft Word or SharePoint interfaces – to personalised, short-term user interfaces that exist only as long as the user requires them for a specific task.

                          This transformation will also address the critical pain point that users typically have – i.e, the crushing cognitive load of navigating bloated, feature-rich software. Instead of searching through endless menus in an overstuffed application like Excel, the user will simply state their goal – “Compare the Q3 and Q4 sales figures for our top 5 products and show me a chart” – and the AI will instantly generate a temporary, purpose-built interface – a “micro-app” – solely designed for that one single task.

                          In the context of dynamically generated user interfaces, both data storage and the creation of bespoke interfaces will be managed by AI. The AI organisations that will truly lead in providing such bespoke user interface-generating capability are those that possess and control their own data.

                          About iManage

                          iManage is dedicated to Making Knowledge Work™. Our cloud-native platform is at the centre of the knowledge economy, enabling every organisation to work more productively, collaboratively, and securely. Built on more than 20 years of industry experience, iManage helps leading organisations manage documents and emails more efficiently, protect vital information assets, and leverage knowledge to drive better business outcomes. As your strategic business partner, we employ our award-winning AI-enabled technology, an extensive partner ecosystem, and a customer-centric approach to provide support and guidance you can trust to make knowledge work for you. iManage is relied on by more than one million professionals at 4,000 organisations around the world.

                          Learn more at imanage.com

                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Strategy

                          Author

                          Sam Kohli

                          Published

                          18 December 2025

                          Estimated Read time

                          6Mins

                          Sam Kohli, CEO at PAYNT, on the need for continued innovation with biometric payments to enhance trust

                          For millions of people, biometric security, or the use of unique personal characteristics such as fingerprints or facial recognition to confirm a person’s identity, has become an everyday process. These technologies are now deeply integrated into a huge variety of activities. From unlocking smartphones to authorising mobile payments. It’s quick, efficient and, compared to many other methods, relatively secure.

                          The underlying principles are long established. Fingerprinting can be traced back to around 500 BC, when it was used on clay tablets as a form of signature. In more contemporary terms, by the 1970s and 1980s, biometric systems began appearing in government and defence environments. Although these nascent technologies were expensive and slow.

                          Commercial adoption only became viable in the last 30 years or so as computing power increased, when applications were focused on workplace access control rather than payments. The real breakthrough came with smartphone integration. This began with fingerprint sensors on consumer devices, such as Apple’s Touch ID and Face ID, which are now extremely popular.

                          A Growing Ecosystem

                          A quick glance at the underlying trends reveals just how rapidly the ecosystem is now expanding. According to Juniper Research, for example, by 2028, the total in-store transaction value for biometric payments is expected to reach $1.2 trillion across 46 billion biometric-enabled transactions globally. While that’s already impressive, there is still enormous growth potential.

                          The problem is, adoption is starting to outpace trust. A recent study published by the Identity Theft Resource Center (ITRC), revealed that while nearly 90% of respondents had been asked to provide a biometric to verify their identity in the past year, nearly two-thirds expressed serious concerns about doing so. Moreover, 39% went as far as to say that the use of biometrics should be banned for both identity verification and/or recognition.

                          So, what can be done to close this trust gap and help ensure biometrics are used across fintechs as a more secure alternative to passwords and PINs? One area that requires more emphasis is consent-based design. Whereby users are given clear and revocable permission regarding how their biometric data is collected, stored, and used.

                          In practical terms, a consent-first design could resemble a digital wallet that provides users with clear, active choices regarding the use of biometrics. During setup, biometric authentication is optional and switched off by default. The app explains what data is collected, where it is stored and how to disable it later. During the payment process, all matching occurs locally on the device, rather than in a central database, and independent certification confirms compliance with data protection standards.

                          These processes must also be designed so they continue to act in the best interests of users. For example, consent should be viewed as an ongoing decision, rather than a one-time formality. Users must be able to revisit and change biometric permissions at any point and without difficulty. Settings should not be buried under layers of menus and options. They should be readily available so that users understand they are in control at all times.

                          Biometric Authentication

                          For example, if a user decides they no longer want to use biometric authentication in their payment app, they should be able to switch that functionality off with a single action. In these circumstances, the app immediately reverts to PIN or password authentication, so access isn’t disrupted. At the same time, any biometric templates held on the device are securely deleted.

                          If the user chooses to close their account entirely, the deletion workflow should extend to all associated data, so nothing is retained unnecessarily. Users should then receive a notification that their biometric identifiers are no longer stored.

                          Even these relatively basic processes can help put users in a much stronger position to understand and control the use of their biometrics. And don’t forget, this isn’t just a nice-to-have; it is increasingly a regulatory requirement issued by the EU and other authorities worldwide. GDPR is a good example, as it classifies biometric data as a special category of data and prohibits processing it unless explicit consent or another lawful basis applies.

                          Closing the Trust Gap

                          Let’s be in no doubt: trust (or the lack of it) is a real problem across the payments ecosystem. Including those organisations that rely on biometrics. In many current environments, a persistent trust gap, uneven implementation and mixed user experiences show that compliance alone does not guarantee confidence. Better progress now depends on practical execution, clear communication at the point of use, and systems that make data handling visible and auditable. Collectively, these processes can help reassure people that organisations are doing the right thing consistently and for the right reasons.

                          As a result, transparency and education are now key to improving confidence, ensuring users understand how their biometric data is protected and how they can stay in control. For many FinTechs, this requires a shift in mindset, where transparency is seen as a core product feature, rather than an afterthought or compliance tick box. With consent first design principles in place, users should be regularly reminded about where their biometric data resides and how to delete it.

                          Additionally, regular external audits or certifications help demonstrate accountability and ensure FinTechs operate to recognised standards. Granted, relatively few consumers are likely to study the fine details, but the act of being credibly audited is an important contributor to the way consumers build trust.

                          Trust as a Competitive Advantage

                          In these circumstances, trust can actually evolve into a competitive advantage. Transparent payment systems and processes will always face fewer adoption barriers, fewer customer complaints and possess stronger reputational resilience in the event of incidents. Ultimately, the more open and consistent the provider, the more users adopt and stay engaged. In markets where penetration is still low, a consent-first design and a focus on trust will reassure users that they will always remain in control of their data. Encouraging increased adoption of newer, seamless payment methods.

                          Regardless of how you look at it, the need for change is becoming increasingly urgent. Biometric payments are evolving beyond single-factor models toward richer, multimodal processes that introduce a combination of fingerprints, facial recognition, voice patterns and behavioural signals. As these capabilities mature, they will be applied in a wider variety of payment contexts, ranging from in-store to remote authentication and open banking apps.

                          This will only serve to heighten expectations around transparency and user control. In this environment, consent-first design does more than support regulatory compliance; it lays the foundation for future adoption by building systems that are flexible enough to accommodate new biometric methods without compromising user trust. As consumers become more digitally savvy and accustomed to a culture where switching between service providers is relatively easy, building trust in biometrics will contribute significantly to FinTech success.

                          Learn more at paynt.com

                          • Cybersecurity in FinTech
                          • Digital Payments

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          5Mins

                          Marcin Glogowski, SVP Managing Director for Europe and UK CEO at Marqeta, on empowering businesses in the UK

                          FinTechs have long supported consumers, with the modern iteration of consumer innovations beginning in the UK in the early 2000s with the launch of the Faster Payments network in 2005. The first peer-to-peer lending platform started in the same year. And in 2007, the UK became one of the first markets to introduce contactless cards. Small and medium-sized businesses (SMB) lending and payment innovation has paled in comparison.

                          SMBs are the backbone of the UK economy, generating an impressive £2.8 trillion in revenue every year. Yet despite their critical role, they remain underserved when it comes to financial support. Only £62.1 billion in business loans were issued in the past year (45 times less than SMBs contribute to annual revenue) highlighting just how difficult it can be for SMBs to access the funding they need to grow. This funding gap limits not only individual businesses but also the wider economy that depends on their success.

                          While FinTechs have poured innovation into consumer products, revolutionising everything from budgeting apps to buy now, pay later (BNPL), SMBs have largely been left behind. This is despite the fact that SMB lending represents one of the fastest growing opportunities for financial organisations. 

                          A report earlier this year by Boston Consulting Group (BCG) highlights that we are on the cusp of a revolution not dissimilar to the one seen decades ago in mortgages, with technological advancements playing a key role. The demand for more efficient payments, smarter cashflow tools and flexible funding solutions is accelerating. Yet many businesses still find themselves navigating outdated systems and slow, manual processes.

                          The issue is not that SMBs do not see the value in modern financial tools. In fact, they are ready to invest. According to our recent Marqeta 2025 State of Payments report 90% of UK SMBs surveyed said they would pay higher upfront costs for tools that deliver long-term savings and efficiency. What SMBs really want is simplicity, speed and control. They are not emotionally invested in payments themselves – they are invested in running their businesses as efficiently as possible.

                          A striking finding from the Marqeta report reveals that nearly half (42%) of UK SMB owners still use personal cards to fund business expenses, citing higher credit limits and better rewards as the motivator behind this practice. This reveals an opening for payment solutions to meet businesses with credit solutions that are tailored and personalised to their specific requirements. Smart, data-driven underwriting models that look beyond traditional credit scores and reward businesses.

                          The SMB Payments Frontier

                          FinTechs have made great strides in improving financial experiences for individuals, but in doing so, may have missed the mark when it comes to understanding the unique needs of business owners. For SMBs, payments are not just a transaction. 

                          As Marqeta’s findings highlight, UK SMBs increasingly view payment tools as strategic assets rather than mere utilities. From social commerce trends to rewards programs and digital asset management, businesses are seeking solutions that actively contribute to the growth and efficiency of their organisations. Payment providers that offer real-time, flexible tools that reward customers for their engagement are best positioned to capture this rising demand and untapped potential.

                          Payments are the lifeblood of their operations, tied to cashflow, customer experience and long-term growth, with 52% of UK SMBs surveyed, as part of the State of Payment report, viewing payments as a tactical lever helping them streamline expenses, boost operational efficiencies, and free up cash flow. When payments work seamlessly, business owners can focus their energy where it matters most, serving their customers and growing their businesses.

                          Beyond payments alone, SMBs are looking for platforms that provide actionable insights and preventative measures that pre-empt major issues. Solutions that anticipate cashflow gaps, suggest repayment plans and automatically identify funding opportunities. The shift from reactive to proactive financial management offers SMBs a market advantage and, critically, represents a new dawn for how fintechs can support their growth.

                          Connecting the Dots

                          That is why bridging the gap between payments and funding represents such a powerful opportunity. Embedded Finance is already starting to move the needle, allowing SMBs to access credit directly through their payment platforms. This integration can transform payments from a passive process into an active growth driver. Imagine a world where a business processing card payments automatically receives insights into cashflow, credit opportunities or flexible repayment options tailored to its transaction history.

                          By combining real-time payment data with intelligent lending models, FinTechs can deliver funding at the point of need, not through laborious processes that may take weeks or months later. This kind of agility can make the difference between a business thriving or merely surviving. It also fosters financial resilience and trust, helping SMBs weather economic fluctuations with greater confidence in their suppliers and improved control.

                          Too often, the financial world can feel overly complex and fragmented for small businesses.  Many rely on multiple providers for banking, payments, invoicing and credit, creating a patchwork of tools that rarely communicate effectively. Fintechs now have the opportunity to simplify this landscape by creating connected ecosystems that serve SMBs holistically. The future lies in frictionless experiences that combine payments, insights and lending under one roof.

                          Riding the Payments Wave

                          For FinTechs, the message is clear. The next wave of financial innovation will be about empowering the businesses that keep the UK economy moving. With the right approach, payment platforms can deliver more than convenience. They can provide SMBs with the confidence, agility and financial durability they need to thrive in an uncertain world.

                          As SMB payments take flight, the question is not whether the opportunity exists, but whether fintechs are ready to power the future and support businesses as they navigate the new payments frontier.

                          Learn more at marqeta.com

                          • Digital Payments
                          • Embedded Finance

                          Author

                          Jamil Jiva

                          Published

                          17 December 2025

                          Estimated Read time

                          4Mins

                          Jamil Jiva, Head of Asset Management at Linedata, on unlocking the benefits of AI for Private Equity

                          Private equity has always been a race against time: identify the right opportunity, execute the deal, and drive growth before the next cycle begins. Traditionally, the competitive edge came from sharp analysis and strategic foresight. But today, as competition intensifies and margins for inefficiency vanish, another advantage is emerging: the ability to reclaim time itself.

                          Generative AI is the force multiplier behind this shift. It’s becoming an extension of the deal team, capable of accelerating the most time-consuming elements of the investment lifecycle. When applied thoughtfully, AI can unlock what may be the most important metric in modern private equity: Return on Time (ROT).

                          ROT measures the hours reclaimed from manual, repetitive work and reinvested in activities that truly drive value. In other words, AI is giving deal teams the gift of time. And in private equity, there may be no greater currency.  

                          AI as an Extension of the Deal Team

                          Many firms have already taken the first step towards using AI to automate the ‘heavy lift’ tasks that have traditionally slowed teams down. 

                          Deal sourcing is where the first savings can be made. Machine learning models trained on past investments, sector trends, and even unstructured data from news and social media are helping teams identify potential opportunities earlier. Sometimes before they even hit the market. Instead of hours spent trawling through databases or reading reports, deal professionals can now focus their energy on strategic decisions and relationship building.

                          Once a target is in sight, due diligence becomes the next time-intensive phase ripe for AI optimisation. Generative and analytical AI tools can now extract and classify data from hundreds of pages of financial documents, contracts, and ESG disclosures in minutes rather than days. 

                          Post-acquisition, portfolio monitoring is where AI is starting to transform how value creation is managed. Natural language processing (NLP) can scan management reports and board decks to flag anomalies or benchmark performance against similar assets. Instead of manually consolidating metrics from scattered sources, investment teams can access real-time, AI-generated insights via live dashboards, giving them more bandwidth and brain space to focus on value creation.

                          At each stage, AI doesn’t replace the expertise of analysts and associates; it amplifies it. By handling the volume and velocity of modern data, AI helps firms make faster, better-informed decisions. The kind that can define fund performance.

                          Measuring ROT

                          In an industry where success is often quantified in basis points, ‘return on time’ may sound abstract (almost as abstract as the concept of time itself). But it’s quickly becoming a very real and measurable advantage.

                          Every hour a deal professional spends wrangling data or formatting reports is an hour not spent nurturing relationships or driving portfolio performance. AI can convert those reclaimed hours into strategic capacity.

                          For example, a mid-market firm that uses AI to automate quarterly portfolio reporting might save its operations team 15 hours per company per cycle. Across a 30-asset portfolio, that’s over 1,800 hours annually. That’s the equivalent of adding a full-time team member, without increasing headcount.

                          More importantly, the quality of those hours improves. Teams can reallocate time to higher-value activities, like mentoring junior talent, exploring new sectors, or deepening engagement with portfolio executives. In private equity, where speed and insight often determine who wins a deal or exits successfully, that time dividend can compound dramatically.

                          Scaling with Governance and Buy-In

                          While the business case is clear, scaling AI across investment teams is littered with challenges. Sensitive financial and portfolio data demand strong governance frameworks, especially as regulations such as the EU Data Act tighten the rules around data privacy and AI accountability.

                          Equally important is cultural buy-in. Starting small is the surest way to build trust and momentum, focusing on high-friction areas like due diligence and fragmented data workflows to deliver quick wins and tangible results. Clear communication is vital, but nothing reinforces confidence like seeing fast, impactful outcomes firsthand.

                          The most successful adopters recognise that AI implementation is an organisational shift that impacts far more than just IT. Analysts, partners, and operating teams all need to understand how AI supports, not substitutes, their expertise. Training programs and visible leadership support are essential to make the change stick.

                          Firms that neglect the human side of transformation risk underutilising their tools or facing quiet resistance from teams that don’t trust or understand the outputs. In contrast, firms that invest in cultural alignment often see adoption take flight organically, as teams begin to experience benefits they can see in their daily work.

                          The Gift of Time

                          AI’s impact on private equity will not be measured solely by reduced costs or faster workflows, but by the strategic capacity it returns to teams.

                          From there, the benefits become both quantitative and qualitative. As critical KPIs see an uplift, so too will more holistic metrics like decision-making confidence, analyst satisfaction, and internal adoption rates. In an industry built on the efficient use of capital, time remains the most precious and finite resource of all. Measuring and maximising Return on Time could be the differentiator that marks the next step up in private equity performance.

                          Learn more at linedata.com

                          • Artificial Intelligence in FinTech
                          • InsurTech

                          Author

                          Dan Brightmore

                          Published

                          16 December 2025

                          Estimated Read time

                          6Mins

                          FinTech Connect was a crossroads for strategy and execution. Global banks, FinTech challengers, regulators and investors gathered to define 2026 priorities, debate operational challenges and benchmark technology roadmaps.

                          A Decade of Fintech Innovation

                          FinTech Connect marked its 10th anniversary at ExCeL London. Drawing 5,000+ industry professionals, 140+ speakers and 100+ exhibitors to explore banking, payments, compliance, digital transformation and blockchain innovation. The co-location with Tokenize: LDN brought deeper coverage of tokenisation and digital-asset infrastructure alongside core FinTech topics.

                          AI in Fintech: From Vision to Practice

                          A theme threaded through almost every theatre was AI adoption in financial services. But unlike earlier years’ speculative hype, this edition focused on practical deployment and risk management.

                          One standout panel, “GenAI That Customers Can Trust: The One Zero Digital Banker Story,” shared how One Zero built responsible generative AI features tailored for banking workflows, emphasising transparency and user trust. Industry leaders underscored that explainability, governance and compliance are no longer optional in enterprise AI.

                          A direct follow-on session, “How Do We Make AI Responsible in Practice?”, featured Rajeev Chakraborty from the Home Office discussing model governance and ethical safeguards for operational AI—an area rapidly becoming central to CIO and risk officer agendas.

                          Across both days, panels also explored how AI can reduce backlog in financial institutions, with Santander UK’s Head of AI demonstrating measurable impact on operational efficiency, and tackling tech debt at scale—a perennial challenge heightened by the influx of automation projects.

                          Key takeaway: AI’s role has shifted from emerging trend to core enterprise infrastructure, but success now hinges on responsible implementation, observable outcomes, and regulatory alignment.

                          Digital Transformation & Core Banking Strategies

                          Transforming legacy systems was another anchor topic. The Digital Transformation stage hosted robust discussions around neobanks and challenger strategies, with executives from TSB Bank and HSBC highlighting how incumbents are adopting agile ways of working while balancing risk and customer expectations.

                          The session “All In on Legacy? Driving Time to Market Without Big-Bang Migrations” resonated with many practitioners: incremental modernisation beats wholesale lift-outs when prioritising stability and customer continuity.

                          Another practical highlight, “Engineering Productivity Measurement: Traditional Bank to UK’s Largest Fintech,” narrated the journey of building measurable engineering benchmarks to align business goals and product delivery.

                          Key takeaway: Attendees left with a reinforced understanding that successful transformation blends cultural shift, incremental modernization, and strategic tech investment—not hurried replacement of core systems.

                          RegTech & Ethical Compliance: Balancing Innovation with Governance

                          RegTech, Compliance & Security sessions tackled the tension between rapid innovation and tightening regulatory guardrails—a debate central to fintech scaling.

                          A standout session titled “Ethical AI in Regulatory Technology: Balancing Innovation & Compliance” featured voices from governance, compliance and data-ethics functions. Panelists discussed strategies for embedding fairness, bias mitigation and traceability into machine-assisted workflows—a crucial step for institutions deploying automated decisioning.

                          Another forward-looking talk, “How Quantum Innovation Will Redefine Regulatory Operations,” examined how future computing paradigms could reshape compliance tooling and data verification—but also stressed the need to prepare today’s infrastructure for tomorrow’s disruptions.

                          Key takeaway: Compliance isn’t just a cost centre; speakers argued that robust RegTech can be a competitive advantage, reducing risk while enabling faster scaling.

                          PayTech & eCommerce: Securing the Digital Commerce Era

                          The PayTech & eCommerce stage delivered insights on securing payment flows and shaping the next wave of commerce innovation.

                          In “Emerging Global Tech Trends in Payments & Cash Management,” HSBC’s payment leaders unpacked how real-time rails and open APIs are influencing cross-border flows. Fintech Connect 2025

                          The panel “Transforming Payment Security with AI” brought together payment experts and academics to examine fraud detection innovations—AI-enabled risk scoring, adaptive authentication and cooperative intelligence sharing—as a defence against evolving threats. Fintech Connect 2025

                          A later session on “Tackling Cyber Threats in a New Era of Digital Payments,” addressed real-time threat detection, third-party risk and securing complex ecosystems, underscoring cybersecurity’s front-and-centre role for digital commerce. Fintech Connect 2025

                          Key takeaway: Payments remain fertile ground for innovation, but trust and security are foundational determinants of user adoption and ecosystem resilience.

                          Tokenisation & Blockchain: Institutional Pathways Ahead

                          The Tokenize: LDN co-located stage brought in robust debate around real-world asset (RWA) tokenization and Web3 infrastructure—not as fringe buzzwords, but as emerging institutional tools.

                          Panels like “Bridging the RWA Infrastructure Gap” unpacked regulatory friction points and scaling challenges, highlighting custody risk, compliance complexity and standardisation needs—critical prerequisites to institutional adoption.

                          Another session on “Expanding Investment Opportunities With Fractional Ownership” featured cross-sector thought leaders, including Dr Lisa Cameron (MP & Crypto APPG Chair), exploring how tokenised assets can democratise access to traditionally illiquid markets.

                          Web3 panels examined trust, privacy and compliance in blockchain ecosystems and navigated the practicalities of smart contracts and decentralised identities—topics that are rapidly gaining traction with enterprise adopters.

                          A key session, titled Blockchain and CBDCs: At the Heart of Public Transformation? featured NatWest’s Head of Group Payment Strategy Lee McNabb, EY’s Emerging Tech & Innovation Leader Igor Mikhalev and Joy Adams, COO for Digital Assets at Deutsche Bank. A lively debate chaired by CommerzBank’s Poonam Ahuja examined the pros and cons of digital currencies and the rise of stablecoins.

                          Key takeaway: Tokenisation is still nascent, but panels stressed it’s transitioning into a practical institutional infrastructure conversation, with regulatory clarity and integration tooling cited as catalysts for broader uptake.

                          Startup Innovation & Demo Highlights

                          The Innovation & Start Up stage and Start-Up LaunchPad provided rapid-fire exposure to emerging companies pushing the frontier.

                          Live demos included:

                          • DaMoney.ai, showcasing AI-guided compliance workflows;
                          • Narrative, an AI-native engagement platform for SMEs;
                          • Profylr, offering comprehensive consumer duty landscapes analytics;
                          • 3AI, demonstrating self-learning investment intelligence models.

                          These sessions were among the most interactive parts of the show, with founders directly answering questions on integration, compliance and product-market fit.

                          Key takeaway: Startups revealed solutions that dovetail with enterprise needs—especially around AML automation, customer engagement and data orchestration—making them compelling partners for larger financial services buyers.

                          Networking, Community & Celebration

                          FinTech Connect didn’t just deliver talks; it facilitated dense networking across peer groups, investors, regulators and tech leads. The AI-powered networking app helped attendees pre-book conversations and tailor agendas, turning serendipity into structured discovery.

                          The 10th anniversary celebration—complete with drinks, a Christmas Market theme and live entertainment—reinforced the community aspect and capped the event on a high note.

                          Conclusion: A Hard-Working Fintech Forum

                          FinTech Connect 2025 proved to be more than a conference—it was a strategic inflection point. While technology and vendor showcases were abundant, it was the panel debates and operational talks that delivered the most actionable insight. Attendees departed with:

                          • A clearer view of AI adoption roadmaps;
                          • Practical frameworks for RegTech and compliance transformation;
                          • Nuanced understanding of payments security and real-time rails;
                          • Emerging tokenisation playbooks suitable for institutional pilots.

                          As FinTech leaders prepare 2026 budgets and technology plans, FinTech Connect has reaffirmed itself as a must-attend forum where strategy, innovation and regulation intersect—and where the next decade of financial services will continue to take shape.

                          • Artificial Intelligence in FinTech
                          • Blockchain & Crypto
                          • Events
                          • Host Perspectives
                          • InsurTech
                          • Neobanking

                          Author

                          Dan Brightmore

                          Published

                          Estimated Read time

                          3Mins

                          Interface issue 68 is live featuring Microsoft, Virgin Media O2, CIBC Caribbean, Telkom, Zoom, ServiceNow, Snowflake and more

                          Welcome to the latest issue of Interface magazine!

                          Click here to read the latest edition!

                          Driving Business Transformation Through Cloud & AI

                          Microsoft’s Shruti Harish, Head of Solution Engineering for Cloud and AI Platforms across the tech giant’s Manufacturing and Mobility vertical, talks to Interface about how to achieve successful AI implementations augmented by Cloud. Our future focused fireside chat covered everything from driving value through cloud modernisation to responsible AI.

                          “Leaders should align AI initiatives with clear business outcomes and foster a culture that embraces change. The focus is shifting toward AI-operated, human-led models where intelligent agents handle tasks and humans guide strategy.”

                          Virgin Media O2: Democratising Data as a Cultural Movement

                          Mauro Flores, EVP for Data Democratisation at Virgin Media O2, talks to Interface about the leading telco’s data journey and how it is supporting colleagues to innovate faster, make smarter decisions and deliver brilliant customer experiences.

                          “Data-driven insights are essential. They’re helping power our decisions like optimising our network performance, anticipating outages before they happen, identifying and preventing fraud, personalising offers and pricing to build customer loyalty, and forecasting demand so we invest in the right things.”

                          CIBC Caribbean: Shaping the future of Banking in the Caribbean

                          Deputy CIO Trevor Wood explains how CIBC Caribbean is blending technology, culture, and customer-centricity to deliver seamless digital experiences across the region with a ‘Future Faster’ strategy.

                          “We want to lead in every market we operate, build maturity across our practices and be architects of a smarter financial future for all.”

                          And read on for deep AI insights from ANS’s CIO on why AI isn’t just for big business, Emergn’s CTO on how your business can get AI-ready and Kore.ai’s Chief Strategy Officer on taming AI-sprawl with governance-first platforms.

                          We also hear from Celonis, Snowflake, ServiceNow, Make and Zoom with their tech predictions for 2026 and chart the key dates for your diary with global networking opportunities at the latest tech events and conferences across the globe.

                          Click here to read the latest edition!

                          • Artificial Intelligence in FinTech
                          • Data & AI
                          • Digital Payments
                          • Digital Strategy
                          • People & Culture

                          Author

                          Berkley Egenes

                          Published

                          15 December 2025

                          Estimated Read time

                          5Mins

                          Berkley Egenes, Chief Marketing & Growth Officer at Xsolla, on the future of frictionless payments in gaming and why convenience is king

                          From subscriptions to battle passes and in-game marketplaces, today’s video games are just as much about payments as they are about play. But with players now used to lightning-fast experiences, the way money moves in gaming is undergoing a dramatic shift. In this kind of space, one truth stands out: convenience is king.

                          In 2025, a slow or clunky payment experience can cost more than just a sale; it can cost a player. As global competition heats up, gaming companies are quickly realising the easier it is for someone to pay, the more likely they are to stay. 

                          Players Expect More Than Just Good Gameplay

                          Video games have come a long way from cartridges and cash registers. With the rise of mobile gaming, free-to-play models, and digital-first ecosystems, the way people pay and what they pay for has changed completely.

                          But something else has changed, too: expectations. Players now want to make purchases without stopping the game. No long card forms, no redirects, no confusing fees. Just a quick tap, swipe, or confirmation, and they’re back in the action. It sounds simple, but delivering that kind of seamless experience is anything but.

                          It’s no longer just about offering the right content; it’s about removing every hurdle between a player and their purchase. Whether it’s a new skin, currency top-up, or unlocking extra content, the process has to feel natural, safe, and crucially, fast.

                          Speed, Security, and Staying Power

                          When payments work well, we barely notice them. However, when they don’t, they stand out for all the wrong reasons.

                          In gaming, timing is everything. A player sees an offer in the middle of a boss fight, and they want to buy. Yet if they’re forced to pause, enter details, confirm identities, or troubleshoot errors, the moment is lost. Consequently, the sale disappears, and the player might even give up altogether. 

                          Security remains essential, of course. As digital fraud evolves, the challenge is building protections without creating extra friction. Gamers expect secure transactions, but they’re not willing to wait around for them. 

                          This is where payments innovation is starting to shine. Tools like tokenised credentials, biometric authentication, and invisible fraud detection are helping strike that delicate balance between trust and convenience. 

                          For game developers, reducing payment friction doesn’t just boost conversions; it also builds trust. A smooth first transaction can turn a casual user into a loyal player. It sets the tone for the entire relationship.

                          Why Global Games Need Local Solutions

                          Gaming is a global industry, but payments are still intensely local. What works for a player in California might not suit someone in Cairo or Jakarta, and this is where games can stumble. 

                          Enter Xsolla, a game commerce company that’s quietly powering payment backbones of some of the biggest games worldwide. Xsolla has only one goal: to make it easy for players to pay for the games they love, wherever they are. 

                          Xsolla supports 1000+ local payment methods across more than 200 countries and geographies, from mobile wallets in Southeast Asia to cash-based options in Latin America. This means players can use the payment tools they already trust, without currency confusion, hidden fees, or extra friction.

                          For developers, it’s a game-changer. Xsolla handles regional taxes, compliance, and localization, making global reach feel simple. The result is that more players complete purchases, higher conversion rates, and greater long-term retention.

                          In a global gaming world, going local is no longer optional – it’s essential. 

                          Embedded Payments are the New Normal

                          Imagine spotting a new item in a game and buying it instantly, without ever leaving the screen. No redirects, no passwords, no second devices, just one click and it’s yours. This is the point of embedded payments, and it’s quickly becoming the gold standard.

                          Rather than treating payments as something which only happens outside the game, developers are increasingly building them right into the experience. Whether that’s a virtual wallet, an in-game currency, or a checkout button inside the character menu, the goal is still the same: to make the payment feel like part of the gameplay.

                          It’s not just about a better experience for players; it also unlocks new possibilities for game economies. Players can trade items, gift content, or top up in real time, without ever breaking immersion.

                          Even more complex technologies like blockchain and NFTs are starting to be embedded in this way. Platforms like Immutable, for example, are working to make digital asset ownership feel as simple as buying a power-up, no crypto know-how required.

                          Web Shops: Gaming’s Direct Line to Players

                          A growing number of game publishers are launching web shops – standalone sites where players can buy in-game currency, cosmetics, or exclusive offers directly, outside traditional app or platform stores.

                          Why? It’s partly about revenue. Many major platforms can charge up to 30% in fees, but developers can offer better prices and keep more of the profits. 

                          It’s also about control. Web shops allow for tailored promotions, local pricing, loyalty rewards, and a wider choice of payment methods – all without platform restrictions. But the experience still matters: web shops must be fast, secure, and mobile-friendly to meet modern expectations. 

                          As regulations evolve, expect web shops to become a key part of the payment strategy – quietly reshaping how games are monetized beyond the app store.

                          The Future of Payments

                          Gaming is no longer just about graphics, storylines, or even community. It’s also about experience and that includes how players pay. Get the payment experience right, and you gain more than just revenue. You gain loyalty, trust, and longevity. Get it wrong and players won’t wait around for you to fix it.

                          Convenience isn’t just king, it’s the kingdom. In gaming, it might just be the most powerful weapon of all. 

                          Learn more at xsolla.com

                          • Digital Payments
                          • Embedded Finance

                          Author

                          John Philips

                          Published

                          12 December 2025

                          Estimated Read time

                          6Mins

                          John Philips, EMEA General Manager at FloQast, on why the secret to happier, more efficient accountants is collaborating with AI – not just using it for menial tasks

                          AI is on everyone’s lips right now. But for teams in small- to mid-sized organisations, it can be hard to know how to practically benefit from this huge, potentially world-changing technology. In some ways its benefits are clear and obvious. Processing information at previously unheard-of speeds, automating menial tasks, and removing the need for complex hard-coding from so many of these processes. But in others, it can be hard to channel your usage. Not just feeding your GPT of choice a bunch of scattergun tasks, but truly harnessing the capabilities of artificial intelligence to transform your work.

                          With that in mind, we’ve been working on research into this exact issue. In our latest report, The Journey to AI Collaboration, produced in partnership with the University of Georgia, we’ve found that it’s the accountants who actively work and collaborate with AI, rather than simply using it for menial tasks, who see real gains. 

                          AI – Good for People, Good for Business

                          In this case, we’re defining ‘collaboration’ as ‘actively working with AI in intentional ways to achieve specific tasks and product deliverables related to accounting.’ And by ‘gains’, I don’t just mean what appears at the bottom of their organisations’ balance sheets. I mean benefits that can be seen in the lives of the accountants themselves. They sleep better, feel less burnt out, and report stronger satisfaction with their work. 

                          For example, when scored on a ‘burnout scale’ from one to 100, AI collaborators registered only 17.5 compared to non-AI-users on 21.6. Likewise, a majority (52%) of AI collaborators reported feeling well-rested from their sleep, compared to only 18% of non-AI users. 

                          Our previous research has shown organisations that improve their employees’ quality of working life and work-life balance tend to see better performance, which in turn supports growth. It’s all a virtuous cycle. So, as companies invest in their stance, they need to ensure it’s based on collaboration, rather than treating it like any other software solution.

                          What’s more, accountants and CFOs who collaborate with artificial intelligence are more likely to report being proactive, staying engaged, and having a valuable voice in their roles. They are almost twice as likely to make choices that impact their organisation’s performance and make suggestions for achieving strategic objectives. They are also more likely to have a valuable voice in strategic direction.

                          A Barn Door to Aim for

                          Only 5–6% of accountants and CFOs have meaningfully integrated AI into their work – yet those are the ones who see the kind of benefits described above. Clearly, this is a bit of a barn door to aim for: the vast majority of accountants aren’t yet collaborating in a truly valuable way with this technology.

                          This doesn’t mean AI is a foreign concept in accounting – quite the opposite. We found that 76% of respondents had used it at work. In other words, at the most basic level, it is already well bedded into our industry. But it’s that ‘meaningfully’ word that makes the difference. ‘Using’ AI covers everything from asking it to write or edit an email, to uploading data and asking a non-company-sanctioned generative AI tool to create a summary.

                          Of that 76%, less than 10 percent say AI has become integral to their work. Crossing the boundary into integral collaboration rather than simply using a tool requires a qualitatively different approach. It means being intentional and specific about what you’re trying to achieve and should result in being able to complete your work more efficiently – not just differently – with that AI assistance.

                          Company-Wide Benefits of AI

                          AI collaboration benefits accountants, but it also transforms entire organisations. Employee retention sits at 59% for ‘AI collaborators’ – companies that fold AI into their processes as a partner, rather than an endpoint solution. In general, we found that organisations that support collaboration do better at keeping their high-value staff, have more trust in the results AI models produce, and a clearer vision for the future.

                          For instance, we asked respondents to indicate their agreement with five statements on the extent to which their work and profession were important to them and their sense of self. Turning those results into a score out of 100, we found that AI collaborators hit a whopping 83, compared to non-AI users on 62. This seems to indicate a positive feedback loop between intelligent, collaborative use of artificial intelligece and a strong sense of identity with the accounting profession.

                          Organisations that support accountant-AI collaboration also see increased productivity. Accountants who collaborate with AI are more likely to report that they have sufficient time to do their work (56%). Accountants in AI-forward organisations also report a lower sense of time pressure (10 points lower) than accountants who use it in a non-integrated way or accountants who do not use AI. These benefits of AI collaboration also help the CFO by making the accounting function easier to operate and freeing up accountants’ time and energy for more strategic tasks.

                          A Leadership Lag

                          Despite the benefits, there are significant barriers to building effective accountant-AI teams. Most accountants and CFOs do not feel prepared for the transition to AI collaboration, and only a small percentage have a complete vision for the role of artificial intelligence in accounting. While AI’s potential is huge, most leaders don’t have
a plan – only 16% of CFOs have a vision for how it will transform accounting in their organisation.

                          Realising the potential of AI collaboration in accounting starts with two steps with which accountants should be familiar. First, organisations need to proactively define roles and responsibilities in relation to AI. Then, with that clarity in place, they need to work on a collaborative, human-AI team tasked with accomplishing certain shared objectives.

                          It’s also crucial to work on growing employees’ trust in artificial intelligence. Knowing the roles that AI is designed to play and understanding your role relative to AI is just as important as knowing how your role connects with the role of a co-worker. Accountants who are actively collaborating with AI are also more likely to view it as auditable – which requires a clear sense of what AI is supposed to do and how it should go about those tasks. Likewise, collaborators are 25 points more likely to view AI as explainable – feeling able to explain how it does what it does.

                          Making the Most of the New World

                          The bottom line of these findings is simple: accountants have made the first move in starting to use AI day-to-day, but the next step is to harness its full abilities in a truly collaborative way. It’s crucial to fold artificial intelligence into accounting processes as a key player, not a standalone tool, fostering greater understanding among employees of who’s responsible for it, what its goals are, how it performs its tasks, and what its goals should be. With that kind of on-boarding, accountants and their companies alike will benefit – unlocking greater efficiency, improved job satisfaction, better work-life balance, and stronger growth.

                          Learn more at floqast.com

                          • Artificial Intelligence in FinTech

                          Author

                          Peter Daunton

                          Published

                          Estimated Read time

                          4Mins

                          Peter Daunton, Chief Product Officer at Sokin, on why embedded B2B banking has flown under the radar and why that’s about to change

                          CFOs are discovering that embedded finance isn’t a feature upgrade, it’s an economic engine. The companies embedding payments, foreign exchange, and financial operations into their platforms aren’t just smoothing workflows. They’re turning cost centres into direct revenue sources. In B2B, where transaction volumes and values dwarf consumer markets, the opportunity is measured in basis points that add up to millions.

                          The Fragmented Finance Problem

                          Modern B2B commerce runs on surprisingly fragmented financial infrastructure. A typical platform operator juggles multiple payment processors, separate FX providers, standalone reconciliation tools, and disconnected reporting systems. Each integration point adds cost in vendor fees, manual processing, and error correction. More critically, fragmentation destroys visibility. When financial data lives across siloed systems, CFOs can’t see real-time transaction flows. It’s tough to understand true unit economics, or identify margin leakage until month-end reports surface it.

                          This complexity has historically been dismissed as “the cost of doing business.” But as platforms have matured and competitive pressure has intensified, CFOs are asking harder questions. Why are we paying multiple vendors to move the same money? Why does reconciliation require a team of three people? And most pointedly: Why are we treating financial flows as overhead when they could be revenue?

                          Automation Unlocks the Business Case

                          Embedding financial capabilities consolidates fragmented workflows into a single operational layer. The immediate benefit of this is cost reduction. Platforms replacing point solutions with embedded infrastructure typically see reductions in vendor fees. Furthermore, automation of reconciliation and reporting can eliminate entire FTE allocations. Transaction error rates drop dramatically when money movement, FX conversion, and ledger updates happen in a single system rather than requiring manual data shuttling between platforms.

                          But cost savings, while compelling, are just the entry point. The strategic opportunity emerges when platforms recognise they’re not just using financial infrastructure, they’re controlling it. And control of financial rails means control of monetisation.

                          From Cost Reduction to Revenue Generation

                          When a B2B platform embeds payment processing, cross-border transfers, or working capital financing, something fundamental shifts: financial operations become a P&L line. The platform captures value at every transaction touchpoint.

                          Payment acceptance generates processing margin, business cards generate interchange, foreign exchange generates spread; all direct revenue that previously went to external processors.

                          Beyond transaction fees, embedded finance enables new revenue models. Float on customer balances generates interest income. Automated reconciliation and real-time reporting become premium features. Transaction data – properly anonymised and aggregated – provides market intelligence that’s monetisable through analytics products. Platforms with deep payment data can even offer embedded lending, using transaction history as underwriting data to extend working capital financing at attractive rates.

                          Why CFOs are Driving the Conversation

                          This economic reality explains why embedded finance discussions have migrated from IT roadmaps to boardroom strategy sessions. CFOs evaluating these integrations aren’t asking “does this improve user experience?”, though it does. They’re asking: “What’s the payback period? How much revenue per transaction? What’s the impact on unit economics?”

                          The answers are increasingly favourable. Embedded finance implementations in B2B typically show ROI within 18-24 months, faster than most enterprise software deployments and with better margin profiles. For high-volume platforms, payback can be measured in quarters.

                          More strategically, CFOs recognise that embedded finance fundamentally changes competitive positioning. A platform that can offer seamless cross-border payments, instant settlement, and integrated reconciliation isn’t just improving automation for the business, it’s also driving more predictable cash flow on repayments for suppliers or enabling market leading payment terms to customers. And in B2B markets where customer acquisition costs are high and sales cycles are long, retention economics matter enormously.

                          The Infrastructure Question

                          None of this works if the underlying infrastructure is fragile. B2B transactions involve larger values, more complex approval workflows, and stringent regulatory requirements. Platforms can’t afford the checkout failures or compliance gaps that might be tolerable in consumer contexts.

                          This is why successful B2B embedded finance implementations treat infrastructure as a first-order concern, not an afterthought. They’re built on banking-grade rails with redundancy, real-time monitoring, and automated compliance checks. When a $2M cross-border payment needs to clear in 24 hours across multiple regulatory jurisdictions, the system either works flawlessly or it destroys customer trust.

                          The platforms winning in embedded B2B finance understand this. They’re not bolting payments onto existing workflows, they’re architecting financial operations as core platform capabilities, with the reliability and visibility their customers’ CFOs demand.

                          The Strategic Imperative

                          Embedded finance in B2B has moved beyond experimentation. The unit economics are proven, the technology has matured, and customer expectations have shifted. Businesses that treat financial capabilities as strategic infrastructure rather than vendor-managed utilities are seeing both cost structures and revenue models transform.

                          For CFOs, the question is no longer whether to embed finance, it’s how quickly they can make it a profit centre.

                          Learn more at sokin.com

                          • Digital Payments
                          • Embedded Finance

                          Author

                          Lyall Cresswell

                          Published

                          10 December 2025

                          Estimated Read time

                          6Mins

                          Lyall Cresswell, Founder & CEO, TEG on how integrated payments are unlocking growth for SMEs in the UK’s £170bn transport and logistics sector

                          Consumer fintech is booming. From instant payments to embedded finance, digital innovation has transformed how individuals manage money, access credit, and transact with businesses. Yet in B2B markets, embedded finance adoption remains stubbornly low. The question is: why?

                          Instant settlement alone doesn’t solve this problem. But when combined with embedded compliance it transforms how fragmented B2B markets operate. This infrastructure enables large enterprises to scale their supplier bases from dozens to thousands while giving SME carriers immediate access to working capital, all without personal financial risk.

                          The answer becomes clear when you examine the UK’s £170 billion logistics sector. Employing over 8% of the workforce, it’s a low margin industry ripe for financial innovation, but in reality, highly fragmented with many SME operators. Large operators at the top of the supply chain are simply unable to verify, onboard and manage large networks of suppliers through traditional methods. This creates delays and friction.  I’ve watched this dynamic play out over 25 years building TEG. Smaller operators tell us the same story: ‘I need money now, not next month’. Cash flow isn’t just an inconvenience, it’s existential.

                          The barrier isn’t payment speed alone. It’s trust at scale. Integrated payment networks, combining instant settlement with embedded compliance and verification, create the infrastructure that enables these fragmented markets to operate differently.

                          Large enterprises don’t limit themselves to a small pool of known suppliers by choice. They do so because onboarding and compliance costs make broader collaboration prohibitively expensive. Each new supplier relationship requires verification of insurance, licensing, VAT status, and payment setup. This friction doesn’t just slow things down, it fundamentally constrains supply chains.

                          Recent research we conducted across six leading UK third party logistics providers (3PLs) revealed the scale of this challenge: 83% audit fewer than 10% of their subcontractors annually, and only 33% use eSourcing technology. These aren’t signs of negligence. They’re symptoms of a system where verification and onboarding are simply too resource intensive to scale.

                          Traditional payment solutions, from early payment programmes to invoice finance, address cash flow symptoms but miss the fundamental barrier. Without infrastructure to verify and onboard new trading partners confidently, enterprises remain trapped working with familiar suppliers even when capacity constraints or cost pressures demand alternatives. Meanwhile, SME carriers aren’t just delayed in payment, they’re excluded from opportunities entirely.

                          This dynamic turns large enterprises into inadvertent gatekeepers, not by choice, but because they lack the infrastructure to safely open their networks. The result is a continuous loop: constrained supplier choice for buyers, limited market access for SMEs, and a fragmented sector unable to collaborate efficiently. The solution requires rethinking the relationship between payments and compliance entirely. Integrated payment networks, embedding compliance verification directly into payment workflows, solve both problems simultaneously.

                          Building Trust Infrastructure Through Verified Payment Networks

                          The breakthrough comes when payment infrastructure and compliance verification integrate seamlessly. At TEG, we’ve built this through SmartPay’s integration with Trustd, our digital identity verification platform, embedding compliance directly into payment workflows.

                          The model is straightforward: carriers are verified once through real time checks of KYC, AML, VAT status, operating licences, and insurance credentials. Once verified, they can transact across the entire network. This “verify once, transact everywhere” approach removes the need for repeated onboarding across different customers or business units.

                          The operational impact has been significant: 90% faster invoice processing, 80% fewer supplier queries, with over 1 million invoices paid through the platform in 2025. By year end, the TEG rollout will connect 2,500 customers with 7,500 suppliers, demonstrating adoption at scale across the logistics sector.

                          But the real transformation lies in shifting from credit based to transaction based finance models. Many carriers have historically relied on credit cards and overdrafts to bridge cash flow gaps, costly stopgaps that eat into already thin margins. Traditional invoice finance excludes many SMEs because lenders must manage risk without transparency, often retaining portions of invoice value and demanding personal guarantees.

                          SmartPay changes this by leveraging verified transaction data to provide instant, non recourse access to full invoice value minus fees. No retention, no personal guarantees, simply immediate working capital based on actual trading activity. This unlocks early payment facilities for carriers who previously had no alternative to expensive short term credit.

                          This creates powerful network effects. As more carriers join the verified payment network, enterprises gain confidence to work with a broader supplier base. More suppliers mean better capacity, more competitive pricing, and greater resilience. For SME carriers, verified status opens doors to opportunities previously out of reach.

                          Verification Infrastructure and Working Capital Access

                          It’s crucial to understand that verified payment networks operate on two distinct but complementary tracks.

                          Unlocking working capital addresses the SME challenge. In a sector where margins run as low as 2% and payment cycles stretch to 90 days, liquidity is existential. Without working capital, SMEs can’t hire staff, expand capacity, or invest in growth. They’re forced to choose clients based on payment terms rather than strategic fit.

                          Instant settlement delivers immediate access to working capital for wages, fuel, and expansion. The UK Small Business Plan identifies late payments as one of the biggest barriers to SME growth—instant settlement directly addresses this constraint, enabling carriers to accept larger contracts and scale their operations.

                          These two tracks reinforce each other. Enterprises gain access to a larger, verified supplier base. SMEs gain both market access and the working capital to serve those opportunities effectively. The result is a more efficient, collaborative market structure.

                          The Fragmented Market Opportunity

                          While logistics provides the proving ground, this model applies to any fragmented B2B sector where compliance complexity limits collaboration. Construction, facilities management, and professional services all face similar dynamics: thin margins, extended payment terms, high onboarding friction, and SME suppliers excluded from opportunities.

                          The key requirement is neutral, collaborative infrastructure that provides a standardised verification model without competing with participants. In sectors where supplier qualification is straightforward, instant payment alone may suffice. But in regulated industries with complex credentialing requirements, verified payment networks become essential infrastructure.

                          The value isn’t in handling compliance alone. It’s in creating a trusted, shared layer that all participants can use without concern that the platform itself will compete with them.

                          The transformation only occurs when you solve both problems simultaneously: enterprises need neutral, trusted verification infrastructure to expand their networks confidently, and SMEs need instant settlement to operate sustainably within those networks. In fragmented markets where no single player can create industry wide standards, this shared infrastructure becomes essential. Address one without the other, and you’ve solved neither.

                          Trusted Collaboration at Scale

                          The narrative around embedded B2B finance needs reframing. It’s not about faster payments. It’s about removing the friction that prevents enterprises and suppliers from working together effectively—it’s about enabling trusted collaboration at scale. True transformation happens when payment infrastructure, compliance verification, and transaction transparency operate seamlessly together to unlock cash flow and expand market access for both sides.

                          Across TEG’s network of over 9,000 logistics businesses, we’ve seen how verified payment networks can reshape fragmented markets. Large enterprises can finally collaborate with the breadth of suppliers their operations demand. SME carriers can access opportunities and capital previously out of reach. The entire sector operates more efficiently.

                          This is the path to unlocking B2B embedded finance adoption: build infrastructure that solves the whole problem. Verify once, transact everywhere, and unlock cashflow. When enterprises can open their networks confidently and SMEs can operate sustainably within them, you create the conditions for genuine market transformation.

                          The technology exists. The business case is proven. We’ve demonstrated it works at scale. The question now is which sectors will move first to build the trust infrastructure their markets desperately need.

                          Learn more at teg.tech

                          • Digital Payments
                          • Embedded Finance

                          Author

                          Alex Mifsud

                          Published

                          9 December 2025

                          Estimated Read time

                          5Mins

                          Alex Mifsud, CEO and co-founder of Weavr.io, on how embedded finance is the perfect solution for employee retention

                          To earn loyalty, stop making your team act like the company’s lender. In the war for talent, few things corrode trust faster than asking employees to bankroll the business they work for. Across the UK and Europe, 42 percent of employees say waiting for expenses harms their financial health, while 36 percent say it affects their mental wellbeing. Behind those percentages are real frustrations: professionals dipping into overdrafts to cover hotel bills, freelancers waiting weeks for per diems that never quite arrive, and finance teams juggling hundreds of delayed claims.

                          In Twisted Sifter recently, one worker described waiting a month for reimbursement of a modest $268 business expense – only to receive $84 and “lose all motivation to go the extra mile”. It’s a small story, but it captures a bigger truth: when employees feel the system works against them, they stop believing in the company that designed it.

                          The Retention Cost of Reimbursement

                          Most businesses don’t connect their expense process with employee retention, yet the link is clear. Work-related stress costs the UK economy £28 billion a year, largely through lost productivity and attrition. Meanwhile, research shows that happier employees drive better results: “Company profits are much higher – and turnover is much lower – when employees feel positive and supported”.

                          Reimbursement systems do the opposite. They impose a financial burden on staff, add administrative friction, and create daily reminders that the company’s systems aren’t designed around their needs. According to HR News, UK employees collectively front an estimated £51 billion a year in work-related expenses before being repaid.

                          The fallout is predictable: financial anxiety, or even just annoyance, leads to disengagement; disengagement leads to turnover. Replacing a skilled employee can cost up to 1.5 times their annual salary once hiring, onboarding and lost productivity are included. That’s a high price to pay for outdated workflows.

                          Where SaaS Platforms Meet the Problem

                          Many purpose-built expenses management SaaS platforms have closed the gap and now offer end-to-end expense experiences, but the opportunity extends far beyond the category itself. HR systems that handle onboarding and travel approvals, accounting platforms that oversee budgets, even workforce and travel platforms that coordinate trips all touch the same underlying workflow;  employees spending on behalf of the business.

                          A common pattern still emerges when employees need to travel for work, for example. They request trip approvals in one tool, capture receipts in another, and submit claims through a third. Finance teams then reconcile spend manually. Even where processes are digital, they often live in separate tools;  approvals in one place, receipt capture in another, reconciliation elsewhere.

                          This fragmentation limits what SaaS platforms can achieve. They automate forms and digitise reports, but the process still ends with an employee waiting for a reimbursement that shouldn’t exist. For product and strategy leaders, this is an opportunity hiding in plain sight: the chance to redesign expense workflows around real-time spending rather than post-hoc repayment.

                          Business Travel: The Perfect Illustration

                          Corporate travel exposes this inefficiency in its rawest form. Most travel platforms monetise only pre-trip spend – flights, hotels and transfers – leaving meals, taxis and incidentals out of their reach. Yet by 2027, global business travel spending is forecast to reach $1.8 trillion, with a significant share of that occurring during the trip itself.

                          It’s also where employees feel the pain most acutely. Travellers frequently use personal cards abroad, juggle currency conversions, photograph receipts on their phones, and then upload them into another system days later. Managers approve after the fact; finance reconciles even later. Three tools, three teams, one frustrated traveller.

                          Now imagine that flow redesigned. Pre-approved budgets are assigned before travel, spend happens seamlessly during the trip, and reconciliation is automatic. Employees never pay out of pocket. Finance teams see every transaction as it occurs. The SaaS platform at the centre of this becomes indispensable – not because it automates forms, but because it eliminates friction. For the traveller, it means simplicity. For finance, control. And for the platform, visibility into the full journey, richer data on spend patterns, and incremental revenue from card transactions that flow through its ecosystem.

                          The Art of The Possible

                          This isn’t about layering FinTech complexity onto software. It’s about simplifying the experience by unifying what should never have been separate: approval, payment and reconciliation. We’ve already seen how embedded finance reshapes customer experience in other industries – e-commerce, ride-hailing, even healthcare. The same logic applies here: when money moves at the speed of the workflow, satisfaction follows.

                          For SaaS platforms, the implication is profound. The closer a product gets to the flow of funds, the deeper its integration into the customer’s operations. That’s not just a revenue opportunity;  it’s a retention strategy. Bain & Company describes embedded finance capabilities as “a way for software platforms to become systemically irreplaceable”. Expense management may be where that principle finds its purest expression. Few workflows touch as many people, as often, or with as much potential frustration. Fixing it is not just good UX; it’s good economics.

                          For SaaS leaders: A Reframing, Not a Roadmap

                          There’s no single architecture for the future of expenses. Each platform,  whether in travel, HR or accounting,  will interpret it differently. The point is to stop digitising the reimbursement process and start designing for prevention, where policy, payment and visibility converge. In practice, that means mapping friction, owning the journey, and measuring how faster, stress-free processes impact satisfaction and retention. When your platform participates directly in how money moves, your relationship with the customer becomes foundational, not functional. The art of the possible here isn’t about FinTech sophistication. It’s about empathy in design.

                          Retention and Reputation are Built, not Bought

                          Retention isn’t earned through perks or slogans; it’s built into experiences that show respect for people’s time and money. Expense reimbursement may seem trivial, but it’s a daily ritual that shapes how employees feel about their work, and how customers feel about the tools they use. A recent survey found that employees left out of pocket by slow expense reimbursements are significantly less likely to recommend their employer to job-seekers. That makes expense friction not only a retention issue, but a reputational one.

                          If the last decade of SaaS was about automating the back office, the next will be about humanising it. When expense workflows are rewired so that approval, payment and reconciliation flow as one, everyone gains: employees, employers and the platforms that serve them. Because when you stop making people act like the company’s lender, they start acting like its advocate.

                          Learn more at weavr.io

                          • Embedded Finance
                          • Neobanking

                          Author

                          Santo Orlando

                          Published

                          8 December 2025

                          Estimated Read time

                          5Mins

                          Santo Orlando, Practice Director – App, Data and AI Services at Insight, on how your organisation can level up with Agentic AI

                          By now, most of us have heard of Generative AI. Many businesses have already adopted the technology for tasks like customer service, code generation and content creation. Generative AI, however, is only the start; we’re only scratching the surface of the potential that AI has to offer

                          Enter Agentic AI

                          Unlike Generative AI, which relies on human input and prompts, Agentic AI can act autonomously to fulfil complex tasks without human intervention. As a result, nearly 45% of business leaders think Agentic AI will outpace Generative AI in terms of impact, and more than 90% expect to adopt it even faster than they did with generative AI. However, despite its promise, our joint understanding of Agentic AI – and how to implement it – is still very much in its infancy.

                          So, where do you start? To kickstart your Agentic AI journey here are five fundamental steps to consider. 

                          Generative AI vs Agentic AI

                          If Generative AI is like having a personal assistant, supporting you one-on-one to speed up your tasks, then Agentic AI is more like having a dedicated team of smart, individual coworkers who can take initiative and get things done across your business – without needing constant oversight. 

                          One powerful example of this in action is in sales. With Agentic AI, organisations are able to receive real-time insights during discovery calls. The AI ‘agents’ allow sales reps to respond with timely, relevant information, helping them build trust, operate faster and close deals more effectively. 

                          By collecting and analysing data from across teams, agents can uncover patterns, translate complex metrics into actionable strategies and even highlight opportunities that might otherwise be unintentionally overlooked. In some early implementations, sales teams have reported saving five to ten hours per rep each month – adding up to thousands of hours redirected toward deeper customer engagement.

                          The one-to-one relationship we’ve grown accustomed to with Generative AI has evolved into the one-to-many dynamic of Agentic AI, which is capable of handling tasks for multiple users and automating entire business processes. Even more impressively, agents can make decisions, control data and take actions on their own. A capability that can seem daunting without a clear understanding of how it works.

                          That’s why businesses need to start small, and here are a few practical steps to get going quicklyand wisely with agentic AI. 

                          Step 1: Getting your data ready

                          Agentic AI is the logical progression for organisations already exploring generative tools. However, the data needs to be in an optimal condition – clean, organised and secure – before autonomous agents can be deployed effectively.

                          As such, eliminating redundant, outdated and trivial (ROT) data is vital. Without removing ROT, agents may rely on obsolete information, leading to inaccurate or misleading outputs. For example, this could happen if a company deploys an HR chatbot that’s connected to outdated data sources. If an employee were to ask about their 2025 benefits, the chatbot might pull information from as far back as 2017, resulting in confusion and misinformation.

                          Proper file labelling, standardised document practices and use of version histories in place of multiple saved versions helps to ensure agents access only the most relevant and accurate information.

                          Step 2: Start with low-risk cases 

                          Agents work on a transactional basis, charging for each operation, which can quickly add up. As such, it’s wise to experiment with simple, low-stakes applications first. This approach allows for quicker deployment and demonstrates immediate value to the business without significant costs or risks.

                          One example could be using an agent to assess sentiment in social media responses following a product launch. This can offer real-time feedback on public perception and inform messaging strategies. Other low-risk use cases include generating reactive press releases and monitoring competitor websites. Additionally, prioritising automation of routine tasks, especially those involving platforms like Salesforce, SharePoint, or Microsoft 365, allows teams to maximise impact without costly system overhauls. 

                          Overall, organisations need to be willing to fail fast and expect failure. It won’t be perfect from the start. However, an experimental pilot approach helps to efficiently refine AI agents, reducing the risk of costly mistakes and making sure that only effective solutions are scaled up.

                          Step 3: Create a single source of truth

                          Establishing a dedicated, cross-functional team to explore agentic AI use cases helps prevent siloed adoption and supports enterprise-wide visibility. This team should span as much of the organisation as possible and include representatives from departments such as marketing, finance and technical solutions.

                          Collaborative workshops can then act as a forum to identify key processes that would benefit from autonomous capabilities and help businesses align potential applications with specific departmental objectives and broader business goals.

                          Step 4: Learn, learn and learn

                          Many companies underestimated the importance of training and governance with Generative AI – and Agentic AI is no different. Organisations need to establish clear governance to define how AI agents should and shouldn’t be used, covering not just technical implications, but HR, compliance and risk concerns as well.

                          Equally, businesses and those employed must understand Agentic AI’s full functionality to get the most out of it. Like with almost all technical training, AI education cannot be viewed as a one-time ‘tick-box’ exercise. Ongoing learning is necessary to keep pace with new capabilities and best practices.

                          For example, consider what’s already emerging, like security agents that automate high-volume threat protection and identity management tasks; sales agents that find leads, reach out to customers and set up meetings; and reasoning agents that transform vast amounts of data into strategic business insights.   

                          Step 5: Reviewing ROI

                          Enthusiasm around Agentic AI is high. But before organisations dive in headfirst, it’s important they first define success. Technology can’t be the solution if there is uncertainty surrounding the goal. Successful deployment requires a clear definition of the problem organisations are looking to solve and knowledge of how to align the solution with measurable business value. Without this, initiatives risk stalling at the experimental stage.

                          Key performance indicators should also be identified early. These may include increased productivity, time savings, cost reduction or improved decision-making. Establishing these benchmarks and taking a data-driven approach ensures that AI initiatives align with business goals and demonstrate tangible benefits to stakeholders.

                          Moving forward

                          The process of switching to Agentic AI is about changing how businesses handle everyday problems with wide ranging effects, not just about using cutting edge technology. Iteration and learning along the way, as well as deliberate, measured adoption are the keys to increasing value. It’s simple. Success with AI starts with small, straightforward actions and use cases.

                          Learn more at insight.com

                          • Data & AI
                          • Digital Strategy

                          Author

                          Abdenour Bezzouh

                          Published

                          Estimated Read time

                          4Mins

                          Abdenour Bezzouh, Chief Technology Officer at myPOS on how AI is revolutionising FinTech from reactive to proactive solutions

                          AI is significantly changing the way small and medium-sized businesses manage their finances. In the UK, the number of SMEs adopting AI tools has increased 32-fold between 2022 and 2024. Meanwhile, average spending on AI tools has risen nearly sixfold over the same period. Once seen purely as a tool for automation, AI now plays a much more proactive role. It helps businesses anticipate cash-flow gaps, prevent fraud, and deliver more personalised customer experiences. 

                          As the technology becomes more embedded, one question looms large. How do we ensure that automation strengthens, rather than replaces, the human relationships at the core of financial services? The answer lies in designing AI to improve human decision-making. Forward-thinking FinTechs are leveraging AI to build trust, enable inclusion, and prevent issues before they ever reach the customer. This shift, from reactive problem-solving to proactive service delivery, represents one of the most significant evolutions in digital finance.

                          At myPOS, we’re focused on designing AI to augment human decision-making, enabling our teams to intervene where empathy, context, or judgement is needed. For example, our AI flags unusual transactions in real-time. But instead of automatically blocking them, it alerts our human teams, who can access the situation and act with the right context.

                          From Reactive to Proactive: The New Standard in Trust  

                          For decades, financial services have operated reactively: a transaction failed, then a customer called; fraud occurred, then an investigation began. AI makes it possible to reverse that logic. By analysing transactions in real time, algorithms can detect unusual patterns that may signal fraud or technical disruptions. This alllows companies to act before the customer even notices a problem. 

                          This proactive approach is becoming central to trust in the FinTech industry, both in the UK and globally. It prevents disruptions, reduces disputes, and allows businesses to run more smoothly. The same principle now applies to onboarding, where document verification and compliance checks that once took days can now be completed in minutes with AI-assisted tools. When technology removes unnecessary friction, users feel more confident that their financial services will ‘just work’. 

                          Augmenting, Not replacing, Human Judgement  

                          Although AI can process information faster and with more accuracy than any human, it lacks emotional intelligence. In fact, a survey found that nearly 70% of UK consumers say AI chatbots fail to understand emotional cues. While AI can identify anomalies in data, it cannot detect the frustration in a customer’s voice or the urgency behind a small business owner’s request. The future of FinTech clearly depends on improving the speed and accuracy of human decision-making.

                          A common mistake organisations make when deploying AI is focusing on the wrong metrics. Success is often measured solely by ‘deflection rates’, or whether a bot resolves an issue without human intervention. This approach overlooks the true indicators of quality service: first-contact resolution, customer trust, and the likelihood that users will recommend the service. Prioritising these outcomes leads to AI supporting meaningful experiences rather than just reducing manual workload.

                          Ethics and Transparency  

                          As AI becomes a key driver of financial decisions, ethical responsibility must be treated as a core design requirement. The principles of fairness, explainability, and accountability need to underpin every aspect of an AI system, from data collection to deployment.

                          For example, transparent decision-making allows customers to understand why a transaction was flagged or a decision made, turning AI into a trust-building tool rather than a black box. At myPOS, for example, every on-device decision is explained and complimented by a ‘request human review’ button. By clicking it, merchants are redirected to a live analyst within two business hours. Crucially, human oversight is needed to interpret AI outputs, make contextual judgments, and intervene when automated systems may misclassify or misrepresent a user’s situation. Ultimately, AI ethics is foundational to trust, which only humans can fully maintain.

                          A Smarter Relationship with Customers

                          AI’s predictive capabilities are also changing the fundamental nature of customer relationships. Instead of responding to problems, FinTechs can now anticipate them: identifying cash-flow gaps before they occur, suggesting actions to improve financial stability, or alerting users to potential risks early.

                          This proactive intelligence significantly enhances trust, shifting interactions from transactional to consultative. It empowers small and medium-sized businesses to make data-driven decisions that once required dedicated financial teams, while freeing human representatives to focus on higher-value conversations – those that demand empathy, judgment, and nuanced understanding.

                          Personal, Prediction, and Human  

                          The next phase of FinTech innovation will be defined by how seamlessly AI blends automation with personalisation. We’re already seeing the rise of conversational commerce, embedded payments, and tailored financial insights delivered directly at the point of sale. As these capabilities expand, so will expectations around transparency, accountability, and empathy in how AI operates.

                          The future of FinTech is smarter, faster and human centric. AI will continue to handle the repetitive and reactive, but people will remain essential for what truly matters: understanding, trust, and connection. When businesses design AI around these core values – fairness, explainability, and empathy – the technology will strengthen the human relationships that keep the financial world moving.

                          Learn more at mypos.com

                          • Artificial Intelligence in FinTech
                          • Digital Payments
                          • Embedded Finance

                          Author

                          Alessandro De Leonardis

                          Published

                          5 December 2025

                          Estimated Read time

                          6Mins

                          From banking to alternative funds, modular architecture is the missing link for effective adoption of artificial intelligence, writes Alessandro De Leonardis, CIO of Armundia Group

                          The global banking industry is approaching a strategic crossroads – one that will prove expensive for those who choose the wrong direction. Financial institutions stand to lose USD 170 billion in profits over the next decade if they do not adapt rapidly to the evolution of artificial intelligence, according to the McKinsey Global Banking Annual Review 2025. Yet the report’s most provocative insight isn’t about AI itself, but the infrastructure required to leverage it effectively.

                          Agentic AI has the potential to reshape banking at its foundations. Early adopters will strengthen long-term advantages, potentially boosting returns on tangible equity by up to four percentage points. On the other hand, laggards face structural declines in profitability. The difference between these outcomes won’t be determined by who adopts AI first, but who has the architectural foundations to implement it effectively. Increasingly, those foundations are modular.

                          From Generative to Agentic AI: Revolution not Evolution

                          To understand why architecture matters so deeply, we must distinguish between the two paradigms reshaping financial services.

                          Generative AI, the star of 2023-24, excels at creating content: automated reports, document summaries, customer-service response, and so on. It is powerful, but fundamentally reactive. GenAI requires human prompts and produces outputs that must still be reviewed and acted upon by humans.

                          Agentic AI represents a step-change. These systems combine autonomous reasoning, planning, and execution. They don’t only generate recommendations, they act on them. An Agentic AI system can autonomously manage an entire loan-approval workflow: collecting documents, verifying information, assessing creditworthiness, checking regulatory compliance, and making approval decisions, all without human involvement at each step.

                          The impact is already measurable. MIT Technology Review Insights found that 70% of banking leaders are implementing agentic AI through production deployments (16%) or pilot projects (52%). Deloitte reports early adopters achieving 30–50% cost reductions in specific workflows. McKinsey anticipates the emergence of a “disruptive agentic business model” within three to five years, with potential cost reductions of up to 70% in some categories. But the benefits are far from evenly accessible.

                          Why Monolithic Architecture are Incompatible with AI

                          The uncomfortable truth is that most banks are attempting to deploy twenty-first-century AI on twentieth-century infrastructure. And it doesn’t work.

                          Legacy systems still absorb around 60% of banks’ technology budgets, according to a 2024 Bloomberg Intelligence survey. These monolithic architectures were never designed for the rapid iteration, continuous integration, and granular governance demanded by AI deployment.

                          Monolithic systems require release cycles lasting months; AI models require continuous retraining and fine-tuning based on real-world performance. The mismatch is structural. Modern Agentic AI relies on orchestrating multiple specialised agents… One for data collection, another for risk evaluation, a third for decision execution. Monolithic architectures struggle to support this level of inter-system communication.

                          Governance is another barrier. AI systems require differentiated risk controls depending on the level of autonomy. A fully autonomous fraud-detection agent needs different guardrails than a customer-service chatbot. Monolithic systems offer all-or-nothing governance, not graduated controls.

                          Financial institutions cannot transform everything at once; they need incremental adoption. Starting with high-impact use cases, learning, then expanding. Monolithic architectures force “big-bang” transformations that almost never succeed.

                          This architectural misalignment explains why so many AI initiatives stall in pilot purgatory, never reaching production scale.

                          Modular Architecture as an Enabler of AI

                          Modular, service-based FinTech architecture solves these problems by design. Instead of monolithic platforms, modular systems are composed of independent, interoperable functional blocks connected via APIs. Each module can be developed, updated, or replaced without affecting the whole.

                          The key is the concept of the service: a module that does not expose standardised technical interfaces simply does not function. Services are the technical objects enabling interoperability:

                          • A compliance module exposes services for regulatory checks,
                          • A data-ingestion module exposes services for data collection and structuring,
                          • An Agentic AI module exposes services for executing autonomous workflows.

                          This architecture creates an ecosystem where each component has clear responsibilities and well-defined interfaces.

                          For AI deployment, this translates into concrete advantages. Banks are implementing Agentic AI systems into specific processes – KYC/AML screening, credit-memo generation, collections monitoring, intelligent communication routing – without rebuilding their entire stack. Service-based modularity allows AI agents to be activated on circumscribed workflows, with impact measured before expansion.

                          Because agents operate within discrete modules, failures remain contained. A malfunctioning fraud-detection agent does not propagate into customer-facing systems. This isolation allows institutions to experiment more boldly.

                          Service-based architectures also enable integration of best-of-breed AI solutions. One module may use Anthropic’s Claude for document analysis, another Google’s Gemini for customer interaction, a third proprietary models for highly specialised credit scoring. Monolithic systems lock institutions into single-vendor dependencies.

                          Different modules can carry different levels of AI autonomy, aligned with risk profiles and regulatory requirements: high autonomy for customer-service bots, human-in-the-loop supervision for lending decisions.

                          As McKinsey notes, the winners of this transformation will practise “precision over heft”- implementing AI surgically where it generates measurable bottom-line impact. Service-based modular architecture is the technical manifestation of such precision.

                          Techfin vs FinTech: When Architecture Comes First

                          There is a fundamental difference between starting from finance and adding technology, and starting from technology and specialising in finance.

                          In the first case, solutions are built top-down – gather functional requirements, then find the technology to satisfy them.

                          In the second, solutions are built bottom-up – design the architecture before the functional requirements, optimising for flexibility rather than feature completeness.

                          When designing wealth- and asset-management platforms – such as FundWatch or 360 FUNDS – this distinction becomes tangible. Being AI-ready does not mean adding an ‘AI layer’ on top of an existing platform. It means the modular architecture allows AI capabilities to be integrated precisely where needed.

                          Modularity operates along two dimensions:

                          • Process modules (compliance, analytics, reporting, client engagement) that can be activated independently;
                          • Target modules tailored for different market participants: custodians, asset servicers, alternative-fund managers, wealth advisers—each activating different module combinations.

                          AI governance is embedded in the architecture, not layered on top. A fully autonomous reconciliation agent operates under different guardrails than a semi-autonomous investment-recommendation agent—different approval workflows, audit trails, and supervision requirements.

                          This approach does not remove the need for transformation, but it changes its rhythm. Instead of three-year platform-replacement projects, institutions can transform progressively: start with a high-impact module, prove value, learn from deployment, scale outward.

                          The key managerial shift is conceptual: the question is no longer “When will our digital transformation be finished?” but “Which module do we activate this quarter, and what do we learn?”

                          The $170bn Question

                          McKinsey’s warning – USD 170 billion of potential profit erosion – is not inevitable. Avoiding it requires strategic decisions today about the technology architecture of tomorrow.

                          The institutions that will thrive are not necessarily the largest or the earliest adopters of AI. They will be those building modular infrastructures engineered for precision, capable of integrating AI surgically, experimenting rapidly, scaling intelligently, and governing rigorously.

                          They will recognise that AI is not merely a technological deployment, it is an architectural imperative. And they will understand the deeper truth: in the Agentic AI era, precision beats scale.

                          The question faced by every financial institution is not whether to adopt AI, but whether its architecture can support it. For most legacy systems built on monolithic foundations, the honest answer is no.

                          The modular imperative is clear. The question remains: are you building for yesterday’s challenges or tomorrow’s opportunities?

                          Find out more at armundia.com

                          • Artificial Intelligence in FinTech

                          Author

                          Dan Brightmore

                          Published

                          4 December 2025

                          Estimated Read time

                          10Mins

                          Chief Operating Officer Bhavna Saraf gives us the lowdown on the genesis of Quidkey and how it is leveraging APIs & AI to transform open banking networks into merchant-ready solutions driving higher conversion and borderless coverage with no-cost simple integration

                          Founded in early 2023, Quidkey has quickly established itself as a trusted provider of next-generation Account-to-account (A2A) payments. Also known as ‘Pay by bank’. Leveraging AI-powered bank prediction, instant settlement, and a streamlined user experience, Quidkey has created a bank-branded checkout system powered by Open Banking. It combines refunds, rewards, and real-time settlement bringing together cash flow, trust, and convenience for merchants. Its growth in the UK and EU is now being expanded to service Australia and the US corridors.

                          Chief Operating Officer Bhavna Saraf met CEO Rob Zeko and CTO Rabea Bader, Quidkey’s co-founders, at the end of her time with Santander. They were pitching Quidkey’s offering to top bank executives. Their vision was ambitious:

                          • Democratising access to bank products amongst its customers through a single channel
                          • Leveraging and monetising its API stack for payments
                          • Providing value add services making open banking usable for businesses

                          “I remember thinking it wasn’t a standard FinTech pitch,” recalls Bhavna. “It was a real infrastructure story that was additive and complimentary to all ecommerce ecosystem players, merchants, banks, PSPs and consumers. When I began figuring the next steps in my career, Rob reached out. The discussion evolved into a collaboration – the timing was serendipitous.

                          Rob believes A2A payments are the future of commerce, and merchants deserve simpler, faster and fairer ways to get paid. “We’ve built a model designed to scale responsibly,” he notes. “Bhavna brings the structure and operational depth to help us do just that.”

                          Rabea is responsible for technology and product at Quidkey. With a seasoned background in technology, he has developed the core engine driving Quidkey’s diverse solutions. These include bank-prediction algorithm, refund automation, and multi-currency settlement, through simple API integrations.

                          “Our aim is to make the technology invisible,” Rabea explains. “If it feels effortless for merchants, it means we’ve done the hard work well.”

                          Together, Rob and Rabea laid the foundation. Bhavna’s arrival added the operational layer needed to take Quidkey global.

                          FinTech Strategy spoke with Bhavna to learn more about her journey. And how her experience is driving Quidkey’s progression across the payments landscape…

                          Bhavna Saraf

                          Tell us about your approach to leadership at Quidkey… How do you reflect on what has been achieved during your time with the organisation?

                          Learning has always meant leaning into the unknown. It’s not just about a strategy, but a mindset. Taking on new business lines, exploring unfamiliar customer segments, getting closer to technology, or stepping into entirely new organisations. It’s important to look outside your comfort zone, because that’s where you find growth. Each pivot builds experience equity. The instinct to link problems with solutions, to adapt with nuance, and to lead effectively no matter the context.

                          It’s the same mindset that underpins my approach to leadership. That it’s not just about hierarchy but influence. Creating an environment where people feel trusted, empowered, and part of something larger than themselves. It’s important to build a feel-good factor where collaboration replaces control and purpose drives performance. Such a philosophy can shape teams and inspire peers. It has helped me forge strong connections across clients, colleagues and ecosystems alike.

                          What drives and inspires you?

                          At the core of my journey is a relentless drive to deliver progress. Time is money. And… Impossible is nothing. Those words capture my pragmatism and optimism. Qualities that have guided me from scaling trade finance at Citi, to launching digital propositions at Lloyds, to leading payments innovation and strategy at Santander UK. Each chapter has broadened my perspective and sharpened my instinct for where financial infrastructure is headed next. At Quidkey, I get to bring all I’ve learned from building at Citi Ventures to leading across banks and apply it where innovation and impact truly meet on a day-to-day basis.

                          Could you share how your extensive experience with the dynamics of payments across your career (Citi, Lloyds, SWIFT, Santander etc) have honed your skills in the space? How is it enabling you to drive positive change in the market through your role at Quidkey?

                          Across leadership roles at Citi, Lloyds, Santander and HSBC, I built and scaled businesses that fuse technology, finance, and innovation. Taking ideas from zero to one or propelling growth to the next level. The focus has consistently been on unlocking near-term value while shaping future-ready roadmaps aligned with market trends, regulatory change, and evolving customer needs.

                          Alongside my day job, at Citi, I first experienced entrepreneurship, as the founder of an intra-bank start-up within Citi Ventures’ D10X program. We raised funding, assembled a team and developed algorithms to match clients across the bank’s global network. The project advanced to Seed 2 funding, earning recognition from Citi’s Global TTS CEO and the Head of Citi Ventures.

                          I caught the founder’s bug. That experience showed me the power of turning an idea into reality. It taught me to balance innovation, risk, and speed. And gave me a deep respect for what it takes to build something new.

                          Tell us about the genesis of Quidkey and its mission…

                          Quidkey was born from a simple idea, that merchants should be able to grow with confidence, scale sustainably, and offer customers a seamless payment experience, at home or abroad.

                          For too long, fragmented rails and card scheme costs have added friction to the payment ecosystem, especially hurting SMBs. Quidkey changes that. Our payment solution requires no change to the checkout experience yet simplifies payment routing, reconciliation, and settlement optimisation behind the scenes.

                          By cutting out unnecessary intermediaries and using Open Banking rails, Quidkey delivers faster, more transparent and cost-efficient payments, empowering merchants to grow and helping banks realise greater value from existing infrastructure.

                          This novel approach sets the foundation for what could evolve into a global clearing layer for digital commerce, removing friction, reducing cost, and reshaping the future of payments.

                          What industry challenges can Quidkey solve?

                          Payments today are still more complicated than they need to be. Merchants face high fees, chargebacks, and slow settlements, while banks and PSPs struggle to turn their Open Banking investments into meaningful value. The result is a fragmented system that creates friction for everyone.

                          Quidkey bridges that gap. By simplifying how money moves between banks, fintechs, and merchants, we make payments faster, cheaper and transparent. The outcome is better liquidity and smoother experiences for merchants, stronger customer relationships, and a real return on infrastructure for the banks that power it all.

                          What benefits are your clients experiencing from Quidkey’s approach to open banking?

                          Open banking adoption is accelerating fast. There are already more than 15 million UK consumers and small businesses taking advantage of open banking-powered services, generating two billion transactions per month and growing. We expect Open Banking payments to generate about 5x more in global revenue by 2030.

                          Quidkey is at the centre of this evolution, turning Open Banking into measurable value through intelligent settlements, stronger customer loyalty, and real returns on investment. We optimise payment rails for merchants, enhance efficiency for banks, and keep payments frictionless for consumers.

                          Why should UK businesses and consumers embrace open banking with Quidkey? How does Quidkey make the cross-border rails more usable so everyone can benefit?

                          With the rapid global expansion in consumer adoption of A2A payments, global A2A transaction volume is expected to increase by 209% in the next 5 years. From 60 billion in 2024 to over 185 billion by 2029. This growth is driven by cost efficiency, speed, convenience and enhanced security compared to traditional card payments. It is especially prevalent across key markets like Europe, where A2A is a leading online payment method in several countries.

                          Quidkey offers merchants the ability to seamlessly integrate this new technology and deploy it both domestically and for cross-border purposes, while simultaneously reducing transaction costs by up to 60-70% as compared to legacy payment models:

                          • Consumers enjoy frictionless, bank-authenticated payments with protections
                          • Merchants save on processing costs, increase conversions, and reduce fraud/chargebacks
                          • Banks strengthen customer primacy and democratise access to their products at checkout.
                          API – Application Programming Interface. Software development tool. Business, modern technology, internet and networking concept.

                          How easy is it for merchants to deploy Quidkey?

                          Quidkey offers easy integrations via Shopify plug-in, WooCommerce, or iFrame with set up in minutes… No code and zero impact to existing payment options – just faster payments that generate capital to invest in growth.

                          With fair fees and no lock-ins, Quidkey’s daily settlement can cut costs and optimise cash flow with product bundles designed for growth. Additionally, Quidkey delivers an Apple Pay–style one-tap experience but over bank rails that reduce fraud and charge back risks.

                          Talk us through some of the big success stories for Quidkey that will provide a platform for future growth?

                          Our early priorities focused on go-to-market execution – getting the Quidkey solution in the hands of consumers to iterate and prove product-market fit. Quidkey is among the few companies approved to service Shopify checkout globally.

                          Additionally, we’ve announced a strategic partnership with Tryp.com to power next-generation ‘Pay by Bank’ travel payments. The collaboration is delivering instant settlement, loyalty rewards, and a frictionless A2A experience – achieving a 12% checkout take-up rate versus <1% for traditional Open Banking solutions. The early data shows strong consumer resonance, with room to grow through education and incentivisation. Quidkey’s tech is industry-agnostic – already extending to sectors like fashion, cosmetics, jewellery, and home goods. And we plan to expand next into globalised B2B payments.

                          What’s next? What forthcoming initiatives are you particularly excited about for 2025 and beyond…

                          “The transition from multinational banking to fintech is less of a leap and more of a return. In a bank, you have all the resources but with layers of bureaucracy; in a start-up, full permission but no resources. The goal is to combine both, the creativity of a start-up with the rigour of an institution.

                          Looking ahead, Quidkey’s focus is clear: scale globally, expand merchant adoption, deepen ecosystem partnerships, and build a sustainable, purpose-driven organisation.

                          Cross-border commerce remains one of the toughest challenges – yet also the biggest opportunity. Global payment flows reached $45 trillion in 2023 across B2B, e-commerce, and remittances, and are expected to hit $76 trillion by 2030. Still, businesses face high fees, slow settlements, and fragmented rails.

                          Quidkey is tackling this head-on by building a merchant-facing clearing layer that harmonises domestic and cross-border payments, making it as easy to sell abroad as it is at home.”

                          Tell us about some of the partnerships Quidkey has forged?

                          Quidkey recognised the geographical limitations in the A2A payments market presented a significant adoption barrier. It’s an increasingly globalised economy, with existing open-banking providers unable to provide full-service cross-border functionality. So, we’ve been hard at work developing a new payments paradigm with mutually beneficial partnerships to help us deliver on the full potential of globalised A2A payments. Now, with our initial solutions fully tested and our user experience optimised to provide seamless integration across channels, we are focusing on cross-border flows to build out the foundations that will underpin Quidkey as the next generation A2A global clearing house.

                          For example, our partnership with Transfermate enables cross-border A2A ecommerce, harnessing open banking technology to replace costly card rails with a faster, more efficient model of payments. TransferMate’s global network of payments, receivables, and local accounts will power Quidkey’s merchant offering, enabling instant or near-instant settlement in domestic markets and accelerated cross-border payments worldwide, with a waiting list of 100+ merchants in Australia selling into EU, UK and US.

                          “We believe execution doesn’t slow down innovation – it amplifies it. I want to make sure Quidkey scales with purpose – fast, but in control, ambitious, yet trusted.”

                          About Quidkey

                          Quidkey is a cross-border payments technology company enabling merchants to accept instant account-to-account payments across the UK, EU, and US. By operating alongside existing PSPs rather than replacing them, Quidkey gives merchants a seamless path to lower costs, faster settlement, and higher checkout conversion. Quidkey is simplifying today’s fragmented payment mix (cards/wallets), enabling tomorrow’s open banking corridors, and preparing for the future of tokenised money – capturing the $2.6tn and growing global e-commerce payments opportunity.

                          Find out more at quidkey.com

                          • Artificial Intelligence in FinTech
                          • Digital Payments
                          • Neobanking

                          Author

                          Emma Steeley

                          Published

                          2 December 2025

                          Estimated Read time

                          5Mins

                          Emma Steeley, CEO of Infinian, the global real time credit intelligence bureau providing data to banks, lenders and other data businesses, explains the consequences of credit data being stuck in the past, and how banks and fintechs can overcome the mounting consequences

                          Despite a cost-of-living crisis and unpredictable economic outlook, too many lenders are forced to make credit decisions using information that belongs to another era. This outdated data is based on small samples, derived from national averages and historical surveys that fail to capture the volatility and diversity of financial realities defining life in the UK today.

                          That disconnect between data and reality harms consumers, distorts pricing, and drags on the wider economy. In short, affordability decisions are outdated before they are made. Borrowers are judged on figures that don’t reflect their actual costs, creditworthy customers are turned away, while others are approved for loans they can’t afford. Real-time, accurate, large-sample data is essential for fair and functional credit markets, and as an industry we must work to ensure decision-making is dragged into the modern day, to support the integrity of financial services and the aims of Consumer Duty for the good of financial services and consumer duty.  

                          Legacy Models Versus Modern Risks

                          For years, affordability models have relied on spending benchmarks from the Office for National Statistics (ONS) and other national-level datasets. ONS data, often sourced from the Living Costs and Food Survey, can lag real-world conditions by more than a year. It captures what households spent yesterday, not what they face today.

                          When models depend on national averages and retrospective surveys, they miss the nuances of how people earn and spend. Workers on variable incomes, renters, and those without long credit histories are most likely to be penalised. They may be financially stable, but legacy data can’t see that, leading to unnecessary declines and reinforcing the gap between those who can access affordable credit and those who can’t. Moreover, outdated data also increases the risk of false positives, meaning lenders may approve those who are likely to default.

                          False positives and negatives aren’t the only concerns, but also compliance – the Financial Conduct Authority’s Consumer Duty makes clear that firms must deliver “good outcomes” for retail customers, including through fair pricing and practical support. If lending decisions are based on incomplete or obsolete data, it becomes difficult to evidence that duty. The FCA’s own CONC 5.2A rules require a “reasonable assessment” of a customer’s ability to repay; data that misrepresents current affordability can’t reasonably support that test.

                          Legacy benchmarks, once a useful proxy, now risk embedding unfairness. They distort pricing, entrench exclusion, and hold back lending when the economy most needs momentum.

                          Gaining a True Perspective on Affordability

                          Fresher, more granular data is changing what responsible lending can look like. Real-time or high-frequency data streams from verified income flows, transaction activity, and recurring payment histories provide lenders with a comprehensive picture of affordability.

                          Unlike static surveys, these sources track actual behaviour. They show how a household’s disposable income shifts month to month, how energy or rent payments fluctuate, and how consistently people meet obligations. When used responsibly, this information enables lenders to make faster, more informed decisions that align with each borrower’s actual circumstances.

                          The payoff is fairer, more inclusive, and more responsible: three goals that don’t have to be in tension. Real-time credit intelligence can also help reduce unnecessary declines, extend access to consumers previously considered “thin-file,” and still maintain prudent risk controls. In other words, responsible lending doesn’t have to mean lending less; it means lending smarter.

                          It also helps lenders identify early signs of financial stress. If outgoings begin to rise faster than income, that signal appears immediately rather than months later, allowing firms to step in with tailored support before problems escalate. By closing the gap between reality and response, real-time data enables lenders to be both fairer to customers and more agile in managing their portfolios.

                          The Commercial Case for Better Data

                          Aside from the moral argument, and the benefits it will bring to compliance and consumer protection, there’s also commercial incentives to modernise credit data.

                          With access to better data, lenders can approve more of the right customers without increasing risk. Decision engines will become sharper, with improved acceptance rates and portfolio performance simultaneously.

                          Speed is another advantage. Consumers nowadays expect instant answers and laggy underwriting processes can make customers shift to faster competitors. Access to real-time credit data enables lenders to expedite these processes, thereby improving satisfaction and conversion rates. In a crowded market, those gains translate directly into loyalty and market share.

                          Basing decisions on current financial behaviours also reduces the need for unnecessary full-bureau checks and manual interventions, lowering the cost per decision and freeing up resources for higher-value activity.

                          Ultimately, modernisation is about competitiveness. Financial institutions, whether banks or fintechs, that invest in real-time credit intelligence today will be well-placed to earn trust, loyalty, and market advantage.

                          The Future of Fair Finance

                          Credit markets rely on accuracy, and accuracy in turn depends on timeliness. When the information behind lending decisions lags behind real life, fairness falters, capital is mispriced, and opportunities are lost.

                          Real-time, representative data allows lenders to extend credit responsibly, price risk precisely, and support customers before problems arise. It strengthens inclusion while improving overall performance.

                          In a world where household finances can change in weeks, lending models must keep pace with reality. Institutions that invest in live, comprehensive data today will set the benchmark for fair and effective finance in the years ahead.

                          Find out more at infinian.com

                          • Artificial Intelligence in FinTech
                          • Digital Payments
                          • InsurTech

                          Author

                          Nell Walker

                          Published

                          Estimated Read time

                          10Mins

                          At the most recent Exiger Executive Forum, we had the opportunity to listen to the experts discuss how supply chains can shore up in chaotic times

                          Most often than not, the control you have over your value chain is an illusion.

                          That’s the bold statement November’s Exiger Executive Forum picked to examine and dissect. The event, entitled False Security: The Illusion of Control in Modern Day Value Chains, was chosen carefully to reflect what procurement and supply and value chain leaders are concerned about today.

                          On the 18th of November, we joined Exiger and its distinguished guests at the beautiful Great Scotland Yard Hotel in London to dig into this topic and hear directly from the best of the best in an expert panel. The guest list reached from defence leadership, supply chain experts, world-leading analysts and senior politicians. 

                          The aim? To challenge that illusion of control, and frame the conversation as a tough love wake-up call. Without open dialogue like this, risks can quietly accumulate in the background, leading to systemic failures.

                          That’s why the Exiger Executive Forum is so important. By giving the most pressing matters – especially the uncomfortable ones – a platform, issues are demystified and disempowered and real solutions to be put into place – both with deep values and credible pragmatism. This allows leaders in procurement and supply chain to  resolve modern day challenges with confidence, regain lost control and determine their future and not merely react.

                          Tim Fowler, Client Engagement  Director at Exiger, acted as moderator for the evening’s discussions. He opened the discussion with a sobering reality: that organisations all over the world are facing systemic risks. “Global supply chains are more data-driven, more regulated, more digitised than ever,” he explained. “But, paradoxically, they’ve never been more fragile with the convergence of geopolitical fragmentation, resource scarcity, technology threats, and regulatory volatility.”

                          The risk caused, Fowler said, is one that “hides in plain sight”. Many enterprises operate under the assumption that they have full visibility of their suppliers, and that as a result, they’re in control. However, dig a little deeper and there are many unseen dependencies, regional concentrations, and of course, human risk. With a more hopeful lilt, Fowler then reminded attendees that the goal of the Executive Forum is to explore what real control and resilience means in a chaotic and ever-changing world, with the help of the expert panel:

                          • Koray Köse, CEO & Chief Analyst, Köse Advisory; Senior Fellow, GlobSEC GeoTech Centre; and Board Member, Slave-Free Alliance

                          • Scott LaFoy, Vice President, Nuclear and Technology Security Programs, Exiger
                          • Sven Markert, Head of Supply Chain & Logistics, Siemens Smart Infrastructure
                          • Angela Qu, Advisor, Strategist, and former Chief Supply Chain Officer
                          • Faysal Rahman, Director, Corporate Coverage – Global Defence Coordinator, Deutsche Bank

                          The illusion of control

                          In the first segment of the evening’s strategic expert exchange, Fowler dug into the concept of this illusion of control with the panel. For Köse, the illusion of control is one of the greatest blind spots in modern business. But why? “It’s all based on our systemic understanding or how we actually created value in the past,” he explained. “Not 50 years ago, but even just 10 or 15 years ago, the world looked very different from what we are facing today. Changing the rules of the game is something many companies still do not examine seriously. It requires a deep review of how their value chains are designed, the governance and compliance structures that guide them, and the intelligence embedded into their processes. Ultimately, it is about building resolve and the capability and capacity to not only survive the challenges of today, but to shape and compete in the markets of tomorrow.”

                          Following this, Qu was asked whether she has also witnessed a false sense of security within governance models in organisations she’s worked with. She pointed out that many companies now have risk mapping, risk monitoring, and risk mitigation as a top agenda since COVID-19, but shortages and disruptions continue. What’s key, for Qu, is “awareness, visibility, and overview. I think we’ve made big steps in the last 2-3 years,” she explained. “There are a lot of conflicts in the classical KPIs, which are still siloed even after the COVID crisis. That’s why you need good visibility of the whole value chain setup – not only tier one.”

                          For Markert, maintaining agility when managing various political, technological, and economic challenges has been a major undertaking. “The truth is, I don’t know if we really maintain the agility or just manage the chaos,” he admitted. “We’re focusing on adaptability over perfection, so we accept that full control is impossible. Then, we’re coming back to basics. This starts with processes, then technology. Lastly, people are the most important and most valuable assets you have. You have to build up cross-functional teams. We don’t want to predict the future; we want to be prepared for the future.”

                          From a financial standpoint, Rahman stated he believes it’s important to take a step back and contextualise the challenge we’re living in. The last few years have seen a pandemic, wars, and geopolitical tensions the likes of which have never been seen, impacting supply chains. With this in mind, Rahman believes that there “couldn’t be more of an emphasis” on supply chain resilience. “How do you make sure your operational resilience is robust so you can withstand black swan events that are becoming more and more common?” he asks. “Diversification of risk is really important.”

                          Sometimes, failure is simply not an option. For LaFoy, who works with national security-grade supply chains, having all of the information in front of you is great, but it means nothing if you don’t use it to take action. “Often people think they can see everything, and that’s only step one of the problem – it doesn’t fully address it,” he said. “You have to be willing to take action within the organisation, to mitigate the problem, fix it, and try to rebuild. People like to say that they’re going to fix their supply chain, but the supply chain is likely supporting a programme that has existed for so long it’s entrenched within the organisation. So it’s almost always too late.”

                          Vulnerabilities and systemic risk

                          Fowler: “Where do you see the biggest unseen vulnerabilities accumulating today?”

                          Köse: “It’s in the KPIs. Companies are measuring themselves against metrics that no longer drive sustainable or resilient value creation in today’s world. They still prioritise short term shareholder returns that evaporate with every risk event. KPIs shift from quarter to quarter, yet value chains take decades to build and mature, just as supplier partnerships and political relationships take decades to cultivate. Both can erode rapidly when interdependent opportunistic and negative actions and disruptions occur.”

                          Fowler: “How do you encourage best practice and good behaviour with your clients?”

                          Rahman: “The number one ingredient is confidence. Having transparency across the value chain, the supply chain, the governance procedures, is super important too. It can take 50 years to build trust and one second to lose it, so it’s important to take a very risk-averse approach while being very commercial and pragmatic.”

                          Fowler: “What have you seen work in terms of breaking down siloes to drive agility?”

                          Qu: “I usually go with strategy, organisation, technology. Technology encompasses risk mitigation, as well as ESG and compliance. We need dedicated projects, working with suppliers and engineers to reduce waste and create internal excellence. Personal resilience is also very important.”

                          Fowler: “How do you balance all the elements of regional concentration and supplier dependency?”

                          Markert: “Efficiency is still key if you want to stay competitive. We cannot optimise purely on costs anymore – that’s gone. We have to take into consideration, as Angela said, the transparency insights beyond tier one. For me, it’s all about continuity and compliance.”

                          Fowler: “What lessons can the private sector draw from defense-grade risk management?”

                          LaFoy: “The defence-grade supply chain has this draconian adherence to certain processes, and that inflexibility doesn’t always translate in a positive way. But in this case, it’s necessary to examine what key things you’re prioritising as a company. 

                          Technology, intelligence, and the myth of visibility 

                          It’s clear, in spite of the warnings about vulnerabilities and control, that the overall feelings for supply chain professionals are hope and determination. Fowler introduced the next segment of the conversation by mentioning that investors and PE companies are now focusing on supply chain risk and resilience as key measures. This bodes well for those in supply chain when they inevitably come to justifying proposed improvements. The fact that supply chain risk ties directly into financial risk proves once again that supply chain is a business-wide concern, if there was any remaining doubt.

                          For Rahman, from a financial perspective, there are a couple of areas clients are focusing on when it comes to their investments. “One is financial risk,” he told Fowler. “What we mean by that is leverage – how much debt and cash they’ve got on the balance sheet. The other is business risk, which is quite broad. It’s about how much the product is needed in the market, whether it’s a diversified product, and so on.”

                          When it comes to questions of compliance and ESG in supply chain, balancing those areas of focus with what investors want can be a challenge. Those investors may have a clear idea of their areas of interest when thinking about risk and resilience, and Qu’s solution for making sure those vital areas don’t get overlooked is to always see things from the customers’ perspective.

                          “That customer, if you want them to choose your product versus a product from competitors, they want to know you’re compliant to all regulations,” she explained. “That results in collaboration among different departments to focus on a common goal and how we achieve it. Also, you need an overview of potential risks and have solutions in place for those focus areas, supported by technology. Things can go wrong, but if that happens when you’re prepared, it’s not the end of the world. There are still activities where humans can take over.”

                          The conversation again turned to leadership, and how that affects organisations in a way that incentivises them to focus on protection and resilience, while not stifling innovation and agility. The key, for Köse, lies in communication and constant messaging, so vital areas don’t get forgotten. “The important factor is drawing the journey very clearly to everyone who is a stakeholder in this process, and make sure that every part of their contribution will become part of the overall value creation process. When we talk about resilience, you always need to think about the next step. We’re not necessarily predicting anything, but we’re preparing for everything.”

                          The conversation shifted to summarising comments, where the panellists highlighted resilience across all functions, with a heavy emphasis on supply chain, utilising AI to help navigate decisions, and simply showing up as being some of the most important aspects to getting the modern supply chain right. “We need to be able to understand, from A to Z, geopolitical interdependencies, financial impact, innovation impact, industrial history, and the most valuable assets – your people and your culture,” Köse concluded. “Showing up in that context, and driving that as leaders, is ultimately really critical.”

                          During the course of the evening, the expert panelists exposed the glaring issues and shattered illusions across the modern value chain, while leaving attendees hopeful that they can achieve operational resilience through a proactive commitment to preparedness. Thank you to Exiger for inviting us to join in this vital conversation; we look forward to the next one.

                          Author

                          Panos Savvas

                          Published

                          1 December 2025

                          Estimated Read time

                          5Mins

                          Kani Payments CTO Panos Savvas on the next generation of banking and payments and why it’s not just about fast banking but complex banking

                          The future of banking won’t be decided by algorithms or apps, but by how well we manage the data that drives them...

                          For years, ‘next generation banking’ has been shorthand for agility, innovation and a clean break from the technological baggage that constrained traditional institutions. Neobanks and fintech challengers built their reputations on speed, automation and digital-first thinking. Yet as the sector matures at a rapid pace, a more layered picture is emerging.

                          Despite their reputation for a ‘tech-centric’ approach, many digital banks are discovering that operational excellence is harder to achieve than customer experience. In some of the most critical areas of financial infrastructure, data management, reconciliation and reporting, modern banks are grappling with challenges that feel decidedly old generation.

                          Of course, this is not a failure of innovation, but a reminder that progress in banking is rarely linear. Building for scale, compliance and resilience inevitably exposes the complexity beneath the sleek surface of digital transformation and in this sense banks aren’t alone with this.

                          The Automation Illusion

                          Being ‘born in the cloud’ should have freed newcomers from legacy infrastructures. Yet research shows that manual processes remain surprisingly prevalent. Kani’s recent survey found that 22 per cent of UK neobanks still use spreadsheets as a standalone tool to perform reconciliation and compliance reporting. A much higher proportion than any other group surveyed.

                          This is a very revealing statistic. While the customer interface has evolved rapidly, the back office hasn’t kept pace. The typical neobank experience may be seamless for users on the surface, but behind the scenes, operations often rely on fragmented data flows, multiple third-party integrations and human oversight.

                          The mismatch doesn’t make them laggards. It simply highlights a structural truth: automation is easy to market, but difficult to master. Data integrity, not digital branding, is what separates the truly next generation from the merely new.

                          Data: The Hidden Legacy

                          Every modern bank understands that clean, reliable data is its most valuable asset. It fuels compliance, supports decision-making and underpins every audit trail. Yet half of neobanks in the same survey said data cleansing was among their most time-consuming reconciliation tasks, with 44 per cent citing auditing and 39 per cent data verification as similar drains on time.

                          These are not edge cases, they are foundational disciplines. When half of a bank’s operational resource is tied up in validation rather than value creation, the issue is not technology but data governance.

                          Traditional institutions often blame legacy systems for inefficiency. For fintechs, the challenge is different. Modern platforms are fast to deploy, but when combined across multiple partners without shared data standards, they can create inconsistencies that require manual resolution. The future of finance depends less on speed and more on how consistently that speed produces trustworthy data.

                          Managing Risk, Not Just Reputation

                          Errors in reconciliation aren’t just accounting irritants, they’re board-level risks. Half of neobanks pointed to compliance exposure as their biggest concern, with 44 per cent linking data breaks directly to market trust.

                          That finding alone reflects sector maturity. Modern institutions now recognise that trust is not simply a brand asset but a measurable operational outcome. The firms investing in traceability, explainability and real-time audit trails are also the ones strengthening their regulatory relationships.

                          It’s important to recognise that regulators are not barriers to innovation. They are collaborators in resilience that want firms to show evidence-based controls. The direction of regulation, particularly under initiatives like the UK’s Consumer Duty and Europe’s PSD3, points toward transparency, not obstruction.

                          Turning Data into Context

                          How a bank enriches and contextualises transaction data is a reliable indicator of operational maturity. Yet many organisations, not only neobanks, still have enrichment processes that rely heavily on human intervention. 61 per cent of neobanks manually add metadata to transactions, while only a third integrate third-party data automatically.

                          That dependence on manual enrichment reflects an industry-wide balancing act. The challenge is not capability but confidence. Integrating external data sources requires robust governance, clear permissions and the ability to trace every enrichment to its origin. For a sector under constant regulatory scrutiny, it’s no surprise that many firms err on the side of caution.

                          The next step is to make enrichment auditable as well as automated, so that data quality, not data quantity, becomes the competitive differentiator.

                          The AI Rush

                          Artificial intelligence (AI) has become the headline act of modern banking, promising to transform everything from fraud detection to credit scoring. Yet there’s a risk in assuming that AI will fix underlying operational inefficiencies.

                          Across the industry, many are racing to bolt AI onto customer-facing functions while leaving back-office processes largely untouched. Without robust data hygiene, reconciliation and enrichment, AI is at risk of improvising around gaps rather than accelerating truth.

                          True next-generation banking will emerge not from the adoption of algorithms but from the discipline of data stewardship. When banks invest in consistent, explainable data architectures, AI becomes a multiplier for accuracy and trust, not a mask for structural fragility.

                          Beyond the Buzz

                          The phrase “next generation banking” has become so elastic that it risks losing all definition. For some, it means AI-driven services; for others, embedded finance or real-time payments. These innovations matter, but they rest on the same foundational truth of, if the data isn’t right, nothing works as it should.

                          A bank that can open an account in minutes but takes days to close its books is not yet fully digital. A platform that deploys AI for insights but can’t trace the lineage of its data is not yet intelligent. The goal of next-gen banking should be to make the invisible visible, ensuring that every process beneath the surface is as modern as the experience on top.

                          The Real Definition of “Next Generation”

                          It’s easy to imagine next-generation banking as something futuristic and abstract. In reality, it’s about something deeply practical: building systems that make data dependable.

                          Neobanks and fintech banking began as the antidote to legacy complexity. Their next chapter will depend on how well they tackle their own hidden legacies and the invisible operational debt that lurks beneath every modern interface.

                          The banks that succeed will be those that blend speed with substance, innovation with integrity, and automation with accountability. In the end, the only kind of innovation that endures is the kind that accelerates truth.

                          Learn more at kanipayments.com

                          • Digital Payments
                          • Neobanking

                          Author

                          Kyle Hill

                          Published

                          28 November 2025

                          Estimated Read time

                          6Mins

                          Kyle Hill, CTO of leading digital transformation company and Microsoft Services Partner of the Year 2025, ANS, explores how businesses of all sizes can make the most of their AI investment and maintain a competitive edge in an era of innovation

                          Across the world, businesses are clamouring to adopt the latest AI technologies, and they’re willing invest significantly. According to Gartner, generative AI has produced a significant increase in infrastructure spending from organisations across the last few months, which prompted it to add approximately $63 billion to its January 2024 IT spending forecast. 

                          Capable of reshaping business operations, facilitating supply-chain efficiency, and revolutionising the customer experience, it’s no wonder major enterprises are keen to channel their budgets towards AI. But the benefits of AI can extend beyond large enterprises and make a considerable difference to small businesses too if adopted responsibly. 

                          Game-Changing Innovation 

                          Most SMBs don’t have the same ability for taking spending risks as their larger counterparts, so they need to be confident that any investments they do make are worthwhile. It’s therefore understandable why some might assume it to be an elite tool reserved for the major players.

                          To understand how SMBs can make the most of their AI investments, it’s important to first look at what the technology can offer. 

                          Across industries, AI is promising to be a game changer, taking day-to-day operations to a new level of accuracy and efficiency. AI technology can enhance businesses of all sizes by:

                          Enhancing customer experience

                          Businesses can use AI tools to process and analyse vast amounts of data – from spending habits and frequent buys to the length of time spent looking at a specific product. They can then use these insights to provide a more tailored experience via personalised recommendations, unique suggestions and substitution offers when a product is out of stock. And, with AI chat functions, businesses can provide more timely responses to any questions or requests, without always needing an abundance of customer service staff on hand. 

                            Powering day-to-day procedures

                            One of the most common and inclusive uses of AI across organisations is for assisting and automating everyday tasks including data input, coding support and content generation. These tools, such as OpenAI’s ChatGPT and Microsoft Copilot applications, don’t require big investments to adopt. Smaller teams and businesses are already using them to save valuable employee time and resources and boost productivity. This also saves the need for these organisations to outsource these capabilities where they might not have them otherwise. 

                              Minimising waste 

                              AI is also helping businesses to drive profit, minimising wasted resources, and identifying potential disruptions. By tracking levels of supply and demand, AI can automatically identify challenges such as stock shortages, delivery-route disruptions, or a heightened demand for a particular product. More impressively, however, they are also capable of suggesting solutions to these problems – from the fastest delivery route that avoids traffic, to diverting stock to a new warehouse. Such planning and preparation help businesses to avoid disruptions which costs valuable time, money, and resources. 

                                According to Forbes Advisor, 56% of businesses are already using AI for customer service, and 47% for digital personal assistance. If organisations want to keep up with their cutting edge-competitors, AI tools are quickly becoming a must-have for their inventory. 

                                For SMBs looking to stay afloat in this competitive landscape of AI innovation, getting the most out of their technological investment is crucial. 

                                Laying down the foundations

                                Adopting AI isn’t as straightforward as ‘plug and play’ and SMBs shouldn’t underestimate the investment these tools require. Whilst many of the applications may be easy to use, it’s important that business leaders take time to fully understand the technology and its potential uses. Otherwise, they risk missing some major benefits and not getting the most from their investment, particularly as they scale out. 

                                Acknowledging the potential risks and challenges of implementing new AI tools can help organisations prepare solutions and ensure that their business is equipped to manage the modern technology. This can help businesses to avoid costly mistakes and hit the ground running with their innovation efforts. 

                                SMB leaders looking to implement AI first need to ask the following:

                                What can AI do for me? 

                                Are day-to-day administration tasks your biggest sticking points? Or are you looking to provide customer service like no-other? Identifying how AI might be of most use for your business can help you to make the most effective investments. It’s also worth considering the tools and applications you already have, and how AI might enhance these. Many companies already use Microsoft Office, for instance, which Microsoft Copilot can seamlessly slot into, making for a much smoother rollout. 

                                Can my business manage its data? 

                                AI is powered by data, so having sufficient data-management and storage processes in place is necessary. Before investing in AI, businesses might benefit from first looking at managed data platforms and services. This is crucial for providing the scalability, security and flexibility needed to embrace innovation in a responsible and effective way. 

                                What about regulation?

                                The use and development of AI are becoming increasingly regulated, with legislation such as the EU AI Act providing stringent, risk-based guidance on its adoption. Keeping up with the latest rules and legislative changes is vital. Not only will this help your business to maintain compliance, but it will also help to maintain trust with customers and employees alike, whose data might be stored and processed by AI. Reputational damage caused by a data breach is a tough blow even for big businesses, so organisations would be wise to avoid it where possible. 

                                Embracing Innovation

                                This new age of AI is exciting; it holds great transformative potential. We’ve already seen the development of accessible, affordable tools, such as Microsoft Copilot, opening a world of new innovative potential to businesses of all sizes. Those that don’t dip their toes in the AI pool risk getting left behind. 

                                The question smaller businesses ask themselves can no longer be about whether AI is right for them; instead, it should be about how they can best access its benefits within the parameters of their budget. 

                                By thoroughly preparing and taking time to understand the full process of AI adoption, SMBs can make sure that their digital transformation efforts are a success. In today’s world, this is the best way to remain fiercely competitive in a continuously evolving landscape. 

                                About ANS

                                ANS is a digital transformation provider and Microsoft’s UK Services Partner of the Year 2025. Headquartered in Manchester, it offers public and private cloud, security, business applications, low code, and data services to thousands of customers, from enterprise to SMB and public sector organisations. With a strong commitment to community, diversity, and inclusion, ANS aims to empower local talent and contribute to the growth of the Northwest tech ecosystem. Understanding customers’ needs is at the heart of ANS’s approach, setting them apart from any other company in the industry. 

                                The ANS Academy is rated outstanding by Ofsted and offers in-house apprenticeships across a range of technology disciplines. ANS has supported more than 250 apprentices to gain qualifications in the last decade via apprenticeships across technology, commercial, finance, business administration and marketing. 

                                ANS owns and operates five IL3‐accredited data centres in Manchester and has an ecosystem of tech partners including Microsoft (Gold Partner), AWS, VMWare, Citrix, HPE, Dell, Commvault and Cisco. It is one of the very few organisations to have received all six of Microsoft’s Solutions Partner Designations. 

                                Find out more at ans.co.uk

                                • Artificial Intelligence in FinTech
                                • Data & AI
                                • Digital Strategy

                                Author

                                Dan Brightmore

                                Published

                                Estimated Read time

                                6Mins

                                With the rise of AI-enabled fraud in mind, Dave Rossi, Managing Director at National Hunter, argues the need for a radical rethink

                                AI is making financial fraud less predictable and far more damaging. With access to new tools like Fraud GPT, deep fakes, and large-scale automated, and agentic, autonomous decision making capabilities to supercharge methods such as spearphishing, fraudsters are now able to target their activity more accurately, convincingly, and at higher volumes than ever before. Add in use of AI to flood the industry with financial applications which increase phishing and identity theft, especially for vulnerable individuals, and the cost of financial fraud continues to explode.

                                As one recent report revealed, in the UK alone, banking fraud caused £417.4 million in losses across 21,392 reported cases over the past year, making it the third costliest fraud type. Combatting this explosion in financial crime requires a different approach. One that not only transforms identity checks through robust, multi-tiered tools but also includes assessment of behavioural signals, transaction monitoring and cross validation to highlight suspicious activity at any point in the customer lifecycle.

                                Critically, argues Dave Rossi, Managing Director, National Hunter, it demands a new mindset based on collaboration, information sharing and a culture that encourages people to raise concerns, call out suspicious activity and prioritise fraud detection at every stage of the customer journey.

                                Financial Fraud Explosion

                                Financial institutions are struggling to adopt the new mindset required to protect customers, reputation and the bottom line from financial fraud. The continued internal conflict between the need to add layers of verification and detection to deliver essential safeguards and a perception that such measures will lead to customer disengagement and loss is adding unacceptable risk in a new era of AI enabled, widescale financial fraud.

                                Financial fraud is no longer opportunistic and small scale. From individuals trafficked to dedicated fraud centres in the Far East to the systematic use of AI to build synthetic IDs at scale and deep fake voice and video calls used successfully for spearfishing activity, financial fraud is a global, organised crime.

                                The ease with which AI can be used to generate synthetic identities alone should prompt a radical overhaul of anti-fraud measures. According to Signicat, AI-driven identity fraud is up 2,100% since 2021. It is now outpacing many traditional forms of financial crime. Rather than stolen passports and forged documents, fraudsters are now using AI to create manufactured personas, ID documents and accounts created using digital footprints that appear legitimate but have been built to deceive. Adding defence measures – both technology and human – to the process may potentially add friction to the customer experience but failing to protect either the business or customers will, without any doubt, cost significantly more. 

                                Synthetic IDs

                                Organisations need to understand the sheer scale of AI-enabled financial fraud. LexisNexis Risk Solutions estimates that there are around 2.8 million synthetic identities in circulation in the UK, and hundreds of thousands more are created annually. They also claim 85% of synthetic IDs go undetected by standard models, creating a potential cost to the UK economy of £4.2 billion by 2027 unless companies adopt more stringent screening measures. 

                                The use of AI at this scale enables criminal gangs to play the long game, with the behaviour of synthetic accounts mirroring real customers over months or years to build a credit history before cashing out and leaving the business and bank to handle the write-off. And this tactic is being used to target business in every industry. According to Experian over a third (35%) of all UK businesses reported being targeted by AI-related fraud in the first quarter of 2025, an increase of more than 50% over the same time period last year.

                                The use of synthetic IDs is just one way in which AI has changed the familiar patterns of financial fraud. The sophistication of deep fake technology is another, with fake voice and video building on chat based social engineering messaging via real-time chat scripts for LinkedIn DMs and WhatsApp messages, to successfully facilitate incredibly sophisticated spearfishing attacks. Mimicking the persona of high value individuals, especially CEOs and CFOs, such attacks have led to devastating losses, including the UK-based fintech which lost £1.8 million in 2024 following an attack using a combination of spearphishing and generative AI to impersonate the company’s CFO.

                                Trust Issues

                                Organisations cannot afford the current levels of (over) trust. Indeed, the success of the majority of AI-enabled financial fraud can be tied to organisational culture. Synthetic IDs succeed when the focus is only on verification – which checks identity – rather than on-going monitoring of behaviour and transactions as well as cross validation, which highlight intent. Spearfishing leverages a culture of uncertainty, succeeding in environments where individuals do not feel confident or are not encouraged to question the veracity of the CFO’s payment orders, for example.

                                The reliance on credentials verification is inadequate in a world of Fraud GPT. With diverse sophisticated technologies now being deployed at scale, it is no longer acceptable to rely on traditional models of verification, such as document validation. Furthermore, organisations are losing trust in newer techniques, such as facial biometric authentication due to the sophistication of AI deepfakes. Concerns are growing about the risks associated with proposed national eIDs: when a digital ID appears to be verified by government there is a temptation to believe without additional, yet essential, scrutiny.

                                Organisations need to consider intention as well as identity. What are the behavioural signals that could indicate fraud? Which transactions are suspicious and what additional insight can be surfaced through continual cross-validation of activity? Adding layers of verification and flagging possibly suspicious activity may initially annoy the odd genuine customer, but the reality of AI-enabled fraud is devastating individuals, businesses and financial institutions. It is now vital to adopt a fraud-first culture, where individuals at every level of the organisation have both the tools and understanding to spot suspicious activity and are encouraged to call out concerns, especially if they relate to senior management requests.

                                Collaborative Model

                                Failure to shift from over-trust to low-trust will continue to play into the hands of criminal gangs. Gangs that are constantly sharing information about weak targets. Innovative, anti-fraud organisations are leading the fight back through intelligence sharing, cross-validation and next generation screening. Adopting both robust verification and validation technologies and culture that encourages suspicion and also fosters cross-industry insight is key to addressing this complex, evolving threat.

                                By proactively sharing the information surfaced through comprehensive verification as well as behavioural and device analytics, the industry can gain rapid understanding of the fast-changing tactics being deployed by these criminal gangs and take the appropriate remedial action to protect, customers, reputation and the bottom line.

                                Learn more about tackling fincrime at nhunter.co.uk/

                                • Artificial Intelligence in FinTech
                                • Cybersecurity in FinTech

                                Author

                                Dan Brightmore

                                Published

                                27 November 2025

                                Estimated Read time

                                2Mins

                                At AWS, we’re obsessed with helping our customers harness the benefits of cloud and AI. While maintaining robust security, resilience…

                                At AWS, we’re obsessed with helping our customers harness the benefits of cloud and AI. While maintaining robust security, resilience and scalability. We believe the true value of he cloud is unlocked when seen as an end-to-end transformation opportunity. A chance for organisations across Asia Pacific and Japan, such as Techcombank (TCB), to seize the innovations Gen AI and Agentic AI can offer today.

                                According to a new AWS-Strand Partners 2025 report, AI adoption among businesses in Vietnam is growing rapidly at an annual rate of 39%. Close to 170,000 businesses in Vietnam have already adopted AI. And 77% of those businesses expect AI to increase their revenue within the next year.

                                Delivering Business Benefits

                                TCB’s journey with AWS exemplifies the transformative power of cloud and AI adoption. Spanning strategic planning and co-innovation, with a shared commitment to transformation:

                                • Within six months, AWS helped TCB migrate retail and corporate banking systems to the cloud. This enabled on-demand scalability, reduced infrastructure costs, improved time to market and enhanced availability for TCB, cutting downtime.
                                • By rapidly scaling infrastructure, reliably and securely, TCB has seen digital transactions grow by 38%.
                                • Today, 55% of new customers now join via digital channels and 97% of transactions are processed digitally.

                                The AWS Data Migration Service is expected to generate projected cost savings of up to $10.4 million over five years. Driven by improved infrastructure efficiency and simplified operations.

                                Harnessing Gen AI & Agentic AI

                                Gen AI is delivering workplace transformations, including enabling contact centre agents to resolve customer concerns. TCB has established itself as a pioneer, becoming Vietnam’s first bank to develop proprietary applications using Amazon Bedrock. Initiatives include customer chatbots for employee use, advanced language translation tools, and SMARTIE – an AI personal assistant built on a custom Large Language Model (LLM).

                                AWS: A Trusted Partner for Cloud at Scale

                                AWS distinguishes itself as a transformation partner through its unique combination of global expertise, strong local partnerships, and proven implementation frameworks. This comprehensive approach enables organisations to achieve meaningful business transformation while staying at the cutting edge of technological innovation.

                                “By enabling financial institutions like Techcombank to innovate at scale, we’re helping create the foundation for Vietnam’s next phase of AI-driven economic growth.”

                                Eric Yeo, Country General Manager – AWS Vietnam

                                Discover more about the ways Techcombank is overcoming challenges on its transformation journey with AWS from Eric Yeo, Country General Manager – AWS Vietnam


                                • Artificial Intelligence in FinTech
                                • Blockchain & Crypto
                                • Cybersecurity in FinTech
                                • InsurTech

                                Author

                                Dan Brightmore

                                Published

                                26 November 2025

                                Estimated Read time

                                10Mins

                                Financial Services Director Arunkumar Gopalakrishnan on how Publicis Sapient is developing the playbook for delivering successful AI-led digital transformations across the financial services landscape

                                Publicis Sapient doesn’t sell tools; it delivers human-led, AI-enhanced solutions that blend proprietary platforms with deep industry expertise across global banking. The organisation is shaping the future of financial services by delivering complex digital transformations across continents – from the UK and Southeast Asia to the Middle East and the United States – anchored by a belief that true innovation lies at the intersection of business insight and technological depth.

                                Publicis Sapient utilises a SPEED philosophy – Strategy, Product, Engineering, Experience and Data. “For us, SPEED isn’t just a framework. It’s the way we align our capabilities to accelerate transformation,” explains Financial Services Director Arunkumar Gopalakrishnan. “My focus is the ‘P’ in that process – Agile Program Management and Product Management; helping clients move from vision to value at pace.”

                                Transformation at SPEED

                                During his time with Publicis Sapient, Arunkumar has seen the power of transformation at SPEED on a variety of high-stakes projects: helping a major UK bank launch a digital-only entity on the cloud; partnering with a leading Thai bank to revitalise its mobile-banking experience for a fast-growing, tech-savvy customer base; supporting a sovereign-funded startup bank exploring blockchain for trade finance in collaboration with Microsoft; building a holistic wealth-management platform for a large US custodian bank; and helping another lead the way in AI adoption. These are the type of innovation journeys where Publicis Sapient excels at moving from groundwork to exponential scale.

                                At the Intersection of Business and Technology

                                Publicis Sapient excels by fusing two disciplines often treated as separate. “We are at the intersection of business and technology,” explains Arunkumar. “You need deep business acumen to understand client challenges. However, you must have enough technical depth to engage meaningfully with engineering teams. That balance is what enables real problem-solving.”

                                From fraud prevention to blockchain and digital banking the industry is changing fast,” he notes. “Working with Generative AI today feels like standing on a new frontier. It keeps us on our toes, but it’s also what drives us – to stay relevant, deliver outcomes and connect both worlds of business and technology.”

                                Meeting the Challenge: Balancing Innovation and Risk

                                The biggest challenges facing financial services clients are not purely technological. They are structural and cultural. “Banks operate in complex regulatory environments,” notes Arunkumar. “There’s always a tension between innovation and risk management. On one hand, you want the next shiny thing; no one wants to be left behind in the technology race. On the other, you can’t bring something to life without going through the proper regulatory and risk-management controls.”

                                That balance defines the work Publicis Sapient does. “We are a people + product business,” he says. “Our strength lies in talented people, strong domain understanding, platforms, tools and a culture that says to clients: We have your back; we get it done.”

                                Many of the firm’s engagements, he explains, involve deep collaboration, experimentation and iteration. “Some of the use cases we take on aren’t easy. We work with partners, we research, we prototype, we unlearn and relearn. Progress in this space is continuous, not linear.”

                                A Digital Transformation Success Story

                                Publicis Sapient partnered with a large US Bank to lead digital transformation efforts focused on GenAI implementation and scaling. It worked in collaboration with Google and the Bank to design, build, and adopt GenAI to spur innovation, enhance risk management and improve productivity.

                                The high-level solution is composed of modular components including a secure GenAI Gateway for LLM access control, RAG framework for contextual retrieval, and Vertex AI integration leveraging Gemini models for high-quality natural language responses.

                                Delivering the Solution

                                The solution delivered integrated, repeatable accelerators designed to solve the central business challenges of speed, risk, and control from the ground up.

                                Publicis Sapient’s Financial Services Director Arunkumar Gopalakrishnan explains how the platform was built upon some core pillars:

                                Unified LLM Access & Model Context Protocol: “We streamlined the model consumption layer with a foundational gateway, built on a resilient Model Context Protocol (MCP). The MCP acts as an essential abstraction layer, ensuring all data streams, model inputs, and application requests are managed consistently, securely, and in compliance with governance rules.”

                                Integrated Governance & Security: “We implemented a ‘shift-left’ security approach, embedding continuous guardrails directly into the GenAI pipelines. This pre-processing step, coupled with adversarial testing, proactively minimizes human error, reduces operational risk, and ensures a continuous, audit trail.”

                                Proprietary Knowledge Grounding (RAG): “The platform enables the Retrieval-Augmented Generation (RAG) pattern. This involves securely indexing the bank’s vast internal repository of operational knowledge and compliance guides – by using this verified knowledge to ‘ground’ LLM responses, the platform ensures every AI output is based on the bank’s accurate, proprietary data. This successfully mitigates factual errors, minimizes hallucination risk, and protects brand integrity.”

                                Agent Orchestration (Agentic AI): “Moving beyond simple chat, the platform includes capabilities for managing Agentic AI workflows which greatly improves efficiency. These agents are goal-directed systems that execute multi-step business processes (e.g., investigating a service ticket, performing patching operations etc. with human-in-loop for oversight). This is the crucial layer for end-to-end automation of complex, cross-functional tasks.”

                                Unified Observability: “The final pillar establishes a system for tracking crucial metricstied to defined business outcomes. This enterprise-level observability framework captures data like response latency, quality, consumption rates etc. This data allows leadership to continuously monitor output quality and reviewing against the standards of accuracy and trustworthiness.”

                                Realising the Benefits

                                The positive impact of the work Publicis Sapient is doing includes:

                                Scalable Framework: Designed as a Platform-as-a-Service (PaaS) model to support future GenAI use cases across the enterprise; agent-driven extensibility enables enhancements with rapid time-to-market deployments.

                                Accelerated Onboarding: Automates the provisioning process by surfacing relevant documentation, policies, and procedures instantly.

                                Knowledge Reuse: Leverages existing enterprise knowledge bases to reduce redundancy and improve consistency.

                                Building with Purpose: From Vision to Scale

                                At the heart of Publicis Sapient’s transformation philosophy is its Digital Business Transformation Framework. Teams use a playbook to take clients from problem definition to scaled delivery. “It starts with Ignite – understanding the problem and bringing in strategic expertise,” explains Arunkumar. “Then comes Hunt & Shape – identifying and defining value, mapping MVPs and roadmaps. And finally Build & Scale – turning ideas into outcomes by building the right solutions.”

                                Scaling, he insists, is not only about size but certainty. “You don’t scale right away. You start small – proofs of concept, limited users, experiments and learn fast. Once you know what works, you can accelerate.”

                                He points to a current AI engagement as an example. “We started with one application hosted on the platform last year. Now we have twenty-plus, and many more coming. Building the foundation took months, but once we understood the landscape, everything else became a fast follower. You develop a playbook, you know the risks, and then it’s about momentum.”

                                Generative AI: A Catalyst for Reinvention

                                Few technologies have captured the imagination of financial services like Generative AI. Arunkumar sees its impact as both profound and pragmatic. “While business leaders talk about productivity gains, CIOs are using GenAI to drive measurable productivity and cost efficiency by modernising high-friction IT Service Management processes,” he notes.

                                Publicis Sapient identifies three areas where the shift is most visible:

                                • Enhanced self-service: Intelligent Virtual Assistants act as the first line of defence. They automate a big chunk of initial inquiries, freeing human agents and improving response times.
                                • IT-agent augmentation: GenAI synthesises ticket histories, diagnoses root causes and drafts expert-level resolutions. It drastically shortens the mean time to Resolution for critical incidents.
                                • Developer velocity: Secure, context-aware coding assistants are improving efficiency, allowing engineers to focus on high-value work.

                                Each example reflects a practical application of AI – not hype but measurable productivity and cost efficiency.

                                The Rise of Agentic AI

                                Publicis Sapient’s next frontier is Agentic AI, where intelligent agents move beyond analysis to orchestration and action. Teams have been testing these systems within IT service environments for major banks. “We started with a simple knowledge-search application,” he recalls. “It consolidated information across multiple systems to provide accurate, high-performance answers.”

                                From there, Publicis Sapient expanded into process automation. “Imagine an IT engineer under pressure to fix issues fast,” he says. “The knowledge-search tool becomes a force multiplier, identifying root causes instantly. Next, you automate the actions – patching servers, routing tickets, escalating tasks – with a human in the loop for control.” The goal is productivity gains with safety.

                                Challenges remain – particularly model drift and AI hallucination – but these can be mitigated with rigorous evaluation frameworks. “AI is probabilistic, not deterministic,” says Arunkumar. “You can’t expect one-plus-one to always equal two. That’s why continuous grounding, validation and human oversight are key.”

                                Innovation in Action: Real-World Use Cases

                                For Arunkumar, the most exciting part of AI transformation lies in the unexpected. “Some of the best use cases aren’t flashy but support everyday processes that, when optimised, deliver outsized value.”

                                He describes one example from a banking client: improving the reliability of customer statements. “A bank may send hundreds of thousands of daily communications – statements, notifications, alerts. Sometimes statements fail to send, and by the time customers notice, the issue snowballs into reputational risk.”

                                AI, he notes, can detect these failures proactively. “If a statement isn’t generated by 7 a.m., the system flags it very soon and resolves it before customers even notice. Predictive AI identifies the anomaly; GenAI drafts the corrective communication for review. It’s small, but it saves time, cost and reputation.”

                                Such “mundane” use cases, he argues, are where the real transformation happens. “Everyone talks about the big, shiny things. But in complex, regulated environments, it’s the subtle automations that drive consistent outcomes.”

                                Platforms for the Future

                                Publicis Sapient’s investment in AI platforms underscores its commitment to innovation. Arunkumar highlights three in particular:

                                • Bodhi, the foundational system for building intelligent agents
                                • Slingshot, designed to accelerate software-development lifecycles
                                • Sustain AI, focused on IT service management and operational resilience

                                “These are our three-pronged approach to transformation,” he explains. “Each builds on the other – Bodhi as the foundation, Slingshot for velocity, and Sustain AI for long-term stability. And there’s more in the pipeline…”

                                Culture, Collaboration and the Power of Small Wins

                                For all the technology involved, Arunkumar insists transformation ultimately depends on people. “In any transformation, you work with stakeholders who have competing priorities,” he says. “The key is to focus on agreements first – find small wins and move forward. Progress builds trust.”

                                Publicis Sapient is a people + product business where Arunkumar encourages his teams to balance ambition with empathy. “If a meeting is contentious, end with one thing agreed. Take the rest next time. Transformation isn’t about forcing alignment; it’s about building it. We tell our clients, and our teams, ‘We have your back’. That trust is what makes complex programs succeed.”

                                Looking Ahead: Building Expertise and Depth

                                The focus for 2026, and beyond, is on cultivating deep, dual-disciplinary expertise. “Our teams sit between business and technology,” he explains. “You must be good at both. No one can master all financial services, it’s too vast, but you can specialise. Pick a niche within key areas – asset management, wealth, retail banking, corporate banking, payments, financial crime – and become excellent at it.”

                                At the same time, he urges his teams to stay curious about technology. “Even if you’re not implementing solutions yourself, you need to understand them and speak the same language as engineers and architects. That’s how collaboration works.”

                                Continuous learning, he believes, is non-negotiable. “There’s so much information out there – training, communities, conversations. We just need to channel it, understand the basics and keep moving forward.”

                                Transformation: A Continuous Journey

                                At Publicis Sapient transformation is never static. “We don’t fix something once and move on,” he says. “We think, test, learn, and build again. You must define the real problem before you solve it, validate your progress and inspire others to see the vision.”

                                Purpose and persistence turn complexity into clarity for Publicis Sapient’s clients. “The journey is continuous,” says Arunkumar with characteristic calm. “But that’s what makes it exciting. Every challenge is an opportunity to learn, collaborate and move forward – one small win at a time.”

                                Find out more at publicissapient.com

                                • Artificial Intelligence in FinTech

                                Author

                                Dan Brightmore

                                Published

                                Estimated Read time

                                3Mins

                                AI commerce is set to transform how people shop and buy. Find out how Visa Intelligent Commerce empowers AI agents to deliver reliable and secure experiences at every step…

                                Artificial Intelligence is transforming the way we shop and pay. Visa is leveraging the power of its network and its decades of experience to bring trust and security to AI-powered commerce, bringing to life Visa Intelligent Commerce, which enables AI to find and buy.

                                This is an innovative initiative that opens Visa’s payment network to the developers and engineers who are building the foundational AI agents that are transforming commerce.

                                “Soon, people will have AI agents that will browse, select, purchase, and manage on their behalf,” according to Jack Forestell, Chief Product and Strategy Officer at Visa. “These agents will need to be trusted with payments, not just by users, but also by banks and merchants.”

                                Similar to the transition from physical to online shopping, and from online to mobile shopping, Visa is setting a new standard for a new era of commerce. Now, with Visa Intelligent Commerce, AI agents can find, purchase, and pay on behalf of consumers according to their pre-selected preferences. Each consumer sets the limits, and Visa helps manage the rest.

                                Creating a Trusted Future for AI Commerce

                                Millions of people will soon rely on AI to find the perfect sweater, search for a new vacation destination, or complete a shopping list. Visa will eliminate the friction from payment, making it possible to transact securely and reliably in an AI-powered world.

                                Visa Intelligent Commerce is built on 30 years of experience working with AI and machine learning to manage risk and fraud and to deliver secure payment experiences. Alongside industry leaders such as Anthropic, IBM, Microsoft, Mistral AI, OpenAI, Perplexity, Samsung, Stripe, and others, Visa will facilitate personalised and secure AI commerce on a global scale.

                                “We are working with companies at the forefront of AI innovation to drive engagement on AI platforms and support new ways to pay, with security and trust as our number one priority,” Forestell added. “Together with our partners, we are fully harnessing the potential of AI to transform every aspect of commerce, payments, and business.”

                                Empowering Consumers, Merchants, and Developers

                                The transformation of AI commerce – today a futuristic and relatively unknown concept – into a frictionless, secure, and personalised experience for both merchants and consumers is underway at Visa.

                                Visa Intelligent Commerce incorporates a set of integrated APIs and a merchant partner program into AI platforms, allowing developers to implement Visa’s AI commerce capabilities securely and at scale.

                                Visa Intelligent Commerce offers:

                                • AI-Enabled Cards. Replaces card data with tokenised digital credentials, which enhances security for consumers and simplifies payment processes for developers. In turn, it confirms that the agent chosen by the consumer is authorised to act on their behalf. This incorporates identity verification into AI commerce. Only the consumer can instruct the agent on what to do and when to activate a payment credential.
                                • AI-Powered Personalisation. The consumer is in control. It shares basic spending and shopping information from Visa with the consumer’s consent to improve the agent’s performance and personalise purchasing recommendations.
                                • Simple and Secure AI Payments. Allows consumers to easily set spending limits and conditions, providing clear guidelines for the agent’s transactions. Additionally, it shares real-time commerce signals with Visa, enabling Visa to monitor transactions and help manage disputes.

                                Visa’s payment technologies, including tokenisation and authentication APIs, will help enable transactions that are secure and frictionless, providing confidence to consumers who use AI to make purchases. Visa has decades of experience in fraud management, along with a robust data platform, and uses this expertise to power the Visa Intelligent Commerce program.

                                Find out more about Visa Intelligent Commerce visa.com/intelligentcommerce

                                And learn how Visa is working with banks like CIBC to build a secure future for AI commerce.

                                • Artificial Intelligence in FinTech

                                Author

                                Dan Brightmore

                                Published

                                Estimated Read time

                                4Mins

                                Read in brochure format

                                Our cover star Scott Gunther, General Partner at IAG Firemark Ventures, reveals how the company is bringing powerful investments to…

                                Our cover star Scott Gunther, General Partner at IAG Firemark Ventures, reveals how the company is bringing powerful investments to life to transform the ways insurance is delivered.

                                Read the latest issue of FinTech Strategy here

                                IAG Firemark Ventures: Transforming Insurance

                                Scott Gunther, General Partner at IAG Firemark Ventures, tells FinTech Strategy how the company is championing key InsurTech investments to transform how insurance is delivered.

                                “We realised that if we were going to bring the best of the outside world in, we needed to be a truly global CVC.”

                                Publicis Sapient

                                Financial Services Director Arunkumar Gopalakrishnan tells us how Publicis Sapient is developing the playbook for delivering successful AI-led digital transformations across the financial services landscape.

                                “Working with Generative AI today feels like standing on a new frontier. It keeps us on our toes, but it’s also what drives us – to stay relevant, deliver outcomes and connect both worlds of business and technology.”

                                Techcombank

                                Chief Strategy & Transformation Officer, PC Chakravarti reveals the operating model, Data & AI foundations, culture and talent playbook, and the partnerships turning ambition into market leading outcomes at Techcombank in Asia.

                                “Tech is not the limiting factor – it’s about supporting people and talent to leverage capabilities to enhance business models.”

                                CIBC Caribbean

                                Deputy CIO Trevor Wood explains how CIBC Caribbean is blending technology, culture, and customer-centricity to deliver seamless digital experiences across the region with a ‘Future Faster’ strategy.

                                “We want to lead in every market we operate, build maturity across our practices and be architects of a smarter financial future for all.”

                                Nationwide

                                Dan Wilson, Head of Customer Journey at the trusted mutual, reveals the strategic ambition driving payments innovation to modernise Nationwide’s platform delivering a resilient and secure financial future for customers across the UK.

                                “We’re seeking to modernise the Society’s core infrastructure but also build the tools and features our customers need to help them manage their money and payments.”

                                Alexforbes: Transforming & Diversifying Financial Services

                                Chief Information Officer, Jan Bouwer, explores the work Alexforbes has undertaken to modernise and expand its financial services for its 1.2 million members and retail customers alike. “Alexforbes can now engage its 1.2 million members more directly, offering a wider range of services.”

                                Read the latest issue of FinTech Strategy here

                                • Artificial Intelligence in FinTech
                                • Digital Payments
                                • InsurTech
                                • Neobanking

                                Author

                                Nell Walker

                                Published

                                25 November 2025

                                Estimated Read time

                                3Mins

                                Frustration with a broken system is a great motivator, and Spencer Penn, CEO and Co-Founder of LightSource, can attest to that

                                LightSource is a business which, in its own words, gives the user ‘superpowers’ – an ambitious statement with plenty of evidence to confirm it. LightSource, based out of San Francisco, functions as a direct materials operating system, but provides so much more with its ‘Spec to Scale’ philosophy.

                                Spencer Penn, its CEO and Co-Founder, spent most of his career at Tesla prior to this role. He helped lead a program there called the Model Three, which was Tesla’s first mass market electric car. That was his first real exposure to the world of procurement and supply chain, and sparked a love and passion for that side of the business that led him to help create LightSource.

                                “I got exposed to a lot of challenges and insights, and some of those insights now underlie the product we’ve built at LightSource,” he explains. That love for procurement was inspired, in part, by the former CFO of Tesla, Deepak Ahuja, who Penn reported to and describes as “legendary”. Additionally, the rush to create the Model Three exposed Penn to the sourcing world for the first time.

                                Addressing a wider problem

                                After around three years of ideation, LightSource was launched officially in 2021. Penn never really planned to do this professionally; he thought he’d continue working in the tech field as an employee. But several things changed his mind.

                                “One, I realised the sourcing issues at Tesla weren’t just a Tesla problem,” he explains. “If you look at the income statements of any big manufacturing business, direct materials is the biggest area of spend, and it’s also the least innovative and underserved in terms of technology. So there’s a big market opportunity there. Many of us see it, but it doesn’t mean we quit our jobs like I did. 

                                “The real thing that made the difference, for me, was meeting my Co-Founder, Idan Mintz. He’s my business partner and a brilliant technologist. He’s an engineer, our CTO, and he came from Google X and Google Research. Meeting him showed me I had a real counterpart on the technology side, and made me feel like we could go and pursue the change we wanted to see.”

                                And so, Penn and Mintz began to bring together the right elements to create a team and draw investor and customer interest. At that point, the risk wasn’t in following through with the idea – the risk was that if they waited any longer, someone else would fill the gap LightSource wanted to fill. 

                                Read the full story here.

                                Author

                                Nell Walker

                                Published

                                Estimated Read time

                                2Mins

                                BeNeering’s MD and CRO dig into the company’s history with AI, and the necessity of seeing beyond the hype of new technologies

                                A privately-owned company with over 20 years of procurement digitalisation experience fueling it, BeNeering is at the forefront of the AI-powered revolution in its sector. BeNeering was founded in 2007. At the time, Christoph Moll, BeNeering founder and Managing Director, was busy implementing SAP SRM systems for large European multinationals. While this provided great experience, Moll could see the limitations – and he wanted to move beyond them. He wanted to do something more.

                                “That’s how BeNeering was founded,” he says. “We focused on consulting until 2013, at which point we transitioned to being a solutions provider. I cherry-picked a great development team, and I’m happy to say that the same strong team is with us still today. Stability is our value.”

                                Natalia Parmenova, Chief Revenue Officer, is an example of one such strong team member, who has enormous faith in the business. “From my perspective, BeNeering is a super special solution provider for a few reasons,” she states. “First of all, we believe in co-innovation. Everything we develop is built together with very large and advanced customers. We try to stay close to those customers, to people who are forward-thinking and developing their procurement functions, so we can develop solutions for them with them. We are not just inventing things we think are useful – we are basing our development on customer needs. That’s really important.”

                                Simplify and optimise

                                Many people fall into procurement accidentally. It’s a common story among procurement professionals, and a source of amusement considering how passionate many of them become about it. For Parmenova and Moll, however, it was a case of seeing the way the wind was blowing and following the scent of change.

                                “In 2001, when I was employed by SAP, I wanted to do things better to help users in procurement,” says Moll. “That was how it started for me. My vision was really to simplify and optimise steps and processes for both requestors and buyers.” 

                                Read the full story here.

                                Author

                                Nell Walker

                                Published

                                Estimated Read time

                                3Mins

                                Dafydd Llewellyn digs into why businesses need to be deliberate about how they use AI

                                Taking on a new leadership role can be both exciting and daunting. Dafydd Llewellyn is the CEO of HICX, having taken up the mantle in September. In fact, by the time we spoke with him, he’d only been at his post for five weeks – but he was buzzing with nothing but pure enthusiasm about it. At this crucial juncture, when the pace of change is faster than ever before, strong leadership is vital. As such, Llewellyn has his hand on the tiller as he guides HICX on the next part of its journey.

                                “We’re a supplier management platform,” he says. “Companies work with us because they want to transform their supplier management to become more unified, more strategic, and more intelligent. Ultimately, what that means is our customers’ businesses are really able to make the most and harness the power of the suppliers.”

                                AI in the real world

                                Being competitive in the modern day means being not just au fait with artificial intelligence, but having a deeper understanding of it. The theme of DPW Amsterdam this year, as with the New York event in June, was ‘Put AI to work’. While AI has been a topic of discussion for several years now, planning (and bracing) for its impact is a different matter. But HICX, led by Llewellyn, is ready.

                                “It’s great that DPW is focusing on AI,” he says. “At HICX, we are being very deliberate about the way we approach AI. The way we’re looking at it is really about using AI to allow procurement professionals to be more value-added in the tasks they do, and remove all the repetitive tasks that they really don’t want to be doing. They can drive more value for the business that way.

                                “So, we are looking at things around business processes and automation, right through to document capture, through to compliance checks; using AI agents enables us to do that,” Llewellyn continues. “I can give you a real-life example. If you think about the workflow for a new supplier in the old days, you’d have to have them involved to create that workflow. Now, we can use AI to create that workflow just by writing it in natural language.”

                                Read the full story here.

                                Author

                                Nell Walker

                                Published

                                Estimated Read time

                                3Mins

                                Richard Hogg and Michael Van Keulen discuss what the business stands for and how it is shining a spotlight on tomorrow’s procurement department

                                Green Cabbage is more than just a memorable name; it’s a procurement intelligence company that promises to deliver. A company that lives and breathes its win-win business model. Richard Hogg is the Managing Director. He is, in his own words, responsible for “planting and growing the cabbage across Europe, the Middle East, and Africa”. Michael Van Keulen, Chief Procurement and Customer Officer, is new to the business. However, he’s brought with him over 20 years of supply chain experience, and calls himself a procurement “enthusiast”. 

                                The aim of Green Cabbage’s unique name is to keep money at the forefront of people’s minds. “Plant the cabbage, grow the cabbage; green cabbage is a colloquialism for money in some places,” explains Hogg. “Just like how, in the UK, we’d say ‘dough’. We’re trying to save our customers green cabbage.”

                                Forging relationships

                                Green Cabbage’s Founder and CEO, Eric Cunningham, created the business with a goal to try to rebalance the relationship between buyers and suppliers. “Effectively, Eric and his colleagues were looking at supporting the procurement community in preparation for negotiations, whether that meant pricing, non-price terms, or negotiation strategies,” says Van Keulen. “After three or four years, in 2019, they had amassed enough data to launch Green Cabbage. That’s how we were born. It was effectively a combination of really deep, broad data sets with subject matter expertise in core indirect categories.”

                                For Van Keulen, coming into a young company as a new recruit, he has the benefit of a fresh pair of eyes with plenty of expertise. “The challenge as a practitioner has always been that, on one side of the table, you’ve got a buyer; on the other side, you’ve got a seller. But how much information do I have as a buyer, and how can I ensure that I’ve optimised that contract, no matter my decision?” says Van Keulen. 

                                “It’s not even just about price, but also terms, conditions, limitation of liability, indemnification, and other elements of that contract. That’s why I decided to join this organisation. I’ve always been passionate about procurement and supply chain. I love this profession, the community, and I really feel like Green Cabbage has the ability to take procurement to the next level of maturity.”

                                Read the full story here.

                                Author

                                Johannes Kolbeinsson

                                Published

                                24 November 2025

                                Estimated Read time

                                5Mins

                                Johannes Kolbeinsson, CEO and Co-Founder of PAYSTRAX, on how retailers can protect themselves and their customers from fraud

                                According to Bloomberg, if cybercrime were a country, it would rank as the world’s third-largest economy. Behind only the United States and China. And it’s growing. By 2027, global scams are projected to cost the world $23 trillion annually, with one in three people likely to fall victim. Already in the UK, a financial scam occurs once every fifteen seconds on average.

                                It is within this backdrop that Black Friday and Cyber Monday have become an increasing focus point for both retailers and scammers. Every year, the digital shopping frenzy grows bigger, faster, and more sophisticated. And so do the criminals who exploit it.

                                Black Fraud-day

                                Behind the flashing banners of ‘limited-time offers’ and ‘doorbuster deals’ a quieter threat lurks in the shadows of the checkout page: digital payment fraud.

                                As customers rush to click ‘buy now’ fraudsters blend into the chaos, exploiting high transaction volumes and confusing customers with highly sophisticated fraud techniques. What was once a celebration of online convenience has, for many businesses, become a test of their cybersecurity resilience.

                                This year, the true cost of Cyber Monday and Black Friday may not be measured in discounts, but in data breaches, chargebacks, and lost trust.

                                The Warning Signs

                                While many expect issues like stolen cards or hacked accounts, one of the most easily overlooked threats actually comes from genuine customers who know how to game the system.

                                Friendly fraud, often called chargeback fraud, is when a customer makes a legitimate purchase but later disputes the transaction to claim a refund. High-volume periods like Black Friday create the perfect cover for this, as retailers process thousands of orders at speed and struggle to keep track of every proof of delivery. Because it is hard to prove intent, merchants often lose both the product and the refunded payment.

                                Another issue that rises sharply during major sales events is card-not-present (CNP) fraud, where stolen card details are used to make online purchases. With such a large jump in transactions during Black Friday and Cyber Monday, fraudulent activity becomes harder to identify because it blends into the surge of genuine spending. Without a physical card involved, it is easier for fraudsters to bypass standard security checks, especially if retailers remove friction to create a faster checkout experience.

                                Retailers also need to look out for account takeover (ATO) fraud, which has been increasing as more people shop through accounts and apps. Criminals use stolen login details to access customer profiles, change passwords, redeem loyalty points or use stored card information to make purchases. Beyond the financial loss, ATO attacks can seriously erode customer trust. Which is even harder to recover than the lost revenue.

                                How Retailers Can Protect Themselves Against Fraud

                                Protecting customers and safeguarding revenue does not have to come at the expense of a smooth shopping experience. The key is to strike the right balance between security and convenience, especially when order volumes surge over Black Friday and Cyber Monday.

                                A good starting point is tightening defences around online payments. Simple measures can go a long way. Strong Customer Authentication and Address Verification Services can help spot suspicious activity early, without placing unnecessary friction on genuine shoppers. For higher value orders or anything that feels ‘off’, a quick email or phone check with the customer can prevent a costly chargeback later.

                                Strengthening account security is equally important. Criminals often rely on weak passwords or reused login details to break into customer accounts and make purchases with stored cards or loyalty points. Encouraging customers to use strong, unique passwords and offering multi-factor authentication can dramatically reduce the chances of an account takeover. Retailers can also set up alerts for unusual behaviour, such as repeated failed logins or access from unfamiliar locations, so genuine customers can be protected before damage is done.

                                Friendly fraud is harder to prevent because it often comes from legitimate customers rather than malicious actors. That makes clear communication your best defence. Transparent returns and refunds policies, visible during checkout and in order confirmations, help avoid confusion that later turns into a dispute. Keeping thorough records of fulfilment, including delivery tracking and proof of receipt, gives retailers the evidence they need to challenge any questionable chargeback claims. Small touches, such as using a clear and recognisable store name on bank statements, can also reduce “I don’t remember this transaction” disputes.

                                Ultimately, the most effective approach is ongoing, not seasonal. Setting up a simple chargeback management process helps retailers learn from disputes, identify patterns, and ultimately reduce risk.

                                Where Now?

                                As the Cyber Five weekend continues to redefine global retail, it’s also redefining the tactics of digital criminals.

                                The same tools that make online shopping faster and more convenient, saved payment methods, one-click checkout, loyalty programs, have become new frontiers for exploitation.

                                For merchants, staying ahead means more than offering the best deals; it means securing every step of the digital customer journey. By investing in layered security measures, promoting account vigilance, and maintaining transparent communication with customers, businesses can turn the tide against fraudsters.

                                The goal isn’t just to survive Cyber Monday and Black Friday, it’s to build the kind of trust that lasts long after the sales are over. Because in the evolving world of e-commerce, security isn’t a seasonal strategy – it’s a year-round commitment.

                                Find out more at paystrax.com

                                • Cybersecurity in FinTech
                                • Digital Payments

                                Author

                                Niamh Kingsley

                                Published

                                21 November 2025

                                Estimated Read time

                                6Mins

                                Niamh Kingsley, Founder & CEO of the the post-digital consultancy firm ace, on the Quantum future for financial services

                                Just last week, I sat across from a head of engineering at a major city-based bank and asked about their quantum preparedness. His response? “As far as I’m concerned, that’s science fiction.”

                                From my perspective, this view is definitely misguided. But more concerning, it’s also really prevalent. Despite some senior leaders dismissing quantum as a distant concern, their organisations are already exposed to quantum-enabled threats, and their competitors are quietly positioning for advantage.

                                Breakthroughs from the likes of IBM, Google, Rigetti, and Quantinuum show the ten-year timeline is a mirage. The quantum threat is not future tense. It is present and accelerating. In the race for computational advantage, the largest institutions are already in the lab. In the race for security, the threat actors are already in your network.

                                The time for planning is over, and the time for migration is now.

                                The Security Imperative: Your Data is Already at Risk

                                When we talk about the quantum threat, we’re primarily talking about Shor’s Algorithm. On a sufficiently large, fault-tolerant quantum computer (CRQC), Shor would break the public-key cryptography (RSA and most ECC) that underpins many secure protocols and systems, including virtually every secure digital communication and transaction globally.

                                But here is the critical point: the impact doesn’t start on the day a CRQC goes live; it began years ago the with ‘Harvest/Store-Now, Decrypt-Later (HNDL/SNDL)’ attack vector, where adversaries record encrypted traffic today to decrypt it once quantum capabilities arrive. (Symmetric cryptography like AES is affected differently by Grover’s algorithm, and it is generally mitigated by larger key sizes.)

                                Why ‘Harvest Now, Decrypt Later’ is the Real Crisis

                                Think about your most sensitive, high-value data:

                                • KYC and client records: Confidential information that must remain private for decades.
                                • Proprietary trading strategies: Models and algorithms that define your competitive edge.
                                • Intellectual property and M&A communications: Data whose confidentiality window extends well beyond the projected arrival of a CRQC.

                                Sophisticated adversaries, often state-sponsored, are already harvesting vast quantities of this currently encrypted data. They are storing it, bit by bit, waiting for the eventual arrival of a cryptographically relevant quantum computer, which they will then use to decrypt later.

                                This means that data encrypted today will be vulnerable to breach tomorrow. The shelf-life of your confidential information directly dictates the urgency of your response. Any financial institution that relies on current public-key cryptography to protect data with a retention requirement of five years or more is already compromised in principle.

                                Post-Quantum Cryptography Migration: Why it’s Non-Negotiable

                                A wholesale migration to Post-Quantum Cryptography (PQC), algorithms resistant to quantum attack, is the only defence. This isn’t a simple software patch; it’s a foundational re-architecture of your digital trust layer.

                                • What institutions should prioritise: Any data requiring confidentiality beyond a ten-year horizon is at risk. The UK’s National Cyber Security Centre and G7 frameworks explicitly call out finance to begin migration planning now, with several guides targeting 2035 completion for critical sectors.
                                • Inventory everything: You cannot protect what you don’t know you have. Conduct a rigorous, firm-wide audit to map every single instance of public-key cryptography, from TLS certificates and VPNs to digital signatures, PKI, and key management systems.
                                • Focus on the long-lived: Prioritise the migration of systems protecting data with the longest necessary confidentiality (the HNDL targets) and those that are hardest to change (e.g., embedded systems, legacy code, or critical, highly-available infrastructure).
                                • Mandate the standards: Adopt the new, standardised PQC algorithms, such as CRYSTALS-Kyber (for key establishment) and CRYSTALS-Dilithium (for digital signatures), as decreed by global bodies like the US NIST.

                                Capturing Computational Advantage

                                But here’s what the industry isn’t telling you: whilst you’re busy securing your systems, there’s a competitive dividend waiting for institutions willing to explore quantum’s computational capabilities.

                                I’m not talking about vague promises of exponential speedups. I’m talking about targeted, measurable advantages in specific use cases where quantum algorithms demonstrably outperform classical approaches.

                                Monte Carlo simulations for derivative pricing, XVA calculations, and Value-at-Risk models are obvious starting points. Amplitude Estimation provides a quadratic speedup over classical Monte Carlo, achieving the same error tolerance with exponentially fewer samples. That means shorter calculation windows, faster intraday rehedging, and material energy savings. For path-dependent options or rare-event tail scenarios, quantum approaches offer better resolution of low-probability events without exploding compute budgets.

                                Portfolio optimisation, collateral allocation, and limit setting are fundamentally combinatorial optimisation problems. Quantum heuristics may deliver quality and latency benefits under complex constraints, including funding requirements, capital adequacy, central counterparty margin rules.

                                HSBC made headlines deploying quantum algorithms for foreign exchange pricing optimisation. That wasn’t a marketing exercise; it was a proof point that the technology has crossed from research into application.

                                But, and this matters, we don’t yet have large-scale, fault-tolerant quantum computers. IBM’s roadmap targets approximately 200 logical qubits by 2029. We’re not there yet. Which means the smart play is running parallel tracks: migrate to PQC now for security; experiment with quantum algorithms in targeted pilots to understand future advantage.

                                The pilot framework should be rigorous. Choose use cases where runtime and tail-risk scenarios dominate P&L. Establish measurement frameworks comparing quantum approaches against equal-error, equal-time, and equal-energy classical baselines. Report outcomes honestly. Build institutional knowledge whilst the hardware matures.

                                The Competitive Landscape: The Window is Closing

                                The quantum era is a global, systemic shift. It is a dual-sided challenge, an existential security risk and an unprecedented performance opportunity.

                                We are entering a phase of hyper-competition. The market is already separating into two distinct groups:

                                • The value capturers: These are the institutions that have already established quantum governance, initiated PQC pilots, and embedded crypto-agility into their DNA. They will be secure against HNDL, will meet regulatory mandates like DORA, and, crucially, will be the first to operationalise quantum speed-ups in pricing, risk, and optimisation. They will gain an insurmountable performance edge.
                                • The vulnerable and disadvantaged: These are the firms facing “crypto-procrastination.” They risk massive compliance penalties, systemic data theft via HNDL, and the competitive disadvantage of relying on slower, less accurate classical models while competitors price derivatives and optimise collateral in real-time.

                                The quantum inflection point is not an event on a distant calendar; it is a process happening right now. The firms that act today are building an unbreakable digital fortress while simultaneously designing the algorithms that will define the next decade of finance.

                                Don’t wait for Q-Day. Secure your future, then innovate in it.

                                Learn more at aceadvantage.io

                                • Blockchain & Crypto
                                • Cybersecurity in FinTech
                                • Digital Payments

                                Author

                                Raman Korneu

                                Published

                                Estimated Read time

                                5Mins

                                Raman Korneu, CEO and Co-Founder of neobank myTU, on how FinTech innovation can push positive payments progression

                                In 2025, you’d think payments would move as fast as the businesses they power. But for many digital-first companies (especially marketplaces, lenders, and online platforms) the basic task of reliably moving money in and out is still a daily struggle.

                                This shouldn’t be the case. The industry has made huge advances in consumer UX, credit innovation, and embedded finance. But when it comes to back-end operations, FinTech has left too many problems unsolved. The result? A silent drag on growth, unnecessary labour costs, and a persistent erosion of customer trust.

                                Broken Payments, Broken Business

                                When payments are slow or opaque, everything suffers. Vendor payouts get delayed. Customer refunds take too long. Internal teams lose hours manually checking for confirmation or chasing missing funds. And while the friction is operational in nature, the consequences are strategic: damaged relationships, regulatory risk, and lost revenue.

                                Take reconciliation, for example. Many businesses still use spreadsheets to match payment events across bank accounts, payment processors, and internal systems. Others run Slack channels to manually track funds. This makes things slow and leads to a complete lack of real-time, reliable visibility.

                                This complexity becomes a serious burden when transaction volumes scale. Time zone differences, batch file delays, poor API support, and siloed software can all contribute to failures or mismatches that cause downstream chaos. According to Modern Treasury’s 2025 Payment Operations report, 98% of businesses still run some payment operations manually, and 49% use five or more systems, making reconciliation slow, error-prone, and expensive.

                                The Core Problem: No One’s Talking to Each Other

                                It’s not payment initiation that’s broken; it’s what happens after. Money gets sent, but teams don’t know if it landed. Banks don’t notify businesses. Systems don’t talk to each other. In many cases, there’s no real-time feedback loop to confirm what worked, what failed, and what needs action.

                                This disconnect is a byproduct of legacy infrastructure and siloed design. Most banks don’t expose real-time payment events, and their APIs (when they exist) are often outdated, cumbersome, or not developer-friendly. This leaves businesses stuck in a limbo where payments can go missing, get delayed, or trigger compliance issues, and no one knows until it’s too late.

                                What Better Systems Look Like

                                FinTechs are uniquely positioned to solve this, not with dashboards, but with infrastructure that integrates directly into the tools businesses already use.

                                Plug-and-play APIs and webhooks are the key. When embedded into CRMs, ERPs, and accounting platforms, they can push real-time payment updates exactly where they’re needed. No more spreadsheet-based tracking, and no more switching between portals.

                                The best systems will feel less like platforms and more like invisible plumbing, meaning that they’re always running, always syncing, always up to date. Businesses won’t want to log into yet another dashboard. They’ll expect payments to “just work” within the flows they already operate in.

                                Cards Help, But They’re Not the Solution

                                Modern business cards can improve control on the front end (think: spend visibility, real-time limits, cash flow planning). But they don’t solve the backend challenge of inter-system communication or reconciliation. What’s needed is a shift in how we think about payments infrastructure. We need to insist on and build for clarity and control after the money moves.

                                Why FinTech Hasn’t Solved This Yet

                                For years, payment operations have been seen as ‘boring’. That’s why so many startups have chased flashier front-end use cases: crypto, neobanking, buy now/pay later, and super apps. But that neglect is catching up with the industry.

                                As the ‘Decoupled Era’ of banking continues to fragment the value chain, the complexity of payments behind the scenes only grows. And with instant payments in the EU projected to surge 10x by 2028 (McKinsey), reconciliation needs to happen in real time, 24/7, without manual input.

                                This isn’t a nice-to-have anymore. It’s an operational baseline.

                                The Competitive Edge No One Talks About

                                Payments should be boring, because they should work flawlessly in the background. But for too many fast-scaling businesses, they’re still one of the most complex and error-prone parts of operations.

                                Ultimately this will create a divide. Businesses that build on flexible infrastructure will outpace and outperform those who constantly hit limits and choose to stick to more manual transaction tracking and the guesswork that comes with it. Pulling ahead of the competition isn’t always a matter of out-innovating them. Smoother operations are a way to steadily and quietly outcompete. Fintech is in the position to build this better, and to give smart businesses the edge they deserve

                                Raman Korneu is CEO and co-founder of neobank myTU, a fully automated, AI-powered and cloud-first digital bank offering smart, secure, and affordable financial services. With over 25 years of experience in banking, Raman has held senior roles across finance, including consulting roles at Ernst & Young and PwC, where he worked on over 100 projects for over 50 major banks and companies, including Merrill Lynch Securities and Raiffeisenbank. Raman holds prestigious qualifications including an EMBA from Judge Business School at Cambridge University, the prestigious Chartered Financial Analyst (CFA), and ACCA membership. Driven by his passion to tackle problems in traditional banking, Raman leverages his extensive expertise to lead myTU in delivering innovative financial solutions.

                                • Digital Payments
                                • Neobanking

                                Author

                                Jalal Charaf

                                Published

                                20 November 2025

                                Estimated Read time

                                5Mins

                                Jalal Charaf, Chief Digital & AI Officer of the University Mohammed VI Polytechnic (UM6P) and Managing Director of Ecole Centrale Casablanca on how Africa can seize its moment to lead on data

                                In today’s world, data is not just about numbers and technology; it shapes how people live, how governments plan, and how businesses grow. It influences who gets a loan, who receives medical care, and who has access to education. That’s why control over data, called data sovereignty, is becoming one of the most important sources of power in the 21st century.

                                Unfortunately, Africa is still on the margins of this new reality. Although the continent is home to over 1.4 billion people, 18% of the world’s population, it provides less than 4% of the data used to train today’s most powerful AI systems. Most African data is stored in foreign data centres, beyond the reach of African laws and courts. This is no longer just a ‘digital divide’, it’s a dependence on outside systems that don’t fully understand or represent African realities.

                                What’s Holding Africa Back?

                                There are several key reasons why Africa remains largely underrepresented in the global digital economy.

                                First, representation. Most AI systems are built on data from outside Africa. As a result, they often misjudge or misrepresent African realities, whether it’s credit scoring, medical diagnostics, or speech recognition. The absence of African data creates blind spots that affect real lives.

                                Second, infrastructure. Africa captures less than 1% of global cloud revenue and has limited data storage and processing capacity. This forces governments and businesses to rely on distant cloud providers. Outages, costs, or policy shifts in other countries can suddenly disrupt services at home.

                                Third, governance. With 29 different national data protection laws, Africa lacks a unified approach to managing data. In contrast, the European Union negotiates data rules as a single bloc. Africa’s fragmented regulatory landscape makes it harder to attract investment or protect citizens’ rights.

                                Momentum is Building

                                Despite these challenges, there are reasons to be hopeful. Africa’s data centre market is expected to grow by 17.5% in 2025, thanks to rising digital demand and support from investors focused on environmental and social goals.

                                Several major projects are already underway. Microsoft and G42 (a technology group from the UAE) are investing $1 billion in a geothermal-powered data centre in Kenya. Equinix, one of the world’s largest data infrastructure companies, plans to spend $390 million expanding into West, South, and East Africa. By the end of this year, Rwanda and Zimbabwe will join the list of countries with carrier-neutral data centres, bringing the total to 26.

                                A Blueprint in Morocco

                                Morocco offers a model of what digital sovereignty can look like. In June 2025, a consortium led by Nexus Core Systems announced a 500-megawatt, renewables-powered AI infrastructure project on the Atlantic coast. Phase one, with 40 MW of NVIDIA’s Blackwell AI chips, will go live in early 2026, exporting compute power across Europe, the Middle East, and Africa.

                                Critically, this infrastructure is under Moroccan jurisdiction, not subject to U.S. laws like the CLOUD Act. The project proves that African countries can host cutting-edge data systems while protecting their own legal and strategic interests.

                                How Africa Can Lead

                                To turn early momentum into lasting sovereignty, African governments, institutions, and partners must work together across four pillars:

                                • Data creation and curation. Countries should invest at least 1% of GDP in digital public infrastructure, such as national ID systems, crop mapping satellites, and open data portals. These systems ensure that African data reflects African lives.
                                • Compute and storage. Regions with access to renewable energy can build local ‘green AI corridors’ linked by neutral internet exchanges. This keeps data close to where it’s generated and cuts dependence on foreign servers.
                                • Policy and regulation. The African Union should lead a continent-wide Data Sovereignty Compact, a framework to harmonise data protection, localisation, and AI ethics. A unified legal environment will attract investment and support responsible innovation.
                                • Talent and research. African universities and public agencies should develop homegrown AI talent. Governments can require that models trained on African data are hosted locally. Research must be rooted in African languages, priorities, and realities, not just imported standards.

                                A Role for Everyone: From Governments to Global Partners

                                Governments should commit at least 10% of their ICT budgets to data sovereignty and adopt AU-wide standards. Local cloud facilities and fibre infrastructure deserve long-term funding, not just short-term pilots.

                                Private industry must shift from short-lived cloud credits to permanent, on-the-ground investment. Companies should publish annual data localisation reports and follow the example set by Nexus Core Systems.

                                Development finance institutions (DFIs) should support 20-year infrastructure partnerships, not just one-off tech grants. According to the Global Partnership for Sustainable Development Data, every $1 invested in data systems brings $32 in economic return. That’s a smart investment.

                                Universities, civil society groups, and non-profits also have a responsibility. Open data repositories, civic tech labs, and ethical data governance initiatives must be scaled up to support innovation that’s inclusive and local.

                                A Strategic Opportunity: OpenAI for Countries

                                OpenAI has recently launched an initiative called OpenAI for Countries, designed to help governments build local data centres, train AI systems in national languages, and support start-ups in their own ecosystems. The program is looking for ten partner countries in its first phase. This initiative aligns well with Africa’s goals for sovereign data and democratic AI development.

                                Africa’s Moment to Lead on Data

                                Africa has everything it needs to become a global leader in digital intelligence. Its young population, growing tech talent, and renewable energy potential are powerful advantages. But sovereignty will not be handed over, it must be built.

                                We must act now, before the rules of the digital world are written without us. Morocco’s Nexus Core project shows what’s possible when ambition meets action. It’s time for the rest of the continent to follow suit, and shape a future where Africa owns its data, tells its stories, and sets its own course.

                                • Data & AI
                                • Digital Strategy

                                Author

                                Anthony Yeung

                                Published

                                Estimated Read time

                                4Mins

                                After a turbulent few years, the crypto sector looks on the cusp of another period of boom. Yet, according to Anthony Yeung, Chief Commercial Officer at CoinCover, the success of this next phase will hinge on embedding responsibility and accountability at its core.

                                A few years ago, the crypto sector found itself grappling with a profound image crisis. A series of high-profile scandals, widespread misconceptions about its place within the broader financial system, and a glaring absence of regulatory oversight led many to dismiss the space as a haven for tech-savvy opportunists peddling dubious tokens in a never-ending cycle of ‘get-rich-quick’ schemes.

                                Fast forward to 2025, and while some of that baggage lingers, public understanding of crypto and its underlying value has matured considerably. Endorsements from major governments, coupled with rising levels of institutional investment, have helped to temper concerns about crypto’s legitimacy and long-term role in the financial ecosystem. Nevertheless, questions around trust and transparency continue to cast a shadow over its progress.

                                A Collective Effort

                                It’s clear that crypto remains a hotbed of innovation, much of it focused on attracting more individuals and businesses into the ecosystem. However, alongside the development of cutting-edge solutions, the sector must also dedicate time and effort to rebuilding and strengthening its public image. As we enter this next phase of growth, reinforcing trust and public confidence is just as vital as technological progress.

                                At CoinCover, we believe that tackling this trust deficit could be the key to unlocking the next billion users of cryptocurrency. Driving such a shift will require more than just our efforts. As an industry, crypto must urgently find more effective ways to tell its story showcasing not only its value but also its security. A collective, coordinated effort from stakeholders across the ecosystem is essential to reshape public perception and build lasting confidence.

                                The Path to the Next Billion Crypto Users

                                That sentiment is unlikely to raise eyebrows. From my experience, there’s broad agreement that crypto must do more to manage how it’s perceived by those outside the space. Yet, when it comes to charting a path forward, consensus becomes far more elusive. Chief among the contentious issues is the role of external regulation; a topic that continues to divide opinion across the sector and spark lively debate.

                                Unlike just a few years ago, when regulation in the crypto space was minimal, businesses today face a growing list of compliance demands. Moreover, expectations are mounting that regulatory oversight will only become more stringent in the months and years ahead. For many within the sector, this external scrutiny sits uneasily alongside the original ethos and mission of cryptocurrencies.

                                Evolution, Not Revolution

                                Many crypto OGs acknowledge that the space was born out of a desire for decentralisation, autonomy, and freedom from traditional financial systems. Yet, as with many movements, that founding mission has evolved over time. Today, crypto no longer exists as a siloed alternative but is increasingly integrated into the broader financial ecosystem that supports the modern global economy.

                                While for some the merits of this evolution remain up for debate, its reality is undeniable. For those of us committed to broadening access to the benefits of cryptocurrency, this moment presents more opportunity than challenge. In terms of user access, the crypto space has reached heights few could have expected. The ideology that shaped the sector’s early days need not be discarded, but elements of it must evolve to reflect the times we live in.

                                Responsible Regulation

                                At present, regulation represents the key tension point between these two opposing worldviews. For some, external oversight undermines the very essence of crypto. For others, the wave of incoming compliance offers much-needed validation, a chance for the sector to shed its chequered reputation and re-emerge as a more trusted, credible, and accessible solution for the next billion global users.

                                As a long-time crypto enthusiast, I appreciate the merits of both sides of the debate. At the same time, I’m realistic enough to acknowledge that the genie is well and truly out of the bottle. There’s no turning back the clock on regulation – and perhaps nor should there be. While few within the sector would advocate for overly stringent measures, there is a clear and pressing need for measures to be introduced and upheld that incentivise good behaviour across the board.

                                Unlocking the Next Wave of Users

                                Embracing responsible compliance, and viewing its introduction as an opportunity rather than a threat would mark a positive step forward for the sector. Additionally, it would help initiate the much-needed process of reshaping crypto’s public image: one that reflects a commitment to accountability, long-term growth, and sustainable progress. It could prove crucial as the sector looks to unlock the next billion global users.

                                At CoinCover, we’re committed to helping shape the conversation around this issue. In the months ahead, we aim to engage openly with all sides of the debate; from regulators to crypto companies. By fostering dialogue across the ecosystem, we believe we can play a constructive role in helping the sector reach a more balanced, sustainable equilibrium — one that serves the interests of all stakeholders, and most importantly, its users.

                                Find out more at coincover.com

                                • Blockchain & Crypto

                                Author

                                Dan Brightmore

                                Published

                                19 November 2025

                                Estimated Read time

                                6Mins

                                PA Consulting’s payments expert Simon Williams on the seismic shift in cross-border electronic payments with ISO 20022

                                November 22nd 2025 marks a turning point in electronic payments. ISO 20022 becomes mandatory for cross-border transactions on the SWIFT network. It requires banks to replace traditional payment messages with a larger, data-rich format called MX. At first glance, it sounds like a technical update – something happening at the edge of banks’ infrastructure. But its impact reaches far beyond compliance. ISO 20022 isn’t just a messaging standard. It opens the door for serious modernisation in banking and finance.

                                A New Era for Payments

                                For decades, electronic payment messages have relied on formats designed in the 1970s. These are messages with rigid structures, fixed-length fields, and little room for complexity. To convey essential details, banks have often resorted to private codes and workarounds. They are greed between one another to pass on critical information about a payment.

                                ISO 20022 changes that paradigm, introducing a richer, more flexible, and globally standardised format. This can carry structured data seamlessly across systems. In doing so, it unlocks opportunities for better fraud detection, customer experience, and operational efficiency. These benefits extend not only to banks, but also their clients and service providers across the financial ecosystem.

                                Firms that haven’t properly prepared for the November deadline risk delays, disruption, and rising costs. With SWIFT charging a penalty for every payment message sent in the legacy format. But beyond compliance, many firms are overlooking the opportunities the change poses. Payments are the lifeblood of a bank, and the data they carry is a strategic asset. So how can firms turn the ISO requirement into a competitive advantage?

                                Product Owners and Customer Journey Managers

                                First, banks should use this moment to strengthen their customer journeys. Starting with a deep dive into customer pain points and breaks in the payment flows. This will involve reviewing existing customer journey maps, analysing complaints data, and gathering fresh qualitative and quantitative customer insights to uncover points of friction.

                                For example, unexpected delays in payments or confusion about correct tax reporting and purpose codes are common issues. Data is often at the root cause of these problems. Which is why ISO 20022’s structured data format can help fix issues. Think how tax and fee codes, transaction references, and other enriched fields could reduce ambiguity and speed up processing. Could this avoid the need for banks to contact clients for further information about the correct coding of payments made? Or prevent clients making complaints about delays and fees deducted? Beyond fixing known issues, firms can also use ISO 20022’s richer data to spot patterns. Such as correspondent banks that consistently slow down transactions. And take subsequent steps to address them.

                                Money Laundering Reporting Officers (MLROs)

                                ISO 20022 could also be a game-changer for economic crime prevention in 2026. Anti-money laundering, transaction monitoring, and other sanctions screening relies on interrogating transactional data. And their effectiveness is often only as strong as the data available.

                                Even seemingly simple improvements to data matter. For example, ISO 20022’s structured fields call for addresses to be stored as distinct elements like ‘street name’ and ‘country code’, rather than the generic ‘line one’ and ‘line two.’ This level of precision makes it far easier to flag suspicious activity, like multiple unrelated accounts tied to the same address, or a mismatch between the street name and country code. In other words, ISO 20022 equips banks with the granular data needed to fight financial crime more effectively.

                                Legal Entity Identifiers (LEIs) add another layer of value, enabling a specific organisation to be uniquely and consistently identified across borders, which could streamline KYC and sanctions screening processes. However, two challenges stand in the way: legacy platforms may not support ISO 20022 data, and other banks may not send useful data if it’s not mandatory, such as LEIs for non-financial institutions.

                                Overcoming these hurdles requires a proactive approach, with banks understanding the potential, prioritising technical upgrades that deliver the greatest compliance benefits, and collaborating with other banks and payment schemes to encourage richer data exchange. The payoff? Reduced compliance burdens and a stronger defence against economic crime.

                                Bank Enterprise and Data Architects

                                Bank enterprise and data architects have a key role to play in helping other functions understand the richness and potential value of the ISO 20022 format. Today, many banks translate data into and out of ISO 20022 as payments move through their systems. A process that introduces risk and inefficiency. Extending ISO 20022 structures deeper into internal systems avoids these pitfalls.

                                Updating customer-facing channels to capture payment instructions in an ISO-compliant format will ensure alignment with the structure of messages transmitted by the bank, avoiding the risks inherent with translation. It will also enable future changes, like annual updates to mandatory fields, to be implemented more easily.

                                Thinking of ISO 20022 as a bank-wide data standard opens the door to reducing complexity and preserving data integrity. Ultimately, ISO 20022 can be used to better describe customers, their addresses, and the relationships between parties in a transaction. While it’s only required at the boundary of a bank – where payments are sent to or received from central infrastructure – aligning internal systems with the standard unlocks additional benefits, creating a more open, flexible banking system.

                                Corporate Treasurers and Finance Teams

                                Looking beyond banks, ISO 20022’s benefits extend to customers, corporate treasurers, accounts payable, and accounts receivable teams. Improved reconciliation, better liquidity management, and greater transparency in payment processing are all within reach. ISO 20022 makes it possible to embed detailed information directly into a payment, down to the invoice line-item level. That level of precision could eliminate misallocated payments or stop transactions from bouncing back because they can’t be reconciled.

                                Many ERP systems already support ISO 20022 for both payment initiation and receiving confirmations and statements, making it possible to transmit and receive this enriched data. But success depends on collaboration across the entire payment chain. Customers should be encouraged to embed remittance data into their payments. Banks should ensure this information flows intact through their systems and into payment networks. And IT teams may need to upgrade ERP platforms or enable the use of ISO messages. When everyone plays their part, payments become faster, smarter, and far more reliable – turning payment operations from a source of friction into a driver of value.

                                FinTechs

                                Fintechs have a natural advantage when it comes to ISO 20022. With fewer legacy constraints, they can embed the standard into their platforms from the ground up – most have been ‘ISO-native’ from day one. The question now is how to turn that technical strength into a competitive edge.

                                Consider looking across the customer ecosystem – and internally – to identify opportunities to outperform the competition and deliver benefits to customers. From delivering richer data insights to enabling faster, more transparent payment experiences, firms that move beyond compliance will stand out in an increasingly crowded market.

                                Moving Beyond Compliance

                                The November deadline marks the end of the readiness phase: most banks have ensured compliance at the boundary, where systems connect to payment schemes. But the real work is only beginning.

                                ISO 20022 should not be seen as a technical mandate. It’s a new language for financial information, one that can unlock efficiency, transparency, and innovation across the ecosystem. We are now entering the most exciting phase; the point where true business benefits can emerge. Has your organisation considered where those opportunities lie?

                                Learn more at PA Consulting

                                • Digital Payments

                                Author

                                Paul Clarke

                                Published

                                17 November 2025

                                Estimated Read time

                                4Mins

                                Paul Clarke, Chief Growth Officer at Cashdflows, on how payments infrastructure can support both trust and scale

                                The UK’s game of skill, competition and raffle sector is undergoing rapid transformation. While data on the sector is limited, UK Government analysis indicates that 14% of UK adults collectively spend a total of £1.3 billion per year. For comparison, 44% of adults spend an estimated £8.2 billion annually on the National Lottery.

                                The same report shows an upward market trajectory with 60% of operators anticipating an increase in ticket sales over the next three years, while only 5% expect a decline. When it comes to the players themselves, 22% have increased their spending in the past year, outpacing the 17% who have reduced theirs.

                                Against this backdrop of sustained engagement, fair access to effective payment solutions is essential to support competition among merchants.

                                The Payments Layer of Trust and Scale

                                As operators mature, they must balance commercial growth with strong operational integrity. Unlike purely entertainment-driven apps, these platforms are rooted in real-money participation, whether through entry fees, prize payouts, or both. This heightens expectations for merchants and consumers around security, compliance, and player protection.

                                Payments infrastructure therefore becomes a fundamental line of defence. Tools such as Strong Customer Authentication (SCA) and two-factor authentication (2FA) provide robust safeguards against fraud, account compromise, and unauthorised transactions, reinforcing trust with both consumers and regulators.

                                Enhanced checkout features also play a significant role. Pre-populated payment details and secure card-on-file capabilities streamline repeat purchases, reducing manual errors and checkout abandonment. Click to Pay and network tokenisation support secure one-click transactions, improving conversion performance while ensuring PCI compliance.

                                Real-time fraud analytics, velocity checks, and dynamic transaction routing help maintain strong approval rates and minimise friction, ensuring legitimate users enjoy a smooth and reliable payment experience.

                                From Back-Office Burden to Brand Advantage

                                Payments were once viewed purely as a back-end process, a necessary function behind the scenes. Today, they are a frontline driver of user experience and commercial differentiation. Deposits and withdrawals bookend the player journey, so speed, transparency, and seamless execution boost satisfaction, reduce churn, and can become pivotal to brand advocacy.

                                In a high-volume environment where microtransactions dominate, even brief delays or failed payments can quickly damage trust. Conversely, efficient transactions turn reliable payments into a competitive advantage – one that encourages repeat play and referrals.

                                Powering the Platform Economy of the Future

                                The broader creator and competition economy is still in its infancy, with new formats emerging at pace but what unites them is a reliance on secure, scalable, and accessible payment systems. What those that succeed will have in common is whether those payment systems can support growth while maintaining compliance and safeguarding trust. As investment continues to flow into the sector, the platforms that thrive will be those that view payments not just as operational plumbing but as a strategic asset.

                                Paul Clarke, Chief Growth Officer at Cashflows, has a wealth of experience successfully leading product, business strategy, and innovation functions in the payments, eCommerce, and digital sectors. He was previously Executive Vice President for Product and Innovation at international payments solutions provider: Network International. Prior to this, Paul held leadership positions at key payment organisations, such as Barclaycard, Elavon, and Worldpay. Having joined Cashflows in 2021, Paul is responsible for leading the product proposition, strategy, go to market and delivery functions of the business. 

                                About Cashflows 

                                Cashflows is a new breed of FinTech payments company that makes it easy for small corporates and SMEs to accept card and digital payments – online, in store and on the move. 

                                Through its own acquiring platform and gateway, Cashflows provides a safe, secure ecosystem for processing payments right across Europe. Cashflows products and services are built with the latest technology and the future in mind, always to meet the specific needs of partners and customers. 

                                Learn more at www.cashflows.com  

                                • Digital Payments
                                • Neobanking

                                Author

                                Dan Brightmore

                                Published

                                13 November 2025

                                Estimated Read time

                                3Mins

                                The Card & Payments Awards Middle East will be taking place on Thursday 5th April 2026 at Atlantis – The Palm in Dubai. Entries are open now and close in December.. Book your table for the Awards now!

                                The Card & Payments Awards is among the leading networking events of the year for the Middle East card and payments industry. With over 1,100 guests attending on the night, from over 300 different companies, and with a compelling list of blue-chip sponsors. Enter here and book your tables now.

                                Recognising Excellence and Innovation in Payments

                                For two decades, The Card & Payments Awards has stood as the premier networking event for the UK and Irish card and payments industry. The event was founded 20 years ago by Michael Harty.

                                Building on this legacy of success, 2025 marked an exciting expansion with the inaugural Card & Payments Awards Middle East. Hosted in Dubai on April 17th, 2025, at the prestigious Ritz-Carlton, DIFC, this highly successful event celebrated best practice, innovation, and excellence within the region’s dynamic card and payments sector. It provided an invaluable networking platform, connecting key players and fostering new partnerships and collaborations to drive continued innovation across diverse verticals.

                                The Card & Payments Awards Middle East welcomes entries from credit, debit, prepaid, and charge card issuers, co-brands, merchant acquirers, payment processors, retailers, and other payments companies worldwide offering programs or initiatives within the Middle East. With a range of categories covering essential disciplines, the awards offer organizations a significant opportunity to showcase their achievements and contribute to a vital industry platform that recognizes and rewards the best in the Middle East.

                                Why Enter

                                Entering your company for an Awards Programme is a fantastic opportunity to showcase your achievements to the industry, while benchmarking against your competitors. Ultimately success at the Awards can be leveraged on consumer facing communications.

                                Some of the many reasons to consider an entry are listed below.

                                • A mark of quality assurance from the leading & longest-standing Awards in the industry
                                • Increase your brand exposure through media & PR coverage
                                • Increase your profile with top industry blue chip companies
                                • Increase your credibility and gain trust from consumers
                                • Differentiate your brand
                                • Gain a competitive edge
                                • Benchmark against others in the industry
                                • Drive best practice
                                • Receive recognition, network & celebrate with others in the industry.

                                Putting together an entry can seem a daunting task, so if you aren’t sure where to begin, get in touch with us and we will be able to advise.

                                Enter here and book your tables now to celebrate the industry’s biggest achievements, whilst meeting the key players from across the sector in the Middle East.

                                Author

                                Alex Carteau

                                Published

                                Estimated Read time

                                5Mins

                                Stock Investing has become increasingly popular over the last few years. The self-directed investing trend is in full swing and…

                                Stock Investing has become increasingly popular over the last few years. The self-directed investing trend is in full swing and retail investors are looking for smarter and better ways of looking at the markets to identify winning stocks. A plethora of web services and chats now exist solely to service this market. Many of these services process the same limited types of data, such as market prices and tape, fundamental data, filings or even news sentiments.

                                Navigating Investment Services

                                How can investors navigate this crowded landscape of services? It all depends on what the investor is looking for. Broadly, there are three levels of investing behaviour and tools:

                                Level 1: The Stock Tip. 

                                This investor just wants a stock tip – simply what to buy and when to sell without trying to understand the why. He may “ask the audience” and use a Telegram chat or Discord chat service for that, “phone a friend” who just takes a “50/50” guess. The platforms providing these services are usually unsophisticated operations often with one or two individuals animating a series of chats. Speculation, misinformation and meme stock “pump and dump” schemes are frequent.

                                Outcome: This looks great on the surface as the user gets an immediate stock tip, but what happens later is worrying. The investor will have no idea about when to sell since they did not work to understand the real reasons of why the trade has been initiated in the first place.

                                Level 2: Raw and Calculated Data

                                The investor relies on data platforms for  research and to decide how to identify promising candidates. From Yahoo Finance to Investing.com, many platforms offer raw and calculated data in tables and charts. These include financial data from the company (either as reported or harmonised), analysts’ recommendation price targets or estimates, company filings (13F, Form 4, 8k …) even public databases of senatorial and congressional registered trades.

                                This overabundance of data can create information overload, sometimes leaving users more confused than when they started. With hundreds of fields and ratios, it takes significant financial literacy and experience to know where to look, which metric to focus on and the ones to leave out. Coupled with the information already available via a brokerage platform, often the investor is now facing a “wall” of data. Recently, new conversational AI tools that use natural language have been touted as game changers that can make sense of it all. Unfortunately these tools come with their own limitations and biases that are not always visible..

                                Outcome: The investor is more confused than at the beginning of the process, unless he is trained in using the right metrics for his analysis, this is a losing game. These ChatGPT-like platforms bring a false sense of intelligence as they combine news and data from various sources in a nice summarized paragraph, which is neither reliable, accurate or fool-proof.


                                Level 3: Derived Proprietary Data

                                At this level, the investor would turn to a team of financial market professionals who would generate proprietary rating or scoring for each stock helping an investor focus on the right opportunities.

                                These methodologies are either “proven” or “tested” representing many years of financial market expertise. This layer of human experience makes all the difference in generating valuable insights. Investor’s Business Daily has one of the best known services, providing ratings alongside a respected news bureau that has helped investors for decades.

                                This approach is probably one of the best for a serious investor – one that would consume this proprietary derived data and combine it with news and other market events for a comprehensive investing picture.


                                Level 4: LLMs

                                This level of investing is where not only human experience and skills are in the mix but also Large Language Models processing vast amounts of unstructured data. It is processed from news or filings for a comprehensive view of market conditions and sentiment from text based data. It also brings the most important “human insight” contained in the ranks and scoring in the service.

                                Stock Investing Solutions

                                Beyond this vertical hierarchy, there is also a horizontal challenge; that is that the breadth of data is also an issue. Many platforms provide their own niche services, such as focusing on 13F filings, a specific technical analysis, earnings estimates or option flow. As a result, investors often end up subscribing to several services to gain a comprehensive view of the market.

                                The solution: a flexible, comprehensive platform that delivers everything an investor may need including scoring, rankings and proprietary indicators but while integrating AI models to enhance and supercharge research efforts.

                                Making data meaningful is the future of investing. Human expertise can be blended with intelligent technology, while modern platforms close the intimidation gap between professional insight and everyday understanding. The world is overflowing with information and trustworthy innovation lies in simplification.

                                Alex Carteau is the CEO and Founder of EPSMomentum, with more than 25 years of expertise in financial market software across Asia, Europe, and the United States. He spent more than a decade at Bloomberg, advising investment managers through advanced data and market insights. Following his work on Bloomberg’s specialised equity derivatives team, he expanded his career with leadership roles at RaisePartner and TradingScreen.  

                                At EPSMomentum, Alex applies his deep knowledge of hedge fund technology, stock-picking analytics and trading systems to create tools that simplify investing for everyday investors. Drawing on his background in financial technology, his work emphasises clarity and actionable insights. With a drive to challenge outdated approaches, he is committed to providing investors with professional-level resources and advancing the evolution of smarter investing drawing on insight gained over decades of experience.  

                                • Artificial Intelligence in FinTech
                                • Blockchain & Crypto
                                • Embedded Finance

                                Author

                                Alan Jones

                                Published

                                11 November 2025

                                Estimated Read time

                                5Mins

                                Alan Jones, CEO and Co-Founder, of YEO Messaging, on the need for secure communications platforms with continuous identity verification

                                When it comes to cybersecurity, the financial sector is among the most heavily regulated globally. Yet even as banks invest billions in network protection and data encryption, they continue to fall at a surprisingly low hurdle: how their own people communicate.

                                In the last three years, global regulators have issued fines totalling more than $2.6 billion against financial institutions. For failures in record-keeping and the misuse of consumer messaging platforms. Behind those headlines sits a deeper systemic issue: the tools most employees use every day were never designed for regulated finance environments. 

                                Consumer messaging apps and collaboration tools excel at convenience. But this convenience and familiarity come at the cost of compliance. These platforms lack audit trails, administrative controls, and the data-sovereignty guarantees demanded by frameworks such as MiFID II, GDPR, and DORA. Messages can be stored across multiple jurisdictions, copied, forwarded, or deleted, usually beyond the institution’s knowledge or control.

                                For compliance officers, that creates an impossible paradox. A conversation that starts as an innocent customer query can instantly become a recordable financial interaction. If it happens outside the approved communication environment, the financial institution has already breached its obligations.

                                The Financial Conduct Authority (FCA) and the U.S. Securities and Exchange Commission (SEC) have both made it clear that ignorance is no defence. Whether the messages were business-related or personal, institutions are accountable for maintaining complete, retrievable records of communications by their staff. 

                                The Multi-Billion-Dollar Messaging Gap

                                The operational and reputational damage of these breaches goes far beyond fines. Investigations can cost millions in legal fees, divert resources for months, and erode customer trust overnight. 

                                Another avenue to consider is the increased impact of cyber incidents, especially ransomware. What’s needed, especially in the first 48 hours of any attack, is an out-of-band communications channel from which management and responders can crisis-communicate with confidence and prove responses after the fact. According to IBM Security’s 2024 Cost of a Data Breach report, the financial industry now suffers the highest remediation cost per incident, averaging $6.08 million. This is primarily due to the sensitivity and volume of information exposed through unmonitored channels. 

                                Meanwhile, legacy systems such as email and call centres offer little relief. They’re slow, fragmented, and vulnerable to both human error and social engineering. The result is a growing communications gap. Institutions are caught between regulatory risk on one side and the demand for instant, mobile-first customer interaction on the other.

                                From Data Protection To Identity Protection

                                The next phase of compliance will hinge on something more profound than encryption and identity verification. Knowing who is actually behind each message has become as important as securing the message itself. When consumer apps are used, only the device is verified, not the person. This is a critical distinction. Traditional platforms authenticate a user once, at login. After that, anyone with access to the device – whether a colleague, a contractor, or a cybercriminal – can read or forward sensitive data. It’s a blind spot that regulators increasingly view as an unacceptable risk.

                                By contrast, identity-verified messaging introduces a continuous layer of assurance. At YEO Messaging, we’ve developed patented Continuous Facial Recognition technology that biometrically validates the authorised user in real time. If the user steps away or an unauthorised face appears, messages blur instantly, preventing exposure even on a compromised device. Consider also, sadly, especially in London of late, the impact of device theft (80,000 iPhones were estimated to have been stolen in the last year alone and shipped to China to overcome their Internet firewall restrictions).

                                Combined with geofencing to restrict message access by location, screenshot blocking, and invite-only network controls, this approach ensures that compliance is enforced not just by policy, but by the technology itself.

                                Turning Compliance Into A Competitive Advantage

                                Forward-thinking financial institutions are already realising that regulatory resilience can be a differentiator. A secure, identity-verified communication channel not only prevents breaches but also builds confidence with clients and regulators alike.

                                Instead of chasing retrospective audit trails, banks can demonstrate proactive compliance: every interaction is automatically encrypted, archived, and attributable to a verified individual. For customers, that translates into trust, knowing that sensitive transactions and discussions are protected from interception, impersonation, and insider threat.

                                And for the business, it delivers tangible efficiency gains. Secure, unified messaging across teams and devices eliminates the sprawl of shadow IT while cutting operational costs associated with manual monitoring and data recovery.

                                The Regulator’s New Focus: Communication Integrity

                                The conversation within global financial oversight bodies is shifting. From London to Paris to Basel, regulators are converging on the same message: communication integrity is no longer optional. The Financial Conduct Authority (FCA) in the UK, the European Banking Authority (EBA) in France, and the Basel Committee on Banking Supervision (BCBS) in Switzerland are all broadening their guidance beyond data security to focus on proof of identity and control.

                                This emerging principle of communication integrity, the ability to verify, in real time, that every message originates from a legitimate, authorised source and remains under institutional control throughout its lifecycle, marks a significant evolution in compliance thinking. The message itself is no longer the sole concern; the continuity of trust around that message is what matters.

                                Identity-verified communication is rapidly becoming the benchmark for meeting this new expectation.

                                Bridging Security & Experience

                                Regulation doesn’t have to come at the expense of usability. The institutions that will thrive in this new landscape are those that integrate compliance into the user experience, not bolt it on afterwards.

                                Today’s banking and insurance customers, especially digital-native generations, expect to interact with their banks as easily as they do with friends on devices. The challenge for fintech leaders is to meet that expectation securely. Platforms that combine military-grade encryption with seamless biometric verification enable both.

                                A Closing Thought

                                Non-compliance is no longer a technical glitch; it’s a board-level risk with financial, reputational, and ethical dimensions. The good news is that the tools to close the messaging gap already exist.

                                By embedding identity verification, auditability, and privacy-by-design into every communication, financial institutions can transform compliance from a reactive burden into a proactive safeguard and in doing so, rebuild the foundation of trust upon which modern finance depends.

                                Alan Jones is the CEO and Co-Founder of YEO Messaging, a UK-based secure communications platform that is pioneering continuous identity verification for regulated industries.

                                • Cybersecurity in FinTech

                                Author

                                Cathal McCarthy

                                Published

                                10 November 2025

                                Estimated Read time

                                4Mins

                                Cathal McCarthy, Chief Strategy Officer at Kore.ai, on why now is the time for enterprises to take stock and set themselves up for a long-term, successful future in applying AI where it can make the most difference

                                The generative AI boom has triggered a wave of enterprise experimentation. From proof-of-concepts to customer-facing AI Agents, which can be launched at pace but too often in isolation. This comes as MIT’s latest report finds that only 5% of Generative AI pilots are successful, with the majority failing due to poor integration with enterprise systems and in-house implementations without engagement with expert vendors.

                                As adoption grows, so does the call for accountability. Control and centralisation is more important than ever. Siloed operations and experimentation pilots have meant that there are a trail of disconnected tools, incomplete experiments and sometimes confusion within enterprises of where AI is being used and who is using it, meaning it can’t be governed effectively.

                                Now is the time for enterprises to take stock and set themselves up for a long-term, successful future in applying AI where it can make the most difference. The state of play today shows where clear changes are needed.

                                AI Islands

                                In a recent report from Boston Consulting Group and Kore.ai, 80% of AI leaders say they now favour platform-based strategies over scattered deployments. These platforms are not just about efficiency; they’re quickly becoming the only viable model for visibility, scalability and governance.

                                The consequences of fragmentation are starting to show. CIOs and CTOs are sounding the alarm on siloed AI solutions that make it harder to measure impact, manage risk, or move quickly. This is often the case when AI tools and solutions are implemented in-house and without proven expertise.

                                These ‘AI islands’ are hard to govern, expensive to integrate and nearly impossible to scale responsibly. More than half surveyed in the report say current AI solutions are slowing them down and nearly three-quarters highlight explainability and compliance as top concerns. Clearly, connecting these AI islands together via a common platform can offer more long-term benefits such as better governance, faster time to market, and cost consolidation.

                                Regulation Demands New Architecture

                                Where governance could have been considered a final step by some, it now has to be a design principle from the outset. Transparency, auditability, and oversight must be built into the very fabric of how AI is developed, deployed and monitored.

                                Take the EU AI Act for example, the world’s first broad AI law, now applying to general-purpose AI models from August 2nd, 2025. The rules aim to boost transparency, safety and accountability across the AI value chain while preserving innovation.

                                According to the BCG report, 74% of leaders believe new regulations will significantly influence how they roll out AI across their organisations. And for good reason. Fragmented systems don’t just introduce inefficiency, they create gaps that regulators, stakeholders and customers are not ready to accept.

                                For all the talk of regulation as a constraint, it’s also an opportunity. Regulations should be seen as catalysts, rather than roadblocks. Companies that ensure governance is hard-wired into their AI projects don’t just avoid risk, they create greater trust. And this means greater adoption. This is what leaders need to see, as increased adoption of AI products ensures sustainable, long-term growth.

                                Enterprises in industries holding sensitive and personal data like BFSI, healthcare and retail, are already adopting a platform-based approach. Not only does this ensure integration across the business but also means it future proofs compliance, meeting industry and government regulated standards today but also building in parameters for upcoming regulations.

                                Gaining Control

                                Adopting a platform model doesn’t limit creativity. And it doesn’t mean sacrificing flexibility. Instead of juggling multiple tools, you get one place to plug in what you’ve built and get the best of what’s out there. By running all of your AI capabilities under one unified platform and set of guardrails, your teams across the organisation move forward with one framework, which means, they move faster, make quicker decisions and have a clear understanding of what is – and isn’t – working.

                                Most importantly, a platform turns compliance into a competitive and operational advantage. You can swap models, scale pilots and grow without silos tripping you up, and bring centralised control. This momentum is crucial for scaling and growing an organisation. Platforms create the foundation to scale AI responsibly and effectively and that’s key for future-proofing AI projects and creating impact that matters.

                                • Data & AI
                                • Digital Strategy

                                Author

                                Robert Kraal

                                Published

                                6 November 2025

                                Estimated Read time

                                6Mins

                                Robert Kraal, Co-founder of Silverflow – a cloud-native payments platform designed to reduce cost and complexity while enabling innovation – examines the future for merchants and digital payments

                                There are dozens of examples of companies, and even whole industries, that have failed because they simply weren’t aligned to what people wanted. Nobody in 1985 was desperate for Coke to taste different. In 2001 no one needed a self-balancing electric scooter. And nobody in 2021 needed to have their ownership of JPEG images recorded on the blockchain. The history of business contains many instances of ideas that seemed to emerge fully formed from the minds of their creators rather than as responses to genuine needs.

                                The payments industry might not make as many headlines. However, it is just as full of companies that don’t seem to address any real need on the part of merchants. Too many providers build technology in search of a market, rather than starting with a clear understanding of the challenges merchants face.

                                As payments evolve, that misalignment becomes more visible. Merchants today operate in an environment defined by thin margins, rising costs, and fast-changing customer expectations. Payment Service Providers (PSPs), PayFacs, acquirers, and processors that fail to adapt risk losing touch with what truly matters. Enabling merchants to grow their business efficiently, securely, and globally.

                                So, what do merchants really want from their payments technology – and how can the industry close the gap between expectation and delivery?

                                Beyond ‘Just Getting Paid‘

                                At first glance, payments can appear to be a simple utility. Money goes in, money goes out. Merchants want to get paid quickly and cheaply – and nothing more. But that view misses the larger strategic role payments play in business operations.

                                Cost certainly matters. With corporate bankruptcies at a fourteen-year high and economic uncertainty still weighing heavily, cashflow is critical. Even a small reduction in processing fees can make a difference over time. But “low cost” doesn’t automatically equal ‘good value’.

                                Think of it like buying cheap shoes: they may save money upfront, but if they wear out quickly, the total cost of ownership is higher. The same principle applies to payments infrastructure. The right technology can reduce friction, improve customer experience, and unlock new revenue streams. Far outweighing a slightly higher transaction fee.

                                For many merchants, payments are not just a back-office function but a strategic lever. The ability to expand into new markets, optimise acceptance rates, or adapt quickly to new consumer payment preferences can directly influence growth.

                                What Merchants Say Frustrates Them Most

                                Across industries, merchants face a familiar set of pain points when dealing with payments providers. These often include:

                                Lack of transparency and control over fees

                                Slow onboarding and inflexible contracts

                                Poor technical support and inconsistent service levels

                                Limited access to useful payment data and analytics

                                Outdated systems that make innovation difficult

                                In short, merchants feel constrained by legacy processes and opaque systems that fail to match the agility of their wider digital operations.

                                What Merchants Want Now

                                To move beyond seeing payments as a commodity, providers must understand the specific outcomes merchants are trying to achieve. In practice, that means focusing on five key areas:

                                Higher Acceptance Rates and Fewer False Declines

                                Every false decline represents lost revenue and potential long-term damage to customer loyalty. According to Aite-Novarica, merchants lose billions each year to legitimate transactions mistakenly flagged as fraudulent.

                                Often, these issues arise from outdated or overly rigid risk rules, or from poor visibility into the transaction lifecycle. Merchants need access to data and tools that help identify patterns, adjust rules dynamically, and balance security with customer experience. Smarter fraud management – not just stricter – is key to protecting revenue.

                                Faster Access to New Payment Methods

                                The payments landscape is diversifying rapidly. Account-to-account (A2A) transfers, Buy Now Pay Later (BNPL), mobile wallets, and super-apps are reshaping how consumers pay.

                                For merchants, staying relevant means supporting the methods their customers actually use – without long integration times or complex vendor dependencies. Providers that can onboard new payment types quickly and seamlessly give merchants a crucial competitive advantage.

                                Simplified Cross-Border Payments

                                Global expansion is a natural ambition for digital-first businesses, but cross-border payments remain a major operational headache. Local regulations, currency management, and consumer habits vary dramatically between markets.

                                Merchants want simplified access to local payment methods, along with dynamic currency conversion and compliance tools that minimize friction when operating internationally. A provider that can simplify this complexity – through unified access to multiple schemes and currencies – creates tangible value beyond simple processing.

                                Intelligent Payment Orchestration

                                Many large merchants now work with multiple acquirers and payment processors to optimise cost, performance, and redundancy. But without an orchestration layer to intelligently route transactions, they risk inefficiency and downtime.

                                Modern payment orchestration platforms can automatically send each transaction through the most cost-effective or reliable channel in real time. That capability depends on robust infrastructure – not a tangle of APIs and patches. Merchants increasingly expect their providers to offer orchestration as a native feature, not an afterthought.

                                Modern, Cloud-Native Infrastructure

                                This is where the real bottleneck lies. Many PSPs and acquirers still operate on systems designed decades ago – architectures built for a different era of commerce. They’ve been maintained with patches, middleware, and manual workarounds that make innovation slow and integration difficult.

                                Merchants now expect cloud-native systems that are modular, scalable, and API-driven. Platforms that deliver real-time data visibility, analytics, and adaptability – allowing merchants to build and evolve without being constrained by legacy code.

                                Providers that cling to old systems risk not just technical debt, but strategic irrelevance. Payments infrastructure should be an enabler of innovation, not an obstacle.

                                Rethinking the Infrastructure Layer

                                The issue isn’t that modern payment solutions don’t exist – they do. The problem is that too many are bolted onto outdated foundations. Layering new features onto old systems is like fitting a Formula 1 engine into a 1970s chassis: technically possible, but structurally unsound.

                                The future of payments lies in rethinking the infrastructure layer entirely. That means building platforms that are natively cloud-based, flexible by design, and ready to integrate with tomorrow’s technologies.

                                Modern infrastructure enables:

                                • Faster onboarding and deployment
                                • Greater transparency into transaction data and fees
                                • Easier compliance with evolving regulations
                                • Continuous innovation without system downtime

                                This shift isn’t just technical – it’s strategic. It’s about giving merchants the confidence that their payment systems can scale with them, wherever their business goes next.

                                A New Standard for Payments

                                The payments industry has reached an inflection point. Merchants no longer see payments as a commodity or cost centre – they see them as a growth driver. Providers that continue to build products in isolation from merchant needs will fall behind.

                                Success will come to those who build with a merchant-first mindset: reducing barriers, improving performance, and enabling future growth.

                                The question for every PSP, PayFac, and acquirer is no longer “What features can we add?” but “Are we ready to deliver what merchants actually need?”

                                About Silverflow

                                Silverflow is a new kind of payment processing platform designed for today’s payment needs and fit for the future. A cloud-native solution with a single API to the card networks. One platform with one connection. Reducing cost and complexity, easy to use, data-rich, Silverflow frees you to innovate. Find out more at silverflow.com

                                Co-founder Robert Kraal is one of the few people in the world with over 20 years of experience in online payments.

                                After completing his degree in Geophysics, he started his career at Bibit, the first global Payment Service Provider (PSP) which was acquired by RBS/Worldpay. At RBS/Worldpay he went on to lead account management, before moving on to Google Netherlands. He joined Adyen in 2010 in the role of COO, where he was responsible for building and running the global acquiring and processing service.

                                As the Business Development lead at Silverflow, Robert is responsible for maintaining relationships with the card schemes, acquirers, PSPs and regulators.  

                                • Digital Payments
                                • Neobanking

                                Author

                                Dan Brightmore

                                Published

                                5 November 2025

                                Estimated Read time

                                3Mins

                                Read in brochure format

                                Welcome to the latest issue of Interface magazine! Click here to read the latest edition! USDA: A Fresh Perspective on…

                                Welcome to the latest issue of Interface magazine!

                                Click here to read the latest edition!

                                USDA: A Fresh Perspective on Digital Service

                                This month’s cover story focuses on the digital transformation journey continuing at the United States Department of Agriculture (USDA). In conversation with Fátima Terry, USDA’s former Digital Service Deputy Director, we revisit the sterling work being carried out and find out how technology is being humanised to deliver value to the American people this organisation serves.

                                “One of the things we did was partner with multiple USDA teams that focused on customer experience and digital service delivery for their programs,” she explains. “We also partnered with other federal-wide agencies and departments to move forward and evaluate the progress of digital transformation by cross-pollinating success models to everyone connected.”

                                Ayoba: A Super-App for Africa

                                Ayoba, part of the MTN telco group, is a super-app platform built in Africa, for Africa. Esat Belhan, Chief Technology & Product Officer, reveals how it is bringing more people to digital so they can be tech-savvy and educated on digital capabilities…

                                “In order to do that, one thing you could do is give away free data, but that data could be easily wasted on another data-heavy app, like TikTok, in just a couple of hours. So, the real solution is that the valuable and insightful content Ayoba provides should be provided for free, and that we provide instant messaging and short video content, to keep people using our platform for their communication and entertainment needs.”

                                Kraft Kennedy: Supporting MSPs with People and Processes

                                Nett Lynch, CISO at Kraft Kennedy, explains how the company’s new division, Legion, solves cyber pain-points for MSPs with a collaborative, business-centred approach.

                                “A lot of MSPs struggle with client strategy, they’re talking tech instead of business. We’re nerds – we love the tech, we love the features. But we need to admit clients aren’t focused on those things. They don’t necessarily care how or why it works. They just want it to work and align to their business goals.”

                                And read on to hear from FICO’s CIO on using AI to transform technical operations; learn from KnowBe4 how AI Agents will be a game changer for tackling cybercrime; and discover how data centres are meeting the demands of the AI boom with Vertiv.

                                Click here to read the latest edition!

                                • Data & AI
                                • Digital Strategy
                                • Infrastructure & Cloud
                                • People & Culture

                                Author

                                Dan Brightmore

                                Published

                                Estimated Read time

                                5Mins

                                Interface hears from Emergn CTO Fredrik Hagstroem on approaches to AI best practice that can drive positive business transformations

                                What does it actually mean for an organisation to be AI-ready, beyond having the right tools and data

                                “Being AI-ready is fundamentally about openness to learning and the ability to react quickly. While having the right tools and well-managed data is essential, true readiness is defined by an organisation’s capacity to operate, monitor, and measure the effectiveness of AI solutions.

                                We often see organisations invest heavily in implementation and tooling, only to realise that no one is prepared to take responsibility for running, monitoring, and improving AI systems.

                                AI-savvy organisations design solutions differently depending on the type of work, operational versus knowledge work, and, for knowledge work, focus on measuring effectiveness rather than just productivity.”

                                Where do most companies go wrong when trying to embed AI into their operations?

                                “Many companies treat AI solutions like traditional IT projects, using user acceptance as a checkpoint between development and handover to IT operations. This approach often fails before it even begins.

                                AI performs tasks that typically require human intelligence, perception, reasoning, and decision-making. While AI can execute these tasks with far greater precision and consistency than humans, someone within the organisation remains ultimately accountable for the results.

                                The most common misstep is underestimating the need to provide users with the right level of oversight and control so they can accept accountability for AI-driven decisions.

                                For example, explaining how AI decisions are made and demonstrating that they are ethical and fair depends not only on transparency and traceability but also on maintaining control and proper training data records.”

                                How can leaders prevent transformation fatigue during AI-driven change initiatives?

                                “Change is inevitable, so responding to it is part of effective leadership. AI will transform how businesses operate, but transformation fatigue arises when people feel constantly subject to change rather than in control of it.

                                Deliberate planning and thoughtful communication help, but the most effective approach is to empower people to feel more in control. This often involves organising teams around value streams that cut across business, technology, and operations.

                                Leaders can ensure teams have the skills and information necessary to take ownership of outcomes and make adjustments based on real results. This is especially important with AI solutions, which should be structured to provide continuous feedback, allowing teams to monitor performance, improve models, and refine processes based on learning.”

                                What kind of mindset and cultural shift is required for AI to deliver long-term value?

                                “Delivering long-term value from AI requires a shift from control to collaboration, and from predictability to adaptability. Organisations focused on individual targets and siloed accountability often struggle to realise AI’s full potential.

                                Value emerges when teams adopt a collective mindset, defining success by shared outcomes, whether customer experience, business impact, or strategic growth. Individual productivity only matters when it benefits the whole system.

                                Another critical shift is embracing uncertainty. Traditional corporate cultures often reward certainty and fixed plans. Cultures that support experimentation, feedback loops, and incremental change are more likely to see lasting benefits from AI.

                                This cultural evolution isn’t just about tools; it’s about how work is structured, how teams interact, and how decisions are made. Empowering teams to act fast, learn fast, and improve fast is central to sustaining AI-driven value.”

                                How can organisations balance AI experimentation with maintaining trust, transparency, and alignment with business goals?

                                “Each AI initiative should be evaluated based on the type of work and value it aims to deliver, whether efficiency, experience, or innovation. Different goals require different levels of oversight and distinct success metrics, making a portfolio approach to investment essential. Maintaining alignment with business goals means focusing on outcomes rather than outputs.

                                This requires systems where feedback, transparency, and learning are built in from the start, allowing initiatives to fail gracefully. Trust begins with a clear governance framework, as AI, like any transformative technology, can have unintended consequences. Transparency is not just audit trails; it’s about inviting dialogue, sharing lessons learned, and adapting as standards and regulations evolve.

                                Experimentation and learning go hand in hand. Delivering incremental value early builds credibility and transparency, helping teams understand what works and what doesn’t. Ultimately, AI is only valuable to the extent that it drives the business toward its strategic goals.”

                                How do organisations deal with some of the risks associated with AI – hallucinations, privacy issues, etc. – and how do they go about both securing essential data and overcoming employee resistance to the technology?

                                “Treating AI adoption as an iterative, feedback-driven process is key to managing risks. Success is less about getting everything perfect from the start and more about structuring work to minimise unintended consequences and adapt quickly.

                                “Hallucinations” is a misleading term. Today’s AI doesn’t imagine things; it follows programmed rules based on probabilities and patterns. Like any software, AI carries risks of errors or mismanaged data.

                                What is new is how AI uses data, to train models that imitate human decision-making. Without careful management, models can produce biased or unethical outcomes. Technology does not remove employee accountability. Recognising this allows organisations to design AI solutions with lower risk.

                                Designing solutions with humans in the loop is critical. It promotes transparency and explainability and is the most effective way to overcome resistance while maintaining control over outcomes.”

                                Find out more from Emergn

                                • Data & AI
                                • People & Culture

                                Author

                                Dan Brightmore

                                Published

                                Estimated Read time

                                3Mins

                                Read in brochure format

                                Welcome to the latest issue of Interface magazine! Click here to read the latest edition! Washington State DNR: People-Led Cybersecurity…

                                Welcome to the latest issue of Interface magazine!

                                Click here to read the latest edition!

                                Washington State DNR: People-Led Cybersecurity

                                Ralph Hogaboom is a seasoned cybersecurity leader, a CISO with a deep commitment to public service and a human-centred approach to information security. Our cover star talks about creating a people-led cybersecurity function for the Washington State Department of Natural Resources (DNR) defined by long-term thinking, commitment to the vision and keeping empathy at the forefront.

                                “Now we’re the team that helps people get to ‘yes’,” says Hogaboom. The core of it, he explains, is an approach to cybersecurity focused on people, their needs and outcomes, rather than a systems or technology-centric approach.”

                                IAG Firemark Ventures: Transforming Insurance

                                We check in again with Scott Gunther, General Partner at IAG Firemark Ventures, on how the company is bringing powerful investments to life to transform how insurance is delivered.

                                “We realised that if we were going to bring the best of the outside world in, we needed to be a truly global CVC.”

                                Delta Dental: Cybersecurity as a Business Enabler

                                Alex Green, CISO at Delta Dental Plans Association, talks cyber risk, resilience, and practicing servant leadership in a uniquely challenging cybersecurity environment.

                                “Cybersecurity isn’t about locking everything down; It’s about managing risk in a way that allows the business to operate, adapt, and grow.”

                                Alexforbes: Transforming & Diversifying Financial Services

                                Chief Information Officer, Jan Bouwer, explores the work Alexforbes has undertaken to modernise and expand its financial services for its 1.2 million members and retail customers alike. “Alexforbes can now engage its 1.2 million members more directly, offering a wider range of services.”

                                University of Tasmania: A Technology Transformation for the People

                                We spoke to four members of the University of Tasmania‘s, research, and student services team to dig into the incredible work the university is doing to support researchers and students, and what such a complex operation entails.

                                “We recognise that not all potential students get the support they need to go to university,” says CIO Kathleen Mackay. “But we want to be able to provide that support.”

                                Click here to read the latest edition!

                                Author

                                Dan Brightmore

                                Published

                                31 October 2025

                                Estimated Read time

                                6Mins

                                Osama Bari, Chief Technology Officer at D24 Fintech on the need for cybersecurity advancement to support the rise of crypto adoption

                                Cryptocurrency adoption has accelerated dramatically, rising in popularity in recent years. Yet the sector remains a prime target for cyberattacks. As digital assets grow in value and popularity, the stakes for both exchanges and users have never been higher. High-profile incidents, such as the CoinDCX breach in July, which saw hackers steal $44 million without touching user wallets, Phemex losing $69 million in a crypto heist, and WazirX losing $230 million, demonstrate the sophisticated tactics cybercriminals now employ.

                                Similarly, the Bybit hack exposed vulnerabilities in multi-signature authorisation and user interface (UI) spoofing. This highlights how even experienced professionals can be caught off guard.

                                These events underscore the urgent need for exchanges and financial institutions to prioritise security. They must implement robust protocols, and adopt comprehensive risk-management strategies. There are several core areas where crypto platforms can significantly reduce the risk of security breaches.

                                Strengthening Cybersecurity Protocols

                                It is vital for exchanges to implement multi-party approval systems for all transactions. By using threshold-based authorisation, combined with real-time monitoring of deposits and withdrawals, platforms can identify unusual activity and flag it for manual verification. Each withdrawal should undergo a transaction audit score assessment before processing. Such measures are critical for preventing attacks that exploit UI vulnerabilities or other operational oversights. This ensures that no single point of failure can compromise user assets.

                                Another essential safeguard is two-factor authentication (2FA). While a long-established security measure, its importance in protecting accounts and verifying users cannot be overstated. By requiring a second form of identification, exchanges can ensure only authorised personnel access accounts and manage balances. In practice, this simple but effective layer of protection increases the difficulty for hackers. It demonstrates an exchange’s commitment to protecting its customers’ funds. All financial providers should offer 2FA as a baseline security measure.

                                Custodians also play a vital role in mitigating risks. For many exchanges, especially those handling large volumes of assets, partnering with a trusted custodian provides additional security and oversight. Custodians safeguard digital assets on behalf of clients, reducing exposure to theft, loss, or mismanagement. In the aftermath of this year’s prominent hacks, the value of external support becomes clear. Custodians enable exchanges to focus on customer experience and platform innovation while ensuring that user funds remain secure.

                                A further innovation gaining traction is liveness verification, which confirms user identity through biometric measures such as facial recognition or fingerprints. With roughly 40% of banks having implemented this measure to counter fraud – up from 26% five years ago – crypto platforms have an opportunity to follow suit. Liveness checks provide an additional barrier to attackers who might otherwise exploit compromised passwords, keys, or devices. The uniqueness of biometric identifiers ensures that users’ accounts are better protected against increasingly sophisticated fraud attempts.

                                Centralised cryptocurrency exchanges (CEXs) continue to demonstrate resilience in the face of attacks. Security must be embedded into operational design. The recent incidents highlight the effectiveness of CEXs’ ability to freeze or recover stolen assets quickly. By collaborating with other platforms and utilising centralised oversight, these exchanges can mitigate the impact of breaches. As crypto continues to gain mainstream traction, balancing decentralisation with strong security infrastructure is essential to maintaining investor trust and market stability.

                                A Holistic Approach to Crypto Security

                                Beyond these specific measures, exchanges must also adopt holistic cybersecurity strategies. Key steps include thorough risk assessments to identify vulnerabilities. Rigorous protection of private keys through encryption and secure storage. Robust wallet security with multi-factor authentication. And secure transaction protocols including encryption and transaction signing. Regular updates to software and firmware, coupled with continuous network monitoring using intrusion detection systems and threat intelligence feeds, further strengthen a platform’s defence.

                                Data encryption and access control are critical to prevent unauthorised access. Furthermore, periodic security audits and assessments ensure protocols remain effective as threats evolve. Smart contract and token security, secure coding practices, and rigorous testing must also be prioritised to safeguard DeFi applications and other blockchain-based services. Importantly, exchanges should implement backup and recovery protocols to safeguard against potential data loss. And maintain clear incident response plans to mitigate the impact of any breach.

                                Educating users remains an underappreciated but crucial aspect of crypto security. Platforms should guide strong password practices, phishing awareness, software updates, and overall security hygiene. Well-informed users are an integral layer of defence, reducing the likelihood of successful social engineering attacks or credential theft.

                                Finally, regulatory compliance is indispensable. Exchanges operating within clear legal frameworks and adhering to anti-money laundering (AML), counter-terrorism financing (CTF), and data protection regulations significantly reduce risk exposure. Partnering with reputable security vendors and maintaining open lines of communication with regulators can enhance both operational security and market credibility.

                                Learning from Previous Incidents

                                The CoinDCX incident serves as a cautionary tale. By exploiting vulnerabilities without ever accessing individual wallets, attackers demonstrated high-value, sophisticated hacks can occur even in the absence of traditional breaches. This reinforces the point that centralised oversight, real-time monitoring, and rapid response protocols are crucial in mitigating damage and protecting customer assets. Exchanges that fail to implement these measures risk not only financial loss but also erosion of trust, which is arguably a more severe long-term consequence.

                                As cryptocurrencies increasingly integrate into institutional portfolios and mainstream finance, robust security is no longer optional; it is fundamental. Investors, funds, and enterprise clients require assurance that digital assets are safeguarded. And that exchanges and custodians adhere to industry-leading security standards. Platforms that prioritise security will not only protect their customers but also foster broader adoption and confidence in the market.

                                The Path Forward

                                The evolution of crypto security is a continuous process. While decentralised networks inherently resist certain forms of attack due to their distributed structure, the human, operational, and software layers of the ecosystem remain vulnerable. The combination of multi-party approval systems, 2FA, custodian partnerships, biometric verification, continuous monitoring, and regulatory compliance provides a robust framework for mitigating these risks.

                                The message is clear: security must be embedded into the DNA of every crypto platform. Only through a proactive, multi-layered approach can the industry protect its users, maintain trust, and continue to grow sustainably. As high-profile breaches like CoinDCX, WazirX, Phemex, and Bybit demonstrate, the cost of complacency is far too great. By prioritising security today, exchanges not only defend against current threats but also lay the foundation for the future of a resilient, trustworthy crypto ecosystem.

                                About D24 Fintech

                                D24 Fintech focuses on developing innovative technological solutions for the evolving digital and fintech landscape.

                                By leveraging innovation and emerging technologies, D24 Fintech engineers integrated solutions designed to enhance transactional security, streamline digital payments, and improve operational efficiency. With a global perspective and a customer-first approach, D24 Fintech aims to redefine industry standards and drive innovation into fintech ecosystems.

                                D24 Fintech’s digital solutions include developing advanced technological platforms and management tools, and more.

                                • Blockchain & Crypto
                                • Cybersecurity in FinTech

                                Author

                                Dan Brightmore

                                Published

                                30 October 2025

                                Estimated Read time

                                3Mins

                                ABBYY survey finds financial services industry leading on innovation, but challenges exist with deployment  

                                New research commissioned by ABBYY has revealed a staggering 91% of financial services organisations are using sophisticated Generative AI tools. However, many experienced major challenges with deployment. 

                                While 98% of banking firms reported positive results from GenAI, many admit to needing to augment it with other technologies for better outcomes, according to the 2025 ABBYY State of Intelligent Automation Report: GenAI Confessions. 44% of financial services companies say their investment in GenAI will rise more than 20% in 2026. 

                                Managing AI Expectations

                                The survey, conducted by Opinium Research, shows that training the GenAI models was harder than expected for 39% of financial services firms, 32% found it difficult to integrate into business processes and 29% found their staff did not have the necessary skills to deploy it. In addition, 26% did not have proper governance. 

                                It meant 42% of companies had to add document AI to improve outputs, while 39% used process intelligence, and the same amount asked staff to manually check results – much higher than the global average of 25%, suggesting too much manual intervention. 

                                Adding other technologies led to 59% of respondents having increased trust in GenAI, 55% seeing better quality outputs, and just over half (51%) benefiting from more cost savings and better integration into their workflows. 

                                “It seems that financial services leaders spent money on GenAI tools that promised more than they can provide. In some cases, they didn’t even need it. Before moving forward with GenAI tools for agentic automation, companies need to first evaluate their current processes and create a visibility map of their workflow with data analytics tools such as process intelligence. When training models prove more difficult than expected, pre-trained, purpose-built AI turns out to be the right solution.” 

                                Maxime Vermeir, Senior Director of AI, ABBYY

                                Generative AI Creating a Buzz

                                While the top reason for introducing GenAI was to increase efficiency and customer service (67%), banking industry bosses are the most concerned about employee wellbeing. Over a third of respondents (35%) hoped the technology would reduce employee burnout and a quarter (25%) cited improving job satisfaction as a key goal – much higher than other industries such as transport and logistics (11%) and manufacturing (15%). 

                                However, the survey also revealed that four-in-ten (40%) of financial services leaders admit that a driving factor for introducing GenAI was that employees were already using it on a Bring Your Own Software (BYOS) basis for personal productivity – which could impact security concerns over Shadow AI. Over half (51%) say employees wanted the technology to “make them look smarter and more professional,” while 67% said it reduces workload and increases productivity.  

                                Generally, staff are optimistic about GenAI, with 88% of leaders saying workers enjoy positive results. 

                                “GenAI is creating remarkable opportunities to reimagine how work gets done, which is rightfully generating a great deal of excitement. However, shadow AI, when individuals use commonly available tools like ChatGPT, Grok, or Perplexity without oversight at work, potentially raises serious data privacy and compliance concerns. The corporate benefits of GenAI’s potential are truly unlocked when leaders drive secure, strategic adoption with risk management as a priority.” 

                                Ulf Persson, CEO, ABBYY

                                Key Findings from ABBYY

                                Other key findings from the report include: 

                                • 65% of financial services organizations are using purpose-built AI – compared to 59% of companies globally 
                                • 62% use agentic compared to 53% on average by other industries 
                                • Top uses for GenAI in banking: data analysis (59%), employee productivity (56%), automating business documents (56%), customer-facing apps like chatbots (55%) 
                                • Departments using GenAI: Finance for fraud detection and cash flow predictions (57%), sales and marketing (56%) compliance and legal (45%) 
                                • Wishlist of improvements for GenAI include being free of human bias and using less resources 

                                Access the full State of Intelligent Automation: GenAI Confessions 2025 report 

                                Methodology 

                                Opinium research of 1,200 senior managers or above in companies of 100+ employees in the US, UK, France, Germany, Australia and Singapore with 110 financial services leaders questioned. Research undertaken between 20th of June and 8th of July 2025. 

                                About ABBYY 

                                ABBYY helps organizations optimize processes, accelerate decisions, and drive better outcomes with Process AI and Document AI. More than 10,000 enterprises, including many Fortune 500 companies, rely on ABBYY’s 35 years of innovation to turn business data into actionable insights that improve the way we work and live. Headquartered in Austin, Texas, and offices in 13 countries, ABBYY leads the way for smarter agentic automation. For more information, visit www.abbyy.com

                                  

                                • Artificial Intelligence in FinTech

                                Author

                                Nell Walker

                                Published

                                Estimated Read time

                                3Mins

                                Exiger has been awarded a huge contract to help modernise the detection of transshipment for the US government

                                Exiger, the market-leading supply chain AI company, announced today that it has been awarded an exclusive, multi-million dollar contract by US Customs and Border Protection (CBP) to modernise the detection of illicit transshipment across global supply chains. Designed to evade tariffs, trade restrictions and sanctions, illicit transshipment is the practice of manipulating supply chains to disguise a product’s true country of origin. Exiger’s Trade AI will be adopted and deployed across CBP, serving as an additional tool for the US government’s transshipment detection capability.

                                Transshipment identification and enforcement are critical priorities for the Department of Homeland Security (DHS) and CBP. Convergent Solutions, Inc., DBA Exiger Government Solutions, will equip CBP enforcement offices and personnel across the US with access to Exiger’s AI platform and data to identify illicit transshipment at-scale and in real-time.

                                “Billions of dollars worth of global trade move through illegal transshipment channels that seek to bypass US restrictions,” said Exiger CEO Brandon Daniels. “A core CBP mission is to enforce US trade and forced labor laws, thereby helping ensure that American manufacturers and workers are competing on a level playing field. Exiger is proud to support this mission, bringing to bear the world’s largest proprietary supply chain database and the market’s most sophisticated AI.”

                                Exiger’s AI will be an additional resource available to CBP personnel to:

                                • Detect illegal transshipment across global supply chains
                                • Monitor and enforce tariff and trade regulations
                                • Leverage Exiger’s proprietary AI models and trade intelligence data to enrich data in CBP systems and enhance decision making
                                • Deploy AI-enabled validations of tariff classification, value and country of origin
                                • Create automated bills of material for products and sub-components
                                • Map the flow of raw materials and sub-components through global supply chains
                                • Risk-score shipments in-real time
                                • Collect tariff revenues earlier
                                • Trace global supply chains to enhance import visibility and risk segmentation

                                Exiger’s proven AI solutions have been deployed across 60+ US Government agencies, including the Department of War, Department of State, Department of Energy, DHS, the intelligence community, and armed forces.

                                Exiger’s technology continues to earn top recognition. In April, Exiger was named an awardee on the Government Services Administration’s Supply Chain Risk Illumination Professional Tools and Services (SCRIPTS) Blanket Purchase Agreement, and was the highest-ranked unrestricted vendor awardee of the 10-year, $919 million contract. This year, Exiger was named a Leader in the 2025 Gartner® Magic Quadrant™ for Supplier Risk Management Solutions, a Best-of-Breed Solution and three-time Value Leader in Spend Matters’ SolutionMap, and a Leader in Omdia’s Market Radar: Firmware and Software Supply Chain Security. Exiger also won a 2025 STEVIE® Award for AI Company of the Year.

                                • AI in Supply Chain

                                Author

                                Dan Brightmore

                                Published

                                27 October 2025

                                Estimated Read time

                                4Mins

                                ClearBank research finds half of large firms say embedded finance will drive new revenue, but concerns over outdated systems, implementation challenges, integration and customer trust loom

                                New research from ClearBank reveals that large UK businesses now view embedded financial services as a strategic boardroom decision and business growth driver.

                                The research, ‘The embedded economy: Why brands are embracing financial services as a driver for innovation and growth’ explores the attitudes of 200 senior business leaders at large UK-based corporates towards embedded finance and the potential for payments, accounts, and lending to enable new services, new revenue streams, and enhanced customer loyalty.

                                It found that despite growing enthusiasm for embedded finance’s potential to deliver these services, many companies are still held back by fears of regulatory requirements, technical complexity, and ongoing concerns around finding the right partner to deliver at scale.

                                A Boardroom Priority: Nearly Half of Corporates see Embedded Finance as a Revenue Driver

                                Implementing embedded finance has rapidly moved from a niche innovation to a strategic boardroom decision. Survey results found that 38% of C-suite leaders cite embedded finance as important for their company’s growth, reflecting the shift in mindset from viewing it as a back-office payments tool to a driver of competitive advantage.

                                Crucially, nearly half (48%) of corporates surveyed see embedded finance as a way to improve payments and launch new revenue-generating services. These services range from offering own brand accounts to saving tools and lending services. For many, the potential increase in revenue is compelling, with more than a quarter (28%) of the view that embedded finance could help drive double-digit revenue growth for their business. 67% believed growth would be at least 5% and just over a third (39%) suggest between 5-10% of revenue growth.

                                “Embedded banking allows businesses to integrate payments, lending and account services directly into their customer propositions. For corporates, this is a real opportunity to create stronger relationships with customers while also building new and potentially significant revenue streams for the business. We believe we’re on the cusp of the embedded economy.

                                “For any business looking to remain competitive in the digital age, these services can no longer be seen as ‘add-ons’. They are becoming essential infrastructure to deepen customer loyalty and open new revenue streams.

                                “We see this shift first-hand through the financial services clients already embedding our infrastructure. That experience gives us a clear view of how the same approach can be applied to corporates more widely and why embedded finance is such a significant opportunity across industries.”

                                Emma Hagan, ClearBank UK CEO

                                Cross-Sector Growth:  Companies Across Consumer Products & Services, Retail and Healthcare Have Biggest Appetite for Embedding Financial Services

                                Although embedded finance has often been associated with the retail sector, interest is broadening across other sectors. Research found that appetite was highest in consumer products and services (23%), retail (20%) and healthcare (18%), with the likes of the payroll and travel industries increasingly seeing the potential to integrate financial services into their customer journeys.

                                Of those companies surveyed that said they are actively considering offering embedded financial services within their own platforms, payment services were most considered (16%), followed by insurance (13%) and lending (13%). This signals a structural change in non-financial companies as they look to add layers of value and deepen engagement and loyalty with customers.

                                Untapped Potential: Only 19% Have launched Embedded Finance Services – Challenges Slowing progress

                                While appetite for embedded finance is growing rapidly, adoption is still maturing. Three-quarters (75%) said they would offer embedded finance today if it were easy to implement. This gap between ambition and reality underlines the perception that embedded finance is still typically difficult to employ and highlights the need for a new type of partner to tackle practical obstacles before broader uptake can occur.

                                When asked about the challenges corporates faced, some firms pointed to the technicalities of setting up such an offering in terms of integration challenges (61%), regulatory compliance (49%) and lack of technical expertise (44%)

                                Beyond the technical barriers, businesses also flagged reputational and regulatory risks such as greater regulatory scrutiny (57%), a loss of customer trust (52%) with reputational damage if the service fails (65%).

                                Taken together, these figures highlight that while embedded finance is seen as a major growth opportunity, corporates remain cautious. Success will depend not only on demonstrating the revenue potential but also on reducing risks during implementation through providing trusted infrastructure, regulatory clarity, and a smooth integration path that allows businesses to move from intent to action with confidence.

                                The Benefits & Motivations: Convenience & Customer Loyalty

                                For many corporates, embedded finance is first and foremost about strengthening customer relationships. Over half of firms 63% highlighted the opportunity to deliver a more seamless and convenient experience, positioning embedded finance as a customer service differentiator as much as a commercial driver. A further (57%) saw offering embedded services as a way of improving customer loyalty through creating more frequent and valuable touch points.

                                “Traditional banks we have found, give you a good brand halo and risk expertise but the cycles are killing us. They are slow, the integrations are not really bespoke and the slower cycle of development and keeping up to track with regulation has been the problem consistently.” (spokesperson from consumer industries)

                                About the Report

                                Ronin conducted interviews with  30 Senior Business Leaders at UK-based organisations across technology, healthcare, consumer, retail, travel, energy, and utilities sectors, along with surveying 200 Senior Business Leaders on the evolving nature of payment strategies, with a particular focus on the role of embedded finance in enabling new services and revenue streams. The interviews took place over August and September 2025.

                                • Embedded Finance
                                • Neobanking

                                Author

                                Gerry Goodwin

                                Published

                                Estimated Read time

                                5Mins

                                Gerry Goodwin, VP Insurance, Western Europe at FintechOS on how InsurTech competition is forcing incumbents to modernise outdated systems

                                Digital-first upstarts that have built their operations around modern technology stacks from day one are placing competitive pressure on the traditional insurance industry. Lemonade and Root Insurance have demonstrated that seamless, instant experiences are possible for insurance, from quote to claim. In the UK market, Marshmallow has similarly disrupted traditional motor insurance by leveraging AI and modern data analytics. They appeal to underserved communities with personalised pricing and streamlined digital experiences. These modern insurers operate with dramatically lower cost bases and faster product development cycles than traditional carriers.

                                This disruption is an urgent imperative for modernisation among traditional insurers. Legacy players are caught between rising customer expectations for slick digital experiences driven by other financial verticals and the limitations of decades-old infrastructure. The result is a widening capability gap that threatens market share, profitability and ultimately survival. Most industry discussions focus on modernisation as a defensive response to competition. However, an equally compelling but less discussed strategic dimension is preparation for M&A opportunities.

                                Modernising Insurance is a Must

                                Mergers and acquisitions in insurance are notoriously complex. Integrating product portfolios, claims histories and policyholder data across multiple lines and regulatory environments can be fraught with risk. The technical reality of merging legacy platforms often determines whether acquisitions deliver their promised value. Or become costly technical quagmires that stall innovation for years.

                                The potential challenges become even more pronounced when considering major legacy players. The rumoured clash of technical complexities between Aviva and Direct Line Group, both legacy giants with significant technical debt, exemplifies this. Merging outdated systems can create integration nightmares. These can compound existing inefficiencies. Such combinations risk creating even more complex, fragmented technology estates that become increasingly difficult to modernise.

                                Davies Group recently secured £275 million for M&A and generative AI investment. This demonstrated that consolidation and digital transformation should be pursued in parallel. Consolidation is accelerating across the insurance industry. Carriers are discovering that reliance on outdated technology stacks doesn’t just hamper competitiveness; it makes them toxic acquisition targets or ill-equipped acquirers. According to ACORD’s 2025 Insurance Digital Maturity Study, only 25% of top insurers have truly digitalised their value chain. Furthermore, over half still exploring how to apply digitalisation to their business models.

                                Legacy Systems Limit M&A 

                                Most insurers’ IT environments are dominated by legacy systems that consume the majority of their resources. PwC estimates that 70% of an insurer’s annual IT budget is spent on maintaining these legacy systems. This leaves little room for innovation or strategic initiatives. While legacy infrastructure may appear inexpensive on paper, acquirers often discover upgrading or replacing core business systems post-merger requires substantial investment. This can erode the ultimate value of the deal or even derail transactions.

                                A 2025 industry survey found 46.4% of insurers cite inflexibility to adapt to market changes as the most significant limitation of their current core systems. This is closely followed by integration challenges with new technologies (45.5%) and high maintenance costs (44.5%). These challenges are not just technical; they directly impact M&A outcomes. Data consolidation becomes exponentially complex when bridging inflexible legacy systems with modern platforms. Even when dealing with standard data structures, product definitions and customer identifiers.

                                Modern Technology Accelerates Deal Flow

                                Insurers are increasingly viewing technology through an M&A lens. The critical question has shifted from “Is this system good enough to run the business?” to “Would a buyer be able to integrate this system with minimal friction?”. Modernisation is now a core rationale for many, with forward-thinking insurers proactively upgrading systems to reduce complexity and improve interoperability.

                                This approach works. The latest tranche of modernisation, including the robust integration of AI capabilities, can reduce annual expenses by as much as $480 billion in property and casualty insurance and $300 billion in life insurance globally. Internally, modernisation improves operations and accelerates innovation. Externally, it signals digital maturity and business agility, qualities that enhance an insurer’s appeal and can increase its valuation in competitive acquisition scenarios.

                                Private equity firms are particularly attuned to the importance of digital maturity. In 2025, 82% of PE-backed insurance consolidators reported focusing on enhanced technology and insurtech capabilities post-acquisition, aiming to avoid the time and cost of transformation while rapidly building market share. For example, Munich Re’s $2.6 billion acquisition of Next Insurance was driven by a strategy to acquire digital capabilities, not just market share.

                                Meanwhile, strategic acquirers, such as Gallagher’s $1.2 billion purchase of Woodruff Sawyer, reflect a focus on operational gains and scale. Both PE and traditional insurers agree 52% of buyers expect significantly more emphasis on technology due diligence over the next two years. This underscores the centrality of digital readiness in dealmaking.

                                Cross-Business Value Creation

                                Modernising legacy systems to become acquisition-ready also opens the doors to a broader pool of potential acquirers. An insurer with digital infrastructure can attract interest not only from traditional players but also from reinsurers and private capital seeking to build scalable platforms in niche segments like embedded insurance or SME cover. A well-executed modernisation programme empowers insurers to court acquisition interest or pursue joint ventures, partnerships, or IPOs from a position of strength.

                                Modernisation has progressed from a back-office IT concern to a strategic enabler of business growth and M&A success. The lower the barriers to an insurer being absorbed into a larger platform, the more attractive it becomes as a target. As the insurance sector’s digital transformation accelerates, those who modernise today will be in pole position for tomorrow’s deals.

                                • InsurTech

                                Author

                                Dan Brightmore

                                Published

                                Estimated Read time

                                4Mins

                                New DeepL research finds AI is now used for over a third (37%) of customer interactions across UK financial services, with multilingual communication as the leading application. However, nearly two-thirds (65%) of UK financial services professionals admit employees are already using unapproved AI tools to communicate with customers

                                Artificial intelligence is rapidly becoming essential to how UK banks and fintechs retain customers in international markets, according to new research from DeepL, a global AI product and research company. A new survey of 1,500 financial services professionals in Europe, including 500 across the UK reveals that AI is now embedded in customer communications – from faster support to real-time multilingual translation – with over a third (37%) client interactions already AI-powered. With nearly half of all client work now cross-border, firms are using AI to deliver consistent, trusted experiences at speed and scale. But the research also highlights growing risks from “shadow AI,” as employees turn to unapproved tools that could undermine customer trust and regulatory compliance.

                                AI’s Developing Role in Financial Services Customer Comms

                                AI is now responsible for a significant share of customer interactions in UK financial services companies. On average, 37% of all client communications already involve AI tools, a figure that is projected to rise to 46% within 12 months and 50% within three years. 

                                The most common uses for AI in UK customer communications include:

                                • AI powered translation (used by 52% of respondents) 
                                • Virtual assistants or chatbots for banking queries with customers (51%)
                                • AI for fraud alerts and transaction monitoring (50%)
                                • Automated responses for credit card or account support (48%)
                                • Wealth management or investment advice (48%)

                                Translation is the most popular use case, reflecting the pressures financial services firms face in serving increasingly international customer bases, overcoming persistent language barriers, and addressing challenges in hiring multilingual staff.

                                How AI is Changing the Face of Cross-Border Comms

                                Over a third (39%) of all customer work in UK financial services companies is now cross-border. Yet firms are struggling to keep pace with the communication demands that come with international business: 85% percent of professionals report that language gaps have slowed down customer activity for non-English speakers, and 84% say it is difficult to hire staff who can communicate effectively across multiple languages and regions.

                                Against this backdrop, AI is emerging as a powerful tool to improve customer communication. Seven in ten UK finance professionals say AI improves the speed and availability of customer support, while the same proportion believe it helps maintain consistent communication quality across languages. Over seven in ten also report that customers are more satisfied when service is available in their preferred language. These findings highlight how AI is not only helping firms manage the complexity of cross-border work but also strengthening customer trust and loyalty in highly competitive markets.

                                Shadow AI Risks the Reputation of Financial Services Firms

                                Alongside rapid adoption of AI in customer facing areas comes increased risk. The research highlights mounting concerns around “shadow AI,” where employees turn to unapproved AI tools to save time but without oversight or safeguards. 

                                Nearly two-thirds (65%) of UK financial services professionals admit employees are already using unapproved AI tools to communicate with customers. This poses serious cybersecurity and compliance concerns, as sensitive data may be exposed without the right safeguards. Shadow AI often arises when teams do not have access to the specialist tools they need — for example, using general-purpose AI tools when secure, purpose-built translation solutions are required. To address this, firms must ensure IT and customer-facing teams work together to choose the right solutions.

                                “In financial services, where every interaction is highly regulated and reputational risk is acute, staff will inevitably look for workarounds if the tools provided don’t meet their needs,” said David Parry-Jones, Chief Revenue Officer at DeepL. “The real risk is not employees experimenting with AI, but companies failing to give them secure, fit-for-purpose solutions. By building a collaborative approach between IT and frontline teams, organisations can avoid shadow AI, protect against cybersecurity threats, and still realise the full benefits of trusted AI.”

                                About DeepL

                                DeepL is a global AI product and research company focused on building secure, intelligent solutions to complex business problems. Over 200,000 customers and millions of individuals across 228 global markets today trust DeepL’s Language AI platform for human-like translation, improved writing and real-time voice translation. Building on a history of innovation, quality and security, DeepL continues to expand its offerings beyond the field of Language, including the soon to be released DeepL Agent – an autonomous AI assistant designed to transform the way businesses and knowledge workers get work done. Founded in 2017 by CEO Jaroslaw “Jarek” Kutylowski, DeepL now has over 1,000 passionate employees and is supported by world-renowned investors including Benchmark, IVP, and Index Ventures. For more information on DeepL, visit www.deepl.com. 

                                Methodology

                                As a part of DeepL’s ongoing effort to analyze industry-specific and regional trends in AI adoption, Censuswide conducted a survey in June 2025 on behalf of DeepL. The research targeted 1501 professionals in financial services, split evenly across commercial banking, retail banking, fintech, and payments. The participants were located in France, Germany, the UK and Ireland, and answered nine multiple-choice questions. The questions gathered insights on how financial services teams use AI in customer service—from multilingual communication and onboarding to fraud alerts, virtual assistants, and the impact on speed, quality, and trust.

                                • Artificial Intelligence in FinTech

                                Author

                                Elina Rayberg

                                Published

                                21 October 2025

                                Estimated Read time

                                3Mins

                                Elina Rayberg, Principal at Valar Ventures, on the changing face of payments across the FinTech ecosystem

                                Wise’s exploration of a UK banking license is more than a single company milestone; it’s reflective of a significant, wider industry trend. Fintechs are no longer content to operate on the periphery of payments; they are stepping out of the shadows to compete directly with traditional banks. The implications for the payments ecosystem are profound.

                                Expanding Beyond the Payments Value Chain

                                For many years now, fintechs have added various components to the payments value chain. From BNPL, cross-border transfers, embedded payments and beyond, building financial infrastructure that allows businesses to integrate simpler, varied payment options for consumers has been a lucrative and innovative industry, one that’s attracted swathes of investment.

                                Until very recently, these fintech players haven’t felt a need to expand into more consumer-facing, traditional banking settings, and particularly not the need to tackle the various compliance and capital requirements needed to become a bank. This is changing.

                                Wise’s Strategic Move

                                Wise is a payments giant. It already operates at a global scale, with over 10 million customers and billions in transfers each quarter. By seeking a banking license, Wise is demonstrating an ambition to move beyond payment infrastructure and offer regulated financial products such as savings and credit. This would open new revenue streams while strengthening its position as a consumer brand, not just a payments rail.

                                A Broader Competitive Landscape

                                Wise is part of a wider movement. Revolut has been pursuing banking licenses in both the UK and US. Block (formerly Square) holds a banking charter, whilst both Stripe and Apple have partnerships with Goldman Sachs to offer banking products and services. Together, these moves illustrate a convergence: fintechs expanding into regulated banking, while incumbent banks adopt fintech-driven product strategies to protect market share.

                                The Full-Stack Future

                                The movement of both fintechs into the banking space and banks integrating fintech product strategies is reshaping the payments ecosystem in real time. Broad advances in technology since the inception of banking and financial services mean that it is entirely possible for one platform to operate as a full-stack digital bank proposition.

                                Traditional banks, challengers, and neobanks are all racing to execute on this opportunity, though with varying degrees of success, often constrained by regulation and the complexity of scaling financial infrastructure.

                                Regulatory Implications

                                As fintechs edge deeper into banking, regulators face the challenge of adapting rules to a landscape where the line between payment providers and banks blurs. This presents both opportunity and risk. Companies that can scale responsibly within regulatory frameworks may unlock significant advantage; those that outpace their compliance capabilities risk severe consequences.

                                Looking Ahead

                                Fintechs have historically been content to capture slices of the payments market. Today, signals suggest they are preparing to compete head-on with traditional banks. Non-bank firms that successfully leverage technology, regulatory approval, and customer reach stand to evolve into diversified, full-stack financial institutions, reshaping the future of payments in the process.

                                • Digital Payments
                                • Neobanking

                                Author

                                Dan Brightmore

                                Published

                                20 October 2025

                                Estimated Read time

                                2Mins

                                New research from bluQube shows that, despite years of digital investment, many finance teams are still stuck in manual mode, with 40% of businesses managing up to half their financial data by hand

                                Despite years of investment in digital transformation, finance functions remain heavily reliant on manual processes that slow down decision-making and increase risk, according to new research from finance and accounting software company bluQube.

                                The survey of 700 finance and business leaders found that 40% of businesses continue to manage up to half of their financial data manually. More striking still, more than a quarter (26%) admitted that the majority of their financial data is still being handled in this way.

                                Digital Transformation Delayed

                                The findings point to a widespread dependence on spreadsheets and manual entry, even as digital finance tools and automation have become commonplace. This reliance is creating significant bottlenecks for organisations, leaving finance professionals tied up in routine processes rather than focusing on analysis and strategy.

                                When asked where they lose the most time, nearly a third (31%) of finance teams said reconciling accounts between entities was their biggest monthly pain point, followed by the month-end close (26%) and audit and compliance reporting (20%). These time-intensive activities underline how far many teams remain from achieving true automation.

                                The research also highlights a confidence gap in financial reporting. While just over half (54%) of respondents said they are very confident their current processes would satisfy investor or audit requirements for accuracy and speed, nearly half (46%) expressed at least some doubt about their data’s reliability or timeliness.

                                The appetite for improvement is clear. A third (33%) of finance leaders said eliminating manual processes would have the biggest positive impact on their work, followed by faster consolidated reporting (26%) and improving cash flow visibility (24%).

                                Facing Up to the Risks of Manual Processes

                                The risks stretch well beyond inefficiency. Manual handling of financial data increases the likelihood of mistakes, duplication, and delays. These errors compromise the accuracy of financial reporting and reduce the confidence leaders need to make critical decisions. place in the insights they need to steer their organisations. 

                                “Finance teams have been at the centre of digital transformation strategies for over a decade, yet our research shows many organisations remain trapped in outdated practices. Too much time is still being spent reconciling spreadsheets rather than generating insights that drive growth. Manual processes not only waste resources but also expose businesses to unnecessary risk. In a business environment defined by economic uncertainty, regulatory pressure, and heightened competition, that lack of reliability can have serious consequences. Automating financial workflows should now be seen as essential, not optional.”

                                Simon Kearsley, CEO of bluQube

                                The survey underscores the urgency for businesses to modernise their finance functions. By adopting intelligent accountancy software and embedding automation, organisations can cut down on errors, free up capacity for strategic projects and base decisions on accurate, real-time information.

                                • Digital Payments
                                • Neobanking

                                Author

                                Dan Brightmore

                                Published

                                15 October 2025

                                Estimated Read time

                                5Mins

                                Five Insurtech companies poised to lead the market in 2026 — firms that combine scale, innovation, and resilience in one of the world’s most complex financial industries

                                As we approach 2026, the global insurance landscape continues to be reshaped by InsurTech innovators combining data, AI, and embedded finance to deliver faster, more transparent, and customer-centric insurance experiences. From underwriting automation to embedded protection and climate-risk modeling, these next-generation firms are redefining how risk is managed and distributed.

                                1. Shift Technology — AI-Driven Decisioning at Scale

                                Paris-based Shift Technology has emerged as a global leader in applying artificial intelligence to insurance decisioning — from fraud detection to claims automation. The company’s latest evolution, Shift Claims, leverages agentic AI models that can interpret complex policy data, assess claims, and detect anomalies faster than traditional systems.

                                Insurers using Shift’s technology are cutting processing times dramatically while improving fraud detection accuracy. With the launch of new AI-powered products designed for both underwriting and claims management, Shift is positioning itself as a core technology partner for global insurers modernising their infrastructure.

                                Why it matters: As AI regulation matures in Europe and beyond, Shift’s explainable AI models could set the standard for compliant automation in insurance operations.

                                Key challenge: Scaling these intelligent systems across legacy insurer environments — where data silos and outdated IT stacks remain the norm.

                                🌍 2. bolttech — Building the Embedded Insurance Ecosystem

                                bolttech, headquartered in Singapore, is one of the fastest-growing insurtech firms in the world. It operates as a technology-enabled insurance marketplace, connecting insurers, distributors, and consumers through a network that spans more than 35 markets.

                                Its embedded insurance solutions allow non-insurance brands — from e-commerce sites to telcos — to offer insurance products at the point of sale. This model aligns perfectly with digital commerce growth trends and customer expectations for frictionless protection.

                                In 2025, bolttech was named among the world’s top 100 insurtech innovators, underscoring its leadership in distribution technology.

                                Why it matters: Embedded finance is becoming a trillion-dollar global market opportunity, and bolttech’s API-driven platform is at the centre of it.

                                Key challenge: Sustaining profitability and navigating regulatory differences across dozens of jurisdictions while maintaining customer trust.

                                3. Parsyl — Smart Insurance for the Global Supply Chain

                                Denver-based Parsyl is redefining insurance for the logistics and marine sectors through IoT and data-driven risk assessment. The firm provides coverage for perishable and temperature-sensitive goods — using real-time sensor data to monitor shipments and proactively prevent losses.

                                As climate change and supply-chain disruptions intensify, Parsyl’s combination of data analytics and specialty insurance positions it uniquely in a high-value, under-served niche. Investors, including The Lightsmith Group, see its model as a blueprint for climate-resilient insurance.

                                Why it matters: Parsyl bridges the gap between traditional insurance and risk prevention — giving clients visibility, not just coverage.

                                Key challenge: Expanding from niche segments into mainstream marine and freight insurance markets while maintaining data integrity and regulatory compliance.

                                4. Weecover — Insurance as a Service for Europe

                                Spain’s Weecover is an emerging star in the Insurance-as-a-Service (IaaS) and embedded insurance ecosystem. Its platform enables retailers, fintechs, and e-commerce businesses to easily integrate insurance offerings into their digital flows through APIs.

                                In early 2025, the company closed a €42 million funding round led by Swanlaab and Nauta Capital, signalling investor confidence in its scalable platform model. With its focus on simplicity, flexibility, and compliance, Weecover is fast becoming a go-to solution for European businesses looking to embed protection products into customer journeys.

                                Why it matters: As Europe pushes for greater digital financial inclusion, Weecover’s B2B distribution model could make insurance more accessible to millions.

                                Key challenge: Ensuring consistent underwriting quality across diverse markets and managing regulatory complexity as it scales across the EU.

                                5. Counterforce Health — AI Meets Claims Advocacy

                                Launched in 2025, Counterforce Health is tackling one of the most persistent pain points in U.S. healthcare: insurance claim denials. The company uses AI and data analytics to help patients and providers navigate appeals, identify errors, and challenge wrongful denials.

                                In an era of escalating healthcare costs, Counterforce Health’s technology-driven advocacy model blends social impact with insurtech innovation, offering a fairer and faster route to claim resolution. If successful, it could redefine how consumers interact with insurers in one of the world’s most complex insurance systems.

                                Why it matters: Counterforce’s AI tools could significantly reduce administrative waste and improve transparency in health insurance.

                                Key challenge: Winning trust from both insurers and healthcare providers while navigating strict health data regulations.

                                The Future of Insurtech: What Will Define 2026

                                As 2026 approaches, the insurtech sector will pivot from hype to sustainable, revenue-driven innovation. The next wave of leaders will stand out not just for their technology, but for their ability to:

                                Achieve profitability at scale — growth must now translate into viable margins.

                                Master regulatory complexity — especially in multi-jurisdiction and cross-border operations.

                                Integrate deeply with ecosystems — through APIs, partnerships, and embedded finance.

                                Leverage ethical, explainable AI — ensuring compliance and consumer confidence.

                                Deliver measurable impact — whether through climate resilience, accessibility, or healthcare fairness.

                                  The insurance industry’s digital transformation is entering its most critical phase. The InsurTechs leading the charge — from Shift Technology and bolttech to Parsyl, Weecover, and Counterforce Health — exemplify how innovation, data, and purpose can combine to reshape an entire sector.

                                  By 2026, these firms won’t just be “startups to watch” — they’ll be the blueprints for how the insurance industry of the future operates: smarter, fairer, and more connected than ever before.

                                  • InsurTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  10 October 2025

                                  Estimated Read time

                                  8Mins

                                  Evident’s annual AI Index reveals the banks making the biggest moves in AI… JPMorganChase, Capital One and Royal Bank of Canada are the three leading banks in AI adoption…

                                  JPMorganChase has maintained its position as the world’s most AI-advanced bank in the Evident AI Index. The global standard benchmark for AI adoption in the financial services sector.

                                  According to Evident, the leading banks for AI maturity have pulled away from their peers in 2025, consolidating earlier gains and – increasingly – realising ROI for their AI investments. 

                                  Evident AI Index

                                  The annual Evident AI Index evaluates the ongoing AI performance of 50 major banks in North America, Europe, and APAC against 70+ indicators drawn from millions of public data points.

                                  It reveals that although nearly every bank is advancing in the Evident AI Index, the top 10 banks are increasing their scores 2.3x faster year-on-year than the rest of the Index.

                                  This year’s top three AI performers – JPMorganChase, Capital One and Royal Bank of Canada – have retained their rankings for a third successive year. JPMorganChase takes the top spot in three of Evident’s four pillars of AI capability – Innovation, Leadership and Transparency. Capital One leads on Talent, and has continued to gain ground on its rival. While the two undisputed leaders have further extended their lead, there is now little to separate the two in terms of overall AI maturity.

                                  The top 10 is increasingly dominated by US-headquartered institutions, but RBC, UBS and HSBC continue to secure places among the global leaders as the top performers in Canada, Europe and the UK respectively. 

                                  Based on the Evident AI Index, the ten banks leading the race for AI maturity are:

                                  BANK2025 INDEX2024 INDEX2024-25Change
                                  JPMorganChase11–
                                  Capital One22–
                                  Royal Bank of Canada33–
                                  CommBank45+1
                                  Morgan Stanley510+5
                                  Wells Fargo64-2
                                  UBS76-1
                                  HSBC87-1
                                  Goldman Sachs911+2
                                  Bank of America1015+5

                                  “Banking is one of the most advanced and competitive industries on the planet when it comes to developing and rolling out AI at scale. While some have described recent history as ‘The Summer AI Turned Ugly’, in the banking industry a different story is playing out. We’re beginning to see clear signs that AI investment is starting to translate into tangible financial gains, both in terms of efficiency and, increasingly, via new revenue opportunities. Banks and their shareholders expect ROI to accelerate over the next few years, and those in our top 10 are in pole position to see their efforts come to fruition.“

                                  Alexandra Mousavizadeh, Co-founder & CEO, Evident

                                  By far, the most competitive segment of the Index was found among those banks ranked just outside the top 10. All five of the banks in this range – BNP Paribas (#11), Citigroup (#12), TD Bank (#13), BBVA (#14), and Lloyds Banking Group (#15) saw a >20% increase in scores year-on-year (compared to ~10% for the wider Index), highlighting the intensity of the battle to keep pace with the leading banks.

                                  Across the regions covered in the Index, all six regional leaders are unchanged from 2024, with the gap between domestic leaders’ and laggards’ AI capabilities also growing year-on-year.

                                  Mousavizadeh adds:

                                  “Bifurcation in AI maturity creates a credibility gap. Banks that fail to keep pace risk losing the confidence of boards, regulators, and investors. At the same time, lagging institutions will struggle to attract and retain top-tier AI talent. This combination of stakeholder doubt and the risk of talent flight slows deployment, undermines momentum, and compounds the difficulty of turning AI investments into measurable business outcomes.”

                                  HSBC Heads Top AI Performing UK Banks

                                  When it comes to AI adoption, the UK is one of the most consistent regions in terms of bank performance. Four of the five UK banks rank in the top half of the Index. Three of the five UK banks advanced their position in the ranking year-over-year. And all five UK banks are tightly clustered – featuring the narrowest spread between the top-performing bank (HSBC) and bottom-performing bank (Standard Chartered) across every region.

                                  Responsible AI continues to be an area of strength, with four of the five UK banks ranking among the top 10 in the Transparency pillar. Conversely, no UK bank places in the top 10 in the Talent pillar.

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                                  HSBC improved its standing by +1 position across both the Talent and Innovation pillars, while ceding ground in Leadership (-10 rank) and Transparency (-3 rank). Consequently, HSBC lost one position in the overall ranking, but maintained a spot among the top 10 banks.

                                  In contrast, Lloyds Banking Group demonstrated the most forward momentum, rising from 27th to 15th in the ranking. This performance was buoyed by significant jumps in Talent (+12 rank), Leadership (+20 rank), and Transparency (+14 rank), with Lloyds one of only four Index banks to improve across all four pillars of the methodology.

                                  Mousavizadeh comments:

                                  “Lloyds Banking Group’s strong performance reflects a significant mindset shift at the bank, with the establishment of a centralised AI team and an increased focus on AI hires to accelerate the execution of its AI strategy. The upshot is that Lloyds is now sharing more details of its active use cases and long-term plans, translating into a much improved ranking in the Index.” 

                                  In a short space of time, Lloyds has matched HSBC in the number of recent AI use cases specifying outcomes. In March, the bank filed a patent for its Global Correlation Engine (CGE) – documenting an AI-driven approach to cybersecurity threats that results in 92% fewer false positives. And in July, the bank rolled out Athena, its first large-scale GenAI product.

                                  Measuring Returns on AI Investment

                                  According to Evident, twice as many banks reported a total number of active artificial intelligence use cases (jumping from 12 to 25 banks since last year), and 32 out of 50 have disclosed at least one use case with an associated financial or non-financial impact – up from 26 in 2024. 

                                  While more banks are reporting returns at the use-case level, only a small group have quantified the performance of their AI portfolios at Group level. Today, eight banks are disclosing portfolio-level ROI estimates – either realized or projected – with just three reporting both.

                                  These frontrunners include BNP Paribas, DBS, and JPMorganChase (all of which have already revised projections upwards). JPMorganChase is at the top of the table, raising its estimates from $1 billion to “heading more towards $2 billion” in AI-driven benefits, according to President and COO Daniel Pinto.

                                  Annabel Ayles, Co-founder & Co-CEO of Evident, comments:

                                  “All banks – regardless of size – are increasing their AI budgets, and our data shows virtually every key metric of AI adoption increasing.We’re already seeing these investments translate into tangible examples of use cases deployment. And our discussions with banking leaders suggest they’re expecting to see material, reportable AI returns in the next 12-18 months. Our data strongly suggests that this achievement is imminent. The question is: how big will the returns be? If they exceed expectations, current AI investment levels could pale in comparison to what comes next.”

                                  Talent, Innovation, Leadership and Transparency in AI

                                  According to Evident, the top 10 banks in the Index all demonstrate industry-leading AI performance across at least one of the four pillars, as follows:

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                                  Talent: 

                                  • Ten banks now employ almost half of all AI talent in the Index (circa 90,000 workers), with US banks dominating the leaderboard.
                                  • The AI talent pool across the top 50 banks grew 25% year-over-year, the fastest on record, nearly 5x faster than overall headcount growth.
                                  • On average, the top 10 banks by talent volume disclosed nearly 2x more use cases than the rest of the banks in the Index.
                                  • 38 of the 50 banks now disclose some form of AI training to its employees (up from 32 banks last year). And 33 banks now offer distinct training for senior leadership.

                                  Innovation: 

                                  • JPMorgan retained #1 spot for Innovation through the unparalleled strength of its AI research team and continued venture investments into AI-focused companies. 
                                  • Capital One overtook Royal Bank of Canada for the #2 spot, partly driven by the Discover merger, doubling its AI research team and showing steady growth in patents.
                                  • HSBC moved up to #8, the leading light amongst the European banks, who otherwise don’t feature.
                                  • Despite banks rushing to fund hyperscalers and the infrastructure that will power the AI era, general investment by banks into AI-focused and Data/Tech-focused companies is down double digits (17% from 2024) for the second year in a row.

                                  Leadership:

                                  • Over the past year, even those organizations that have traditionally chosen to keep their progress behind closed doors, are making their AI activities more visible.
                                  • Five banks maintained their top 10 ranks in Leadership: JPMorganChase took the top spot, strengthening its external AI communications efforts considerably, and Royal Bank of Canada jumped +5 ranks to take #3 position, publishing projected financial returns from AI for the first time during its Investor Day in March.
                                  • New entrants to the top-10 included: Natwest, UBS, and Morgan Stanley – and while they did not go as far disclosing financial targets for AI value, they each provided richer updates on use cases and impact than ever before.

                                  Transparency: 

                                  • JPMorganChase retained the top position for Transparency and seven of the top 10 banks carry over from 2024.
                                  • Responsible AI activity continues unabated across the industry – over the past year, the volume of RAI-specific talent found across the 50 banks more than doubled, and nearly 300 RAI-specific research papers were published, a +60% increase year-on-year. 
                                  • 35 of the 50 banks engage in partnerships with academic institutions, government bodies, or private companies (up from 31 banks last year), with nearly 80% of these partnerships yielding published case studies or use cases (up from 45% last year), demonstrating the increasingly tangible outcomes of their RAI efforts.

                                  Evident AI Index Methodology

                                  Since launching in January 2023, the Evident AI Index has quickly become established as the leading independent source of data and insight on artificial intelligence adoption across the banking industry.

                                  The Index combines extensive research, automated data capture from public sources, consultation across Evident’s network of AI experts, and ongoing dialogue with featured banks.

                                  Drawing from millions of public data points spanning 70+ individual indicators, it ranks each bank across four key capability areas which collective signal AI maturity:

                                  • Talent: measures the depth, density and development of AI talent within each organisation.
                                  • Innovation: captures long-term investment in AI-related innovation, including research, patents, partnerships and engagement with the open-source ecosystem.
                                  • Leadership: assesses the role of leadership in setting and communicating the organisation’s AI agenda.
                                  • Transparency: evaluates public engagement with Responsible AI (RAI), from internal talent and frameworks to external partnerships and disclosures.
                                  • Artificial Intelligence in FinTech
                                  • Neobanking

                                  Author

                                  Dan Brightmore

                                  Published

                                  8 October 2025

                                  Estimated Read time

                                  2Mins

                                  The Global FinTech Ecosystem. Connected.

                                  This year marks the 10th anniversary of FinTech Connect. The UK’s largest FinTech conference and exhibition, bringing together over 5,000 global attendees from across the financial services and technology landscape.

                                  FinTech Connect

                                  For a decade, FinTech Connect has been the launchpad for the ideas, partnerships and technologies driving the evolution of digital finance. It’s where banks meet breakthrough platforms. Where startups connect with major buyers. And where leaders across digital transformation payments, regtech, financial security and blockchain converge to shape what’s next.

                                  In 2025, we’re scaling up. With 100+ exhibitors, seven world-class conference tracks, live demos and the return of the Start-Up LaunchPad. This year’s event will deliver more connections, more innovation and more opportunity than ever before.

                                  Join us to celebrate a decade of FinTech excellence. And experience the future of finance, powered by cutting-edge tech, real-world insights. And the partnerships that will define the next 10 years.

                                  “FinTech connect is a great place to learn about the latest trends, concerns and enhancements in the FinTech space. Furthermore it is a fantastic opportunity to meet with up and coming companies; or names that you are already in contact with, in one convenient location.”

                                  Nicholas Nicolaides, Associate Director, Barclays

                                  Tokenize: LDN at FinTech Connect

                                  In 2025, FinTech Connect is growing in scale and ambition. For the first time, it will be co-located with Tokenize: LDN, the UK’s leading event for blockchain, web3 and real-world asset tokenisation. Creating a powerful convergence of FinTech and digital asset innovation under one roof.

                                  At Tokenize: LDN, you’ll dive into the latest developments in decentralised finance, custody solutions, tokenised infrastructure and emerging use cases across capital markets. The co-location opens the door to unparalleled cross-industry networking. Connecting FinTech professionals, institutional players and blockchain pioneers in one dynamic space.

                                  Tokenize: LDN is the UK’s leading showcase of the technologies, projects and investment strategies shaping the future of tokenized real-world assets (RWAs). From tokenised treasuries and real estate to on-chain credit, funds, financial infrastructure and more.

                                  Whether you’re navigating tokenisation for the first time or scaling existing strategies, Tokenize: LDN is where serious conversations turn into real-world innovation.

                                  Join asset managers, banks, institutional investors, regulators, custodians, blockchain developers and fintech innovators shaping the future of global capital markets. 

                                  Held in London and co-located with FinTech Connect, Tokenize: LDN is where the global conversation on liquidity, regulation, interoperability and institutional adoption comes to life. 

                                  Together, these two events offer a unique opportunity to explore the future of finance from every angle. Technological, Regulatory, Decentralised and Institutional.

                                  Register now for free tickets for general access. Join 5,000+ industry professionals for two days of talks, exhibitors and networking.

                                  • Blockchain & Crypto
                                  • Cybersecurity in FinTech
                                  • Digital Payments
                                  • Event Newsroom
                                  • Events

                                  Author

                                  Dan Brightmore

                                  Published

                                  7 October 2025

                                  Estimated Read time

                                  3Mins

                                  Samsung and OpenAI Announce Strategic Partnership to Accelerate Advancements in Global AI Infrastructure

                                  Samsung will bring together technologies and innovations across advanced semiconductors, data centres, shipbuilding, cloud services and maritime technologies

                                  OpenAI, Samsung Electronics, Samsung SDS, Samsung C&T and Samsung Heavy Industries have announced a letter of intent (LOI) for their strategic partnership to accelerate advancements in global AI data centre infrastructure and develop future technologies together in relevant fields. This expansive collaboration will bring together the collective strengths and leadership of Samsung companies across semiconductors, data centres, shipbuilding, cloud services and maritime technologies.

                                  The signing ceremony was held at Samsung’s corporate headquarters in Seoul, Korea, attended by Young Hyun Jun, Vice Chairman & CEO of Samsung Electronics; Sung-an Choi, Vice Chairman & CEO of Samsung Heavy Industries; Sechul Oh, President & CEO of Samsung C&T; and Junehee Lee, President & CEO of Samsung SDS.

                                  Samsung Electronics

                                  Samsung Electronics will work with OpenAI as a strategic memory partner to supply advanced semiconductor solutions for OpenAI’s global Stargate initiative. With OpenAI’s memory demand projected to reach up to 900,000 DRAM wafers per month, Samsung will contribute toward meeting this need with its extensive lineup of high-performance DRAM solutions.

                                  As a comprehensive semiconductor solutions provider, Samsung’s leading technologies span across memory, logic and foundry with a diverse product portfolio that supports the full AI workflow from training to inference.

                                  The company also brings differentiated capabilities in advanced chip packaging and heterogeneous integration between memory and system semiconductors, enabling it to provide unique solutions for OpenAI.

                                  Samsung SDS

                                  Samsung SDS has entered into a potential partnership with OpenAI to jointly develop AI data centre and provide enterprise AI services.

                                  Leveraging its expertise in advanced data center technologies, Samsung SDS will collaborate with OpenAI in the design, development and operation of the Stargate AI data centers. Under the LOI, Samsung SDS can now provide consulting, deployment and management services for businesses seeking to integrate OpenAI’s AI models into their internal systems.

                                  In addition, Samsung SDS has signed a reseller partnership for OpenAI’s services in Korea and plans to support local companies in adopting OpenAI’s ChatGPT Enterprise offerings.

                                  Samsung C&T and Samsung Heavy Industries

                                  Samsung C&T and Samsung Heavy Industries will collaborate with OpenAI to advance global AI data centers, with a particular focus on the joint development of floating data centers.

                                  Floating data centers are considered to have advantages over data centers because they can address land scarcity and lower cooling costs. Still, their technical complexity has so far limited wider deployment.

                                  Building on their proprietary technologies, Samsung C&T and Samsung Heavy Industries will also explore opportunities to pursue projects in floating power plants and control centers, in addition to floating data center infrastructure.

                                  Starting with the landmark partnership with OpenAI, Samsung plans to fully support Korea’s goals to become one of the world’s top three nations in AI and create new opportunities in the field.

                                  Samsung is also exploring broader adoption of ChatGPT within the companies to facilitate AI transformation in the workplace.

                                  About OpenAI

                                  OpenAI is an AI research and deployment company. Our mission is to ensure that artificial general intelligence benefits all of humanity.

                                  About Samsung Electronics Co., Ltd.

                                  Samsung inspires the world and shapes the future with transformative ideas and technologies. The company is redefining the worlds of TVs, digital signage, smartphones, wearables, tablets, home appliances and network systems, as well as memory, system LSI and foundry. Samsung is also advancing medical imaging technologies, HVAC solutions and robotics, while creating innovative automotive and audio products through Harman. With its SmartThings ecosystem, open collaboration with partners, and integration of AI across its portfolio, Samsung delivers a seamless and intelligent connected experience.

                                  • Digital Strategy

                                  Author

                                  Dan Brightmore

                                  Published

                                  Estimated Read time

                                  3Mins

                                  AccessPay, the leading bank integration provider, has completed the roll out of its SWIFT connectivity solution for Finseta, an international payments…

                                  AccessPay, the leading bank integration provider, has completed the roll out of its SWIFT connectivity solution for Finseta, an international payments and alternative banking provider. This will ensure a reliable, secure way to process cross-border payments.

                                  To support its global expansion strategy and service-led business, Finseta wanted to launch a new agency banking solution. And improve payment processing automation. It implemented AccessPay’s SWIFT connectivity solution, building a seamless integration between digital currency exchange platform FXPal and Barclays Bank. This enables transparent pricing, automated reporting and analytics, and full back-office-to-bank connectivity.

                                  The four-way project, including Barclays and SWIFT, was implemented in just six months. An impressive achievement for a first-time SWIFT user. Finseta benefits from cost savings, improved competitive advantage and a scalable architecture.

                                  AccessPay’s tailored, integrated solution, includes:

                                  • End-to-end workflow automation: A seamless integration between FXPal and Barclays Bank using AccessPay’s SWIFT connectivity through Alliance Lite2 for Business Application service. Payment files are now automatically validated, processed and monitored in real time.
                                  • Real-time visibility and reconciliation: Provides Finseta’s customers full transparency into payment status. Along with the ability to instantly reconcile transactions against bank statements.
                                  • Seamless customer experience: With AccessPay’s SWIFT capabilities, Finseta created a smooth, efficient experience for its clients. Reducing manual errors and delays.

                                  SWIFT Connectivity

                                  Finseta’s experience shows the value of working with a third-party specialist in SWIFT connectivity. AccessPay’s knowledge ensures smoother implementation and faster issue resolution. Additionally, leveraging a trusted partner helps future-proof Finseta’s payment infrastructure. Making it easier to scale globally and maintain service reliability.

                                  “Of the many SWIFT projects I’ve been involved in over the past dozen years, this has probably been one of the smoothest and fastest. With the service delivered in just six months. I attribute this to the strong four-way relationship. As well as the teams’ motivation and responsiveness, and a well-defined project strategy.”

                                  Tom Livock, Head of Enterprise Sales, AccessPay

                                  “AccessPay did the heavy lifting involved in implementing SWIFT connectivity. The quick route to go-live has meant that we can start realising the benefits sooner than if we built the solution in-house. I’d rather double down on what sets Finseta apart from our competitors, than trying to be an expert in SWIFT.”

                                  Declan Jones, Chief Product Officer, Finseta.

                                  Finseta will use AccessPay’s SWIFT connectivity solution globally for all its customers (high-net-worth individuals, large institutions and corporates).

                                  About AccessPay

                                  AccessPay is a leading provider of bank integration solutions, pioneering finance transformation for the Office of the CFO. AccessPay helps finance and treasury teams modernise their operations through secure, cloud-based bank connectivity. Our platform connects back-office systems to banks, enabling the automated flow and transformation of payment, bank statement and other financial data. 

                                  Thousands of businesses around the world partner with AccessPay to automate supplier and client payments. Alongside Direct Debit collections, and bank statement retrieval – improving efficiency, reducing fraud risk, and gaining real-time cash visibility. 

                                  Founded in 2012 and headquartered in Manchester, UK, AccessPay is trusted by global enterprises to automate finance and treasury operations and build a future-ready Office of the CFO. 

                                  About Finseta

                                  Finseta is a foreign exchange and payments company offering multi-currency accounts and payment solutions to businesses and individuals. Headquartered in the City of London, Finseta combines a proprietary technology platform with a high level of personalised service. It supports clients with payments in over 165 countries in 150 currencies. With a track record of over 15 years, Finseta has the expertise, experience and expanding global partner network to be able to execute complex cross-border payments. It is fully regulated, through its wholly-owned subsidiaries, by the Financial Conduct Authority as an Electronic Money Institution. By the Financial Transactions and Reports Analysis Centre of Canada as a Money Services Business. And by the Dubai Financial Services Authority under a Category 3D licence.

                                  • Digital Payments

                                  Author

                                  Dan Brightmore

                                  Published

                                  6 October 2025

                                  Estimated Read time

                                  3Mins

                                  Our round up of the five neobanks best positioned to lead the space in 2026… Nubank (Nu Holdings) Why It’s…

                                  Our round up of the five neobanks best positioned to lead the space in 2026…

                                  Nubank (Nu Holdings)

                                  Why It’s Likely to Lead in 2026: Nubank already has over 100 million customers in Latin America and is actively pushing into new markets, including applying for a U.S. banking charter. This international expansion, combined with strength in credit, deposits, and FinTech adjacent services, gives it a shot at becoming a truly global neobank.

                                  Risks/Challenges: Breaking into the U.S. (or other mature markets) is tough. Regulatory compliance, competition from domestic digital banks, and local consumer trust are big hurdles.

                                  Revolut

                                  Why It’s Likely to Lead in 2026: Revolut has deep product breadth (multi-currency, trading, credit, crypto, business accounts), and is aggressively expanding globally. It also has strong brand momentum. For instance, it was named the fastest-growing bank brand in the UK. Revolut’s capacity to cross-sell services and innovate puts it in a strong position.

                                  Risks/Challenges: Scalability of regulatory compliance across many jurisdictions, potential regulatory crackdowns, and maintaining profitability with heavy investment costs are key risks.

                                  Monzo

                                  Why It’s Likely to Lead in 2026: Monzo recently crossed into profitability, bolstered by rising interest rates and growth of its lending and subscription services. It also has ambitions to expand beyond the UK into broader Europe and the U.S. As more neobanks are judged by their ability to monetise at scale, that profitability is a strong signal.

                                  Risks/Challenges: Expansion outside the UK will test its product-market fit, regulatory compliance in new regions, and capital backing. Also, competition in the mature markets is fierce.

                                  Bunq

                                  Why It’s Likely to Lead in 2026: Bunq is one of the stronger pan-European neobanks, with multi-IBAN accounts, a broad user base across Europe, and deposit protections under EU frameworks. Its steady growth in deposits and commitment to European expansion gives it a solid foundation in its home turf.

                                  Risks/Challenges: Scaling beyond Europe (or outside the EU regulatory regime) is harder. Also, its earlier ambition in the U.S. seems to have been pulled back, demonstrating how regulatory unpredictability can slow growth.

                                  U.S. Digital Banks

                                  Why It’s Likely to Lead in 2026: While Chime, SoFi, Varo, and others aren’t “neobanks” in the same exact model in all respects, they are dominant digital banking players in the U.S. market. Their deep user bases, product stacks (savings, credit, investing), and ability to leverage scale make them key contenders in the “neobank era”. As the U.S. digital banking adoption continues, one or more of these could claim leadership by 2026.

                                  Risks/Challenges: U.S. regulation, interest rate cycles, competition from incumbents and fintechs, and margin pressures are big challenges. Also, converting free users to revenue-paying ones is an ongoing tension for all these models.

                                  Honorable Mentions / Dark Horses

                                  • Starling Bank (UK) — It already has a strong UK presence, though regulatory scrutiny (e.g. fines) is a risk.
                                  • Kroo (UK) — Newly licensed, growing deposits quickly, potentially disruptive in niche markets.
                                  • Regional & Asia / Africa challengers — Several neobanks in Asia, Africa, Latin America, and Southeast Asia are scaling fast; some may emerge as leaders in their regions (and eventually go global).

                                  Conclusion & What to Watch

                                  By 2026, what will separate the winners from the also-rans are:

                                  1. Profitability / Unit Economics — It isn’t enough to grow; you need sustainable margins.
                                  2. Regulatory & Compliance Strength — Multi-jurisdiction operations demand strong controls.
                                  3. Platform / Ecosystem Expansion — Embedding finance (e.g. via APIs, partnerships), offering non-bank products (insurance, investing) will be key.
                                  4. Global Reach & Localisation — The ability to expand across borders, but localise offerings to fit each market.
                                  5. Trust & Resilience — In banking, trust is critical. Neobanks will be judged harshly on outages, fraud, security, and financial stability.
                                  • Digital Payments
                                  • Neobanking

                                  Author

                                  Dan Brightmore

                                  Published

                                  2 October 2025

                                  Estimated Read time

                                  3Mins

                                  Plumery’s expansion, collaborating with Vancouver-based Aequilibrium, brings specific Canadian market capabilities to support credit unions delivery of personalised, compliant, and elevated member experiences

                                  Plumery, the digital banking experience platform, today unveiled Canada-specific features and integrations giving Canadian credit unions a clear path to deliver personalized, compliant, and modern digital banking experiences.

                                  Canadian financial institutions are facing heightened customer expectations, stiff competition from FinTechs, and growing pressure to modernise legacy systems. These pressures have been amplified by Central 1 Credit Union’s announcement that it will wind down its Forge (formerly MemberDirect) digital banking platform. The system, until recently, served over 170 credit unions across Canada.

                                  This represents both a risk and an opportunity for credit unions. They must now plan for a replacement quickly, and also have the chance to adopt a platform that gives them greater control and the ability to compete on user experience.

                                  The collaboration with Aequilibrium, with their deep knowledge of the Canadian regulatory landscape and user experience design ensures Plumery’s Canadian-ready platform is built around how Canadians, especially credit union members, expect to bank.

                                  Though Canada’s banking sector is among the most advanced globally, many credit unions are held back by outdated infrastructure.

                                  Plumery Tailored for Canadians

                                  Meanwhile, members are demanding hyper-personalised, mobile-first and intuitive digital journeys. To meet these needs, Plumery has localised its platform with out-of-the-box features tailored for how Canadians bank. These include:

                                  • Everyday payments and transfers such as bill payments, cheque deposits, and Interac e-Transfers.
                                  • Support for Canadian savings and lending products including GICs, mortgages, and student loans.
                                  • Business banking capabilities like bulk payments and payroll management.
                                  • Compliance and user experience features including bilingual English/French support, privacy and data residency adherence, and accessibility standards.

                                  Ben Goldin, CEO & Founder of Plumery, said: “With Forge winding down, Canadian institutions have a rare opportunity to modernise on their own terms, rather than being tied to outdated systems. Our platform provides an immediate, future-ready option that puts control back in the hands of credit unions. By working with Aequilibrium, we are combining global banking innovation with local expertise to deliver experiences that meet the unique needs of Canadian credit unions’ members.”

                                  Plumery’s Canadian-ready platform is available now, and the company is already in discussions with multiple credit unions evaluating their digital futures beyond Forge.

                                  About Plumery

                                  Headquartered in the Netherlands, Plumery has a mission is to empower financial institutions worldwide, regardless of size, to craft distinctive, contemporary, and customer-centric mobile and web experiences.

                                  Plumery operates with a diverse team that embodies a unique combination of seasoned expertise and vibrant innovation. This blend has been cultivated through years of experience at start-ups, scale-ups, and established financial institutions, and most notably at globally leading financial technology companies, where they were instrumental in creating disruptive digital banking solutions and platforms that now serve 300+ banks globally. 

                                  Plumery’s Digital Success Fabric platform provides banks with the foundation for success beyond fast-time-to-market by expediting the development of their digital front ends while significantly cutting costs compared to in-house initiatives or solutions with high total cost of ownership (TCO).  

                                  About Aequilibrium

                                  For over 13 years, Aequilibrium has supported small to large-sized credit unions globally, helping them modernize their digital banking, elevate their training practices through VR + AI, and create member-first experiences that leave a lasting impression. They simplify technology, co-create strategies, and deliver personalised experiences that enrich people’s lives.

                                  • Digital Payments
                                  • Neobanking

                                  Author

                                  Richard May

                                  Published

                                  1 October 2025

                                  Estimated Read time

                                  5Mins

                                  Richard May, director of product development at virtualDCS, on navigating cyber regulation, assessing risk, and building digital resilience in a cloud-first financial landscape

                                  In 2025, financial services are deeply reliant on digital infrastructures. Cloud services, especially, are reshaping how the sector operates.

                                  The cloud offers both established and challenger companies the ability to improve flexibility, efficiency, and analytics capabilities. When deployed properly, it can deliver integrated security across an organisation, but also introduces new vulnerabilities.

                                  Due to the sensitive nature of financial data, the sector remains a target for cyberattacks. This, combined with strict regulatory oversight, means firms must continuously align with evolving legislation while enhancing service functionality.


                                  Which regulations do financial services need to be aware of?

                                  There are several specific regulatory requirements that financial institutions must follow. These pieces of legislation are designed to ensure customer data is protected from attackers:

                                  Payment card information and PCI-DSS

                                  For businesses that handle payment card information, PCI DSS requirements dictate security and operational requirements for protecting cardholder information during storage, processing, and transmission. In practice, these requirements are 12 mandatory security controls that cover network security, data protection, vulnerability management, access control, monitoring and logging, physical security, testing, and policy enforcement. Failure to comply with the 12 security controls can lead to severe financial penalties and even liability for compensation costs.

                                  GDPR implications

                                  GDPR regulations categorise financial data as sensitive personal data. This refers to bank details, transaction histories, assets, credit scores, and anything else that might concern the overall financial health of an individual. Firms must take measures to prevent unauthorised access or risk facing fines.

                                  Basel III considerations

                                  The third Basel Accord, Basel III, sets the international standards for capital requirements, stress tests, liquidity regulations, and leverage. It is designed to reduce the risks of phenomena such as bank runs and bank failures, as we saw in the 2008 financial crash. Due to this, most of Basel III focuses on financial requirements such as liquidity to ensure banks are more resilient to changes in the international financial markets. However, it still communicates standards in relation to information and communication technology (ICT),‍ cyber incident response and reporting, and‍ third-party risk management (TPRM).

                                  Digital Operational Resilience Act (DORA)

                                  Introduced in January 2025 by the European Union (EU), DORA addresses rising digital dependency in finance. It covers ICT risk management, third-party oversight, operational resilience, incident reporting, and information sharing.

                                  Compliance with these regulations is essential. Beyond avoiding penalties or criminal charges, it strengthens protection against growing cyber threats.

                                  Assessing Vulnerability and Risk in the Financial Services Industry

                                  Risk assessments are critical to business continuity and reducing the impact of cybersecurity breaches. A task of identifying threats and vulnerabilities, and quantifying the consequences of threats if they were to materialise, enables firms to rank services and ensure the most critical systems are protected first.

                                  The Financial Services Information Sharing and Analysis Center (FS-ISAC) identified several key threats to the global financial sector in its latest report, including: 

                                  Supply Chain Incidents

                                  Businesses should remain alert to the competencies and overall security of service providers they utilise. As reliance on external providers is increasingly integral to many core business strategies, firms cannot afford to overlook the cyber maturity of their partners. To mitigate potential security risks, organisations should ensure and verify that all service providers meet robust cyber-security standards.

                                  Fraud

                                  The universality of real-time payments has led to a surge in fraud action in all sectors for which financial channels and services are used. The immediacy of payment has also created a scenario where it is almost impossible to retrieve stolen funds. Online scammers are building complex operations to take advantage of this. Fraud prevention and detection are becoming more and more important to companies in the sector. Increasing friction for payments through two-factor authorisation, along with other strategic obstacles, reduces fraud risks. Without cross-border partnerships tackling this global issue, however, this is set to remain a growing threat for businesses.

                                  Ransomware

                                  Ransomware has long been a cybersecurity threat. Many victims are often opportunistically targeted by hackers, rather than chosen specifically. Incidents of spear phishing are also on the rise – attackers research individuals or organisations to create personalised messages to convince them to click on infected links. Creating barriers to stop or delay ransomware attacks is therefore essential to reduce the threat. Ransomware’s targeting of customer data also means detection and recovery protocols are critical for firms that want to reduce the threat from malicious actors.

                                  Distributed Denial-of-Service

                                  The FS-ISAC revealed that financial services accounted for a third of all distributed denial-of-service (DDoS) attacks in 2023. DDoS attackers bring down an area of a network or application and extort the affected organisation for financial gain. Motivations may also include political statement-making, competitor sabotage, and cyber vandalism, simply to cause chaos and disruption. The increasing use of application programming interfaces (APIs) in the sector means that denial of service can have a devastating effect on financial service businesses. Firms should implement mitigation strategies to protect customer trust and service availability. 

                                  When, Not If: Building Cyber Resilience Through Disaster Recovery

                                  While cybersecurity defences are essential, effective disaster recovery is vital to reduce the impact of incidents and maintain operations.

                                  Speed of recovery has become the main point of difference for organisations attempting to recover from cyber incidents. Prolonged downtime can lead to reputational damage, regulatory penalties, and lost customers. Without effective disaster recovery, continuity efforts are undermined.

                                  Firms should develop a ‘when’, not ‘if’, mindset when it comes to disaster recovery. A comprehensive disaster playbook provides a manual in the event of a cyber incident. This plan must incorporate tools to allow for early detection of malicious action. Your plan for disaster recovery should be printed as a hard copy or saved on an external device (to ensure it remains accessible if your primary system is compromised). It must consider the first steps of: documenting evidence for cyber insurance and law enforcement, identifying and isolating infected systems, and informing relevant stakeholders an attack has taken place. Furthermore, the plan should contain information around communication and key contacts, an agreed chain of command and designated person to lead the ransomware response, and assurance the plan comes under regular review with ‘fire drill’ rehearsals.

                                  Financial institutions face some of the most severe cyber risks in the world. Abiding by regulatory requirements goes some way to protect against threats, but organisations must go further – by proactively assessing threats, incorporating security measures, and preparing for disruptions. Resilience isn’t just about avoiding breaches. It is about ensuring trust, safeguarding sensitive data, and maintaining the ability to deliver reliable services in a digital-first landscape.

                                  Learn more at virtualDCS

                                  • Cybersecurity in FinTech
                                  • Risk & Resilience

                                  Author

                                  Dan Brightmore

                                  Published

                                  29 September 2025

                                  Estimated Read time

                                  6Mins

                                  Chirag Shah, Founder & CEO of Pulse, on ULI, and what it could mean for lenders and their customers

                                  The UK’s financial services ecosystem is currently in the process of profound transformation. Traditional lending frameworks, characterised by siloed systems, static risk models, and manual processes, are no longer fit for purpose. They’re outdated and ineffective, unable to answer the needs of today’s digital economy. With the growth of embedded finance, real-time data, and rising customer expectations, financial institutions, platforms, and regulators are having to rethink their infrastructure from the ground up.

                                  Initiatives like Open Banking, Making Tax Digital (MTD), and Open Accounting have already laid the groundwork for greater data accessibility, meaning that data is not only available but useable. But with that usability comes greater expectations – both businesses and consumers expect instant decisions, seamless experiences, and personalised products. The problem is that the lending infrastructure that should be able to deliver on this promise remains fragmented. Lending decisions are still difficult to make because data is scattered, while processes are duplicated and manual. While lenders, platforms, and regulators are unable to work in unison. The Unified Lending Interface (ULI) is emerging as both a technical solution and the next generation of lending infrastructure in the UK.

                                  What is ULI?

                                  ULI is a standardised interoperability framework that governs the exchange of credit-related data, events, and permissions across lending ecosystems. Unlike a product or single platform, ULI acts as an underlying protocol, a form of modular APIs, data schemas, and event models that make it easier for lenders, platforms, and borrowers to interact in a consistent, secure, and scalable way. The idea being that if data can be standardised and exchanged in real time, credit decisioning and servicing can become significantly more efficient, transparent, and inclusive.

                                  What this looks like in real terms is:

                                  • The use of standardised data models for origination, underwriting, and loan servicing
                                  • Real-time event streaming for repayments, defaults, and restructures
                                  • Cross-lender affordability and exposure checks
                                  • Secure, user-driven consent mechanisms
                                  • Customisable APIs to suit various regulatory and operational contexts
                                  • In-built analytics and reporting tools for compliance and performance

                                  ULI is not yet a formal regulatory term, but its equivalents are already emerging in industry-led pilots and fintech platforms. In my view, its adoption would be the next logical step in the evolution of UK lending.

                                  The Challenges That ULI Could Solve

                                  Despite the rapid uptake of embedded finance, the underlying infrastructure that should power and enable it has begun to fall behind. This disconnect has created multiple pain points that need to be addressed if innovation and effective risk management are to continue.

                                  One major challenge lies in siloed integrations. Many lenders rely on custom-built connections with each distribution partner, which typically results in fragile systems that are difficult to scale and costly to maintain. This is not only inefficient, it makes it harder to respond to changing market demands.

                                  Risk visibility is another concern. As things stand, most lenders assess credit exposure in isolation, which means that a business could have multiple existing loans on different platforms with no aggregated affordability assessment. This creates obvious blind spots, increasing the chances of overextension and missed risk signals.

                                  Borrowers themselves are often unaware of why or how credit decisions are made or how their data is used. This opacity leads to a lack of trust, and can deter people from responsible borrowing. And regulatory friction adds further strain. Many institutions still rely on outdated tools for supervisory reporting, including batch files and CSVs, which are prone to error and inefficiency. This creates compliance burdens and slows down oversight.

                                  Lastly, customer concerns around data sharing presents another barrier. Without clear, user-driven consent frameworks, individuals and businesses are reluctant to share financial data. This not only limits lenders’ ability to personalise offerings but also undermines accurate risk assessment.

                                  The ULI directly addresses these challenges by introducing a common framework for interoperability. It brings much-needed structure to an otherwise fragmented ecosystem, enabling lenders and platforms to work together more efficiently without stifling innovation. It also helps restore trust to all users.

                                  How ULI Works

                                  Rather than acting as a centralised system, ULI operates as a distributed interoperability layer, purpose-built for credit. It works in four general phases:

                                  Standardising loan origination

                                  ULI defines a shared schema for different credit products, whether that’s long-term loans, merchant cash advances, invoice financing, or credit lines. This shared language allows platforms and lenders to integrate quickly and consistently. My company has already pioneered this approach, embedding ULI frameworks into platforms that support the entire loan lifecycle, from application to disbursement, collections, and ongoing management.

                                  Affordability and risk aggregation

                                  A critical ULI function is its ability to aggregate exposure across multiple lenders in real time. This enables federated credit checks, prevents borrower overextension, and enhances regulatory oversight. Again, this is something that my company is already doing, with a solution that integrates with ULI to assess a borrower’s receivables, providing granular visibility into cash flow and repayment capacity.

                                  Real-time event notifications

                                  With ULI, you also introduce real-time event notifications that allow key loan events, such as repayments and missed instalments, to be monitored in real-time. This enhanced visibility enables lenders to monitor risk continuously, rather than relying on retrospective data. It also allows for the automation of collections to streamline the response to any such events. Additionally, lenders can make easy adjustments to credit limits based on a borrower’s behaviour and financial performance over time. Essentially, bringing both control and flexibility to lending.

                                  Streamlined application journeys

                                  ULI also helps streamline multi-lender application journeys through a single interface. Our system, for example, allows for automated underwriting, with over 95% of applications decisioned in under 60 seconds. This means that loan applications can be completed in minutes, drastically improving both lender efficiency and borrower experience.

                                  Is The UK Ready For ULI?

                                  Several recent developments suggest that, from a regulatory standpoint, the UK is uniquely positioned to adopt ULI, or similar. First, there’s the government’s Smart Data agenda, which is expanding the legal framework to support cross-sector, user-permissioned data sharing, which is an essential foundation for interoperable lending. While the ongoing development of Open Finance reflects a clear determination to build modular, interconnected financial services systems that mirror the goals of ULI. At the same time, increased regulatory scrutiny of traditional credit bureaus signals a broader appetite for more transparent, real-time credit models that can better serve both lenders and borrowers. As such, ULI wouldn’t replace existing financial infrastructure, it would complement it. Helping to modernise business lending and improve access to credit.

                                  Financial services have become increasingly modular. It’s an approach that answers the evolving needs of today’s digitally driven businesses. A side effect of that is a lack of standardisation and agility. ULI provides a solution to resolve that problem. By empowering lenders with real-time data, simplifying compliance, and creating a more inclusive and transparent borrower experience, it signals a move towards more responsible finance. In my book, that’s the future of lending.

                                  • Embedded Finance
                                  • Neobanking

                                  Author

                                  Dan Brightmore

                                  Published

                                  26 September 2025

                                  Estimated Read time

                                  2Mins

                                  Join 3,000+ industry decision makers and influencers at Smart Retail Tech Show for your opportunity to gain the tools to stay ahead in a competitive market

                                  If you’re in retail and looking to stay ahead in a fast-changing market, the Smart Retail Tech Expo is a must-attend event. With thousands of industry professionals, the show is a hub for innovation, showcasing the latest technologies to enhance the customer journey, streamline operations, and drive growth. Whether it’s improving operations, enhancing safety, enabling contactless payments, or elevating the customer experience, it’s all on the show floor.

                                  Regardless if you’re an independent retailer or part of a global chain, this is your chance to explore cutting-edge solutions!

                                  Why Attend Smart Retail Tech Expo?

                                  With only pre-qualified decision-makers and key influencers in attendance, it’s the perfect place to network, learn, and invest in the future of retail.

                                  Visitors include Key Decision-Makers: CTO | Director of Retail Experience | Digital Transformation Director | Director of Innovation | Head of Customer Experience | Head of Digital & E-commerce

                                  • 3,200 visitors in attendance
                                  • 86% have purchasing authority
                                  • 76% are looking to source new products & services
                                  • 95% are senior management or above

                                  Smart Retail Tech Expo is where retail innovation happens! Small business or global, discover cutting-edge solutions and in one place and shape retail’s future.

                                  “Thanks @smartretailexpo! Packed with innovation, connected with lots of great problem solving startups doing amazing work in the space!”

                                  Daniel Himsworth, Marks & Spencer

                                  Keynote speakers include experts from e-commerce, retail, and tech backgrounds, alongside many more. They will be sharing insights from their personal journey and future-proofed strategies on customer engagement, globalising your business, social media commerce, and lots more. Come and hear from the industry’s biggest voices and learn about how to keep ahead in the white and private-label sector. Keynote speakers include expert insights from Pinterest, Tik Tok, Uber Eats, Alibaba and many more…

                                  Register now for free tickets and gain insider knowledge… Beyond networking, Smart Retail Tech Expo offers expert-led sessions and insights into emerging trends, sourcing strategies, and retail technology—giving you the tools to stay ahead in a competitive market.

                                  Author

                                  Dan Brightmore

                                  Published

                                  Estimated Read time

                                  2Mins

                                  Join over 25,000 entrepreneurs, SME owners, and senior professionals at Excel London for The Business Show London 2025

                                  The world’s largest award-winning business event, The Business Show London 2025, is returning to Excel London on the 12th and 13th of November 2025. Join over 25,000 SMEs and startups at this premier London business expo, designed to provide the support and resources you need to start, grow, or scale your business.

                                  As always, the event offers free expert advice and insights from some of the biggest names in the industry. Building on last year’s impactful keynotes, this year’s business conference features fresh faces—business leaders who have thrived in recent years. In today’s digital landscape, this is a rare opportunity to gain face-to-face experience, advice, and inspiration from those who have been in your position and succeeded.

                                  Whether you’re looking to network at one of the best business networking events in London or seeking new business partnerships, this event is your gateway to unlocking growth. For enquiries, registration, or to book a stand, contact the team today and secure your place at the UK’s leading SME business event.

                                  Why Attend The Business Show London?

                                  This flagship London business expo offers unparalleled opportunities to connect with industry leaders, discover cutting-edge solutions, and gain practical insights to accelerate your business.

                                  “Vibrant, electric and inclusive ….the atmosphere I felt today at The Business Show, London excel as a keynote speaker representing Google. Such an incredible turn out, engaged listeners and wonderful to also have 121’s with many entrepreneurs on business growth utilising AI!”

                                  Harmony Murphy, Google

                                  With thousands of exhibitors, inspiring keynote speakers, and interactive show features, the show caters to startups, established businesses, and everyone in between. Whether you’re looking to connect with startups, explore small business exhibitions, or attend the UK’s leading business growth conference, this event will equip you with fresh ideas and practical strategies to help your business succeed.

                                  • 500+ exhibitors
                                  • 86% attendee satisfaction rate
                                  • 75% attendees plan to return
                                  • 6 show features

                                  Don’t miss your chance to participate in one of the top business networking events in London.

                                  Register now for free tickets and join the UK’s most ambitious business minds to gain new partnerships, expert advice, and business development opportunities.

                                  Author

                                  Ribert Cottrill

                                  Published

                                  25 September 2025

                                  Estimated Read time

                                  4Mins

                                  Robert Cottrill, Technology Director at digital transformation company ANS, explores how businesses can harness the potential of AI while mitigating the growing risks to cybersecurity and privacy

                                  AI can transform businesses, but is it also opening the door to cybersecurity risks?

                                  Fuelled by competitive pressure and rising government support through the UK’s Industrial Strategy, it’s no surprise that more and more businesses are racing to adopt AI.

                                  But there’s a catch. The more businesses scale their AI adoption, the bigger their attack surface becomes. Without a proactive and structured approach to securing AI systems, organisations risk trading short-term efficiencies for long-term vulnerabilities.

                                  The AI Boom

                                  AI investment is skyrocketing. Businesses are deploying generative AI tools, machine learning models, and intelligent automation across nearly every function, from customer service and fraud detection to supply chain optimisation. Platforms like DeepSeek and open-source AI models are now part of the mainstream tech stack.

                                  Initiatives like the UK’s AI Opportunities Action Plan are fuelling experimentation and adoption. AI is now seen not just as a productivity tool, but as a critical lever for digital transformation.

                                  However, the rapid pace of AI deployment is outpacing the development of the security frameworks required to protect it. When integrated with sensitive data or critical infrastructure, AI systems can introduce serious risks if not properly secured. These risks include data leakage through AI prompts or model training, as well as AI-generated phishing and social engineering attacks

                                  So, it’s no surprise that our research found that data privacy is the top concern for businesses when adopting AI. As these threats evolve, businesses must treat AI not just as an enabler, but also as a potential vector for attack.

                                  The Governance Gap

                                  While technical threats often take centre stage, businesses also can’t forget the increasing regulatory requirements surrounding AI. 

                                  As AI systems become more powerful, enabling businesses to extract valuable insights from vast datasets, they also raise serious ethical and legal challenges. 

                                  Regulatory frameworks like the EU AI Act and GDPR aim to provide guardrails for responsible AI use. But these regulations often struggle to keep up with the rapid advancements in AI technology, leaving businesses exposed to potential breaches and misuse of personal data.

                                  The Need for Responsible AI Adoption with Cybersecurity

                                  To build resilience while embracing AI, businesses need a dual approach: 

                                  1. Prioritise AI-specific training across the workforce

                                  Cybersecurity teams are already stretched. Introducing AI into the mix raises the stakes. Organisations must prioritise upskilling their cybersecurity professionals to understand how AI can both protect and threaten systems.

                                  But this isn’t just a job for the security team. As AI tools become embedded in daily workflows, employees across functions must also be trained to spot risks. Whether it’s uploading sensitive data into a chatbot or blindly trusting algorithms, human error remains a major weak point.

                                  A well-trained workforce is the first and most crucial line of defence.

                                  2. Adopt open-source AI responsibly

                                  Another key strategy for reducing AI-related risks is the responsible adoption of open-source AI platforms. Open-source AI enhances transparency by making AI algorithms and tools available for broader scrutiny. This openness fosters collaboration and collective innovation, allowing developers and security experts worldwide to identify and address potential vulnerabilities more efficiently.

                                  The transparency of open-source AI demystifies AI technologies for businesses, giving them the confidence to adopt AI solutions while ensuring they stay alert about potential security flaws. When AI systems are subject to global review, organisations can tap into the expertise of a diverse and engaged tech community to build more secure, reliable AI applications.

                                  To adopt responsibly, businesses need to ensure that the AI they are using aligns with security best practices, complies with regulations, and is ethically sound. By using open-source AI responsibly, organisations can create more secure digital environments and strengthen trust with stakeholders.

                                  Securing the Future of AI

                                  AI is a transformative force that will redefine cybersecurity. We’re already seeing AI being used to automate threat detection and response. But it’s also powering more advanced attacks, from deepfake impersonation to large-scale automated exploits.

                                  Organisations that succeed will be those that embed cybersecurity into every stage of their AI journey, from innovation to implementation. That means making risk management part of the innovation conversation, not a downstream fix.

                                  By taking a responsible approach, investing in training, leveraging open-source AI wisely, and embedding cybersecurity into every layer of the business, organisations can unlock AI’s potential while defending against its risks.  

                                  AI is a double-edged sword, but with thoughtful adoption, businesses can confidently navigate the complex landscape of AI and cybersecurity.

                                  • Cybersecurity
                                  • Data & AI

                                  Author

                                  Dan Brightmore

                                  Published

                                  Estimated Read time

                                  2Mins

                                  Wells Fargo and Google Cloud have expanded their strategic relationship to deploy Agentic AI tools across the bank. As an early…

                                  Wells Fargo and Google Cloud have expanded their strategic relationship to deploy Agentic AI tools across the bank. As an early adopter of Google Agentspace, Wells Fargo is equipping teams with AI agents that will help improve the customer experience, automate routine tasks, and unlock new levels of innovation.

                                  With a strong commitment to responsible AI, Wells Fargo and Google Cloud are focused on modernising financial services and empowering employees with Generative AI solutions to deliver more personalised support and services. This strategic relationship reflects Wells Fargo’s dedication to innovation and transforming how the bank serves its customers.

                                  About Wells Fargo

                                  Wells Fargo is a leading financial services company that has approximately $2.0 trillion in assets. We provide a diversified set of banking, investment and mortgage products and services, as well as consumer and commercial finance, through our four reportable operating segments: Consumer Banking and Lending, Commercial Banking, Corporate and Investment Banking, and Wealth & Investment Management. Wells Fargo ranked No. 33 on Fortune’s 2025 rankings of America’s largest corporations. News, insights, and perspectives from Wells Fargo are also available at Wells Fargo Stories.

                                  • Artificial Intelligence in FinTech
                                  • Digital Payments

                                  Author

                                  Dan Brightmore

                                  Published

                                  24 September 2025

                                  Estimated Read time

                                  3Mins

                                  Integration connects Franklin Templeton’s proprietary tokenisation platform to BNB Chain’s growing ecosystem of institutional and retail investors, supporting secure, compliant on-chain financial products

                                  BNB Chain, leading L1 ecosystem, and Franklin Templeton, a global investment leader with $1.6 trillion in assets under management, today announced the expansion of Franklin Templeton’s Benji Technology Platform onto the BNB Chain. This integration allows Franklin Templeton to leverage BNB Chain’s scalable, low-cost, compliance-ready, and enterprise-grade infrastructure to provide global clients with seamless access to tokenized investment products.

                                  Benji Blockchain Technology Platform

                                  The Benji Technology Platform is Franklin Templeton’s proprietary blockchain-integrated stack, designed to facilitate trading, management, and administration of token-based investments. Using this platform, Franklin Templeton launched the world’s first U.S.-registered mutual fund in 2021 using blockchain-integrated technology to process transactions and record share ownership. The firm has since launched several tokenized investment products, fully on-chain, that support a wide range of global client needs across retail, wealth, institutional, bank and collateral use cases.

                                  By deploying on BNB Chain, Franklin Templeton gains access to a growing ecosystem of institutional and retail participants while demonstrating the network’s ability to support real-world, on-chain financial products at scale. 

                                  “Our goal is to meet more investors where they’re active, while continuing to push the boundaries of what tokenization can deliver with security and compliance at the forefront. Together, Franklin Templeton and BNB Chain will work to deliver tokenized assets with greater utility, and enhanced features for retail and institutional clients across the globe.”

                                  Roger Bayston, Head of Digital Assets, Franklin Templeton

                                  BNB Chain has become a premier destination for tokenized financial products, including money market funds, public equities, credit instruments, and other real-world assets. It enables tokenisation at scale through its powerful tech stack designed for secure, low-cost execution with real-time finality.

                                  “BNB Chain has a purpose-built environment that issuers can’t find elsewhere: fast settlement, low fees, and compliant data tooling in one ecosystem. Franklin Templeton’s decision to expand the Benji Technology Platform to our network demonstrates that BNB Chain can support regulated, real-world assets at scale and continues to strengthen our ecosystem of tokenised financial products.”

                                  Sarah Song, Head of Business Development at BNB Chain

                                  About BNB Chain

                                  BNB Chain is a community-driven blockchain ecosystem that is removing barriers to Web3 adoption. It is composed of:

                                  • BNB Smart Chain (BSC): A secure DeFi hub with the lowest gas fees of any EVM-compatible L1; serves as the ecosystem’s governance chain.  
                                  • opBNB: A scalability L2 that delivers some of the lowest gas fees of any L2 and rapid processing speeds.
                                  • BNB Greenfield: Meets decentralized storage needs for the ecosystem and lets users establish their own data marketplaces.

                                  Setting a high bar for security, the AvengerDAO community protects BNB Chain users while Red Alarm provides a real-time risk-scanner for Dapps. The ecosystem also offers a range of monetary and ecosystem rewards as part of its Builder Support Program. For more, follow BNB Chain on X or start exploring via our Dapp library.

                                  About Franklin Templeton

                                  Franklin Resources, Inc. is a global investment management organisation with subsidiaries operating as Franklin Templeton and serving clients in over 150 countries. Franklin Templeton’s mission is to help clients achieve better outcomes through investment management expertise, wealth management and technology solutions. Through its specialist investment managers, the company offers specialization on a global scale, bringing extensive capabilities in fixed income, equity, alternatives and multi-asset solutions. With more than 1,500 investment professionals, and offices in major financial markets around the world, the California-based company has over 75 years of investment experience and [$1.64 trillion] in assets under management as of August 31, 2025. For more information, please visit franklintempleton.com 

                                  • Blockchain & Crypto

                                  Author

                                  Dan Brightmore

                                  Published

                                  23 September 2025

                                  Estimated Read time

                                  2Mins

                                  CIBC launches GenAI platform, CAI, for data analysis, accelerated research, light coding and more…

                                  CIBC today announced the bank-wide launch of CIBC AI (CAI), its in-house Generative AI platform, to help drive further productivity across the organization and enable team members to deliver on the bank’s client-focused strategy.

                                  CIBC AI (CAI)

                                  CAI launched a pilot phase in July 2024 with an initial group of team members across Canada, the US and the UK. The AI platform has saved team members an estimated 200,000+ hours during the pilot by enabling team members to automate common tasks such as summarizing documents, drafting emails, compiling research and other text-based content.

                                  “It’s been tremendous watching the uptake of CAI across our bank and how it has helped simplify routine tasks for team members, better enabling them to focus on delivering value to our clients. What sets CAI apart is its adaptability to the unique needs of each team, from writing to research and analysis or even light coding suggestions, CAI has had a positive impact across all lines of business.”

                                  Dave Gillespie, Executive Vice-President, Infrastructure, Architecture and Modernisation, CIBC

                                  CAI is a custom-built Generative AI platform that was designed by CIBC from the ground up to support team members with a task-driven approach. It features an intuitive dashboard that allows users to easily navigate through various functionalities such as data analysis, accelerated research and preparing presentations. With the adoption of CAI, team members are able to focus their time on higher value activities.

                                  Responsible AI

                                  Team members need to complete a mandatory training course in order to access CAI, which provides an understanding of CIBC’s approach to AI and data, as well as the responsible governance framework in place to guide the use of AI at the bank.

                                  “Innovation has long been a hallmark of CIBC’s approach to meeting client needs, and we’re incredibly proud to take another exciting step forward in enhancing everyday experiences for our team members.” added Gillespie.

                                  CIBC reinforced its commitment to responsible AI by becoming the first major Canadian bank to sign the Government of Canada’s Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems in March. 

                                  About CIBC

                                  CIBC is a leading North American financial institution with 14 million personal banking, business, public sector and institutional clients. Across Personal and Business Banking, Commercial Banking and Wealth Management, and Capital Markets and Direct Financial Services businesses, CIBC offers a full range of advice, solutions and services through its leading digital banking network, and locations across Canada, in the United States and around the world. Ongoing news releases and more information about CIBC can be found at www.cibc.com/ca/media-centre.

                                  • Artificial Intelligence in FinTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  18 September 2025

                                  Estimated Read time

                                  2Mins

                                  Embat and MicroFin strategic alliance delivers AI-powered cash management, reconciliation and real-time visibility for finance teams managing complex, multi-entity operations

                                  Embat, the leading European financial management and treasury platform, has formed a strategic partnership with MacroFin, part of Cooper Parry Digital and the UK’s leading NetSuite Alliance Partner. The collaboration combines MacroFin’s market-leading NetSuite implementation expertise with Embat’s next-generation treasury technology. The alliance will help finance teams tackle the growing complexity of international operations.

                                  MacroFin has been recognised as NetSuite Alliance Partner of the Year since 2021, reflecting its reputation and expertise for delivering the UK’s most complex ERP implementations. Following its acquisition by Cooper Parry, MacroFin has further solidified its position as one of the UK’s premier NetSuite partners.

                                  Facing the Challenge to Transform

                                  As companies scale – particularly in sectors such as SaaS, e-commerce, retail, and hospitality – their finance teams face challenges to transform that outgrow traditional tools such as Microsoft Excel. Multi-currency operations, multiple legal entities, high transaction volumes, and increased regulatory demands. This partnership ensures NetSuite clients have access to Embat’s treasury platform bidirectionally connected to NetSuite, offering:

                                  • Real-time cash visibility across accounts and currencies
                                  • AI-powered bank reconciliation that cuts manual processing time by up to 90%
                                  • Advanced forecasting to support strategic planning
                                  • Automated treasury operations to streamline day-to-day processes
                                  • Seamless NetSuite integration for consistent, efficient workflows
                                  • TellMe, Embat’s AI-powered treasury analyst, which enables finance teams to save up to 75% of their time on manual tasks. Freeing them to focus on strategic decision making

                                  Treasury Management

                                  “Treasury management has evolved from a back-office task to a strategic driver of business growth and efficiency. By working with MacroFin, we’re making advanced treasury technology accessible to NetSuite clients who need real-time visibility and automation to manage complexity with confidence.”

                                  Theo Wasserberg, Head of UK&I at Embat

                                  “When clients face complex international and multi-entity challenges, we look for solutions that go beyond NetSuite’s native functionality. Embat’s direct integration and AI-driven automation deliver the clarity and efficiency CFOs need in today’s environment.”

                                  Ross Latta, Co-Founder of MacroFin

                                  This partnership underscores Embat and MacroFin’s shared commitment to innovation in financial technology and toempowering CFOs and finance teams with tools that enhance both operational efficiency and strategic insight.

                                  About Embat

                                  Embat is a leading European financial management and treasury platform that enables finance teams in medium and large companies to centralise all operations from banking relationships to their financial management processes. It allows finance teams to save up to 75% of their time on manual tasks by using TellMe, our AI-powered treasury analyst, so they can focus on strategic decision-making. The main functions of Embat are treasury automation, automated accounting, and payments. Clients experience cost savings (by optimising their working capital management), time savings, reduced errors and an increased quality of life.

                                  About MacroFin with 3RP and the CP Digital Family

                                  MacroFin is a UK-based consultancy specialising in finance-led ERP (Enterprise Resource Planning) transformations centred around the NetSuite platform. Founded in 2018 by chartered accountants, their approach emphasises embedding finance expertise at every stage of implementation. They offer services including NetSuite implementation, optimisation, training, support, and custom development. 

                                  In 2024, MacroFin joined Cooper Parry to form CP Digital alongside 3RP and Cloud Orca, creating a digital transformation hub with wider expertise and tech partnerships.

                                  MacroFin has implemented NetSuite for leading brands like Babylon, Depop, PensionBee, and Zego, achieving average go-live in four months.

                                  • Artificial Intelligence in FinTech
                                  • Digital Payments

                                  Author

                                  Anna Collard

                                  Published

                                  Estimated Read time

                                  8Mins

                                  Anna Collard, SVP Content Strategy & Evangelist KnowBe4 – Africa, on leveraging AI-driven cybersecurity systems to fight cybercrime

                                  Artificial Intelligence is no longer just a tool. It is a game-changer in our lives, our work as well as in both cybersecurity and cybercrime. While businesses leverage AI to enhance defences, cybercriminals are weaponising AI to make these attacks more scalable and convincing​.  

                                  In 2025, research shows AI agents, or autonomous AI-driven systems capable of performing complex tasks with minimal human input, are revolutionising both cyberattacks and cybersecurity defences. While AI-powered chatbots have been around for a while, AI agents go beyond simple assistants. They function as self-learning digital operatives that plan, execute, and adapt in real time. These advancements don’t just enhance cybercriminal tactics, they may fundamentally change the cybersecurity battlefield. 

                                  How Cybercriminals Are Weaponising AI: The New Threat Landscape 

                                  AI is transforming cybercrime, making attacks more scalable, efficient, and accessible. The WEF Artificial Intelligence and Cybersecurity Report (2025) highlights how AI has democratised cyber threats. Thus enabling attackers to automate social engineering, expand phishing campaigns, and develop AI-driven malware​. Similarly, the Orange Cyberdefense Security Navigator 2025 warns of AI-powered cyber extortion, deepfake fraud, and adversarial AI techniques. And the 2025 State of Malware Report by Malwarebytes notes, while GenAI has enhanced cybercrime efficiency, it hasn’t yet introduced entirely new attack methods. Attackers still rely on phishing, social engineering, and cyber extortion, now amplified by AI. However, this is set to change with the rise of AI agents. Autonomous AI systems are capable of planning, acting, and executing complex tasks—posing major implications for the future of cybercrime. 

                                  Here is a list of common (ab)use cases of AI by cybercriminals:  

                                  AI-Generated Phishing & Social Engineering 

                                  Generative AI and large language models (LLMs) enable cybercriminals to craft more believable and sophisticated phishing emails in multiple languages. Without the usual red flags like poor grammar or spelling mistakes. AI-driven spear phishing now allows criminals to personalise scams at scale, automatically adjusting messages based on a target’s online activity. AI-powered Business Email Compromise (BEC) scams are increasing. Attackers use AI-generated phishing emails sent from compromised internal accounts to enhance credibility​. AI also automates the creation of fake phishing websites, watering hole attacks and chatbot scams. These are sold as AI-powered ‘crimeware as a service’ offerings, further lowering the barrier to entry for cybercrime​. 

                                  Deepfake-Enhanced Fraud & Impersonation 

                                  Deepfake audio and video scams are being used to impersonate business executives, co-workers or family members to manipulate victims into transferring money or revealing sensitive data. The most famous 2024 incident was UK based engineering firm Arup that lost $25 million after one of their Hong Kong based employees was tricked by deepfake executives in a video call. Attackers are also using deepfake voice technology to impersonate distressed relatives or executives, demanding urgent financial transactions.  

                                  Cognitive Attacks  

                                  Online manipulation—as defined by Susser et al. (2018)—is “at its core, hidden influence, the covert subversion of another person’s decision-making power”. AI-driven cognitive attacks are rapidly expanding the scope of online manipulation. By everaging digital platforms, state-sponsored actors increasingly use generative AI to craft hyper-realistic fake content. They are subtly shaping public perception while evading detection. These tactics are deployed to influence elections, spread disinformation and erode trust in democratic institutions. Unlike conventional cyberattacks, cognitive attacks don’t just compromise systems—they manipulate minds, subtly steering behaviours and beliefs over time without the target’s awareness. The integration of AI into disinformation campaigns dramatically increases the scale and precision of these threats, making them harder to detect and counter.  

                                  The Security Risks of LLM Adoption 

                                  Beyond misuse by threat actors, business adoption of AI-chatbots and LLMs introduces significant security risks. Especially when untested AI interfaces connect the open internet to critical backend systems or sensitive data. Poorly integrated AI systems can be exploited by adversaries. This enables new attack vectors, including prompt injection, content evasion, and denial-of-service attacks. Multimodal AI expands these risks further, allowing hidden malicious commands in images or audio to manipulate outputs.  

                                  Moreover, many modern LLMs now function as Retrieval-Augmented Generation (RAG) systems. Dynamically pulling in real-time data from external sources to enhance their responses. While this improves accuracy and relevance, it also introduces additional risks, such as data poisoning, misinformation propagation, and increased exposure to external attack surfaces. A compromised or manipulated source can directly influence AI-generated outputs. Potentially leading to incorrect, biased, or even harmful recommendations in business-critical applications. 

                                  Additionally, bias within LLMs poses another challenge. These models learn from vast datasets that may contain skewed, outdated, or harmful biases. This can lead to misleading outputs, discriminatory decision-making, or security misjudgements, potentially exacerbating vulnerabilities rather than mitigating them. As LLM adoption grows, rigorous security testing, bias auditing, and risk assessment, especially in RAG-powered models, are essential to prevent exploitation and ensure trustworthy, unbiased AI-driven decision-making. 

                                  When AI Goes Rogue: The Dangers of Autonomous Agents 

                                  With AI systems now capable of self-replication, as demonstrated in a recent study, the risk of uncontrolled AI propagation or rogue AI – AI systems that act against the interests of their creators, users, or humanity at large – is growing. Security and AI researchers have raised concerns that these rogue systems can arise either accidentally or maliciously. Particularly when autonomous AI agents are granted access to data, APIs, and external integrations. The broader an AI’s reach through integrations and automation, the greater the potential threat of it going rogue. This means robust oversight, security measures, and ethical AI governance essential in mitigating these risks. 

                                  The Future of AI Agents for Automation in Cybercrime 

                                  A more disruptive shift in cybercrime can and will come from AI Agents. These transform AI from a passive assistant into an autonomous actor capable of planning and executing complex attacks. Google, Amazon, Meta, Microsoft, and Salesforce are already developing Agentic AI for business use. However, in the hands of cybercriminals, its implications are alarming. These AI agents can be used to autonomously scan for vulnerabilities, exploit security weaknesses, and execute cyberattacks at scale. They can also allow attackers to scrape massive amounts of personal data from social media platforms. They can automatically compose and send fake executive requests to employees. And, for example, analyse divorce records across multiple countries to identify individuals for AI-driven romance scams, orchestrated by an AI agent. These AI-driven fraud tactics don’t just scale attacks, they make them more personalised and harder to detect. Unlike current GenAI threats, Agentic AI has the potential to automate entire cybercrime operations, significantly amplifying the risk​. 

                                  How Defenders Can Use AI & AI Agents 

                                  Organisations cannot afford to remain passive in the face of AI-driven threats. Security professionals need to remain abreast of the latest developments. Here are some of the  opportunities in using AI to defend against AI:  

                                  AI-Powered Threat Detection and Response

                                  Security teams can deploy AI and AI-agents to monitor networks in real time, identify anomalies, and respond to threats faster than human analysts can. AI-driven security platforms can automatically correlate vast amounts of data to detect subtle attack patterns. These might otherwise go unnoticed. AI can create dynamic threat modelling, real-time network behaviour analysis, and deep anomaly detection​. For example, as outlined by researchers of Orange Cyber Defense, AI-assisted threat detection is crucial as attackers increasingly use “Living off the Land” (LOL) techniques that mimic normal user behaviour. Making it harder for detection teams to separate real threats from benign activity. By analysing repetitive requests and unusual traffic patterns, AI-driven systems can quickly identify anomalies and trigger real-time alerts, allowing for faster defensive responses. 

                                  However, despite the potential of AI-agents, human analysts still remain critical. Their intuition and adaptability are essential for recognising nuanced attack patterns. They can leverage real incident and organisational insights to prioritise resources effectively. 

                                  Automated Phishing and Fraud Prevention

                                  AI-powered email security solutions can analyse linguistic patterns, and metadata to identify AI-generated phishing attempts before they reach employees, by analysing writing patterns and behavioural anomalies. AI can also flag unusual sender behaviour and improve detection of BEC attacks​. Similarly, detection algorithms can help verify the authenticity of communications and prevent impersonation scams. AI-powered biometric and audio analysis tools detect deepfake media by identifying voice and video inconsistencies. However, real-time deepfake detection remains a challenge, as technology continues to evolve. 

                                  User Education & AI-Powered Security Awareness Training

                                  AI-powered platforms deliver personalised security awareness training. They can simulate AI-generated attacks to educate users on evolving threats, helping train employees to recognise deceptive AI-generated content​. And strengthen their individual susceptibility factors and vulnerabilities.  

                                  Adversarial AI Countermeasures

                                  Just as cybercriminals use AI to bypass security, defenders can employ adversarial AI techniques. For example, deploying deception technologies – such as AI-generated honeypots – to mislead and track attackers. As well as continuously training defensive AI models to recognise and counteract evolving attack patterns. 

                                  Using AI to Fight AI-Driven Misinformation and Scams

                                  AI-powered tools can detect synthetic text and deepfake misinformation, assisting fact-checking and source validation. Fraud detection models can analyse news sources, financial transactions, and AI-generated media to flag manipulation attempts​. Counter-attacks, like those shown by research project Countercloud or O2 Telecoms AI agent “Daisy” show how AI based bots and deepfake real-time voice chatbots can be used to counter disinformation campaigns as well as scammers by engaging them in endless conversations to waste their time and reducing their ability to target real victims​. 

                                  In a future where both attackers and defenders use AI, defenders need to be aware of how adversarial AI operates. And how AI can be used to defend against their attacks. In this fast-paced environment, organisations need to guard against their greatest enemy: their own complacency. While at the same time considering AI-driven security solutions thoughtfully and deliberately. Rather than rushing to adopt the next shiny AI security tool, decision makers should carefully evaluate AI-powered defences to ensure they match the sophistication of emerging AI threats. Hastily deploying AI without strategic risk assessment could introduce new vulnerabilities, making a mindful, measured approach essential in securing the future of cybersecurity.  

                                  To stay ahead in this AI-powered digital arms race, organisations should:  

                                  • Monitor both the threat and AI landscape to stay abreast of latest developments on both sides. 
                                  • Train employees frequently on latest AI-driven threats, including deepfakes and AI-generated phishing. 
                                  • Deploy AI for proactive cyber defense, including threat intelligence and incident response. 
                                  • Continuously test your own AI models against adversarial attacks to ensure resilience. 
                                  • Cybersecurity
                                  • Data & AI

                                  Author

                                  Dan Brightmore

                                  Published

                                  17 September 2025

                                  Estimated Read time

                                  3Mins

                                  New data from Evident shows banks are increasingly turning AI research into real-world tools

                                  AI benchmarking and intelligence platform Evident has published its latest report… The State of AI Research in Banking, analyses over 2,700 AI-specific papers from 50 of the world’s largest banks. 

                                  The State of AI Research in Banking

                                  The report shows that the big banks have increased their annual artificial intelligence research output by 7x over the past five years. The most AI-advanced institutions are focusing on research areas that directly serve their AI production pipelines.

                                  Since 2019, the number of banks publishing AI research has nearly doubled from 25 to 46 from 50 banks tracked by Evident. Last year, two-thirds of this research (65%) was driven by just five banks. They are JPMorganChase (37%), Capital One (14%), Wells Fargo (5%), RBC (5%), TD Bank (4%).

                                  According to Evident, it’s possible to map the banks’ historic research pipelines directly to their artificial intelligence use cases and products. From RBC’s ATOM model powering responsible lending to Capital One’s multi-agent systems for customer service. Examples of banks where research papers have served as blueprints for production include:

                                  • Capital Markets & Trading: Scotiabank, RBC Borealis, BlackRock, JPMorganChase
                                  • Transactions, Risk, AML, and Fraud: RBC Borealis, NatWest, CommBank
                                  • Agentic AI and Workflow Automation: Capital One, JPMorganChase, UniCredit
                                  • Causal AI and Personalisation: BBVA, TD Bank
                                  • Customer Experience and Summarization: NatWest, JPMorganChase

                                  “Through their research programmes, banks like JPMorganChase, Capital One, RBC, Wells Fargo, and TD Bank are setting the tone for how AI will be deployed in high-stakes, regulated environments. In contrast to the more commercially-guarded R&D practices of Big Tech, these banks are signalling the future of applied AI in financial services. And, most impressively, moving from research pipelines into production at scale within two to three years. Which is lightning fast by academic standards.”

                                  Alexandra Mousavizadeh, Co-founder & CEO, Evident

                                  The Rise of Agentic AI

                                  The State of AI Research in Banking report also points to the rise of Agentic AI as a priority within the world’s largest banks. 

                                  Evident’s data shows that AI Agents and Agent-based Systems research is now the fifth most popular research paper theme. Agentic themed research accounts for nearly 6% of year-to-date 2025 publications – or twice the current share of public agentic use cases Evident found across banking. 

                                  As more resources pour into agentic research, there has been an accompanying year-over-year decline in papers focused on Computer Vision (-0.7%), Scientific Discovery (-1.8%), and Healthcare / Biomedicine (-2.2%). This data further underscores where and how banks are shifting efforts away from open inquiry, in favour of applied research that clearly relates to immediate business applications.

                                  “While academic research within big business is often dismissed as a vanity exercise to keep PhDs happy, our analysis shows the opposite. The leading banks are pushing the frontier on emerging technologies like agentic AI – building the architectures and workflows that will soon underpin real-world applications. This isn’t research for research’s sake: it’s laying the foundation for faster deployments, smarter trading agents, and the next frontier of AI-driven financial services,” added Mousavizadeh.

                                  About Evident

                                  Evident is the intelligence platform for AI adoption in financial services. The company supports leaders stay ahead of change with in-depth insights, benchmarking, and real-time data through its flagship Indexes, Insights across Talent, Innovation, Leadership, Transparency and Responsible AI pillars, a real-time Use Case Tracker, community and events. Evident also provides private outcomes benchmarking, enabling firms to understand how their adoption of artificial intelligence compares to peers. Learn more at www.evidentinsights.com

                                  • Artificial Intelligence in FinTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  Estimated Read time

                                  3Mins

                                  Revolutionary integration ensures real-time policy verification, combats document fraud, and boosts trust in the supply chain

                                  Business Choice Direct (BCD), a leading insurance provider to the transport and logistics industry, has announced a groundbreaking partnership with Trustd, the government-certified digital identity platform for transport and logistics. They launch the logistics sector’s first use of verifiable insurance credentials. This platform facilitates digital proof of valid insurance which can be instantly confirmed by any third party. Whether a fleet owner, freight forwarder, or shipper.

                                  Traditionally, insurance documentation relied on physical or emailed copies. These were easily outdated or manipulated, leaving carriers, subcontractors, and freight businesses vulnerable to fraud. In many cases, invalid or refunded insurance policies are mistakenly accepted because there’s no easy way to verify them when they are presented as proof of cover. Figures from BCD renewal statistics 2025 show that on average almost 30% of insurance policies are cancelled per month. Whilst not all of these cancellations are due to fraudulent activity, this statistic highlights the significant volume of potential risks that could have gone undetected by manual paperwork processes.

                                  The Trustd and BCD Partnership

                                  Through integration with Trustd, BCD policyholders can now generate secure, real-time verifiable credentials. These provide instant verification of valid insurance coverage, accessible and confirmable by any third-party. Including fleet owners, freight forwarders, or shippers, at any time. The insurance details can be instantly authenticated through secure digital channels, ensuring full transparency and reliability. In addition, the credentials are portable and tamper-proof and can be checked without relying on phone calls or visual inspections.

                                  These digital insurance policies benefit over 10,000 logistics businesses on the TEG platform, one of Trustd’s flagship customers. This is achieved by providing instant updates if a policy is cancelled or expires, allowing quick action without confusion. This prevents instances of fraud and unauthorised carriers moving freight without the correct insurances. 

                                  The First Industry Compliance Tool for Insurance 

                                  “This is a game-changer for insurance verification in logistics,” said Tristan Scaife, Director of Commercial at Business Choice Direct (BCD). This is the first time the logistics industry has access to real-time confirmation that a courier genuinely holds the right insurance. Through our partnership with Trustd, we’ve eliminated the uncertainty that risk and compliance teams have faced for years. No more guesswork, just instant, verified proof. We’re proud to be the first insurer to offer this capability with Trustd. Ensuring our customers’ credentials are both secure and effortlessly verifiable”. 

                                  A Breakthrough for Trust, Safety, and Transparency in the Sector

                                  For drivers and carriers, this means an easy way to share and verify your insurance with anyone or any company for transport work.

                                  For businesses, it offers a reliable and seamless method to ensure every policy presented is current and valid. Minimising risk, improving compliance, and streamlining operations.

                                  Scaife continues: “In terms of our partnership with TEG, we can offer exclusive savings and drivers can be confident that everyone on the platform and insured with BCD, is a Trustd courier. We’re proud to offer exclusive insurance discounts to Trustd users who choose to verify their credentials digitally”.

                                   “I’ve always known providing credible documents can be a challenge, so this integration with BCD is exciting. Together, we’re pioneering innovation in logistics by solving problems that have existed for decades. This partnership with BCD is just the beginning. Our platform is designed to support a growing network of credential issuers, creating a single source of truth for all the verifiable credentials that carriers and drivers need. It’s a win for everyone, from fleet operators to individual drivers.”

                                  Lyall Cresswell, Founder & CEO of Trustd

                                  Once launched, this solution will be available to all TEG users with a BCD-issued policy.

                                  About Business Choice Direct

                                  Business Choice Direct (BCD) is a specialist insurance broker offering tailored cover for professionals in the transport, courier, and logistics industries. With expert advice and competitive pricing, BCD helps small and large businesses stay protected on the move.

                                  About Trustd

                                  Trustd is the first government-certified digital identity management platform designed specifically for transport and logistics. It digitises key industry documentation into secure, verifiable profiles that enhance trust and security across the supply chain.

                                  • InsurTech

                                  Author

                                  Nell Walker

                                  Published

                                  Estimated Read time

                                  6Mins

                                  The proof, as they say, is in the pudding – and the evidence of TealBook’s increasingly-successful evolution lies in its client relationships

                                  We talked endlessly about data and AI at DPW New York 2025. A universal truth is that the successful implementation of AI requires clean data; it doesn’t have to be perfect, but businesses certainly need to have a decent handle on their data before adopting AI tools successfully. 

                                  To help make this a reality, North American data and software company TealBook has recently announced a legal entity-based data model. It’s designed to resolve supplier records to the correct legal entities, map parent-child relationships, and enrich profiles with verifiable attributes, enabling accurate supplier data to flow seamlessly into procurement systems and AI applications. “This is part of a 12-year journey for TealBook,” says Stephany Lapierre, the company’s Founder and CEO. “Our vision has always been to build a way to enable procurement organisations to have high quality data with a lot of integrity, in order to give them the trust they need to put data directly into their systems. 

                                  “Twelve years ago, we underestimated the complexity of getting large enterprises to trust a third-party data solution. As part of our journey, we started using AI early on to find information where it exists on supplier websites and databases, and start creating digital profiles in a structured way for procurement to access it, match it to their vendor master, and use it.”

                                  TealBook’s evolution

                                  But, again, at the beginning, TealBook couldn’t be sure whether the data was high enough quality. In 2017, the company was primarily known as a supplier discovery application, positioned as a pre-sourcing engine to help procurement teams identify alternative suppliers. At the time, TealBook’s data and models enabled it to determine which companies were similar to others, allowing users to search and find comparable suppliers to expand their sourcing options.

                                  “But that was just a way for us to deliver something that was underserved in the market,” Lapierre continues. “Then our customers started asking for certificates, which are hard to collect and match. They needed cleaner data. They felt they were under-reporting. So in 2018, we started to see whether our technology could refine the data more, and focused on certificates and supplier diversity. We collected great use cases along this journey, and the vision never wavered.

                                  “Just last year we released a new technology – completely different, really sophisticated – allowing us to pull from a lot more data sources, and we have provenance so our customers can actually verify where the data’s coming from. We can match it to vendor masters. And now, we also have this new model that includes 230 million verifiable global legal entities from across 145 countries’ registries. We marry this with global parent and child hierarchy, which is really hard for our customers to match themselves.”

                                  Partnership with Kraft Heinz

                                  Now, after 12 years of that vision, TealBook is deeply proud of what it’s achieved. Part of its ability to get to this point is due to early adoption from key customers. Kraft Heinz is a business which Lapierre describes as a “co-innovation partner”, and has been invaluable in helping TealBook achieve its recent goals.

                                  From the perspective of Stefanie Fink, Head of Global Data and Digital Procurement at Kraft Heinz, the partnership has been an immediately valuable one. “It really started with having a visionary, like-minded relationship,” she says. “That’s an important piece of it, because my vision for procurement is that we are partners in our enterprise. 

                                  “In order for us to do our jobs, we have to bring in the right data for use. This is where Stephany’s partnership and vision really resonated. We were really looking for diversity and we could make things easier for our partners, while making sure we had the right people in our ecosystem. We also had to lift up the hood and see what was underneath everything we’ve got. Stephany brought our vision to life. TealBook has evolved too, as we’ve seen; it’s more about orchestration and software-as-a-service. It has been a partnership of need and we cannot continue to do other things without this kind of partnership around data.”

                                  When initially dabbling with this relationship, Fink was clear that Kraft Heinz had no desire to be taking care of more stuff. What she wanted from TealBook was a strong focus on good quality data. After last year’s product release from TealBook, Kraft Heinz already saw its data enriched by 25%. The recently-announced new data model gives the business and TealBook’s other customers the right structure tied to a legal entity, which is a highly credible anchor. “We’re able to do entity resolution – all automated – remove all the duplicates, and then you start with a clean, digitised vendor master,” says Lapierre. “That’s what brings further enrichment.”

                                  The challenge of assessing data quality

                                  Assessing its data before involving TealBook was important for Kraft Heinz, but challenging for such a large organisation. “We had to fail first and fail fast,” says Fink. “We tried some AI around fixing things early, but that didn’t work for us. It was a real eye-opener, realising where this next evolution could take us regarding focusing on AI and agents for the right things, not the meaningless things. Before, we were asking agents to tell us if things were duplicates, when we should have been asking: what do these suppliers offer? Where is the innovation? Where is the value?”

                                  What surprised Fink most when looking under Kraft Heinz’s hood was the lack of attention that was being paid to what the business was doing. “It was amazing that nobody had questioned it sooner,” she says. “So I said, let’s take this as a crawl, walk, run approach, and I have a wonderful CPO who really understands where we want procurement to go as a function. She was excited about us just getting it done and getting people involved, and that’s what it takes: real pride in ownership of the data.”

                                  Getting engrossed in GenAI

                                  True partnership and an all-in approach has enabled Kraft Heinz to work successfully with AI – something some businesses are struggling with as the conversation around artificial intelligence grows louder. For Lapierre, as the CEO of a tech company, adopting AI successfully has meant trying and failing and being fully entrenched in AI as it has evolved.

                                  “We’ve been using AI in our technology since 2016,” she states. “We’re an early adopter. We’d be talking about scraping data, and data in the cloud, and AI models, and our customers’ pupils would widen in surprise. We’ve come a long way and the market has come a long way. 

                                  “The technology we deliver today wouldn’t be possible without the AI tools now at our disposal. We used to build models; we don’t do that anymore. We spend a lot of time investing in engineers to build and test models, and that’s made us so much more efficient. I use GenAI every day for so many things now, and I’m encouraging my team to be so involved in AI. That’s how you build expertise, and you need really strong expertise to use GenAI well. 

                                  “Getting good with AI is about taking risks and having a leadership team that pushes for new things, and suddenly the successful use of AI becomes a habit.”

                                  Author

                                  Dan Brightmore

                                  Published

                                  16 September 2025

                                  Estimated Read time

                                  2Mins

                                  The deadline for entries for the National DevOps Awards is September 19th. Finalist will be announced September 26th. Don’t miss out – book your place before the October 14th deadline.

                                  For nearly a decade, the DevOps Awards have celebrated innovation and excellence in DevOps, recognising the hard work and achievements driving the community forward. As an independent awards program, it highlights leaders who are shaping the future of DevOps.  

                                  Being shortlisted is a significant achievement, marking you as a key player in the industry. The awards are open to businesses of all sizes, as well as teams and individuals worldwide. With 16 diverse categories, entries are judged against a clear set of criteria, ensuring fairness and prestige. 

                                  The awards offer a unique platform to showcase your expertise, gain visibility, and connect with top professionals in DevOps and quality engineering.  

                                  Join us in London this year and share your insights with some of the brightest minds in the field.  

                                  To enter and book your place at the awards visit the National DevOps Awards website.

                                  A Truly Independent DevOps Judging Process​

                                  The DevOps Awards ensures fair and unbiased judging through an anonymous evaluation process. All judges -led by Dávid Jámbor
                                  Senior Director – Technology and Secure Infrastructure BCG – are seasoned senior professionals and they assess award entries purely on merit, with all identifying information removed. This guarantees that every winner is recognised solely for their exceptional achievements, regardless of company size, budget, or market influence.​

                                  Author

                                  Nell Walker

                                  Published

                                  Estimated Read time

                                  7Mins

                                  The march towards agentic AI can be a daunting thing, but it’s important to get over that fear in order to make strides

                                  A common question when discussing AI is ‘where do humans fit in?’. The fear of technological advancements stealing our jobs is an old one, but the conclusion is always the same and always true: there will never be a time when human judgement and teamwork isn’t required.

                                  At DPW New York 2025, we sat down with Rinus Strydom, Chief Revenue Officer at Pactum AI, and Steven Velte, Executive Director Procurement Transformation at Honeywell – a customer of Pactum AI – to discuss AI’s evolution and the human connection. As AI develops, for Strydom, Pactum’s focus is on agentic, rather than generative. There’s a key difference there, especially for initial adoption at large enterprises. 

                                  “A lot of enterprises feel a little bit afraid, because generative AI can go a little off the rails,” he explains. “But when you put agents to work, they’re always within the rails that are defined by the customers. Once we get over that hurdle and can make clients see that they can take their procurement operating model and have it just run at scale with agents, rather than being afraid that their image will get tarnished, AI can be put to work much faster.”

                                  Putting AI to work

                                  When it comes to strategies procurement leaders can adopt to make AI work for them, it’s a major discussion point for Strydom and Velte. As a customer, it’s important for Honeywell to feel like its work with Pactum AI is a collaboration; it’s part of what makes its strides into AI work successfully. “This collaboration goes deeper than what we’ve typically had in the past,” says Velte. 

                                  “When we go through organisational changes, we need a true partner, And when that partner gets into the elevator with you, they don’t just push the button with you – they go up to the next floor with you and sit at the table to talk about what’s happening. So a barrier to AI adoption is not having that deep collaboration and partnership.” 

                                  “I think another thing leaders can do today is really help with that psychological change management to make it feel like a safe thing,” Strydom adds. Mindset shift is such a vital part of this change, especially when it comes to successful collaboration. “It’s important to embrace agentic AI, to encourage people to become managers of agents and not run away or become fearful.”

                                  Identifying the opportunities

                                  The true benefits of AI are now beginning to present themselves, as people increasingly embrace AI. For Velte, businesses have to get going with their AI plans in order to realise where the real opportunities lie. “I can make a business case with tons of ROI, potential productivity gains, revenue uplift, bottom line, profit line – all of that. But the real benefits that come from AI are those hidden benefits we don’t realise. When you start looking at it, there’s a common theme of saving time, and time becomes the real benefit. Unlocking better use of time gives you more potential to work on other creative aspects of the business.”

                                  For Strydom, the true value lies in achieving things that used to be extremely difficult to achieve. Pactum AI’s customer base is broadly looking at 10X ROI, which, now, is easily done thanks to the use of AI agents. Agents also allow procurement teams to scale extremely fast, which is something that has, historically, been hard-won. 

                                  “For example, if you need to change payment terms across your entire supply base, you can do that with thousands of agents in parallel. You could never do that before. It gives you the agility to react to global macro risk issues, like tariffs.”

                                  Start now; perfection comes later

                                  One of the loudest topics of conversation at DPW New York 2025 was data quality and the challenge of cleaning that data up. It’s a huge topic, and a daunting one. Many businesses fall into the trap of thinking their data has to be perfect before they can get fully involved with AI, but the conclusion many procurement leaders are coming to is that getting started is more important than perfection.

                                  “Data quality is always the holy grail going forward,” says Velte. “Everyone’s going to look for it, and try to attain it. When you start implementing within an AI framework, you just need to go in there and know that you’re going to constantly evolve in a good way, thanks to the agents, AI programs, and initiatives. They’re going to uncover and unlock a lot of data and inconsistencies that you have. You won’t get there unless you start looking into them as an opportunity area. Data perfection is not the way to go; it’s about getting in there, starting to look at the opportunities, and being willing to be creative, disruptive, and innovating quickly.

                                  “There’s never going to be a time when everything is 100% correct and accurate, because data is always evolving,” adds Strydom. “Start now. The data can be enriched over time with the agents’ help.” 

                                  Maximum savings, maximum momentum

                                  Pactum is using AI specifically to enable it to be a strategic advisor for customers like Honeywell. The use cases coming out are very new, and changing fast. What Strydom and his team want is to be able to guide customers on the right strategies for them, how to get maximum savings, and maximum momentum. As this landscape becomes more complex, human intervention and guidance is more important than ever, which links back to the topic of mindset and change management. 

                                  There’s been a lot of debate within Pactum AI as to how the business embraces this. “From a marketing perspective, too, there’s the question of whether we should make our agents look human,” says Strydom. “Actually, what we’re seeing is that suppliers actually enjoy interfacing with a bot. Walmart, one of our customers, did a survey where they found that 85% of their suppliers actually prefer to negotiate with Pactum than with a human. It’s more efficient, fair, and unbiased.”

                                  Speaking of humans, shortage of talent has been a talking point within procurement for some time. That was, until advanced tech became more widely adopted, and bringing in procurement experts became less important than bringing in technology experts who are willing to learn. With the advent of agentic AI, according to Strydom, procurement leaders are now acting as managers of agents.

                                  “All the analyst surveys say that procurement organisations are being asked to do more with less every year,” he says. “So the type of talent is definitely transforming. What we see is that the procurement organisations of the future are much more strategic. They’re focusing on creating strategy and procurement policies and procedures, and then having the agents actually go out and do the menial day-to-day work – entering things into ERP, turning requisitions into purchase orders, onboarding suppliers, and so on. All of that can now be done very quickly and efficiently by agents. This really elevates the role, and allows procurement to become a partner to the business.”

                                  Velte adds: “When you talk about talent shortage, it’s also that shift in the mindset we’re going through right now. The expertise is changing, and we want to be able to bring in talented people with that technology flare. When we look at the next generation of leaders coming out of university and college, they’re AI enabled already. They’re expecting AI to be available to them to accelerate their development, career goals, and ambitions.”

                                  Making sense of the landscape

                                  As DPW New York 2025 unfolded around us, the discussion inevitably turned to the ways in which DPW helps procurement make sense of the AI landscape. Pactum AI is actually a perfect example of how useful DPW is. Only four years ago, the business was a startup, and won a pitch contest at DPW Amsterdam. “That catapulted the business, and got us a lot of visibility,” says Strydom. “It’s a great place for visibility with practitioners, investors, and partners.”

                                  Again, it comes back to people. Being able to meet them in real life, communicate face-to-face, and learn from one another. “It’s about reconnecting with a lot of our partners,” says Velte. “But it’s also about seeing what is out there on the forefront that’s becoming available. It’s an amazing opportunity for us to really benchmark ourselves, while also getting a glimpse of what’s coming around the corner.”

                                  Author

                                  Dan Brightmore

                                  Published

                                  15 September 2025

                                  Estimated Read time

                                  6Mins

                                  Enterprise-wide AI platform security protects sensitive data and governs integrations to help organisations scale Agentic AI with confidence

                                  ServiceNow the AI platform for business transformation, has unveiled its new Zurich platform release. It delivers breakthrough innovations with faster multi-agentic AI development, enterprise-wide AI platform security capabilities, and reimagined workflows. New intelligent developer tools enable secure vibe coding with natural language. This helps turn employees into high-velocity builders and creators and lower the barrier to app creation. Built-in security capabilities, including ServiceNow Vault Console and Machine Identity Console, natively secure sensitive data across workflows. This governs integrations to help organisations scale Agentic AI and innovations with confidence. The introduction of autonomous workflows turns data into action through agentic playbooks. Uniquely offering the flexibility to apply AI and human input in workflows where and when it’s needed for greater control and efficiency. 

                                  AI Transformation with ServiceNow

                                  Enterprise leaders are racing to move beyond table-stakes AI implementations to unlock transformative, tangible results.  According to Gartner, “By 2029, over 60% of enterprises will adopt AI agent development platforms to automate complex workflows previously requiring human coordination.” The ServiceNow AI Platform delivers this transformational promise across the enterprise. It underpins a new era of highly efficient human-AI collaboration. 

                                  “Zurich marks a turning point for enterprise AI. ServiceNow is delivering multi-agentic AI systems in production that are not just powerful, but governable, secure, and built for scale,” said Amit Zavery, president, COO, and chief product officer at ServiceNow. “We are transforming the enterprise tech stack to be AI-native. From autonomous workflows that act on data with precision, to developer tools that democratise high-velocity innovation. With built-in controls for security, risk, and compliance, we’re helping organisations move beyond experimentation. And into a new era of intelligent execution.” 

                                  Vibe Coding Meets Enterprise Scale 

                                  According to Gartner, “Agentic AI features will be near ubiquitous, embedded in software, platforms and applications, transforming user experiences and workflows.” The introduction of ServiceNow Build Agent and Developer Sandbox provides resources for employees to work with AI more efficiently. They can now do this conversationally, and at scale, to solve real problems in every corner of the business. 

                                  • Build Agent is a breakthrough for enterprise app creation—bringing vibe coding to the rigor of the ServiceNow AI Platform. In seconds, employees can turn an idea into a production-ready application by asking in natural language. Say, “Create an onboarding app that assigns tasks to HR, IT, and Facilities,” and Build Agent handles the rest. Design, build, logic, integrations, testing, and industry-leading governance included. What sets it apart is enterprise discipline: every app comes with audit trails, security, and compliance built in. Developers and citizen creators alike get the speed of AI with the confidence of enterprise-grade control, in a streamlined interface. 
                                  • Developer Sandbox empowers developers to build better applications, faster, while maintaining the highest standards of quality. Sandboxes provide isolated environments within a single instance, so multiple teams can collaborate, build, and test new features without conflicts, and rapid scale doesn’t come at the cost of control. Teams can version, iterate, and deliver without waiting in line for developer resources. Developers can safely experiment with vibe coding, test AI-powered workflows, and resolve version control issues before changes go live. This reduces rework, shortens feedback loops, and helps teams ship higher-quality applications rapidly with lower risk. 

                                  Security That Enables AI Strategy 

                                  As enterprises adopt autonomous workflows powered by agentic AI, securing how these systems access data and communicate across environments is essential. Zurich introduces new built-in AI platform security capabilities to make it easier to protect sensitive information. It can also govern integrations and manage growing AI footprints. 

                                  • The newServiceNow Vault Console provides a guided experience to discover, classify, and protect sensitive data across workflows. For example, an admin managing customer service operations can now identify personal data across tickets, apply different types of protection policies, and track compliance activity. The console also offers recommendations for protecting newly discovered sensitive data, along with customizable dashboards to monitor key metrics. What used to require manual configuration across multiple tools can now be managed in one place, with intelligent insights and a streamlined experience. 
                                  • Machine Identity Console addresses the need for integration security with enterprise-grade authentication and authorization, delivering control over bots and APIs head on. As the ServiceNow AI Platform scales, every API connection, including those from AI agents, introduces another identity to manage and determine what it can access. This console gives platform teams visibility into all inbound API integrations using machine identities such as service accounts and keys, flags outdated or weak authentication methods, and provides clear steps to strengthen security. If an integration is using basic authentication or hasn’t been active in 100 days, the console spots it and helps resolve it. 

                                  Digital Transformation

                                  “At Kanton Zürich, digital transformation is central to how we deliver secure and efficient public services. Since 2018, ServiceNow has enabled us to centralize and standardize our processes with data security as a top priority,” said Jürg Kasper, head of business solutions, Kanton Zürich. “Zurich’s latest advancements in both security and AI will allow us to automate more complex workflows, unlocking new efficiencies that enhance how we serve our citizens—with greater speed, clarity, and assurance.”  

                                  Without built-in security and trust, scaling AI comes with risk. These new security features in Zurich build upon ServiceNow’s AI Control Tower, announced in May 2025, which provides enterprise-wide visibility, embedded compliance, and end-to-end lifecycle governance for Agentic AI systems. By centralising oversight of every AI agent, model, and workflow, native or third-party, the AI Control Tower ensures organisations can scale AI with confidence, aligning innovation with enterprise-grade security and trust. 

                                  Turn Data Into Outcomes With Autonomous Workflows 

                                  As organisations rapidly scale AI, they face the added challenge of delivering solutions consistently, reliably, and responsibly. Enterprises need the right guardrails, full visibility, and strong governance to achieve service delivery. Or they risk eroding trust and slowing results. ServiceNow’s AI Platform does all this in a single platform. It sets a new standard for how organisations can create autonomous workflows to turn data into action and AI into measurable business impact. 

                                  • Agentic playbooks from ServiceNow bring people, automation, and AI together seamlessly, powering autonomous workflows. A traditional playbook is a structured sequence of automated steps. These are based on predefined business rules and processes—ideal for ensuring consistency, efficiency, and trust. Agentic playbooks amplify this model by embedding AI into the trusted framework. AI agents eliminate manual effort, completing tasks in seconds and accelerating execution. This frees employees to focus on higher-value work where human judgment matters most. For example, in a credit card support situation, an agentic playbook can guide an AI agent to verify someone’s identity. It can freeze a card, send a replacement and notify the customer while allowing a human agent to step in. The result: governed, efficient, and trusted work—supercharged by AI to deliver faster, smarter outcomes. 
                                  • The ServiceNow Zurich platform release also seamlessly combines Process and Task Mining insights within a unified platform. These new capabilities give organisations an end-to-end understanding of how work gets done. Revealing where human expertise is essential, and where AI agents can deliver the greatest impact. With process intelligence built directly into the platform, customers can move seamlessly from insight to action. Streamlining operations, applying AI where it matters most. And accelerating real business outcomes without the complexity of disconnected legacy tools. 

                                  All features announced as part of the ServiceNow AI Platform Zurich release are generally available and can be found in the ServiceNow Store

                                  • Data & AI
                                  • Digital Strategy

                                  Author

                                  Dan Brightmore

                                  Published

                                  Estimated Read time

                                  2Mins

                                  Integration of open banking technology and digital banking experience platform delivers seamless, standards-compliant customer experiences

                                  Ozone API, the global leader in open banking and open finance technology, and Plumery, a digital banking experience platform, today announced a strategic partnership for true customer-centric banking. The collaboration combines Ozone API’s specialist open banking platform with Plumery’s Digital Success Fabric, to empower financial institutions to deliver seamless, compliant, and innovative digital banking experiences.

                                  The partnership combines Ozone API’s standards-based open API technology, built to support all global open banking standards and regulations, with Plumery’s modern, cloud-native digital banking experience platform. This integration empowers banks and financial institutions to rapidly deploy customer-centric mobile and web applications. These can seamlessly incorporate open banking capabilities without compromising on compliance or security.

                                  Ozone API & Plumery – A Digital Partnership

                                  “At Ozone API, we do one thing better than anyone else – provide standards-based open API technology to banks and financial institutions. Our partnership with Plumery represents the perfect orchestration of market-leading technologies. By combining our specialist open banking technology with Plumery’s innovative digital banking platform, we’re enabling financial institutions to deliver truly differentiated customer experiences with an accelerated time to market.”

                                  Huw Davies, Co-founder and CEO of Ozone API

                                  “Our partnership with Ozone API represents a significant milestone in our mission to empower financial institutions with truly customer-centric digital banking experiences. The integration enables banks to not just meet regulatory requirements, but to transform open banking from a compliance necessity into a competitive advantage. Through future-proof architecture our clients can now deliver innovative, personalised services that leverage open banking data while maintaining the flexibility and speed-to-market that our platform is known for.”

                                  Ben Goldin, CEO, Plumery

                                  The joint solution addresses the growing demand from financial institutions for integrated digital banking platforms that can harness open banking capabilities. These can enhance customer engagement and create new revenue streams. Banks can now utilise Plumery’s flexible, developer-friendly platform to craft tailored digital experiences. Meanwhile, seamlessly integrating Ozone API’s robust open banking functionality.

                                  About Ozone API

                                  Ozone API empowers banks, fintechs, and financial institutions worldwide to thrive in the world of open banking. Founded by the team behind the UK’s open banking standards, our platform delivers secure, compliant, and high-performance APIs that unlock the potential of open finance. We help clients across multiple continents comply with evolving standards, create commercial value from their data, and deliver innovation at speed. Learn more: https://ozoneapi.com

                                  About Plumery

                                  Founded in 2016 as a private consultancy collaborating with leading global banking companies, Plumery became a registered brand in 2017 and evolved into an independent product company in 2022. Backed by renowned venture capital firms, Plumery now offers a modern, cloud-native digital banking experience platform. Headquartered in the Netherlands, Plumery operates with a diverse team that embodies a unique combination of seasoned expertise and vibrant innovation. Operating across Amsterdam, Lisbon, and Vilnius, Plumery’s mission is to empower financial institutions worldwide, regardless of size, to craft distinctive, contemporary, and customer-centric mobile and web experiences. Learn more: https://plumery.com/

                                  • Digital Payments
                                  • Neobanking

                                  Author

                                  Andrew Woods

                                  Published

                                  Estimated Read time

                                  5Mins

                                  At Kinexions 2025, Jennifer Roberts, Supply Chain Leader, IBM who talked us through how the supply chain is transforming at the global giant

                                  Jennifer Roberts, Supply Chain Leader at IBM, is visibly buzzing as she shares her favourite Kinexions moments so far. “Kinexions is really exciting,” she says, having flown in from Raleigh-Durham, North Carolina to be here. “The first thing for me is getting to see the people I work with at Kinaxis who help advance the solution within IBM,” she explains. “We have a great account management team that’s helping us look to the future. And the energy here is always exciting. They really are a motivating company when it comes to thinking about the future. I’m really thankful that IBM invested in the ability of our teams to join the event this year.”

                                  Roberts and IBM’s C-level executive suite for supply chain are located at Raleigh-Durham’s Research Triangle Park where IBM has a large facility covering 600 acres. “It’s a good place to be,” she says. “But a large part of my team is broadly located throughout the US in Poughkeepsie, New York, Rochester and Minnesota. And then we also have a team down in Guadalajara, Mexico. The global supply chain is located everywhere, but the people I work with are primarily in those locations.” 

                                  Roberts leads Demand Planning Operations for IBM’s hardware manufacturing division, supporting mainframe, power, and storage products across both internal and contract manufacturing. She supports transformation efforts within the Demand Supply Planning and Inventory organisations.

                                  Supply chain transformation

                                  Roberts specialises in configuring and modelling planning architecture in Kinaxis and SAP, translating, automating and transforming business processes, while identifying and collecting the relevant data from various large unstructured data sources. Her goal is to optimise supply chain processes and tools, reduce costs, improve efficiency and enhance customer satisfaction. 

                                  The words “revolution” and “transformation” have embodied the discourse at Kinexions and these are two concepts that play out in a major way at IBM. “Our business is all about transformation,” she explains. “We are constantly looking to evolve to solve a variety of different areas of opportunity. There’s certainly never a day where we aren’t thinking about what the next disruption may be. And so within our organisation, we focus a lot on resiliency, protecting our supply chain and ensuring we can deliver quality to our clients.” Indeed, IBM onboarded Kinaxis around five years ago to help transform Demand Planning and Supply Planning. Kinaxis Maestro provides IBM with the transparency needed to see how changes in demand and supply affect each other, utilising the most current data to run multiple concurrent scenarios.

                                  AI in supply chain

                                  IBM’s supply chain transformation efforts are currently focused heavily on AI. Of course, IBM has been leaders in the AI space for quite some time with the Watsonx products, but supply chain is considered client zero within IBM for that platform. “We are focused on efficiencies in the organisation, digital transformation, developing digital twins and taking enterprise data and bringing it together so that we can orchestrate a plan that is visible to all through one source of truth,” she reveals. “And that’s something we can all execute against seamlessly.”

                                  “Everyone wants data in real-time. Everyone is looking for accuracy of data. They’re looking for answers to problems faster than we’ve ever been able to perform before,” she explains. “When the next big diversion comes, the next big distraction, we need to be able to quickly align ourselves, not just within the supply chain, but upstream with our sales organisation, who are feeding us all the sales opportunities and giving us insight into where the business is going. And then our downstream suppliers need to be equally connected. So, we partner with those organisations to ensure it’s all very seamless and that our data flows in both directions so we can manage results. So, one of the advantages of our internal AI supply chain tool, which we call CSCA 360 (Cognitive Advisor), is to get a 360-degree view of the world considering all those products. And access is a big part of that because we run our S&OP and MRP (Material Requirements Planning) processes through that tool, along with our inventory management process as well.”

                                  According to Roberts, the biggest opportunities for Supply Chain at IBM lay within ways to mitigate disruptions earlier, boosting resiliency and agility, while protecting the supply chain. “There are things that hit us between the eyes at the last minute, and we have to be as responsive as possible to solve those problems. Data insights and being able to assess them proactively, is so important. And that’s where I see our organisation heading more strategically, through taking the data, ingesting it faster, making decisions on it, using generative AI and focusing on allowing people to dig into the data more quickly and get answers on information they’re seeking. We’ve been using agentic AI for years, but we’re really starting to dig into what it can do for us now in terms of impacting productivity.”

                                  The human touch

                                  Although Kinexions has been showcasing transformation and technological revolution it has also stressed the importance of work culture, something vitally important to Roberts. “Our leadership drives the mindset of transformation being at the forefront of where we’re going, in order to keep up with the demands of the future,” she tells us. “We’re always being asked to look at where we can create opportunities within the business and not just taking the leadership’s advice on what we should be doing. We look to all our employees and get their ideas from the bottom up; deciding whether or not there’s business value that can be returned from things that aren’t always visible.

                                  “I think the most important part of your business is your people. Without having the ability of your people to be transparent in where they see opportunities, you really are going to hold yourselves back. Keep an open mind, ask a lot of questions, listen closely. I’m always told you have two ears and one mouth. And I think as a leadership team, you should allow your employees to come forth with ideas, plus, we need to think about why they are suggesting them – well, it’s because they’re impacted every day by what’s going on around them. So, listen.”

                                  Author

                                  Jess Shanahan

                                  Published

                                  12 September 2025

                                  Estimated Read time

                                  7Mins

                                  From automating decisions to redefining procurement talent, AlixPartners lays out why risk-takers lead the way.

                                  The use of artificial intelligence (AI) in procurement is gaining traction with many organisations already looking at how the technology can improve processes. However, there’s scope to go beyond efficiency and instead focus on transforming value delivery. 

                                  At DPW New York, we spoke to Amit Mahajan and Aaron Addicoat from AlixPartners, a management consultancy firm doing things a little differently. The organisation is advising its clients on how to implement AI to drive value, but it’s also using AI internally, too. 

                                  “AlixPartners has a unique business model,” explains Addicoat. “We have a very senior model, very few junior resources. So now you imagine taking people with 10 or 15 years experience and now you equip them with AI… For us, it’s a huge unlock.”

                                  This is about more than just productivity gains. AlixPartners focuses on using AI to transform the way procurement teams work, while crucially, maintaining the human touch.

                                  How procurement professionals are using AI

                                  With the support of technology, it’s possible to shift procurement from a cost-saving exercise to a potential revenue driver. Procurement teams are already looking for these opportunities, as Mahajan explains. “They’re starting to think about new ways of doing things,” he says. “It’s not just automation, but asking how do I leapfrog and do something differently?”

                                  There are plenty of use cases where AI is helping with automation. This is a great place to start as it frees up human workers to do more valuable jobs that need a personal touch. “I have a client who’s using AI every day,” says Addicoat. “This allows them to review documents and contracts rapidly, to find key clauses and termination dates. They’re also using it in spend control processes to identify which things need to be reviewed more thoroughly.”

                                  Many organisations are also using AI agentically to create their own bots. This gives teams a more accessible way to review information. “One example is a client who’s using AI for their business to help with acronyms,” says Addicoat. “They built it as an acronym tool to help break down the language barrier between different functions using different terms. This led to better engagement.”

                                  This empowers employees across an organisation to be more autonomous while still getting the full picture. Agentic AI, especially, allows them to interact with information in a way that previously would’ve required specialist technical knowledge. Now, it’s possible to query information within a contract directly. 

                                  “It’s about using agents and AI to look at anomalies within your procurement contracts,” explains Mahajan, “and be able to help the category analysts, the category specialists, and others to get more of those insights.”

                                  While generative AI might be a hot topic, it’s not the only way to use the technology. In combining several sources of data and using AI to spot trends, it’s possible to create workflows tailored to the current environment. Addicoat explains: “We take a series of data inputs, such as weather patterns, lead times, contractual terms, inventory, and forecast. Then the AI generates the purchase order, queues it for review, and upon approval, places the order.”

                                  This can help an organisation to place orders with the right supplier in the most timely fashion to avoid delays, and optimise for cost, for example. This fully automates the end-to-end process, using AI to interpret those important data signals.

                                  While this is useful for procurement teams, it’s only the start. “Using AI in this way is really cool,” says Addicoat, “but what I found most fascinating is that you’re building a data model, and with AI layered into it, that over time can tell you how to optimise itself.”

                                  This has huge implications for procurement teams looking to save money and drive revenue. “For example, it could tell us the commodity price at a certain point in time was low,” says Addicoat, “but because inventory capacity to hold resin was maxed out the client could only buy so much at that low price. So now investing in a new storage unit at a cost of a few hundred thousand dollars could, under the same scenario in the future, save millions of dollars..Data quality challenges

                                  A roadblock that can stop procurement teams from fully embracing AI is a lack of quality data. With so many sources of information, often including paper-based documents, some might think it’s difficult to get the data AI needs to be truly useful.

                                  “Don’t wait for everything to be perfect before you get started,” says Addicoat. 

                                  This is a sentiment echoed by Mahajan: “Use AI to solve your data problem before solving your business problems.”

                                  This requires a mindset shift. While AI can help cleanse, enrich, and structure existing unstructured data, it’s important to take the right approach. Shift from asking ‘what can we do with our data?’ to ‘what value do we need to create?’ and work backwards from there.

                                  With this approach, the questions are less about the data and more about the business problem. This then allows you to use AI to work with the information you have to help answer those questions.

                                  “Start with the value proposition in mind and work backwards,” explains Addicoat. “You can get data from anywhere — it has to serve a purpose.”

                                  Bringing back the human touch

                                  AI can free up procurement teams to focus on tasks that need more nuance and expertise. Using technology to automate workflows and make information more accessible has a huge impact on employee productivity. “It’s fundamentally transforming the way they work, the amount of work they can do, and the type of work they’re able to do,” says Addicoat.

                                  There’s always the worry that with any new technology, the human element will be forgotten. “With every new advancement that comes in,” says Mahajan, “whether that was a steam engine or when computers came along, everybody wondered what they were going to do. But as humans, we always find ways to start doing higher-level work.”

                                  This means that many professionals will find new ways of doing things. “Imagine all the mundane tasks you have to do in your daily job now,” Addicoat continues. “With these new ways of working, imagine the speed with which you can turn an idea into something real. All that time you free up allows you to go talk to people and build relationships that mean something.”

                                  On the other side of things, the sheer volume of AI-generated content out there is going to drive people towards those more meaningful interactions. “You don’t know what to trust and what to believe anymore,” Addicoat says. “That’s going to lead to a resurgence in face-to-face content, being at the office, and being at events.”

                                  AI’s impact on procurement talent

                                  The talent landscape is changing. With technology playing a larger part than ever before, organisations don’t just need procurement professionals, they need adaptable, tech-savvy people. The nature of the job means that those in procurement need a wide range of skills. 

                                  “We do everything,” says Addicoat, “legal, operations, supply chain, negotiation, analytics. Procurement professionals are generalists.” 

                                  Tech plays into every element of that skillset, which means tech skills are becoming even more important for candidates applying for procurement roles. “Nobody goes to college thinking they’ll be a procurement professional,” says Mahajan, “but with AI and tech, that’s changing.”

                                  With procurement often seen as a proving ground for leadership, embedding these tech-minded generalists could have a huge impact on the future. “We have a shortage of talent,” explains Addicoat. “But with more and more CEOs and COOs coming from procurement, that speaks volumes to what procurement does and the value it brings, as well as what the future holds.”

                                  At AlixPartners, the passion for procurement is very clear with Addicoat saying: “There are only two kinds of people in the world: those who love procurement and those who don’t know it yet.”

                                  Change is coming

                                  With AI of all forms steadily gaining traction, procurement could change dramatically in the coming years. It’s the organisations that are willing to take risks and embrace change that will come out on top.

                                  “AI has the potential to disrupt the whole management consulting world,” says Mahajan. “Firms focused on transformation will thrive.” 

                                  With AI’s capabilities increasing rapidly, it’s difficult to predict what comes next. However, adaptability is key. “Hold onto your hat. In a year and a half, the world’s going to look very different,” concludes Addicoat.

                                  Author

                                  Jess Shanahan

                                  Published

                                  Estimated Read time

                                  8Mins

                                  AI is already transforming procurement, but meaningful value depends on more than just tools. At Beroe, that starts with aligning AI to real business problems

                                  As AI continues to dominate conference stages and boardroom discussions, the pressure to use it is everywhere. As this technology becomes further embedded in enterprise strategy, many organisations are still grappling with how to apply it in a way that delivers real, measurable value.

                                  Rather than focusing on AI for the sake of innovation, the question now is how to align new tools with real business problems. That means looking beyond dashboards and pilots to deploy AI where it can simplify decision-making and improve processes.

                                  At Beroe, this principle is central to how AI solutions are developed, deployed, and scaled. As the company behind the world’s leading procurement intelligence platform, Beroe provides real-time market data, cost analysis, and supplier risk assessments, empowering thousands of organisations globally to streamline operations and mitigate risks. Its latest advances in autonomous negotiation, supplier discovery, and predictive analytics show what it means to align AI with business objectives.

                                  Speaking with Prerna Dhawan, Chief Product Officer at Beroe, during this year’s DPW New York conference, the discussion explored how procurement leaders can move beyond hype and start unlocking the full potential of AI.

                                  Misalignment with business needs

                                  There are plenty of real-world examples of how AI can improve efficiency within a business, from automating manual tasks like invoice processing to identifying new suppliers based on complex sourcing criteria. Accessing this technology is easier than ever with a wide range of tools available to procurement professionals. It can be tempting to jump on the bandwagon and integrate AI across every area of an organisation, but success requires a more nuanced approach.

                                  The key is to ask the right questions, Dhawan explains: “We talk about all the latest and greatest technology out there, but what does it mean in practical terms? We need to ask, ‘How can I apply it today in the work I am doing as a head of product or as a procurement professional?’”

                                  The allure of generative AI is especially strong, but business leaders should ask whether that’s the right solution for their needs. As with any decision, it’s important to consider the business problem. “It starts with a little bit of knowledge about what you’re looking for,” says Dhawan. “What are some of your biggest challenges, and which of those challenges could AI technology solve?”

                                  Matching the right tool to the job

                                  Once an organisation has identified a specific problem, it’s possible to find the AI solution that fits. While generative AI gets a lot of attention, other AI technologies and machine learning based systems might be more appropriate. 

                                  In some cases, prescriptive, rule-based, or predictive AI could be a better choice to solve a problem without the need for a large language model. For example, forecasting commodity prices doesn’t require generative AI, just strong, contextual machine learning. 

                                  “We are looking at AI across two dimensions,” says Dhawan. “Firstly, what is our offering to customers, in terms of procurement intelligence and autonomous negotiation technology. Second, we are looking at AI internally. Let’s say in product development, how do we use the latest AI solutions to accelerate our product development cycles so we can release new modules and capabilities more quickly.”

                                  Regardless of the type of tool chosen, it should cover a high-impact use case. Integrating AI to solve a problem that only surfaces for a small group of people a couple of times a year won’t have a great return on investment. Instead, look for regularly occurring problems that, if fixed, could have a huge impact on productivity or quality. 

                                  Reducing the cognitive load

                                  We’re already bombarded by information, and the use of AI to add to this doesn’t make sense. “I don’t need another dashboard in my life,” says Dhawan. 

                                  When implemented correctly, AI can make data more accessible while reducing cognitive load for users. The result is increased productivity and faster decision-making. 

                                  “I think the power of AI is to simplify access to data. This is why ChatGPT has been a success: it democratises access to information. That’s what our B2B technology world is waiting for. It gives me something simple that allows me to talk to my data. Then I can focus on what insights I need to make a decision or take action.”

                                  For most B2B users, the key is intelligent simplification. Look for ways to simplify access to data through agent AI tools and conversational interfaces. This brings the focus back to action rather than dashboards.

                                  Inside Beroe

                                  While many procurement teams are still exploring AI’s potential, Beroe has already embedded it across both its platform and internal operations. The company, founded in 2006, provides procurement intelligence to thousands of organisations worldwide. Its platform delivers the critical data that professionals need to make informed sourcing decisions, from commodity prices and risk indicators to ESG scores and supplier intelligence.

                                  “We provide all data that procurement needs for decision making, whether it’s cost data, risk data, ESG data or price data,” says Dhawan. “Our reimagination of the future is not just giving access to more data but creating that layer of recommendations that help you make decisions at speed and scale.”

                                  One of the clearest examples of this in action is Beroe’s new ‘autonomous negotiations’ platform resulting from its recent acquisition of negotiation technology business, nnamu.  Delivering a significant evolution in the procurement technology landscape the platform enhances the foundational elements of AI and game theory with Beroe’s industry-leading market intelligence and, according to Dhawan, it’s being deployed successfully in live sourcing scenarios.

                                  “This is a technology that is being used for multilateral negotiations,” Dhawan explained. “It’s no longer just a POC or prototype, it’s live and being used at scale.” These new tools reflect Beroe’s core mission: to help procurement professionals minimise surprises and maximise margins. 

                                  Crucially, Beroe isn’t waiting for perfect data to apply these technologies. Instead, the company is using AI to work with what’s available — cleansing, interpreting, and extracting value from both structured and unstructured sources.

                                  “You can use AI for cleansing data – even paper contracts,” Dhawan says. “Historically, we thought data had to be structured. But now, with vision models and image analytics, that’s no longer the case.”

                                  Rather than striving for 100% accuracy before taking action, Beroe embraces a more agile mindset that balances speed and precision. 

                                  Is mindset holding procurement back?

                                  The technology is ready. The use cases are proven. So why do so many procurement teams still hesitate to embrace AI? “There’s this subconscious fear that I think is a barrier to adoption,” she said. “And to some extent, it’s to do with our friends in Hollywood.”

                                  There’s the myth that AI is a job-threatening black box, especially in industries where trust and experience are the backbone of good decision-making. For procurement, where professional judgement and business context are critical, the idea of handing over tasks to AI can feel risky.

                                  But Dhawan believes this fear is misplaced. At Beroe, AI isn’t replacing procurement professionals, it’s augmenting them. Whether it’s surfacing new suppliers, automating elements of negotiation, or flagging risks earlier in the sourcing cycle, the aim is to enhance human decision-making. She says: “I think with the new kinds of AI technology that’s available to us, it is an opportunity for us in B2B tech to embrace more human-centred design with higher focus on UX.”

                                  Looking ahead

                                  Looking ahead to 2026 and beyond, Dhawan sees procurement evolving into a more personalised and responsive function – one where AI plays a critical role in both strategy and execution.

                                  “We see hyper-personalisation coming, both in supplier relationships and internal stakeholder engagement,” she explains. “AI will be at the centre of that.”

                                  Rather than one-size-fits-all sourcing strategies, AI will enable procurement teams to tailor their approaches to specific business units, categories, or even individual suppliers. This means smarter segmentation, more relevant insights, and stronger commercial outcomes.

                                  Another key shift is the growing ability to connect macro events, such as geopolitical shocks or regulatory changes, with micro actions inside the business. AI can help procurement teams identify these signals earlier, respond faster, and still align with long-term goals such as cost efficiency or sustainability.

                                  “It’s about balancing your fire-fighting reactions to market events with your long term goals and strategy,” says Dhawan. “Procurement needs visibility and flexibility at the same time.”

                                  Beroe is already moving in this direction. Alongside its growing AI capabilities, the company is refining how it delivers intelligence, building agents and recommendation layers that not only inform decisions, but also help teams take action on them. Whether that means automating routine negotiations or proactively flagging supply risks, Beroe is evolving to meet the needs of a procurement function that’s more dynamic than ever.

                                  As Dhawan points out, the goal isn’t to overwhelm teams with more tools, it’s to make their lives easier. “It’s about reducing complexity and giving procurement professionals confidence in what to do next,” she concludes.

                                  For many procurement leaders, AI still feels like a long-term ambition. But the solutions are already here, and through companies like Beroe, they’re already in use. The challenge now is not whether AI can deliver value. It’s whether teams are ready to adopt the mindset and cultural shift that will allow them to unlock that value.

                                  Author

                                  Dan Brightmore

                                  Published

                                  11 September 2025

                                  Estimated Read time

                                  3Mins

                                  Franklin Templeton and Binance are harnessing blockchain tech to create solutions that merge the scale of traditional finance with the speed and accessibility of decentralised markets

                                  Binance, the world’s leading cryptocurrency exchange by trading volume and users, and Franklin Templeton, a global investment leader with $1.6 trillion in assets under management, have announced a collaboration to build digital asset initiatives and solutions tailored for a broad range of investors.

                                  Binance and Franklin Templeton Innovating with Tokenisation

                                  The firms will explore ways to combine Franklin Templeton’s expertise in the compliant tokenisation of securities with Binance’s global trading infrastructure and investor reach. The goal is to deliver innovative solutions to meet the evolving needs of investors. By bringing greater efficiency, transparency and accessibility to capital markets with competitive yield generation and settlement efficiency.

                                  “As these tools and technologies evolve from the fringes to the financial mainstream, partnerships like this one will be essential to accelerating adoption,” said Sandy Kaul, EVP, Head of Innovation at Franklin Templeton. “We see blockchain not as a threat to legacy systems, but as an opportunity to reimagine them. By working with Binance, we can harness tokenisation to bring institutional-grade solutions like our Benji Technology Platform to a wider set of investors and help bridge the worlds of traditional and decentralized finance.”

                                  “Investors are asking about digital assets to remain ahead of the curve, but they need to be accessible and dependable. By working with Binance, we can deliver breakthrough products that meet the requirements of global capital markets and co-create the portfolios of the future,” said Roger Bayston, EVP and Head of Digital Assets at Franklin Templeton. “Our goal is to take tokenisation from concept to practice for clients to achieve efficiencies in settlement, collateral management, and portfolio construction at scale.”

                                  “Binance has a record of innovating first-in-crypto solutions that unlock access and opportunities for investors. Our strategic collaboration with Franklin Templeton to develop new products and initiatives furthers our commitment to bridge crypto with traditional capital markets and open up greater possibilities,” said Catherine Chen, Head of VIP & Institutional at Binance.

                                  More details of the collaboration and new product launches will be shared later this year.

                                  About Binance

                                  Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. It is trusted by more than 280 million people in 100+ countries for its industry-leading security, transparency, trading engine speed, protections for investors, and unmatched portfolio of digital asset products and offerings from trading and finance to education, research, social good, payments, institutional services, and Web3 features. Binance is devoted to building an inclusive crypto ecosystem to increase the freedom of money and financial access for people around the world with crypto as the fundamental means. For more information, visit: https://www.binance.com

                                  About Franklin Templeton

                                  Franklin Resources, Inc. is a global investment management organization with subsidiaries operating as Franklin Templeton and serving clients in over 150 countries. Franklin Templeton’s mission is to help clients achieve better outcomes through investment management expertise, wealth management and technology solutions. Through its specialist investment managers, the company offers specialization on a global scale, bringing extensive capabilities in fixed income, equity, alternatives, and multi-asset solutions. With more than 1,500 investment professionals, and offices in major financial markets around the world, the California-based company has over 75 years of investment experience and $1.64 trillion in assets under management as of August 31, 2025. For more information, visit: franklintempleton.com 

                                  • Blockchain & Crypto
                                  • Digital Payments

                                  Author

                                  Andrew Woods

                                  Published

                                  Estimated Read time

                                  5Mins

                                  Brent Wilson takes time out at Kinexions 2025, to talk us through rapid change in supply chain operations at Qualcomm

                                  Over the past five years, supply chain disruption has been relentless, causing many companies to rethink how they handle ongoing delays and uncertainty. Few companies have undertaken a transformation as profound as Qualcomm, a multinational corporation that designs and develops semiconductors, software, and services related to wireless technology. 

                                  At the heart of that transformation is Brent Wilson, Senior Vice President of Global Supply Chain Operations, who joined the tech giant when supply chain volatility was at its peak. Speaking at Kinexions, the flagship supply chain conference hosted by Kinaxis in Austin, Texas, Wilson shares Qualcomm’s transformation story. 

                                  A full change in thinking

                                  “When I joined Qualcomm,” Wilson explains, “they had lost control of the supply chain. There was no confidence in being able to promise orders to customers, and there was no real connection between the many parts needed to build a working product.” During the chaos of COVID, that was a wake-up call for the business.

                                  Tasked with rebuilding the entire supply chain, Wilson implemented a comprehensive sales and operations planning (S&OP) process powered by Kinaxis Maestro, a process that went beyond software implementation; Qualcomm required an organisational and cultural shift.

                                  “Up to that point, supply chain was seen as the supply chain team’s job,” Wilson explains. “We made a conscious effort to get everyone involved to get them to understand the process. This meant every department was going to have a say in what the data should be. And I think that really alleviated some of the fears.”

                                  That shift in mindset allowed the entire business to see the supply chain as a shared responsibility. But what truly accelerated Qualcomm’s evolution was the technological backbone of Kinaxis Maestro.

                                  Real-time impact

                                  “The power of Maestro is its concurrency,” Wilson says. “The visibility allows us to have conversations around what might happen at the leading edge. In some cases, it allows us to change where we might point a particular design or take a softer approach into a market.”

                                  Qualcomm’s business has evolved dramatically in recent years. Once focused almost exclusively on handsets, the company now operates in diverse markets including automotive, compute, XR (extended reality), and hyperscale servers. These sectors operate at different speeds, with different expectations and constraints. “Having better control of our supply chain means we can enter these markets flawlessly,” Wilson explains. “We can service the customers at a very high level from the very first day we start shipping, all in an efficient and cost-effective manner.”

                                  Maestro enables Qualcomm to model what-if scenarios, evaluate long-term constraints (some as far out as three years), and even make early calls about where to push or pull investment. Wilson details: “We’ve had cases where we planned to go hard into a market, but the data showed a constraint coming years down the line, so we changed strategy. That kind of foresight was unheard of at Qualcomm before Maestro.”

                                  Letting the results speak

                                  Wilson’s metrics for measuring success might be simple, but that doesn’t make them easy to achieve. The first is response time — how quickly the company can commit to a customer order. The second is accuracy — how reliably they hit that first committed delivery date. “When we started tracking the metrics before the planning system, we were at 65%. Now we’re over 95%,” Wilson says.

                                  Those gains are not only operational but also strategic. For many, supply chain disruption has become the norm. The organisations able to respond quickly and reliably have a distinct competitive advantage. Wilson believes that’s exactly what Maestro has unlocked.

                                  People-powered transformation

                                  While technology has been central to the change, Wilson points out that tools alone aren’t enough. “You can have the best systems in the world,” he says, “but if your people aren’t behind it, it won’t work.” To that end, Wilson and his team invested heavily in alignment. They mapped out roles and responsibilities, built transparency into data-sharing, and emphasised the principle of one source of truth. That meant breaking down silos and agreeing on common data sources, even when the data didn’t originate within Wilson’s team.

                                  “There were fears,” he admits. “People thought this new process would take control away. You see, you have to convince people that this is going to be better for the corporation. I always say supply chain is a team sport, so it’s important to make sure everyone understands their role.”

                                  Kinaxis became a strategic partner in Qualcomm’s transformation. Maestro’s ability to unify planning across time horizons and business functions made it the right fit for a company with Qualcomm’s complexity.

                                  Making Kinexions

                                  At Kinexions, Wilson finds true value in the network. “The presentations are great,” he says, “but the real value is found in the peer connections. It’s good to hear how others are implementing Maestro at different stages and to get some references from what people are going through.”

                                  Kinexions isn’t just a stage for Kinaxis to show off its AI-driven platform. It’s a gathering of supply chain professionals all facing similar pressures: geopolitical volatility, inflation, talent shortages, and the increasing demand for agility. Wilson sees opportunity in all of it, especially when it comes to technology. He says, “The things that are being introduced with AI are really exciting, and I think we’re just tapping into the potential of what that can be.”

                                  For Qualcomm, the transformation is ongoing, but there’s a clear trajectory that goes beyond the supply chain team. The whole organisation approach provides greater visibility, greater agility, and a deeper understanding of how supply chain touches every corner of the business.

                                  Author

                                  Dan Brightmore

                                  Published

                                  10 September 2025

                                  Estimated Read time

                                  6Mins

                                  Mike Puglia, General Manager, Kaseya Cybersecurity Labs, on how the need for regulatory support to better support industries when tackling cybercrime

                                  Cyberattacks keep coming hard and fast, but things are beginning to change. In the past few months, law enforcement has announced arrests of three people in the Marks & Spencer breach, seven members of the hacking group NoName057, five affiliates of Scattered Spider and also disrupted the infrastructure of gangs such as Flax Typhoon, Star Blizzard and others.  

                                  Earlier this year, the UK retail industry felt the pressure. Brands, including Marks & Spencer, Harrods and Co-op – and by proxy, their customers – became victims of the hacking group, Scatter Spider. Other businesses are now on high alert as this wave of security breaches is expected to continue. For as long as bad actors can reap rewards and the risk of consequences remains small, they will keep attacking. Ransomware-as-a-service lowers the bar to entry further, allowing even those without specialised skills to launch successful ransomware campaigns.

                                  Along with the threats, regulatory pressure on businesses is growing. Organisations must be able to prove they have strong security defences in place or risk paying hefty fines for non-compliance. However, this means we are essentially punishing the victim, not the perpetrator. By putting the onus on the victims to protect themselves, we are missing an important truth… Because there is no bullet-proof defence, even the best security strategies will not end cybercrime for good.

                                  It’s Time to Treat Cybercrime as Crime

                                  What the industry needs instead is a change in how we approach cybercrime. Rather than blaming the victims, we must start treating it as the serious criminal activity it is. It is high time we addressed cybercrime’s fundamental drivers. Opportunity, motive and the widespread perception that criminals can still get away without punishment. As is the case with physical crime, it takes a two-pronged approach to curb cybercrime: Prevention – and an effective response.

                                  Those who attempt physical theft, for example, face trials and potentially prison. While we have seen a growing number of cybercriminals arrested in recent months, the truth we are only scratching the surface. In the digital world, everything is accessible from everywhere, all the time. This creates an inherent vulnerability that makes perfect protection impossible. In many cases, it also makes it much harder to track down the offenders and hold them accountable.

                                  The Problem with Cryptocurrency and Jurisdiction

                                  The cybercrime landscape has also undergone a significant transformation. While in the past, hackers were mostly focused on stealing financial data, there has been a dramatic shift towards ransomware. It’s far easier to encrypt an organisation’s data and demand a ransom than finding buyers for stolen credit card info.

                                  This transformation has further accelerated because cryptocurrency allows cyber attackers to be paid in anonymous currency. Anywhere in the world, at any time. Previously, criminals had to physically collect payments or transfer money to traceable bank accounts. Now, they can operate with anonymity whilst easily converting their loot into real euros, pounds and dollars. This means ‘following the money’ is no longer a useful way for law enforcement to track nefarious activity. If we made it impossible for criminals to anonymously convert cryptocurrency into real currency, we could change the risk-reward calculation.

                                  The second key issue with fighting cybercrime is the question of jurisdiction. Many cybercriminals are based in countries where western governments have no recourse. When hackers operate from non-cooperative jurisdictions, it may be impossible to extradite them. And they may find their activities tolerated by their local government or even supported.  As we have seen with the recent arrests – the threat actors were outside of Russia and China – where many attacks come from.

                                  These two factors – anonymous payment systems and safe havens – create an environment where cybercrime can and will continue to flourish. While organisations can do their best to make it harder for criminals to attack, it is foolish to believe individual businesses will be able to solve the cybercrime problem on their own.

                                  Stop Blaming the Victim

                                  So, what needs to happen? First, the victim-blaming approach must change. We simply cannot regulate every business to become an impenetrable fortress. When a person is physically robbed, police respond to investigate the crime and help recover stolen property. With cybercrime, victims face reputational damage, fines and higher insurance premiums. Incidents often raise questions about where the business’ cybersecurity strategy failed, rather than a recognition that a crime has been committed against them.

                                  A first step forward towards solving the cybercrime problem would require governmental and societal recognition that cyberattacks represent crimes against businesses and individuals, not merely failures of those organisations to adequately defend themselves. While many countries have ramped up policing efforts against cybercrime, these are generally underfunded considering the scale of the problem.

                                  Secondly, we need to urgently address the anonymous payment systems that keep fuelling cybercrime. This is not an easy problem to solve, but governments must find better ways to trace and regulate how cryptocurrency is converted into real money.

                                  It is also time we introduced real and severe consequences for cybercriminals. The number one deterrent to any type of crime is fear of being caught and punished. The internet has essentially eliminated this, enabling hackers to operate from nations that turn a blind eye. To address this will require more political pressure on ‘safe harbour’ countries to charge, punish and extradite cybercriminals. Where nations refuse to cooperate, potential sanctions such as restrictions on internet connectivity might force governments to reconsider their tolerance for criminal activities.

                                  Finally, we need to acknowledge that regulations such as GDPR, PCI and NIS have their limits. Despite increasingly complex compliance requirements, cybercrime has continued to grow. While regulations can provide critical and much-needed guidance to businesses, they must be combined with properly funded law enforcement – empowered with tools to bring criminals to justice across jurisdictions.

                                  To truly disrupt the criminal ecosystem, systemic changes are needed. We are starting to see governments give law enforcement the tools they need, but it is very early in that process. Because ultimately, we will not solve the cybercrime problem with defence measures alone.

                                  About Kaseya

                                  At Kaseya, our mission is to empower you to simplify and transform IT and cybersecurity management with innovative platform solutions.

                                  Our Mission:

                                  Since 2000, Kaseya has delivered the technology that IT departments and managed service providers need to reach new heights of success. More than 500,000 IT professionals globally use Kaseya products to manage and secure 300 million devices.

                                  Kaseya’s commitment to our customers goes beyond listening to your needs and puts words into action to deliver innovative solutions that empower your business. But we don’t stop there. Kaseya’s first-of-its-kind Partner First Pledge program shares the risk our partners experience because we know a true partner is with you through the ups and downs of life.

                                  • Cybersecurity
                                  • Digital Strategy

                                  Author

                                  Dan Brightmore

                                  Published

                                  Estimated Read time

                                  2Mins

                                  Data from Mangopay’s global fraud detection solution Nethone shows UK online platforms among most frequently attacked countries, driving a 48% year-on-year rise in fraud checks

                                  New data from Nethone, Mangopay’s global fraud detection solution, reveals online fraud pressure rising to record levels and breaking out of traditional holiday cycles. 

                                  From January 2024 to July 2025, monthly inquiries (events assessed for fraud risk such as transactions, logins and sign-ups) grew from around 240 million to over 525 million. More than doubling in 18 months. Peaks landed outside classic shopping windows, notably Sep-Oct 2024 (480m) and set a new all-time high in July 2025 of 525m. 

                                  The year-on-year picture tells the same story: between January and July 2025, Nethone processed an average of 470 million inquiries per month, compared to 300 million in the same period in 2024 – an increase of 48% year-on-year. 

                                  Nethone’s full risk profiling analyses (“profilings”), which combine device fingerprinting, behavioural biometrics and account history checks, also rose from an average of 110 million per month (January-July 2024) to 170 million (January-July 2025), a 37% year-on-year increase, with an all-time high of 245 million in June 2025. 

                                  Geographically, the UK emerges as one of the most targeted hubs for online fraud, alongside France, Germany and Spain. Sector patterns underscore the year-round threat. E-commerce accounts for the majority of fraud events detected across the year. This is consistently driving volumes well above 400 million monthly checks in 2025. Travel and mobility platforms bring in seasonal spikes during summer holidays, while FinTech platforms show sharp surges in specific months, reflecting event-driven criminal activity. Gaming platforms follow a similar pattern around promotional campaigns. 

                                  Mark Burton, VP Engineering, Fraud Platform, Nethone

                                  “Fraud is no longer a seasonal threat. Our data shows that criminal activity has become a year-round pressure on UK and European platforms. Fraudsters now exploit promotional cycles and refund windows just as much as traditional shopping peaks. They are becoming more persistent and opportunistic, driving higher costs for businesses and risks for consumers. Online marketplaces, travel providers, and FinTech platforms need to be prepared for a constant baseline of risk, not just one-off surges.”  

                                  About Mangopay 

                                  Founded in 2013, Mangopay powers a wallet-based payment infrastructure specifically designed for organizations with complex, multi-party fund flows. Our programmable wallet solution optimizes fund management, allowing platforms to regain control over payments, secure transactions, and automate payouts.  

                                  By leveraging Mangopay’s end-to-end white-label infrastructure, clients generate additional revenue and enhance operational efficiency while remaining compliant and protected with 360° AI-driven fraud prevention. 

                                  With over 250 million end users and more than €130 billion in processed transactions, Mangopay continues to lead in the fintech industry, providing flexible wallets designed to move money your way. 

                                  About Nethone, a Mangopay solution 

                                  Nethone, a Mangopay solution, is an AI-powered fraud detection system that offers the most in-depth user analysis and precise risk analysis for merchants and fintech companies.  The proprietary profiler analyzes thousands of data points for a 360° view of every user, detects fraudulent behavior with 130 signals combined with AI-based models, and keeps companies safe from account takeover, payment fraud, bots, and organized attacks.  

                                  • Cybersecurity in FinTech
                                  • Digital Payments

                                  Author

                                  Nell Walker

                                  Published

                                  Estimated Read time

                                  8Mins

                                  We sat down with Abe Eshkenazi, CEO of ASCM, to dig into the organisation’s focus points, and how CHAINge is addressing supply chain’s needs

                                  Tell me a bit about your background, and how you got into supply chain.

                                  Early in my career, I spent quite a bit of time in operations and materials management. We didn’t call it supply chain back in the day – it went by a number of different terms. Not surprisingly, given my role within ASCM, I worked closely with supply chain professionals, not only to elevate the role of the supply chain professional, but to understand the impact that supply chain has on business and society. 

                                  At ASCM, we’re focused on not only supporting that competent, capable individual, but ensuring that organisations are responsible in terms of using supply chain to really enable consumers and patients to get what they need at a reasonable price and reasonable time. This is what supply chain is about. My background combines that business management education and deep engagement with supply chain professionals. This gives me a strong appreciation for not only their challenges, but the opportunities the field faces today.

                                  Tell me about the planning for CHAINge NA this year. What were you looking to achieve when putting ideas together?

                                  Today, supply chain professionals are trying to balance efficiency with geographic diversity and political resilience. They’re trying to put those things together and identify what would make an individual do their job better and exchange that information with others. So our planning is centered around a key theme, which is: how do we equip supply chain professionals for what’s next? 

                                  The systems that we built for speed and cost optimisation are under stress right now. They’re struggling under the weight of complexity, volatility, consumer demands, and all the disruptions that we’re facing today. We’re being called today to rethink not only how quickly and cheaply we can move things and get them to the consumer, but how responsibly, transparently, and resiliently we can operate today. Our hope is that the engagement part of the event enables individuals to exchange information and walk away with insights and actionable strategies that can be taken back to their organisations and implemented. We’re truly looking for that engagement from the attendees. This is an event for the attendees, by the attendees.

                                  It’s also about making the contact and relationships that we all depend on. We’re all seeking opportunities and examples of organisations that have done it better or have responded easier to the challenges that we’re facing today. This provides individuals with an opportunity to engage. We had an opportunity to do this at our European event, after which attendees overwhelmingly indicated that the engagement part – the opportunity to exchange information learned from each other – was a key element of the event itself. We’re trying to replicate that, but with the amount of issues that the US is facing versus the rest of the world, the topics are going to be a little bit different here.

                                  What are the core topics covered at CHAINge NA that you think are most helpful for supply chain professionals?

                                  We need to take a temperature of the current environment, and not surprisingly, we structure the event around several core themes that we’re all facing today. First, resilient and agile supply chains. The adaptability that’s required today is unlike any time that we’ve ever faced. We’ve had disruptions before, and we’ve responded as an industry. Today, we’re continuing to respond, but the pressures on these individuals due to day-to-day uncertainty has created a very different environment.

                                  The second core topic is emerging technologies. As the focus on resiliency and agility becomes much more critical, there are only a few ways to gather the data necessary to enable organisations to make informed decisions. Not surprisingly, AI, digital twins, and a whole host of scenario planning technology tools are a focus for a lot of organisations today. Digital transformation is happening in almost every organisation to shore up their visibility, their transparency, and their traceability.

                                  Also, advancing sustainability practices. We can’t forget that at the end of the day, we still need to be sustainable as an industry. This has been a huge focus within supply chain. It’s taken a little bit of a backseat in the current environment, but organisations are still focused on ensuring that they are sustainable and ethical in their business practices. Lastly, no discussion can be had without understanding what the talent availability is, what their capabilities are, and whether we are ensuring that we do have the right talent.

                                  How important is collaboration (accelerated by things like CHAINge) in supply chain, especially as the landscape becomes more complex?

                                  In today’s environment, as we focus on visibility and on connecting all parts of our supply chain end-to-end, we understand the demand signals clearly so that we can address them appropriately. Collaboration is no longer optional – it’s essential. No single individual organisation can solve today’s challenges on their own, whether it’s navigating geopolitical tensions, managing risk in a global network, or even driving sustainability. The solutions demand cross-functional and industry collaboration. It used to be that the Chief Supply Chain Officer in the back room was only called upon when there was a crisis. Well, I think we’ve got enough crises today that we need to push that individual into the front office.

                                  First, we need to enable them to use their voice at the table to advocate for appropriate supply chain practices, but also in combination with a wide range of other roles. These are the teams that are now addressing these issues. It’s no longer just a supply chain issue; it’s an organisational issue. It’s a societal issue that we now need to address, and there’s only one way to address that; that’s through collaboration within the organisation, as well as with your partners, your vendors, and your vendor’s vendor. This is a very dynamic environment today, and enabling organisations to have that complete visibility and connectivity is critical.

                                  There’s been a lot of talk about a shortage of talent across supply chain; how big an issue is this, from your perspective? And how can it be overcome?

                                  From our perspective, it’s one of the defining issues of our time. As supply chain has moved from the back office to the boardroom, so has the demand for skilled professionals. More often than not, supply chain people come out of finance or engineering. In today’s environment – a very diverse workforce – digital natives are coming into the workforce. They’re not only adaptable, but very comfortable with modern technology. It’s a little bit of a reverse from the leadership that we have in supply chain today, that may still be using that Excel spreadsheet on their systems. Supply chain has the demand for those skilled individuals.

                                  To address this, we’re focused on a number of things. First, expanding the awareness of supply chain as a rewarding career path, which our salary and satisfaction surveys confirm. Secondly, talking openly about investing in ongoing professional development. We’ve been to a lot of conferences and whether we’re talking about AI, sustainability, or disruptions, at the end of the discussion, it always comes down to people. We should be talking about the people at the beginning of the discussion as opposed to the end of it. We need to create that opportunity for individuals to see that they can not only make a difference, but that their voice is heard and followed on within their organisation. That’s what we’re preparing supply chain professionals for. 

                                  We need to provide an inclusive workplace that attracts and retains that diverse talent. As I indicated before, individuals coming into the workforce are digital natives. They’re very adept at AI and they’re more than willing to jump in with the technology. We need to enable them with problem solving, critical thinking, and experience on the job. I couldn’t be more excited about the individuals coming into the workforce today and the focus, and they’re able to change the world through supply chain.

                                  How can supply chain professionals approach the challenge of ever-changing regulatory requirements?

                                  Financial markets and supply chains do not like uncertainty. We like certain demand signals so we can ensure that our supplies are appropriately managed. Supply chain professionals need to have robust systems to monitor changes and provide that data, or the regulatory information and policy individuals reporting become significant. Among the concerns that we have is that more often than not, it’s become regulatory or policy and it becomes a checklist. Part of that concern is whether we’re really focused on really making a change, or focused just on those compliance checklists that often drive down to minimum effect.

                                  Today, technology helps, but so does developing a culture of compliance and resiliency. Once again, collaboration matters, sharing best practices across industries, and enabling individuals to understand that there are ways to respond to the regulatory and the policy changes. 

                                  What are some of the most exciting innovations happening in supply chain today?

                                  I think the combination of the people and technology is what’s going to make an exponential difference. On the technology side, tools like advanced analytics, AI, and digital twins are transforming how we forecast, manage risk, and build resiliency. The real innovation is combining cutting edge technology with a highly skilled, adaptable workforce. I heard a fantastic quote the other day: ‘AI is not going to take your job; an individual using AI is going to take your job’. That’s where the focus is right now – enabling individuals to use technology to really leverage that and enable organisations to be much more responsive and agile, as they address demands.

                                  Author

                                  Dan Brightmore

                                  Published

                                  9 September 2025

                                  Estimated Read time

                                  3Mins

                                  CI&T and Reuters Events report – poor data quality is the biggest barrier to AI transformation, says almost ¾ of UK underwriters

                                  CI&T, a global AI and tech acceleration partner, has released new research highlighting the AI opportunity in the UK. The report, created alongside Reuters Events, reveals poor data quality, rather than technology limitations, is the number one obstacle preventing UK underwriters from accelerating AI adoption. 

                                  Strategising for the AI Insurance Revolution

                                  The report, Strategising for the AI Insurance Revolution, draws on original UK survey data and real-world case studies. It aims to uncover how insurers are tackling the AI opportunity. And what’s holding them back. Much of the market discussion focuses on technology capabilities. The findings show that data fragmentation, unstructured formats and siloed systems are the real roadblocks. The goal is to deliver faster, more accurate underwriting and pricing.

                                  Key Findings from the Study

                                  • Efficiency over personalisation: Just 15% of claims leaders believe greater personalisation will significantly improve customer satisfaction. Compared with 41% prioritising streamlined internal processes and 39% favouring a blend of digital and human touchpoints.
                                  • AI as a cost shield. 60% of claims leaders believe AI-led efficiency will be crucial to offset rising claim costs and premiums.
                                  • Sandbox before scale. Insurers are adopting Generative AI cautiously, testing in sandbox environments. This mitigates risks such as hallucinations, bias, and data privacy breaches.
                                  • Proven ROI in action:
                                  • Working with CI&T, Mitsui Sumitomo (part of Asia’s largest insurance group) saved £800,000 annually. And cut quotation times by 54% through strategic modernisation.
                                  • A leading Brazilian insurer cut SME onboarding time by 46%. And achieved a 26% fraud denial rate, automating 2.2 million claims.

                                  Mike Young, VP Insurance Industry Growth at CI&T

                                  “AI’s success in insurance won’t be determined by how advanced the algorithms are, but by the quality and accessibility of the data that feeds them. This research shows UK insurers are ready to innovate—but they need to get their data house in order first.”

                                  With deep experience in the insurance sector, CI&T has helped insurers modernise legacy systems, improve customer journeys, and achieve measurable operational gains. Central to this is CI&T FLOW, CI&T’s enterprise-grade GenAI platform. It is designed with rigorous governance and privacy safeguards so insurers can innovate without compromising sensitive data.

                                  About CI&T

                                  CI&T is an AI and tech acceleration partner. We help businesses navigate the complex, changing European technological landscape to unlock real, measurable impact with digital-first solutions. CI&T brings a 30-year track record of helping clients deliver accelerated impact through tech-integrated business solutions, with deep expertise across AI, strategy, customer experience, software development, cloud services, data and more. As one of the world’s first digital native companies, innovation is in our DNA, helping us empower clients to win by embedding digital maturity into the heart of their operations. With over 7,400 employees across 10 countries, we combine the expertise of a global business with an entrepreneurial mindset to drive transformation at scale and turn strategy into action.

                                  About Reuters Events

                                  Reuters Events is one of the largest and fastest growing events companies anywhere in the world. Reuters Events serves a diverse range of industries and places a focus on the challenges and opportunities resulting from technological and strategic innovation. Our purpose is to provide senior level executives with the trusted insight and meaningful connections they need to confidently navigate change, unlock opportunity and inform their strategy. We curate world-class events and content that are high value to our customers. For more information, visit reutersevents.com. .

                                  Read the full report here

                                  • Artificial Intelligence in FinTech
                                  • InsurTech

                                  Author

                                  Andy Swift

                                  Published

                                  8 September 2025

                                  Estimated Read time

                                  4Mins

                                  Andy Swift, Cyber Security Assurance Technical Director at Six Degrees on

                                  According to AV-TEST, the independent IT security institute, every day sees at least 450,000 new malware variants added to its database. In June this year, for example, cybercriminals are thought to have used malware to steal over 16 billion login credentials across various major platforms in what is thought to have been the largest breach of its kind in history. For security teams, this represents a relentless challenge that demands constant attention and consumes significant resources.

                                  Malware-Free Attacks

                                  As if that wasn’t enough, malware-free attacks are increasingly favoured by cybercriminals as a way to circumvent organisational security. Typically using legitimate programs and tools, these stealth attacks are particularly complex to detect. And they are invisible to most automated security protection options that are available to buy.

                                  With no obvious malware signatures to detect, automated defences are often powerless to respond. And without robust security foundations, even advanced detection tools offer limited protection once an attacker gains a foothold. When that happens, the consequences can be significant.

                                  At the heart of the matter are the limitations of many traditional security tools, which are simply not designed to stop what they cannot see. Malware-free attacks do not rely on external payloads or binaries with known malicious signatures. This renders many automated detection systems, including standard antivirus solutions, effectively useless. As a result, the burden falls elsewhere.

                                  For most organisations, that means having the right expertise in place to recognise unusual behaviour, supported by technologies that can identify behavioural anomalies quickly. Endpoint detection and response (EDR) platforms offer some of these capabilities. But even the most advanced solutions rely on proper configuration and human oversight to be effective. In an ideal world, every business would have round-the-clock monitoring in place, but in reality, very few do.

                                  Challenging Assumptions Around Risk

                                  So, how can organisations fill the gap? When assessing how to protect against malware-free attacks, many organisations begin with the assumption that they will need to buy new tools or licenses. This can form part of a rounded solution. However, leading with this mindset often overlooks a more fundamental and cost-effective question: What can be improved with the tools already in place?

                                  Reviewing existing capabilities should be the first step. For example, most environments already have some level of EDR, behavioural monitoring or identity protection deployed. Yet these are often underutilised or misconfigured. This can result from a lack of understanding around tool capabilities (and limitations), paying for the wrong level of license coverage, and failing to ensure configurations support behavioural analysis rather than just malware scanning. In many cases, even minor adjustments can significantly increase effectiveness without any additional spend.

                                  Cost vs Risk

                                  Organisations should also reconsider how they approach the question of investment. The cost vs risk conversation needs to shift from what they should buy to what they should fix. Even the most expensive detection tools can be rendered ineffective if attackers can exploit basic oversights such as poor configuration, excessive access rights or the absence of multi-factor authentication. In contrast, identifying and addressing these gaps in existing systems is not only more cost-effective but also more impactful in stopping attacks before they gain momentum.

                                  This kind of review process is also an opportunity to identify gaps and prioritise actions that reduce risk without escalating costs. For example, many organisations find that network segmentation, strict privilege controls and enforcing least-access policies can help prevent lateral movement and minimise credential misuse – two of the most common techniques used in malware-free attacks. Putting these capabilities in place are security fundamentals that often determine whether an attack is stopped early or is able to spread.

                                  In this context, a best practice approach matters more than ever. Not as a one-off initiative, but as a continuous effort to close the windows of opportunity that attackers rely on. This includes reducing privilege levels, adopting MFA by default, limiting binary access and educating users on social engineering techniques. All of which are good examples of cost-effective steps that can limit the opportunity for malware-free attacks to take hold. These are not headline-grabbing technologies, but they remain the strongest defence against attacks that thrive on poor hygiene and overlooked gaps.

                                  So, rather than investing in yet another layer of detection, organisations should focus on strengthening what they already have. This approach not only helps avoid unnecessary expense but also delivers a stronger, more sustainable defence posture in an environment where threat actors continue to be extremely effective.

                                  • Cybersecurity
                                  • Cybersecurity in FinTech
                                  • Infrastructure & Cloud

                                  Author

                                  Trilliam Jeong

                                  Published

                                  Estimated Read time

                                  3Mins

                                  Trilliam Jeong, CEO at Wealthblock, analyses the key investment industry trends in 2025 so far…

                                  Every year the once-staid investment management industry experiences trends in technology, markets, and services that are viewed by many as sure to change the next year’s ways of doing business. Here are three key trends shaping up in 2025… And three from the recent past that turned out to be not-so-trendy.

                                  Trend 1: AI Will Continue to Transform All Areas of Investment Management

                                  Artificial Intelligence (AI), like cloud computing a decade ago, is reshaping the way investment firms acquire, onboard, and manage clients. In 2025, we expect to see deeper integration of AI, providing real-time portfolio insights and automating client communications. Firms will increasingly rely on AI to enhance efficiency and reduce operational costs.

                                  Throughout the past year, leading investment firms have been upgrading their platforms to automate tasks like investor onboarding, marketing, and reporting. This has reduced manual work and human errors. Today, with rapidly advancing technologies like AI and cloud-based solutions, firms are creating customised workflows. These solutions not only benefit clients but allow firms to quickly adjust to changing compliance needs.

                                  Trend 2: Secondary Market Growth

                                  The market for private stakes is likely to expand, offering clients liquidity options beyond traditional public market exits like IPOs. Investors may look to secondary markets for more flexible and immediate exposure to private equity investments. With IPOs remaining limited, secondary market transactions (where private equity stakes are bought and sold) are expected to grow. Both Limited and General Partnership secondaries provide liquidity without requiring a full exit, making them appealing in a market with constrained traditional exit options.

                                  Trend 3: Hyper-Personalisation Through AI

                                  The move toward hyper-personalisation will intensify, with AI tailoring investment firm client interactions to individual preferences. This is crucial for retaining clients in a competitive market. To ensure continues success in 2025, organisations should focus on adopting AI, strengthening their capabilities in secondary markets, and enhancing cybersecurity to protect client data.

                                  Investors now expect quicker, more transparent communication. The state-of-the-art engagement and analytics tools available today have helped reduce delays, but demand for even faster response remains. We foresee further advances in 2025 and beyond.

                                  Beyond these positive trends it is interesting to take note of some oft-hyped predictions in investment technology over the recent past that have not exactly worked out as predicted:

                                  ESG Investing

                                  ESG investing, which gained significant traction between 2019 and 2022, is now witnessing a notable decline. The percentage of new funds labeled as ESG has sharply decreased, and online searches for ESG investing have reverted to 2019 levels.

                                  Tokenisation of Investments

                                  Blockchain and tokenization initially promised a revolution in private investments. But adoption has been slow, primarily due to complex regulations. Firms are now being more selective about blockchain’s real value.

                                  Neobanks and Digital Wallets

                                  Neobanks for private investors have struggled to compete with traditional banks’ digital offerings, leading to a shift in focus. Digital wallets also face security and compliance hurdles in private investment.

                                  AI and Cloud Takes Centre Stage

                                  Generative AI is clearly transforming private equity, with firms exploring AI tools for due diligence, portfolio optimisation, and cost reduction in portfolio companies. While this area is rapidly growing, the high cost and expertise required could limit smaller firms from fully implementing AI solutions across the board.

                                  In 2025, you can expect to see AI more deeply integrated, from real-time portfolio insights to automating investor communications. Firms will likely lean on AI to cut costs and improve response times, making operations smoother overall.

                                  We see the continued investment in AI and Cloud as the overriding trend in 2025. As AI is deployed to help streamline everything from data analysis to investor communication, firms that focus on automating routine tasks will find their team can spend more time on high-level strategy.

                                  • Artificial Intelligence in FinTech
                                  • Digital Payments

                                  Author

                                  Dan Brightmore

                                  Published

                                  4 September 2025

                                  Estimated Read time

                                  5Mins

                                  The Financial Transformation Summit (FTS), presented by MoneyNext, took place June 18-19 2025 at London’s ExCeL Centre, Royal Victoria Dock. With over 2,000 attendees, 300+ speakers, and 400 roundtables, it stood out as one of the most immersive and interactive events in the financial services calendar.

                                  FinTech Strategy hit the conference floor at the heart of the action delivering insights from experts across Banking, Insurance, Wealth, and Lending at Financial Transformation Summit (FTS).

                                  Financial Transformation Summit attendees from banking, insurance, wealth, lending, fintech, consultancy, and regulatory sectors convened for two days packed with keynotes, panel talks, immersive demos, and networking among 60+ exhibitors and startups.

                                  Co-located streams – Banking, Insurance, Wealth, and Lending part of themed zones – meant that ticket-holders could explore adjacent sectors fluidly across a guiding theme: culture, collaboration, and customer centricity driving tech adoption and transformation.

                                  Programme Highlights

                                  Keynotes & Panels

                                  1. Data Silos & Cross‑Institutional Collaboration

                                  A panel featuring senior leaders from EVLO, Aon, Schroders, and Brit Insurance tackled how institutions – despite collectively spending over $33 billion annually on data – still struggle to collaborate due to privacy concerns and regulation. Innovative solutions included federated learning, anonymised client IDs and consent-backed APIs.

                                  2. Digital Insurance via Wallets

                                  Anna Bojic (Miss Moneypenny Technologies) unveiled a fresh take on insurance – embedding policy and claim data into Apple/Google Wallets. The idea: dynamic customer interaction directly from smartphone wallets, enhancing real‑time engagement and retention.

                                  3. ESG Economics & Market Reality

                                  Marc Kahn (Investec) challenged ESG orthodoxy, urging firms to emphasise human and planetary wellbeing – beyond purely financial returns – to capture stakeholder trust and sustainable growth.

                                  4. People & Psychological Safety

                                  Kirsty Watson (Aberdeen Group) and Vikki Allgood (Fidelity International) underlined that technological investments are futile without organisational design and psychological safety. Allgood cited a McKinsey study revealing only 26% of leaders build teams with a sense of safety – a critical step toward innovation.

                                  5. Human‑Centred AI

                                  Monica Kalia (Planda AI) championed AI that models individual financial contexts – recognising diversity within demographic cohorts and personalizing services accordingly.

                                  Roundtable Experiences at FTS

                                  At the event’s heart were the TableTalk roundtables – 400+ small-group sessions, each led by a subject-matter expert. These were limited to six participants each, enabling deep, peer-led discussions on themes like:

                                  • AI in risk and compliance
                                  • Open banking integration
                                  • ESG data standards
                                  • Cyber resilience
                                  • Change management and culture adaptation

                                  Attendees consistently praised their interactive nature – far removed from the stage‑focused “listening” format often critiqued at other conferences.

                                  Demonstrations & Exhibitor Showcase

                                  Over 60 exhibitors presented tech-driven innovations: Generative AI, open‑banking APIs, ESG reporting tools, embedded finance solutions, and more. A few standouts were:

                                  • CRIF highlighted AI-powered credit scoring with ESG overlays – promising dynamic risk assessments backed by sustainability data
                                  • Emerging FinTechs demoing AI compliance engines, digital wallet insurance packaging, and data-sharing platforms
                                  • Hyland demonstrated the intuitive end-user experience of its Hyland Content Innovation Cloud™ and showed how easy it is to configure, tailor and deploy solutions that can empower key stakeholders across any business

                                  The demo zone allowed engaging, hands-on exploration and real-time Q&As; it complemented the content with practical insights.

                                  Standout Themes & Strategic Insights

                                  1. Tech is Not Enough Without Culture

                                  Recurrent messaging emphasised that culture, trust, governance, and psychological safety are foundational – not secondary – to digital initiatives. Technology alone won’t deliver transformation without a people-first mindset.

                                  2. Cross‑Sector Data Collaboration

                                  Despite heavy investment, institutions still operate in silos. Shared, secure infrastructure and regulatory-aligned frameworks are being prototyped, but broad adoption remains a work in progress.

                                  3. AI-as-a-Personalisation Backbone

                                  AI is shifting from automation to empathy. Organisations showcased tools to hyper-personalise offers yet maintain privacy and inclusion – moving beyond outdated demographic frameworks into genuine behavioural understanding.

                                  4. Embedded Finance & Digital Wallets

                                  Insurance via wallet applications and embedded finance models point to seamless customer journeys – less app hopping, more value delivered at the point of need.

                                  5. Rebalancing ESG & Profit Metrics

                                  Speakers emphasised integrating ESG factors into performance metrics – not just for compliance, but as an operative advantage anchored in long-term stability and stakeholder trust.

                                  Who Should Attend FTS Next Year?

                                  Ideal for:

                                  • Transformation and change leaders
                                  • CTOs, CIOs, and Heads of Innovation
                                  • Data and AI strategists
                                  • Operational and HR leaders focused on culture
                                  • FinTech innovators and solution providers

                                  If you’re crafting digital transformation strategies, an attuned leader in financial services, or a consultant embedding tech in legacy environments, this summit provides rich, actionable content.

                                  Expect next year’s event to build on this foundation:

                                  • More AI-specific tracks, possibly Generative AI streams
                                  • ESG deep-dives with case studies on implementation
                                  • Expanded regulator involvement around data governance and cross-border compliance

                                  FTS: Final Verdict

                                  Overall, the FTS 2025 delivered on its brand promise:

                                  • Interactive and inclusive: 400 roundtables empowered voices across levels.
                                  • Cross‑sector learning: Banking, Insurance, Wealth, and Lending streams offered both breadth and depth.
                                  • Insightful keynotes: Big ideas on AI, ESG, data-sharing, and culture were well-explored.
                                  • Real-world relevance: Exhibitor demos connected theory with practice.
                                  • Networking with purpose: Opportunities to engage, learn, and collaborate were abundant.

                                  The Financial Transformation Summit struck a compelling balance between big-picture vision and granular, execution-level insight. It emphasised that while technology enables; culture, customer centricity and collaboration drive real progress. The format – with its roundtables, demos, and keynotes – offered a dynamic platform for knowledge exchange.

                                  If you attended, chances are you left with practical next steps. If you didn’t, you missed one of the most interactive, future-focused events shaping financial services transformation today.

                                  • Artificial Intelligence in FinTech
                                  • Digital Payments
                                  • Embedded Finance
                                  • Events
                                  • Host Perspectives
                                  • InsurTech

                                  Author

                                  Alexandra Mousavizadeh

                                  Published

                                  3 September 2025

                                  Estimated Read time

                                  4Mins

                                  Alexandra Mousavizadeh, CEO and Co-Founder of Evident, with her top five AI innovations advancing financial services in 2025

                                  AI is no longer optional for the world’s biggest banks, it has become a fundamental part of their operations, rapidly transforming modern banking. As the industry faces mounting pressure to innovate, the technology is emerging as a critical tool for achieving a competitive advantage. From automating processes and enhancing customer experiences to improving risk management, banks are investing heavily in artificial intelligence to boost productivity, efficiency and profitability.

                                  2025 has been a pivotal year for AI adoption, as banks shift their focus from strategy development to demonstrating measurable value. Stakeholders will increasingly demand clear evidence of AI’s impact on efficiency gains, revenue growth, employee productivity and customer satisfaction. The next phase of AI adoption will distinguish early adopters who leverage it effectively from those who fall behind.

                                  Here are five predictions for how artificial intelligence will reshape banking in 2025 and beyond.

                                  1. Banks focus will shift from AI strategy to measuring value creation

                                  The big banks are well on their way to operationalising AI at scale and, consequently, it now has to prove its ROI.

                                  Capturing ROI has been one of the most discussed topics internally at banks this year but noticeably absent from the industry disclosures so far. In 2025 realised results are going to be needed to justify ongoing investments. Equity analysts will be asking for clear evidence of the value AI is delivering whether that’s efficiency gains, revenue growth, staff productivity or customer satisfaction.

                                  With just six banks disclosing the realised business impact of artificial intelligence in financial terms so far, it’s time for everyone else to step up.

                                  2. AI Training will take Centre Stage: Ensuring employees can use AI tools effectively

                                  AI training is shifting downstream, so the focus is no longer just having AI tools but ensuring that employees are able to use them properly.

                                  Our talent data suggests that 60% of incoming AI talent arriving at banks is sourced straight out of university. Banks need to ensure AI-focused training and career development opportunities are available across all levels of their organisation to fast-track adoption and start seeing a return.

                                  Specifically, in 2025 we expect to see banks investing in training programmes that shift the emphasis from early internal adopters and specialist hires to the rest of the bank. This could be training ‘leaders’ in AI literacy or upskilling ultimate ‘users’.

                                  3. Unstructured data is no longer a problem

                                  Whether banks are building their own AI or buying in third-party solutions, the end result will only be as good as the underlying infrastructure. Banks made these investments years ago; in 2025, as the drive towards organisation-wide AI deployment ratchets up, we’ll start to see which institutions have placed the right bets.

                                  However, advances in handling unstructured data may ease the burden of cleaning up legacy data pools, providing a lifeline to institutions weighed down by outdated systems. Emerging technologies like AI-powered data wrangling and natural language processing are enabling banks to extract value from messy or siloed data. This is reducing the dependency on large-scale data overhauls.

                                  4. We’ll see the first ‘killer app’ for Agentic AI documented at a major bank

                                  As trust in the technology grows, and banks continue to build artificial intelligence capabilities, we’re expecting to see more use cases that let the AI operate and make decisions without human intervention.

                                  2025 should be the year when the first killer apps for agentic AI surface, although it’s worth noting that, at the time of writing in January, Australia’s CommBank is the first and so far, only big bank out with a live agentic AI use case. The bank is deploying agents to solve some of the 15,000 payment disputes raised by its customers every day. The rest of the major players are yet to show their hand on the agentic front.

                                  5. Trump’s AI Executive Order: A rebrand, not a repeal

                                  Despite President Trump’s pledge to repeal President Biden’s AI Executive Order, this move resulted in a rebranding rather than a full repeal. Biden’s order primarily focused on federal government AI adoption rather than regulating the private sector, leaving industries like banking largely unaffected. Financial institutions are already collaborating with regulators to ensure AI safety and to avoid deploying contentious use cases.

                                  Overall, US regulations will focus on competitiveness, growth and spending cuts. As a result, we anticipate a more liberal approach to AI regulation aimed at staying ahead of China. With the recent appointments of Sriram Krishnan, Michael Kratsios and Lynne Parker we expect regulation will support open source development and avoid a pause on research, an approach that may clash with Musk’s views.

                                  While US AI safety advocates continue to monitor developments, Europe is likely to press ahead with its regulatory agenda regardless. This could create an uneven playing field if Europe’s approach ends up being significantly more heavy-handed than that of the US.

                                  • Artificial Intelligence in FinTech

                                  Author

                                  Rob Israch

                                  Published

                                  29 August 2025

                                  Estimated Read time

                                  4Mins

                                  Rob Israch, President at finance automation specialists Tipalti, reflects on the post-hype AI landscape for innovation in financial services

                                  The initial excitement around AI In finance is shifting toward a more practical focus on real business value. Many companies were swept up in the early enthusiasm. However, companies are now leaning toward integrating artificial intelligence more meaningfully into core workflows to deliver lasting value.

                                  While 92% of companies plan to increase their AI investments over the next three years, just 1% of leaders say their organisations are truly AI mature. True maturity means AI drives measurable outcomes and is central and streamlined into daily operations.

                                  So for finance teams, this shift is critical. In an economy shaped by changes in inflation, tariffs and taxes, every investment must deliver clear ROI and help the business by streamlining operations, enhancing forecasts and adopting predictive analytics.

                                  As companies push for sustainable growth and a thawing IPO market signals possible opportunities, scalable and integrated AI solutions will be key to business success.

                                  Building for Real Problems, Not Hypothetical Gaps

                                  Most companies agree that innovation in the finance department is key to unlocking the next level of growth. However, despite growing ambition to adopt AI and automation, 84% of finance teams still rely heavily on manual processes. Leaving little leftover time for strategic thinking.

                                  To truly drive value, AI must be applied not just tactically, but strategically for each business. Research shows that while 74% of companies have adopted AI, only 4% have advanced capabilities that drive clear business value. Real impact is delivered when the technology goes beyond simple workflow automation and becomes a source of real-time, predictive insight across the finance function.

                                  Take treasury operations, for example. Traditionally, treasury teams have faced mounting challenges in managing cash flow, forecasting liquidity, and overseeing global bank relationships. With AI-powered tools, finance teams can now gain real-time, intelligent cash visibility across thousands of banks, ERP systems, and data sources. This transformation not only empowers leaders to make faster and smarter decisions but also underscores the importance of streamlined systems within the finance function.

                                  From a Surplus of Tools to One Unified Platform

                                  What businesses don’t want is extra layers of complexity; they need a straightforward, unified platform that solves real problems.

                                  Large enterprises may seek ‘AI-first’ products and invest in cross-functional AI platforms. But they typically have the resources to fund extensive IT teams or consultants to customise these systems. However, for most businesses, this level of support isn’t a reality. So, businesses without reams of IT people, benefit more from a consolidated system that delivers efficiency and scalability. This allows them to stay focused on growth and innovation. 

                                  If AI is seamlessly embedded within these solutions, it can enhance performance without increasing complexity. Whether improving automation, workflow management or operational efficiency, AI should be an integral part of the product.

                                  Staging the Runway for the Next Stage of Growth

                                  Companies that fully integrate AI will be more ready for sustainable growth. However, integration is just the start… Once AI is embedded, organisations must focus on how it can deliver real, strategic value. This means designing solutions not only to automate processes but to provide actionable insights. Currently, only 26% have developed the skills to move beyond AI conceptually and deliver real value. In the finance function, using AI strategically can lower processing costs by 81% and speed up processing times by 73%.

                                  As more advanced models are integrated into workplaces systems, they can predict payment patterns, cash flow trends, and vendor behaviour. In today’s dynamic environment, companies that have sustainable, AI-powered solutions centred on usability and scalability are best positioned for the next stage of growth.

                                  The Continued Road to AI Maturity

                                  As finance teams navigate a more mature AI landscape and prepare for future growth, the focus is shifting from individual features to foundational value. With investors sharpening their focus, they seek durable business models. The companies that succeed will be those that have applied AI to maximise their investment.

                                  These companies haven’t just chased metrics; they’ve spent the past few years strengthening their foundations and embedding AI deeply into their architecture.

                                  • Artificial Intelligence in FinTech

                                  Author

                                  Nell Walker

                                  Published

                                  Estimated Read time

                                  2Mins

                                  Collaborating with Amdocs has been a game-changer for Telkom. Here’s why.

                                  As telecom companies race to adopt generative AI, a critical shift is underway – from generic copilots to deeply verticalised, telco-grade agents. Amdocs, in collaboration with AWS and NVIDIA, is leading this evolution with its amAIz Agents – introducing a new class of AI agents built specifically for the telecom industry.

                                  Unlike general-purpose AI, verticalised agents are built with domain-specific knowledge, reasoning, and telco ontology that reflect the complexity of telecom operations. These agents understand service plans, billing structures, and network topologies, enabling them to deliver context-aware responses and take meaningful action.

                                  Amdocs, NVIDIA and AWS released a publication that defines and showcases how AI agents can be tailored for specific telecom domains, illustrating the concept of ‘agent verticalization’ and its impact on operational efficiency and customer experience. These domain-specific agents, across every telco domain like care, sales, network, and marketing, work in coordination, enabling end-to-end automation and intelligent customer engagement through seamless orchestration.

                                  In the whitepaper, ‘AI Verticalization for Telco’, Amdocs outlines the essential traits of telco-grade agents such as composable architecture, reasoning, and agentic experience, and enterprise-grade traits such as trust, security, and cloud-native scalability. 

                                  Amdocs: Three decades as a key transformation partner

                                  It’s a rare thing, in the fast-paced world of technology, for partnerships to last decades. However, for Telkom, Amdocs has been by its side for almost 30 years. The latter has played a critical role in supporting both mobile and wireline operation through its B/OSS platforms. These platforms are regarded as industry leaders, and Telkom has been able to navigate major shifts with Amdocs’s help, from legacy to next-gen digital stacks.

                                  “We have been in this game for some time, being the digital backbone of choice for South Africa, really, Amdocs has been a strategic partner of Telkom for over 30 years,” says Dr Noxolo Kubheka-Dlamini, Chief Digital and Information Officer at Telkom. “We have a shared goal of delivering a better, faster, and more seamless experience to our customers. What stands out about Amdocs is their deep domain expertise, strong delivery capabilities, commitment to our success, and ability to evolve with our ambitious goals. We see them as an extension of our own teams.”

                                  Read the full Telkom and Amdocs story in the latest issue of Interface Magazine.

                                  Author

                                  Andrej Persolja

                                  Published

                                  28 August 2025

                                  Estimated Read time

                                  5Mins

                                  We Fix Boring founder Andrej Persolja on why investors are making bigger bets on fewer teams via the impact of AI, enhanced profiling and better targeting

                                  How founders can improve their chances of raising investment – team alignment, production and business differentiation, and customer-centered strategy. Creating a story that investors can easily understand and buy into.

                                  If you’re planning a FinTech investment pitch, the chances are that your first thoughts will relate to the numbers. You’ll open your spreadsheets and dig out your margins, forecasts, CAC-to-LTV ratios, and KPIs. You’ll do everything you can to make your brand look impressive on paper. It’s what you’ve been taught to do because metrics matter. Of course they do. However, what many founders don’t realise is that although metrics clearly carry value, they should only ever be the starting point of any investment pitch. Because, at the end of the day, investors are people first, and their decisions are based on emotion as much as they are on money.

                                  The Human Factor in FinTech Funding

                                  Investors are not machines. It might sound like stating the obvious, but when so much hinges on investor approval, it can be hard to remember that you’re dealing with human beings. So, you focus on upselling your financial model, growth projections, and market opportunity, entirely overlooking the value of an emotional response. One influenced by your narrative, your team, your product vision, and your belief in your startup’s ability to reshape an industry. And that’s where so many fintechs go wrong.

                                  In sectors like FinTech, where technical innovation is everywhere, what often sets a pitch apart is its ability to tell a compelling story. One that communicates not just what the product does, but why it matters. That emotional connection can often provide the edge that secures the deal.

                                  Innovation often outpaces regulation in fintech, and profitability can be years away. So, what convinces an investor to take a bet on an early-stage startup? The potential return on investment matters and will always be a factor. But it’s rarely the only factor. Because there are countless high-growth opportunities out there. So why choose yours?

                                  The answer is belief. Belief in your vision. Belief in your ability to execute. And the belief that your product solves a real, meaningful problem in a way that others haven’t. That’s why positioning, and the emotional resonance behind it, plays such a critical role in raising capital.

                                  When fintech investors evaluate opportunities, they aren’t just looking at your tech stack or your runway. They’re asking themselves: What does this company stand for? What kind of disruption do I want to back? What values do I want my capital to reflect? If your pitch doesn’t communicate that clearly and emotionally, it becomes just another deck in a crowded inbox.

                                  Strong positioning grounds your FinTech in something bigger than features or metrics. It communicates purpose. And when you pair that with an emotionally resonant brand narrative, you give investors a reason to care. Not just about your product, but about why it exists and where it’s going. Because trust, change, and vision are core themes that can move an investor from ‘interested’ to ‘committed.’

                                  Crafting a FinTech Brand Narrative to Drive Investment

                                  Building a compelling brand narrative in FinTech is no longer optional. It’s a critical part of your investment strategy. And it all starts with one fundamental question: What is your why? Beyond monetisation and market sizing, what real-world problem are you solving? Why does it matter now? Whether you’re streamlining payments, reimagining lending, or building infrastructure for digital finance, your deeper purpose is what sets your FinTech apart. And it’s what investors are really looking for. That, and a strong user experience (UX) that shows commitment to your customers and the potential to build loyalty.

                                  The Role of UX in Investment Pitching

                                  Traditionally, FinTech companies have been held back by one major challenge: compliance. But in today’s digital-first environment, where every player in banking, insurance, and payments is competing for speed, convenience, and trust, the challenge has become twofold: compliance and user experience.

                                  In digital finance, the core area of competition is how quickly you can get the user to value. That means having crystal-clear user journeys and a focus on where and how users perceive value. Using one of my clients – a SaaS solution for institutional investors – as an example, by simplifying the user experience across our landing pages and onboarding, we increased conversion from 0% to 37%. That didn’t just improve user experience. It provided quantifiable traction that could be shown to investors. And if you need to prove traction to investors, every click matters.

                                  With FinTech investment rebounding – up 5.3% in H1 2025 compared to 2024 – now is the time to act. But standing out means more than just showing attractive metrics. Investors want a clear narrative that combines numbers with a strong strategic story. They’re looking for confidence in the team, clarity in the vision, and proof that your product is ready to scale. Both operationally and emotionally.

                                  So, to reiterate. Yes, if you’re preparing an investment pitch for your FinTech, the financial model matters. But seasoned investors know markets shift, projections change, and competition intensifies. A fintech company that can articulate a powerful vision, show traction through product-led growth, and tell a story that resonates on a human level will always have an edge.

                                  So, take your ideas and take your numbers, and make them look as pretty and appealing as possible. But don’t forget to wrap them in a story if you want to spark your investor’s imagination. 

                                  We Fix Boring

                                  • Artificial Intelligence in FinTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  Estimated Read time

                                  5Mins

                                  Read in brochure format

                                  This month’s cover star, Dr. Noxolo Kubheka-Dlamini – Chief Digital and Information Officer at Telkom Consumer & Small Business, speaks to the process of leading an ongoing digital transformation

                                  Welcome to the latest issue of Interface magazine!

                                  Click here to read the latest edition!

                                  Telkom: More Than a Telco

                                  Our cover star talks us through the process of leading an ongoing digital transformation that is pragmatic, strategic and embedded in business goals at South Africa’s largest telecommunications platform provider. “By the time we entered the mobile space in 2010, the market was already saturated,” explains Dr. Noxolo Kubheka-Dlamini, Chief Digital & Information Officer at Telkom Consumer & Small Business. “Our ambitions were constrained by limited capital, inherited legacy systems, regulatory shackles, and the sheer inertia of being a former state-run monopoly.” However, Telkom’s “willpower and commitment never faded” resulting in “notable and consistent performance against all odds”. Today, Telkom is playing a pivotal role in ensuring access to meaningful connectivity, driven by the company’s vision to become South Africa’s digital backbone: bridging the digital divide and enabling inclusive participation in its digital economy.

                                  Kynegos: Shining a Spotlight on Transformation, Innovation and Sustainability

                                  Kynegos, a spin-off from Capital Energy, is a business built on strategy. It exists to develop technological solutions for strategic industries. Capital Energy needed an independent platform that could scale digital solutions beyond the energy sector, and foster collaboration with startups and technology centres. Kynegos has filled this gap, and is being leveraged to create co-innovation ecosystems. This allows Capital Energy to develop digital tools that address current and future industrial challenges, keeping the company’s finger on the pulse. We spoke to CEO Victor Gimeno Granda, about its backstory, its values, and the road ahead. “Not only do we develop digital assets for the renewable sector, but for green data centres as well. My perspective is that sustainability is going to be more relevant than ever in the next 18 months.”

                                  York County: The Human Side of AI

                                  York County’s IT team has spent the past decade redefining what local government tech can and should be. From pioneering community cybersecurity workshops to forging statewide collaboration through ValGITE, the county has systematically brought innovation into its operations. This broad portfolio of initiatives has strengthened infrastructure and elevated service delivery. And also earned York County the number one spot in the Digital Counties Survey for jurisdictions under 150,000 population.

                                  “Since I became deputy director eight years ago, this has been one of my goals,” reflects Tim Wyatt, director of information technology at York County. “And over the last eight years, we’ve been in the top 10, but we finally landed that number one place. I think it’s a great reflection for my team, the county, and all the dedication to try to do what’s right by the citizens. It’s just something I’m incredibly proud of. I think it accurately reflects the hard work of my team.”

                                  Wade Trim: Bridging the Cybersecurity Skills Gap

                                  Wade Trim provides consulting engineering, planning, surveying, landscape architecture and environmental science services to meet the infrastructure needs of government and private corporations. With a cybersecurity skills gap leaving vacancies unfilled, Wade Trim’s Senior Manager of Information Security, Eric Miller, spoke with Interface about how stepping away from education-focused rigidity could unlock swathes of latent talent. “Our industry puts emphasis on certifications. However, being passed over for jobs because you don’t have a particular certification or degree in favour of someone fresh out of college has shown me that the best candidates are those that can tell me their story. What brings them to this point in their career? Tell me what qualifies you for this role. That’s how I interview.”

                                  York Catholic District School Board: York Catholic District School Board: Community and Communication at the Heart of IT Strategy

                                  The challenges facing an IT leader in 2025 call for a new kind of approach. One that favours partnerships over transactions, collaboration over competition, and centres people rather than technology for technology’s sake. These perspectives ring especially true in an organisation like the York Catholic District School Board (YCDSB). It emphasises values like “service, community, collaboration, and fait rather than academic excellence alone,” explains Scott Morrow, YCDSB’s Chief Information Officer (CIO). “It’s not actually about the technology; it’s about enablement.”

                                  We spoke with Morrow to learn more about his approach to IT leadership. From building and maintaining a team amid the IT talent crisis, to driving digital transformation initiatives across the organisation. And broader strategic objectives across a changing technology landscape increasingly defined by cybersecurity and the rise of AI.   

                                  Click here to read the latest edition!

                                  • Cybersecurity
                                  • Data & AI
                                  • Digital Strategy
                                  • People & Culture

                                  Author

                                  Dan Brightmore

                                  Published

                                  26 August 2025

                                  Estimated Read time

                                  3Mins

                                  In 2025, Blockchain has stopped auditioning and started plumbing real money flows. Tokenised funds are attracting institutional assets. Stablecoins are…

                                  In 2025, Blockchain has stopped auditioning and started plumbing real money flows. Tokenised funds are attracting institutional assets. Stablecoins are wiring into mainstream settlement. Banks and central banks are experimenting with programmable money. Treasury teams are moving value 24/7 on tokenised rails. And compliance rules are finally catching up. Below are the five breakthroughs that matter now—and why they’re reshaping how finance moves.

                                  Tokenised funds & collateral move into production


                                  BlackRock’s tokenised BUIDL fund surged past $1B AUM in March—proof that on-chain money-market exposure is crossing the credibility gap. Franklin Templeton, meanwhile, has pushed BENJI into new markets and chains. These include a European launch under local rules and integrations on public networks geared for enterprise use. On the collateral side, Euroclear and Digital Asset began the first phase of tokenised collateral mobility on the Canton Network. This is laying the pipes for faster margining and securities financing.

                                  Stablecoin settlement becomes a mainstream payment rail


                                  Visa announced it is expanding stablecoin settlement. More USD and EUR-backed coins, more blockchains, and broader use cases for issuers and acquirers. Stripe re-enabled stablecoin acceptance (USDC) after a six-year hiatus. It has been vocal that a meaningful share of its future payment volume will ride stablecoins. On the bank stack, FIS is integrating USDC into its Money Movement Hub. Making stablecoin payments available to U.S. financial institutions through existing treasury pipes.

                                  Bank-led “programmable money” via tokenised deposits


                                  The BIS Project Agorá—with seven central banks—entered design to prototype tokenised commercial bank deposits. These settle against wholesale central bank money on a unified, programmable ledger. In the UK, the Regulated Liability Network (RLN) brought together all major banks to prove shared-ledger capabilities for always-on, programmable, multi-asset settlement. Together, these efforts point to bank-grade programmability—smart-contract settlement with the finality and legal clarity of today’s two-tier system.

                                  Institutional on-chain payments & programmable treasury


                                  JPM Coin is quietly doing real work. JPMorgan confirmed the platform processes ~$1B in daily transactions, and says programmability has made volumes “explode.” Corporate treasurers are following… Payoneer now uses Citi Token Services for 24/7 blockchain-enabled intracompany transfers. Demonstrating how programmable liquidity is leaving the lab for day-to-day treasury ops.

                                  Compliance rails mature: MiCA + travel-rule guidance


                                  The EU’s MiCA regime has applied to stablecoins since 30 June 2024 and to broader crypto-asset service providers since 30 December 2024. These timelines have shaped 2025 product launches and licensing. The EBA’s “travel-rule” guidelines now spell out what information must accompany crypto-asset transfers, giving banks and CASPs a clearer path to compliance and interoperability.

                                  In 2025, Blockchain in FinTech is realising its potential. Tokenised funds and collateral are moving real money at scale; stablecoins are quietly becoming a dependable settlement rail; and “programmable money” is shifting from whitepapers to pilots with central banks and tier-one banks. Corporate treasuries are embracing rules-based, 24/7 transfers, while clearer rules (MiCA, travel-rule guidance) are reducing compliance friction.
                                  The arc is clear: finance is converging on interoperable, programmable assets and payments that settle faster, with better transparency and control. Winners will be the firms that pair regulatory credibility with real utility—bridging today’s balance sheets to tomorrow’s on-chain operating model.

                                  • Blockchain & Crypto

                                  Author

                                  Rob Vann

                                  Published

                                  22 August 2025

                                  Estimated Read time

                                  7Mins

                                  Rob Vann, Chief Solutions Officer at Cyberfort, on the importance of the human factor for successful AI integration in financial services

                                  Financial service institutions are currently navigating an increasingly complex digital landscape where opportunity and risk walk hand in hand. According to The Bank of England’s 2024 report, 75% of financial service firms are already using Artificial Intelligence (AI). Afurther 10% are planning to use AI over the next three years.

                                  It goes without saying that the rapid uptake can be attributed to the benefits of AI for financial service firms. These include enhancing fraud detection and automating customer service, to improving risk assessment and streamlining compliance processes. Financial institutions are undeniably seeing faster, more accurate decision-making and cost saving as a result of AI integration.

                                  However, the reality is more complicated. The same report also reveals security has emerged as the highest perceived risk of AI integration. Both now and looking three years ahead. With this in mind, banks and fintechs alike are struggling to address these immediate security concerns. As well as implementing and keeping ahead of new AI regulation. Meanwhile, also trying to prepare and anticipate what is next for AI technology. With AI becoming essential to the future of financial services, is there too much focus on technical integration and not enough on the human element?

                                  The Current Limitations to AI Integration

                                  While Generative AI’s (GenAI) ability to understand plain language makes it easier to use, this creates an abundance of potential security risks. Financial staff using these tools might accidentally share sensitive data when asking questions, or the AI could reveal confidential trading information if it’s not properly trained or restricted. This can also work in reverse, by continually telling the AI tool that an untrue thing is correct, the AI tool will adopt this position and present it as fact. For example, if a GenAI tool was trained that people called ‘Rob’ are always bad credit risks, it would quickly factor that into its answers irrespective of the clear (to humans) fact that it is nonsense. This of course works equally well accidentally and maliciously.

                                  Another considerable limitation of current GenAI systems lies in how the mechanisms are set to prioritise delivering information. Unlike seasoned human financial analysts who possess the experience and time to make informed decisions, GenAI mechanisms are set to prioritise over a number of known and unknown criteria, that are not necessarily trained from that specific use to the model. For example, a user disconnecting without an answer may mean the Gen AI tool prioritises responding within a specific time frame over providing correct information. This is especially prevalent in public GenAI tools where the context and desire of the user will be different to the current question but may be applied as universal learning. Furthermore, Public GenAI rarely sees the reaction to the output, so it is unable to differentiate between the good and bad answers its given, meaning training on dumb makes the GenAI less smart, not more. 

                                  This can lead to potentially dangerous scenarios in critical financial operations. Where the GenAI tool simply guesses or creates an answer that isn’t based on fact, potentially enabling or making the wrong decisions.

                                  A Comprehensive Approach to AI Integration

                                  Instead, financial services and institutions must focus on creating and adopting a comprehensive approach to AI integration and security to address these challenges and limitations.

                                  Firstly, firms should invest in building their own AI models that follow their company’s security rules, rather than relying on unreliable public systems. If public systems are being used by staff though, setting clear rules about, and controls when using these tools, like ChatGPT, will also be essential in ensuring the safety of company information. Staff need to know what they can and can’t share, and monitoring and controls should create clear boundaries and limitations to the use of open AI models.

                                  Companies must also train staff on how to use AI systems safely, as even the best security measures can fail if employees don’t know how to use them properly.


                                  Finally, organisations should also use multiple AI systems that work together with human experts to double-check results, making sure no single system can make unchecked decisions without a human AI partnership.

                                  So, what does a good human AI partnership look like?

                                  How to Leverage Human-AI Partnerships

                                  Finance services institutions need to recognise that the solution should focus on allowing AI and human skills to compliment each other. It isn’t just about better AI – it’s about enabling human expertise to scale efficiently.

                                  The simple principle of “the right tool for the right job” needs to be at the forefront of users minds. A GenAI platform can search through billions of records and identify six that are anomalous in some way. A second AI platform can ask it to validate its findings against the original question. And then a human expert can identify which 4 of the 6 are expected behaviours. And which 2 are malicious, dangerous, or need further action.

                                  In the same way as asking the human to search through billions of records manually is unachievable, asking the GenAI platform to apply context it doesn’t have or retain causal experience is equally unrealistic.

                                  AI excels at processing vast amounts of data to recognise patterns, but humans bring crucial understanding, ethical judgment, and strategic thinking. Working in unison, taking a partnership focused approach can allow organisations to leverage both the processing power of AI and the nuanced decision-making abilities of experienced professionals.

                                  Risk management within this partnership becomes absolutely essential. For instance, if AI flags potential money laundering, a compliance officer needs to review this before any action is taken. Or if AI suggests changes to investment portfolios based on market trends, investment managers must validate these recommendations against their market knowledge and client needs.

                                  Banks too need clear procedures for escalation. If AI suggests unusual trading patterns, there should be a defined process for who reviews this. Whether that’s the trading desk, a separate compliance team, or even senior management. The same applies for credit decisions, fraud alerts, or risk assessments. 

                                  The Real Risk: Avoiding AI Altogether

                                  Interestingly, the biggest risk to financial institutions isn’t from those using AI – it’s from those avoiding it altogether. The key is finding the right balance – embracing AI’s capabilities while maintaining strong human oversight and security measures. Financial institutions must create protected data environments and train AI platforms for specific tasks with specific information. They must establish clear guidelines for AI tool usage. And conduct regular security audits to ensure their AI systems remain both effective and secure.

                                  An AI’s development, training, utilisation and continued learning should be planned monitored and developed. This should be longside its human partner’s usage and of course the overall outputs and results.

                                  GenAI Platform Best Practice

                                  When building a GenAI platform, the following principles should be considered.

                                  1. Design it carefully, with a restricted scope and a set of agreed outcomes, how will it learn? What makes this the best learning data? And of course GenAI supervised by humans can play a big part in this.

                                  2. Validate its learning, tell it what’s right and wrong – a GenAI  model will learn (like a human) through mistakes. But it won’t hold the knowledge of why? Or what? So keep the feedback relevant, continuous and tight.

                                  3. Try to break it – ask it random things. For example, when it replies “I don’t know” tell it that’s a good answer. When it makes something up, be clear and provide feedback.

                                  4. Ensure the human partners understand its limitations – people don’t get to outsource their thinking. They get to participate with a low level, high volume intelligence. Make sure they know that and are checking every answer.

                                  5. Measure against your original outcome goals. Don’t scope creep without following the above principles. Yes it can analyse data, but it can’t think if what you’re asking is stupid or not.

                                  6. Enjoy the financial, time, accuracy and speed benefits of your human/ai partnership

                                  The future of financial services lies in effective human-AI collaboration, not just AI adoption. Success requires building secure, well-trained AI systems that compliment human expertise rather than replace it. Embrace this partnership mindset while maintaining strong security measures and human oversight. Then financial institutions can harness AI’s power while mitigating its risks.

                                  • Artificial Intelligence in FinTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  21 August 2025

                                  Estimated Read time

                                  3Mins

                                  The Card & Payments Awards will be taking place on Thursday 5th February 2026 at the famous JW Marriott Grosvenor House Hotel in Mayfair, London. Entries are open now and close in October… Book your table for the Awards now!

                                  The Card & Payments Awards remains the longest-standing and leading networking event of the year for the UK and Irish card and payments industry. With over 1100 guests attending on the night, from over 300 different companies, and with a compelling list of blue-chip sponsors. Enter here and book your tables now.

                                  Recognising Excellence and Innovation in Payments

                                  The Card & Payments Awards has been instrumental in recognising excellence and innovation across the industry from a diverse range of corporations for the past two decades. Each year many eligible organisations compete for one of the prestigious awards which are judged by an independent panel of industry experts. The Awards concludes with its infamous Industry Achievement Award each year. 

                                  The Card & Payments Awards are open across the different categories to credit, debit, prepaid and charge card issuers, co-brands, merchant acquirers, payment processors, retailers and other payments companies worldwide who are offering programmes or initiatives within the UK and Irish market. There are a range of categories covering key disciplines and offering organisations the opportunity to showcase all of their achievements. 

                                  Why Enter

                                  For over 20 years, The Card & Payments Awards have been recognising excellence across the industry.

                                  Widely regarded as the Oscars of the card and payments world, this is your opportunity to stand out and celebrate your achievements.

                                  An entry gives you the chance to:

                                  • Gain recognition from respected industry leaders
                                  • Build brand credibility and consumer trust
                                  • Increase visibility through press and media coverage
                                  • Extensive networking opportunities with senior industry leaders
                                  • Demonstrate your commitment to excellence
                                  • Assessment by an independent panel of experienced industry judges

                                  Entries are judged on the strength of the submission and how well it meets the category criteria. Categories include: Best Industry Innovation, Best Payment Facility, Best App User Experience (CX Initiative), Best Product Design and the Financial Inclusion Award. Last year’s winners include moneyhub for Open Banking, Dojo for Innovating Customer Service with AI, and Nationwide for Product Design.

                                  Enter here and book your tables now to celebrate the industry’s biggest achievements, whilst meeting the key players from across the sector.

                                  Author

                                  Dan Brightmore

                                  Published

                                  20 August 2025

                                  Estimated Read time

                                  3Mins

                                  The financial services industry has been in a state of disruption for over a decade. However, this year has marked…

                                  The financial services industry has been in a state of disruption for over a decade. However, this year has marked a new chapter for neobanks. What began as lean, digital-first challengers to traditional banks has now evolved into a powerful movement reshaping the definition of what a bank can be. Today’s neobanks are no longer just mobile apps for managing money…. They are full-fledged lifestyle platforms blending finance with connectivity, commerce, and personalisation.

                                  From telecom integration to global market expansion, neobanks are aggressively diversifying their services. They are embedding themselves deeper into everyday life. Consumers are no longer just looking for convenience. They expect financial tools that anticipate their needs, adapt to their behaviours and seamlessly connect with the services they use daily.

                                  Here are the top five Neobanking innovations defining 2025:

                                  1. Neobanks Entering the Telecom Arena with Digital SIMs & Mobile Services

                                  • Monzo (UK) is planning to launch a digital SIM card service. Marking its entry into telecomms to improve customer convenience and diversify its revenue streams.
                                  • Klarna (Sweden/US) has taken a similar step by launching a $40/month unlimited 5G mobile plan in the U.S. Available via MVNO partnerships with AT&T and Gigs.

                                  This trend highlights a notable shift: neobanks are blending finance with connectivity to create new customer touchpoints and offerings.

                                  2. Embedding Finance Across Everyday Activities

                                  Neobanks continue to evolve from stand-alone financial apps into integrated platforms within broader ecosystems:

                                  • monobank (Ukraine) introduced “Shake2Pay”—a quick, seamless way to pay for fuel at WOG gas stations by just shaking your phone.
                                  • It also launched “Group Expenses” for effortless bill splitting and a “market by mono” in-app marketplace offering over 20,000 products with installment buying.

                                  These features demonstrate how everyday financial actions—from buying to splitting bills—are becoming deeply integrated into daily life.

                                  3. AI-Driven Personalisation & Behavioural Coaching

                                  Personal finance is becoming highly tailored:

                                  • Industry reports emphasise that AI and machine learning are being leveraged to deliver advanced personalisation, predicting spending behaviors and recommending tailored products.

                                  The result is a more proactive and individualised banking experience, where the app anticipates your financial needs rather than just reacting.

                                  4. Global Expansion Through Licensing and Market Architecture

                                  Neobanks are aggressively scaling internationally:

                                  • Revolut has reached 60 million users in over 48 countries by mid‑2025 and is expanding further—setting up Paris as its Western European HQ and investing $1.1 billion in France.
                                  • It’s also pursuing a ‘lean bank’ licence in Israel, expanding its regulated banking footprint.

                                  This approach underscores a strategic push to become truly global, not just by user count, but by regulatory presence.

                                  5. Diversification Beyond Traditional Banking Services

                                  Traditional neobanking isn’t enough—2025 is about expanding into new verticals:

                                  • Klarna’s shift from BNPL to full-scale digital banking—with debit cards, mobile plans, and FDIC-insured deposit services—signals how neobanks are making themselves indispensable.
                                  • Neobanks increasingly aim to become one-stop financial destinations rather than niche providers.

                                  Neobanks have moved far beyond their origins as scrappy FinTech challengers. In 2025, they are building holistic ecosystems that combine money management, AI-driven personalisation, mobile connectivity, and lifestyle integration. This transformation signals a broader truth: banking is no longer just about finance… It’s about creating seamless experiences that fit into every corner of modern life.

                                  • Neobanking

                                  Author

                                  Matt Whetton

                                  Published

                                  19 August 2025

                                  Estimated Read time

                                  5Mins

                                  Matt Whetton, Chief Technology Officer, Acquired.com on the future of payments with cVRPs, AI and vertical integration

                                  There are three powerful forces shaping the future of payments and how businesses pay and get paid today. Commercial variable recurring payments (cVRPs), AI, and vertical integration. These forces are transforming the way that businesses can interact with their customers. They are still in the early stages of their development. As these technologies evolve, they hold great potential to redefine payments, benefiting both businesses and consumers alike.

                                  cVRPs – recurring commerce done smarter

                                  When open banking is discussed, many people are familiar with options like “pay by bank” at checkout. While this is mostly used for one-time purchases, recurring payments like bills and subscriptions still rely heavily on direct debits. Businesses serving British consumers, who collectively spend almost £30 billion a year on subscription services, face challenges with slow settlements. There are also high fees (especially for failed transactions), and limited customer control.

                                  cVRPs, the latest evolution of open banking, promise to ease many of the challenges. For example, cVRPs enable businesses to securely collect payments from customers’ bank accounts within agreed limits. These include the amount, frequency, or duration, without requiring customers to re-authenticate each time, reducing friction yet increasing optimisation.

                                  In addition to providing the same benefits as ‘pay by bank’ at checkout, such as the convenience of not having to enter your card details and security of not sharing these details with the retailer, cVRPs can unlock new business models for businesses dependent on recurring revenue. The open banking infrastructure which powers cVRPs allows businesses to gather data insights from these transactions. This enables the introduction of offers like dynamic pricing for subscriptions, or variable insurance premiums based on usage. Not only does this help operational efficiency, but it ultimately enhances the customer experience, encouraging them to keep coming back.

                                  Critically, cVRPs are more likely to successfully complete compared to traditional direct debits, as businesses leverage advanced capabilities like smarter retry logic and dynamic payment routing. These are typically implemented by providers offering VRP services. With open banking making real-time account balance checks possible, businesses can determine the best time to retry a failed payment, such as after payday. Dynamic routing enables merchants to route transactions based on pre-defined business rules, such as transaction value, geographic region, or acquirer performance. This flexibility ensures that payments are directed to the most suitable acquirer or provider. Therefore ncreasing the likelihood of successful transactions and optimising cost efficiency. Together, these capabilities help reduce failed payments, keep customers subscribed, and increase revenue over time.

                                  However, its nascence means there are still potential threats ahead. Regulators need to learn lessons from the growth of ‘pay by bank’. There are 27 million monthly payments now taking place after a slow start, as well as already piloted sweeping VRPs to ensure a solid business model for open banking. With collaboration from banks, FinTechs, business, and government, the ecosystem can take full advantage of these innovative capabilities to reduce friction.

                                  AI/ML’s transformative impact

                                  The advances in AI and machine learning (AI/ML) are written about every day. So, it’s perhaps no surprise that they are having a profound impact on how businesses process payments, detect fraud, and improve customer service. AI’s ability to process large volumes of transaction data efficiently helps businesses identify patterns, trends, and anomalies that would otherwise be difficult to detect.

                                  Not only does this capability benefit fraud prevention, but it can also help businesses gain meaningful insights from the data. Allowing them to expand their service offerings. For example, businesses can apply AI/ML to automate tasks enabled by open banking, such as income verification, affordability checks, and financial health scoring. This helps speed up onboarding and approval processes. Meanwhile, giving consumers access to more sophisticated services. These include spend forecasting, budgeting nudges, and alerts for unusual activity, thereby helping them manage their money more effectively.

                                  Looking ahead, AI/ML will be central to unlocking the full potential of open banking. By improving operational efficiency and enabling richer customer experiences, AI will help businesses transition from reactive to proactive financial services. Currently, the best use cases for AI are assistive, not autonomous. AI is at its most powerful when it augments human decision-making, particularly in nuanced or regulated environments. We’re still early in the maturity curve. As the technology becomes more affordable and the technology within it more explainable, it’s hard to imagine the full potential impact of AI in the payments industry.

                                  Tailored Solutions

                                  The combination of open banking and AI has led to a more tailored and specialised approach to payments technology, particularly for businesses in specific industries. While these powerful tools offer great potential, it is crucial that they are applied in the right way, at the right time, and for the right business.

                                  To move beyond generic payment solutions, the industry is seeing increasing vertical integration. Instead of simply processing transactions, payment providers must now deliver more comprehensive solutions that address the needs of specific sectors. In industries where payment needs are more complex, vertical integration ensures that payment solutions are tightly aligned with business operations. For example, businesses in the construction sector often require project-based billing and payment systems that reflect the way projects are managed. Elsewhere, hospitality providers need solutions that integrate payment systems with real-time inventory tracking and booking management.

                                  It’s fair to say firms will always be looking for any place to optimise to gain an edge. The trend towards vertical integration, combined with cVRPs, and AI are redefining the future of payments. There is a move away from a technical area of the business, to become a core operational function. Businesses adapting to leverage these technologies are well placed to create stronger connections with their customers and drive long-term growth.

                                  • Digital Payments

                                  Author

                                  David Sewell

                                  Published

                                  18 August 2025

                                  Estimated Read time

                                  4Mins

                                  David Sewell, Chief Technology Officer at Synechron on why robust digital infrastructure is the missing link in the UK’s AI ambitions

                                  The current British government wants everyone to know that it sees opportunity in AI. Across a series of flashy public events this spring, Prime Minister Keir Starmer announced a string of support packages. Culminating in a £2 billion AI investment pledge. Standing next to the Prime Minister, Nvidia’s Jensen Huang addressed a gathered audience of businessmen and politicians by mentioning the “extraordinary” atmosphere in the UK. Huang also mentioned that the UK is now the third largest AI venture capital market in the world.

                                  The UK has set an ambition to be a global powerhouse in artificial intelligence – building on what it’s already done. The question now is how to ensure it gets there.

                                  The financial industry, centred in The City but now in every corner of the nation, is core to getting there. As James Lichau, financial services co-leader at BPM said: “AI presents immense opportunities for the FinTech industry”.  From better banking applications to bespoke advisory and vastly improved investment theses, Britain’s AI dream will flower with its fintech ambitions.

                                  The Global AI Momentum and Infrastructure Reality

                                  The UK has been quick to realise the importance of the moment, but others are moving too. Two billion pounds is a sizeable commitment but compared to the United States’ $4 billion CHIPS and Science Act AI investments and China’s estimated $15 billion in annual public and private AI spending, it’s not the largest in the world.

                                  Capital investment is accelerating as nations and corporations are pouring large sums into artificial intelligence capabilities.  What might have previously been seen as “unnecessary spend” is now being approved as essential infrastructure. The best engineers now command salaries the equivalent of city budgets. Financial companies of all sizes have placed substantial wagers on AI’s ability to create new value.

                                  This means Britain will need to be smart and targeted in where to place support. The most obvious place is infrastructure. Infrastructure is critical because ambition without infrastructure is unsustainable. Even the most sophisticated AI strategies, backed by some of the largest companies in the world, will fail without the foundational digital systems to support them.

                                  The UK’s AI aspirations face a fundamental test: can government investment translate into real-world capability when the underlying infrastructure remains underdeveloped? History shows that technological leadership demands comprehensive ecosystem development encompassing everything from basic connectivity to advanced computing resources.

                                  Infrastructure: the foundation for progress

                                  A successful AI ecosystem requires three interconnected elements.

                                  First, compute capacity represents the engine of AI development. Training sophisticated machine learning models demands enormous computational resources, often requiring specialised hardware configurations that can process vast datasets efficiently. Without adequate compute infrastructure, AI development becomes expensive and time-consuming, forcing organisations to seek resources elsewhere or abandon projects entirely. Peter Kyle, Secretary of State for Science, Innovation & Technology described the possibilities this way: “Giving our researchers and innovators access to the processing power they need will not only maintain our standing as the world’s third‑biggest AI power, but put British expertise at the heart of the AI breakthroughs.”

                                  Second, power supply infrastructure must support the energy-intensive operations that modern AI systems require. Data centres housing AI workloads consume significantly more electricity than traditional computing facilities, creating new demands on national energy grids. This is why countries like Iceland with large geothermal and hydroelectric energy capacity typically outperform in power-intensive industries. Meanwhile, the massive grid outage this spring showed the fragility of Spain’s power system. The UK’s AI Energy Council is holding discussions about upgrading the national grid, with plans to power the next wave of AI using nuclear and renewable energy.

                                  Third, connectivity is crucial for reliable movement of large data sets. Networks enable real-time deployment of AI services, allowing organisations to access and process data across real-world applications. Without robust connectivity, AI remains confined to isolated research environments rather than driving economic productivity. The UK has a longstanding programme of investment in broadband infrastructure although the speed requirements represent a significant expansion of current capabilities.

                                  Beyond Headline Commitments: The Implementation Challenge

                                  The caveat frequently used by investment managers applies here as well: “Past performance is not a guarantee of future results.” Some regions have built a head start in the race for AI supremacy. That doesn’t mean they will stay in the lead.  From algorithmic trading to fraud detection, fintech applications will be among the first to falter if infrastructure lags behind innovation

                                  Countries that address infrastructure limitations decisively can leapfrog competitors and establish sustainable competitive advantages.

                                  The UK must be unafraid to copy success from elsewhere, while also finding areas to break new ground. The UK AI Opportunities Action Plan is a strong start. Government, business, and investment leaders must now collaborate to turn ambition into execution.

                                  • Artificial Intelligence in FinTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  13 August 2025

                                  Estimated Read time

                                  2Mins

                                  Join industry leaders and innovators in London at the 5th Annual Digital Banking Summit – October 21-22, a premiere event designed to explore the most transformative trends shaping the banking sector in the digital era.

                                  The Digital Banking Summit two-day conference covers a range of critical topics. From AI-driven banking and open finance to financial inclusion and the future of digital identity. Discover how cutting-edge technologies like edge computing, hyper-personalisation and APIs are redefining corporate and retail banking. Engage in discussions around legacy system modernisation, sustainability through ESG initiatives and the regulatory landscape, including DORA and GDPR.

                                  With sessions led by top executives from global financial institutions (including Santander, Revolut, Citi and Lloyds), attendees will gain actionable insights on leveraging innovation to streamline operations, enhance customer experience, and build resilient financial ecosystems. Take advantage of networking opportunities and 1:1 meetings to connect with senior leaders and experts. Don’t miss this opportunity to be part of the conversation shaping the future of digital banking.

                                  Book your place here

                                  Digital Banking Summit Day 1

                                  • Revolutionising Banking in the Digital Era
                                  • Open Banking and Open Finance
                                  • Financial Inclusion in Banking
                                  • Digital Identity: Onboarding, Compliance and Embedded Finance
                                  • Cross-Industry Collaboration in Banking
                                  • Banking for a Digital Workforce
                                  • Hyper-Personalisation in Wealth Management
                                  • Edge Computing
                                  • The Role of APIs in Transforming Corporate Banking
                                  • Digital Resilience
                                  • Legacy Systems vs Modernisation
                                  • AI in Banking

                                  Digital Banking Summit Day 2

                                  • Automation and Cloud Banking
                                  • Data Monetisation: Ethics and Opportunities
                                  • Digital Marketing in Banking
                                  • CBDCs
                                  • Sustainable Banking Future with ESG
                                  • Navigating DORA, GDPR and Beyond
                                  • Digital Wallets
                                  • Mobile Banking
                                  • Crypto, Instant Transfers and Banking
                                  • AI-Driven Fraud
                                  • Customer-Centric Innovation
                                  • Cybersecurity: Deepfakes, AI Attacks and Quantum Risks

                                  What Attendees Really Think About the Digital Banking Summit

                                  “Very well organised conference with a lot of possibilities to meet people and very interesting topics in the banking world”

                                  Director, ERI Bancaire S.A.

                                   “The energy at the event was truly invigorating, as industry leaders shared innovative ideas that are reshaping the future of banking”

                                  Digital Product Lead, Unicredit

                                  “Great experience! In order to meet with professionals from the industry, a lot of networking opportunities. Great topics!”

                                  Strategy Manager, Akbank

                                  “Valuable learning and interesting conversations”

                                  Director, Wise

                                  “The audience is on a very senior level, a lot of participants. Speakers are also on a very high level, everybody learned a lot. We are very, very happy!”

                                  Head of Regional Marketing CEE & CIS, Finastra

                                  “A great opportunity to meet the industry experts and get inspirational thoughts!”

                                  Digital Product Manager, Innovation at Erste Bank

                                  Book your ticket here

                                  Author

                                  Dan Brightmore

                                  Published

                                  12 August 2025

                                  Estimated Read time

                                  8Mins

                                  Read in brochure format

                                  FinTech Strategy meets Eastern Horizon Founder & CEO Christine Le to discuss client expectations and the changing landscape of wealth management

                                  Financial Transformation Summit 2025 EXCLUSIVE

                                  At Financial Transformation Summit, Christine Le, a Chartered Financial Planner and Founder & CEO of Eastern Horizon Wealth Management, spoke on an investment panel – “Generational Wealth Transfer: Meeting the Expectation of Younger Clients”. Appearing with industry colleagued representing Citi Global Wealth, HFMC Wealth and Lightbox Wealth, Le considered: What trends and technologies are shaping NextGen investment decisions, and how can WMs stay ahead? Can digital wealth platforms meet the demand for hyper-personalised, user-friendly experiences? How does social responsibility & ESG investing influence younger investors, and how can advisors align with these priorities? How can wealth managers build and maintain trust with NextGen investors?

                                  Following the panel, we spoke with Christine to find out more…

                                  Hi Christine, tell us about your role at Eastern Horizon?

                                  “I’m a Chartered Financial Planner and the Founder & CEO of Eastern Horizon Wealth Management. We are a financial advisory firm and also a partner practice of St. James’s Place. They are among the biggest wealth management firms in the UK based on assets under management. We get a lot of support from St. James’s Place in terms of technology compliance and investment solutions. At my practice, we focus on a diverse range of clients including ethnic minorities, especially British Asians in the UK. I’m also the president of the Vietnam Investment and Finance Association in the United Kingdom (VIFA). We aim to provide useful financial information for Vietnamese people in the UK and become a bridge between Vietnam and the UK.”

                                  You were part of a panel at this Summit focused on Generational Wealth Transfer. Can you give us an overview of your thoughts?

                                  ‘’Having worked in the financial services industry for over 15 years, I’ve observed a persistent gap in how the industry serves diverse client segments – particularly ethnic minority communities in the UK. This gap is especially pronounced when it comes to financial education and long-term planning, including wealth transfer across generations. When I speak to members of my own Vietnamese community, I often find that there’s a limited understanding of how to navigate financial systems effectively – from managing investments and pensions to planning for intergenerational wealth. It’s not due to a lack of interest or ambition, but rather a lack of access to culturally relevant and accessible financial advice.

                                  “This is where I believe I can make a meaningful difference. I not only bring professional expertise and technical knowledge to the table, but also a deep understanding of the cultural values, family dynamics, and communication styles that shape financial decision-making in the community. That cultural insight is key to building trust, something that is essential when discussing personal finances and planning for the future. My goal is to help bridge that gap – to empower families with the knowledge and tools they need to make informed financial decisions, preserve their wealth, and pass it on confidently to the next generation.’’

                                  Why is this an exciting time for the business?

                                  “At the moment the world is so integrated, and many people can benefit. A lot of people want to go to the UK, invest into the UK. I think with that in mind this is an exciting time to run my business and to be able to bridge that gap, providing sufficient knowledge for people as a trusted source when they come to the UK and need to understand the financial regulations. We can give people solid support to understand the financial processes of settling and building wealth in the UK.”

                                  “Right now, everyone is talking about AI, and for good reason. In my business, we rely heavily on digital tools to streamline administrative tasks. It’s truly a game-changer. Compared to starting a business 15 years ago, when I would have needed a full-time assistant just to take meeting notes and summarise action points, many of those processes can now be automated, saving both time and cost. Another advantage is in how we communicate. Many of my clients are British Vietnamese. While they understand and speak English, they often feel more comfortable communicating in Vietnamese. We use AI-powered translation tools to make this process faster and more seamless. These technologies are allowing us to broaden the range of services we offer and tailor our support to each client’s needs.”

                                  What pain points are your clients experiencing that you need to address?  How are you meeting the challenge?

                                  “It’s about meeting the client’s highest priority. When people come to me, they maybe want to support their children to get onto the property ladder or plan for their retirement. They might be looking to buy a new car or move home. So, as a regulated financial advisor, I can sit with a client and talk them through key priorities and tailor the solutions best for them and help them overcome the pain points of decision-making.

                                  “Additionally, the UK’s financial regulations are complex and changing all the time. It’s very difficult for people to follow. It’s my job as a financial advisor to follow up those changes and stay up to date with the regulations to assess how it can impact our clients and then give them the best recommendations. Allied to this, many of our clients will need support with cross-border services as they move freely between different countries they need somebody they can trust, an expert that knows what they’re doing and who can provide the right financial services for them.”

                                  Tell us about a recent success story…

                                  “Success for Eastern Horizon is to know that our clients feel they have somebody to rely on. For example, I have an old friend who came to me as a client. She was based in Vietnam but wanted to relocate to the UK. She had assets across Europe and in Vietnam and needed to understand the big picture of financial planning in the UK. We examined her assets across different countries to bring them into the UK and find the best solution for her to utilise tax efficient savings, pensions and investments to support her family and her business in the long term.”

                                  What’s next for Eastern Horizon when it comes to wealth management? What future launches and initiatives are you particularly excited about?

                                  “Over the next few months, we are keen to collaborate with different associations and communities across the UK – whether that’s related to Vietnam or British Asian communities and offer useful information and workshops and webinars tailored to different audiences. Also, with my work for the Vietnam Investment and Finance Association I want to organise workshops for those keen to invest in the UK but don’t know where to start. They often don’t have anyone to support them so I would like to focus on building a network to offer that bridge to investment in the UK.”

                                  Why do you think the evolution of collaboration between traditional institutions and FinTechs is set to continue? What are you excited about?

                                  “I spent five years working at the intersection of FinTech and WealthTech – where wealth management meets technology. During that time, I witnessed firsthand how the financial services landscape is evolving. Large incumbent banks bring undeniable strengths: scale, regulatory rigour, and long-standing client trust. However, they often struggle with agility. Their legacy infrastructures, many of which still aren’t cloud-based, make digital transformation slow and complex. On the other hand, FinTechs are born digital. They’re nimble, innovative, and quick to adapt to changing customer needs. But without the reputation and stability that traditional institutions have built over decades, they can face challenges in gaining consumer trust or navigating regulatory environments alone. What became clear to me is that banks and FinTechs cannot operate in silos.

                                  “Collaboration is not just beneficial, it’s essential. When they work together, they combine the best of both worlds: the reliability and compliance of traditional finance with the innovation and customer-centric design of new technology. With my own practice, we apply this mindset. We actively look for ways to streamline administrative processes using digital tools – reducing costs, improving efficiency, and freeing up more time to focus on what matters most: building strong, human relationships with our clients. The goal is to use technology not to replace that human connection, but to enhance it. By doing so, we can deliver modern, efficient, and deeply personalised financial services that clients trust.”

                                  Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for Eastern Horizon?

                                  “I’ve attended several events this year, and this has truly been one of the most enjoyable and well-organised in the UK. What stood out was the impressive mix of voices – from established financial institutions to bold, forward-thinking startups. Engaging with such a diverse group of speakers has been both insightful and thought-provoking. I’ve come away with fresh perspectives, challenged some of my own assumptions, and found new ideas to explore as we continue building meaningful partnerships for Eastern Horizon Wealth Management.”

                                  Find out more at easternhorizonwealth.co.uk

                                  About Christine Le and Eastern Horizon Wealth Management

                                  As an Appointed Representative of St. James’s Place, Practice Lead, and business owner, Christine leverages over 15 years of experience in financial services and wealth tech to serve our clients, acquired through extensive work in multinational financial services firms in the UK. This rich background has equipped Christine with the skills and knowledge necessary to effectively oversee the business, ensuring that every facet is managed with the highest level of professionalism.

                                  Christine founded and built this Practice to help clients prosper, build financial security, and attain peace of mind while overcoming financial obstacles. 

                                  Her primary focus is on nurturing enduring relationships with her clients, offering them trusted guidance as their financial requirements evolve over time. Throughout her advisory process, clarity remains paramount. By closely collaborating with her clients, Christine strives to identify the most efficient and tax-effective strategies to help them achieve their objectives. Specialising in tailored solutions, Christine is dedicated to understanding her clients’ financial goals and crafting strategies that align with their vision for the future.

                                  Author

                                  Dan Brightmore

                                  Published

                                  11 August 2025

                                  Estimated Read time

                                  8Mins

                                  Read in brochure format

                                  FinTech Strategy meets with Citigroup’s Head of ESG Credit Management, Mauricio Masondo, to discover the future for ESG and sustainable finance

                                  Financial Transformation Summit 2025 EXCLUSIVE

                                  At Financial Transformation Summit, Mauricio Masondo, Head of ESG Credit Management at Citigroup, featured on a sustainability panel – ‘The Future of ESG and Sustainable Finance: Balancing Profit and Purpose’. Alongside peers fromGenerali AM, Gallagher Re and Arma Karma, Masondo considered: What key metrics should FIs use to track ESG progress, and how can they ensure authenticity in their sustainability efforts? Developing a holistic ESG strategy amid evolving regulations – key challenges and solutions. How can FIs leverage technology to meet sustainability goals and drive long-term profitability? How can FIs move beyond offering ESG products to embedding sustainability into their core business models?

                                  Following the panel, we spoke with Mauricio to find out more…

                                  Hi Mauricio, tell us about your role at Citigroup?

                                  “In my 32 years with Citi my career has primarily focused on wholesale credit, and in recent years I built out our portfolio management function. For the past year specifically, I’ve been leading the integration of ESG and climate considerations into our credit processes. As Head of ESG Credit Management, my role is to embed ESG requirements into our credit processes in a way that’s consistently and efficiently applied through technology, policies, training, and governance frameworks. Our strategic approach was not to create an ESG silo that replicates existing processes, but rather to integrate ESG considerations seamlessly into our current workflows. This means any credit analyst can now underwrite ESG credits, sustainable loans, or green loans, rather than requiring dedicated specialists. We’ve equipped our entire team with the knowledge and tools they need to handle these transactions effectively.”

                                  You were part of a panel at this Summit focused on the future for ESG and sustainable finance. Can you give us an overview of your thoughts?

                                  “Data standardisation is absolutely critical, especially as we advance into the AI era. I often reference Moody’s as an excellent example of strategic foresight. Moody’s operates two key businesses – credit ratings and data analytics – and early in their AI journey, they made the strategic decision to structure and normalise all their credit research data. This proved to be transformational because it enabled them to deploy AI solutions much more rapidly with clean, structured datasets. We’re working to apply this same principle at Citi. We’re developing processes to structure climate-related data in a way that will be usable across multiple applications. For example, we’re working on integrating emissions data and climate risk assessments into our credit risk rating models. We’re also exploring how this structured approach could support underwriting processes and securitisations, where comprehensive data packages could facilitate risk transfer transactions with institutional investors. The goal is to build normalised, structured data as the foundation for various applications, from portfolio management to AI-driven solutions. While we’re still in the early stages of many of these initiatives, the potential is significant.”

                                  Why is this an exciting time for the business?

                                  “We’re witnessing the convergence of several transformative trends. However, one of our biggest challenges is policy divergence across jurisdictions. Countries are taking vastly different approaches to ESG requirements, and for a global bank like Citi, this creates significant complexity in standardising processes across multiple regulatory environments. While challenging, this divergence also creates opportunities to develop scalable, cost-effective solutions that can adapt to various regulatory frameworks. Second, AI is revolutionising how we approach ESG challenges. It’s helping us structure data more effectively, enhance reporting capabilities, contextualise information, and identify trends that would have been impossible to detect manually.

                                  “Previously, comprehensive ESG analysis required significant time, resources, and personnel. AI has made these processes more accessible and cost-effective. Most importantly, there’s been a fundamental shift in how the industry, and governments, view ESG. It’s evolved beyond compliance and emissions reporting to become a significant business opportunity. We need to capitalise on this transition – moving from reactive reporting to proactive opportunity capture. The capital is there, and if traditional banks don’t seize these opportunities, asset managers, private credit firms, and private equity will. We’re partnering strategically with reinsurance companies and asset managers to develop innovative solutions that unlock transition capital and help companies fund decarbonisation projects.”

                                  “Trade flows are experiencing significant disruption due to current tariff policies. This creates both challenges and opportunities for our clients. Companies are reassessing their supply chain vulnerabilities and seeking greater resilience in their operations. I anticipate we’ll see a regionalisation of trade flows rather than a complete deglobalisation. European companies will likely increase intra-regional trade while reducing intercontinental transactions. We’re seeing similar patterns emerging in Asia and the Middle East. This shift requires banks to be more agile in how we structure trade finance and working capital solutions to meet these evolving needs.”

                                  What pain points are you experiencing that you need to address?  How are you meeting the challenge?

                                  “Working capital finance requires increasingly creative solutions that leverage advanced technology. Banks are recognising that FinTechs often have greater agility in developing and implementing these technologies. There’s significant efficiency in having one FinTech serve multiple banks rather than each institution developing independent solutions. This collaborative approach allows us to move faster while reducing development costs and time-to-market.”

                                  Tell us about a recent success story…

                                  “I designed and led the implementation of an early warning monitoring system for Citi’s credit portfolio. The project began with a fundamental concept: create a data lake, develop meaningful metrics, and engage data scientists to interpret the insights. We collaborated with trade officers and partnered with external specialists to enhance our capabilities.Initially, there was scepticism about the system’s value, particularly because we built it as an independent function within our portfolio management organisation, separate from traditional banking and risk management structures. However, this positioning allowed us to collect unique client data and develop insights that weren’t available elsewhere in the organisation. A critical component of our success was establishing a dedicated credit expert team that oversees the entire process.

                                  “This team leads the engagement and communication of alerts, ensuring that insights are properly interpreted and actionable recommendations reach the right stakeholders. The evolution was remarkable. We progressed from generating a few alerts daily to dozens per day, and eventually to hundreds of alerts weekly. More importantly, we developed sophisticated processes for interpreting and acting on these alerts, with our expert team serving as the bridge between data insights and business action. Bankers and risk managers began to recognise the value, and today, three years later, the system is integral to how we conduct annual reviews and client presentations. It’s incredibly rewarding to provide our bankers with comprehensive data and insights that strengthen their client relationships.”

                                  What’s next for Citigroup when it comes to ESG? What future launches and initiatives are you particularly excited about?

                                  “While it may sound clichéd, AI truly is transformative for our industry. The breadth of use cases and the rapid pace of learning make it essential to our strategic direction. We’ve established a strategic partnership with Google and are investing significantly in AI use case development and implementation across our operations. From an operational perspective, AI will undoubtedly increase our efficiency as an industry. More importantly, it’s enabling us to evolve our business models and create client solutions that weren’t previously feasible. This opens entirely new avenues for innovative product development. Additionally, since CEO Jane Fraser joined, we’ve embarked on a comprehensive transformation program that’s delivering strong results in terms of financial performance and returns. We’ve restructured and simplified our operations, which positions us more competitively as we refresh our leadership teams and attract new talent. The trajectory is very promising.”

                                  Why do you think the evolution of collaboration between banks and FinTechs is set to continue? What are you excited about?

                                  “The current tariff environment is creating opportunities for FinTechs that facilitate connections between banks, investors, and corporations. It’s also presenting consolidation opportunities for private equity firms within the rapidly expanding FinTech ecosystem.”

                                  Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for Citigroup?

                                  “The panel brought together diverse perspectives from FinTech, asset management, insurance, and banking – all addressing common challenges that span our sectors. This cross-industry dialogue creates tremendous opportunities for collaboration and mutual understanding. The key now is translating these conversations into action. We need to maintain these connections, expand the dialogue, and avoid making decisions in isolation. FinTechs possess the agility to implement changes in their operating models far more quickly than large incumbents like us. However, our procurement systems and processes aren’t always conducive to collaborating with smaller, innovative companies. Events like this highlight the need to streamline how institutions like Citi can collaborate with and learn from FinTechs. We must accelerate our ability to adapt to a rapidly changing world.”

                                  Learn more at citigroup.com/global/our-impact

                                  About Citgroup

                                  A human bank…

                                  We’re helping build more sustainable, economically vibrant communities around the world.

                                  At Citi, helping our clients navigate the challenges and embrace the opportunities of our rapidly changing world is fundamental to our mission of enabling growth and economic progress.

                                  Author

                                  Dan Brightmore

                                  Published

                                  8 August 2025

                                  Estimated Read time

                                  7Mins

                                  Read in brochure format

                                  FinTech Strategy spoke with Veritran’s CMO, Jorge Sanchez Barcelo, at Money20/20 Europe to find out more about the tech firm’s partnership with Manchester City reimagining CX to create a frictionless digital experience for fans

                                  Money20/20 Europe Exclusive

                                  In an era where technology defines the customer journey, Jorge Sanchez Barcelo, Chief Marketing Officer at Veritran, is leading a bold charge into a new frontier: one where financial technology fuses with fandom, and CX becomes both frictionless and deeply personal.

                                  Jorge’s professional journey has always followed the arc of digital transformation. From his earlier roles at AT&T and Banorte to now helming marketing at Veritran, a global technology company, his mission is clear: make life easier, better, and more secure for end users – whether they’re banking customers or football fans.

                                  “Our technology without a purpose is nothing. It’s just code,” Jorge says. “We build for people. And that purpose has taken us far beyond banking.”

                                  From Buenos Aires to Global Ambitions

                                  Founded in Buenos Aires almost 20 years ago, Veritran started building mobile applications before the iPhone even existed – when, as Jorge jokes, “phones were just for calls, texts, and the occasional game of Snake”.

                                  “Our guys were visionaries,” he continues. “They were talking about applications when we didn’t even have smartphones. Back then, you had to build a separate app for every phone model because we didn’t have iOS or Android,” he recalls.

                                  Despite those early technical hurdles, the company maintained a singular focus: democratising access to financial services. “Once a person starts managing their own finances, they gain control,” reasons Jorge. “And control is the first step toward growth.”

                                  That mission has proven timeless, and borderless. Today, Veritran has a solid footprint across Latin America and has expanded into the US and Europe.

                                  Why Experience Matters More Than Ever

                                  Jorge is acutely aware that in financial services, trust is everything. A slick PowerPoint is not enough to win over banks.

                                  “When I meet with a financial institution, they don’t want theory. They want proof. They want to see our tech working in the real world. But many banks are reluctant to share their strategies, even with non-competitors.”

                                  This desire to demonstrate capability led Veritran to seek a bold new marketing approach – one that would provide a visible, secure, and non-competitive environment to showcase its tech.

                                  Enter Manchester City: A Blueprint for CX Innovation

                                  The solution arrived via the pitch, not the boardroom. Veritran entered into a partnership with Manchester City, one of the best football teams in the world.

                                  “Manchester City is digitally five to seven years ahead of most clubs,” says Jorge.

                                  Veritran’s technology now supports key digital operations at Manchester City, helping the Club streamline processes such as user registration, membership management, and ticketing. This collaboration reflects a shared commitment to innovation and operational excellence.

                                  What began as a strategic partnership has evolved into a strong example of how financial technology can reinforce digital infrastructure in the sports sector. As more organisations seek reliable and scalable solutions, the model developed with Manchester City demonstrates the value of secure, efficient platforms designed to support long-term digital growth.

                                  Breaking the Sponsorship Mold

                                  Unlike traditional sports sponsorships, which often come with hefty price tags and limited strategic collaboration, Veritran’s deal with City was rooted in partnership.

                                  “Our partnership is beneficial for both companies, we share value,” explains Jorge.  “With the brand reach of Manchester City’s clubs we have been able to promote our company worldwide.”

                                  This model has opened the door to future collaborations, not only with sports clubs, but also with entertainment companies in the US who are eyeing similar digital transformations.

                                  Applying FinTech Learnings in New Territories

                                  As Veritran enters new markets, they carry the lessons of regulated finance into less restricted sectors.

                                  “In banking, every innovation has to pass through layers of regulation,” notes Jorge. “But in entertainment or sports, you can think outside the box and start with the experience, not the compliance checklist.”

                                  That freedom has allowed Veritran to experiment with new ideas, such as smile-based stadium access or face-based payments.

                                  “We call it ‘mouthful access’ – just smile, and you’re in. You can’t do that in banking… yet.”

                                  Blending Brand and Utility: A New Era for Embedded Finance

                                  What sets Veritran apart isn’t just its technology stack – it’s the way it applies that stack to create emotional resonance and operational value in new settings. For Jorge and his team, the convergence of financial services and lifestyle touchpoints is the most exciting, and underexplored, frontier.

                                  “When we embed finance into a stadium or a music festival, we’re not just processing payments,” he explains. “We’re creating seamless, branded experiences that extend customer relationships beyond the bank branch or app.”

                                  This philosophy echoes a wider FinTech trend: the shift from siloed services to contextual, embedded finance – delivered where customers already are, not where institutions want them to be.

                                  As financial brands seek new ways to engage digitally-native consumers, Jorge believes partnerships with lifestyle, sports, and entertainment brands offer huge untapped potential.

                                  Jorge notes that younger generations expect everything to be digital, instant, and intuitive. They don’t separate banking from shopping or attending an event, it’s all part of one journey. “If we can integrate services invisibly into those moments, that’s where the magic happens.”

                                  He’s quick to add that the financial industry still has work to do in aligning with this shift – both culturally and technologically.

                                  “It’s not just about APIs or infrastructure. It’s about mindset. The organisations that embrace this new way of thinking – who see CX as a shared responsibility across ecosystems – will lead the next decade.”

                                  With Veritran’s cross-industry collaborations accelerating, Jorge is confident they’re not just shaping financial journeys – they’re reshaping everyday experiences.

                                  Embedding Finance in the Fan Journey

                                  Jorge sees a massive opportunity to embed financial services into sports and entertainment ecosystems, particularly in underbanked regions like Latin America.

                                  “In the UK, stadiums are already cashless. In Latin America, we still have guys walking around selling Coca-Cola for cash from their pockets. We want to change that.”

                                  By introducing digital wallets, biometric payments, and embedded insurance services (e.g., ticket protection at the point of sale), Veritran enables clubs to become financial service providers.

                                  “Imagine buying a match ticket and adding travel insurance in one click. That’s the level of seamless we’re aiming for.”

                                  Pain Points Driving Demand

                                  So what are clients asking for?

                                  Jorge says it comes down to three priorities:

                                  1. Integrated Payments Ecosystems
                                    Clients want unified platforms that support seamless payments across channels and partners
                                  2. Digital Onboarding & Identity
                                    Reducing friction while enhancing security is top of mind – especially in customer acquisition
                                  3. End-to-End Security Suites
                                    With AI-driven fraud and evolving regulations, security isn’t optional; it’s a strategic asset

                                  Veritran’s flexibility as a tech partner, not just a vendor, allows it to co-create with clients. This often means integrating with their existing partners, such as banks, card networks, or insurers.

                                  What’s Next for Veritran?

                                  According to Jorge, the company is at a pivotal moment. Its technology is gaining traction in new verticals with strong investment appetite – such as entertainment and live events.

                                  “These sectors have the budget and the ambition. No one’s serving them with the kind of Fintech-grade CX we provide.”

                                  The company is also exploring opportunities in public transportation and other infrastructure-heavy sectors where transactions are frequent and still inefficient.

                                  “Everywhere there’s a transaction, there’s an opportunity to simplify.”

                                  FinTech is set to play an expanding role in everyday life whereJorge believes the very definition of FinTech is evolving.

                                  “It’s not just about banks anymore. If you buy a coffee, book a train, or enter a concert – those are all transactions. And if we can simplify them, that’s FinTech too.”

                                  That’s why Veritran sees future growth in collaborative ecosystems where banks, brands, and non-traditional players converge to serve the customer journey holistically.

                                  Why Money20/20?

                                  Jorge credits the annual Money20/20 Europe conference with helping shape Veritran’s partnerships – including the initial connection with Manchester City.

                                  “It’s one of our top five global trade shows. We don’t just send a team – we send our top execs, including our CEO. It’s where deals happen.”

                                  Building with Purpose for the Future

                                  In an industry flooded with features and hype Veritran differentiates by staying grounded in user value.

                                  “Tech for tech’s sake is meaningless. But tech that improves how someone lives, spends, or connects – that’s everything,” says Jorge.

                                  From its Argentine roots to a global stage, Veritran’s journey underscores one enduring truth: In customer experience, the future belongs to those who build it with purpose.

                                  Veritran: A CX FinTech Trailblazer

                                  Author

                                  Dan Brightmore

                                  Published

                                  7 August 2025

                                  Estimated Read time

                                  7Mins

                                  Read in brochure format

                                  FinTech Strategy meets with Seema Desai, COO at iwoca, to hear how customer experience is being redefined in a digital lending era

                                  Financial Transformation Summit 2025 EXCLUSIVE

                                  At the Financial Transformation Summit, Seema Desai, COO at iwoca, spoke on a panel (alongside representatives from Zopa Bank and Citibank) about the shifting needs for customer experience in digital lending. How can lenders create hyper-personalised loan products to meet diverse customer needs? What are the best practices for maintaining a human touch in automated lending processes? How can lenders build and maintain customer loyalty in a competitive market? What role does omnichannel strategy play in delivering a seamless lending experience?

                                  Following the panel, we spoke with Seema to find out more…

                                  Hi Seema, tell us about your role at iwoca?

                                  “I am the Chief Operating Officer at iwoca. We provide fast and flexible finance to small businesses across the UK and Germany. In my role as COO, I’m responsible for all of our UK operations teams. So, all of our agents that engage with customers throughout the customer journey. And I make sure that we’re offering a really high quality service that is also highly efficient.”

                                  You were part of a panel at this Summit focused on redefining CX in the era of digital lending. Can you give us an overview of your thoughts?

                                  “So, maintaining that personal touch is really important because that personal touch helps us to build trust with our customers. We all know that when dealing with money, that trust element is super important. There’s lots of things that iwoca does to maintain that. For example, every customer has a dedicated account manager. They can get through to them via a direct number. We also respond to emails fast, every email on the same day. And then we commit to answering at least 80% of calls in less than 60 seconds. We’ve got 10,000 new applications every month and about 30,000 customers making repayments currently. We’re doing all of this with an account management team of just 30 people. So, to maintain that level of personal touch whilst also being able to deal with that volume of customers, we absolutely have to leverage digital technology to be able to do that really efficiently. And there’s many ways that we do that…

                                  “First of all, we make sure that our account pages and our signup flow is as clear and seamless as possible so that customers can self-serve if they want to. But we also make sure that with our operations activities, we’ve broken down every step of every operational process into a task that is visible on our in-house built CRM system. And then what we can do is run tests on every single step of those to see where having human interaction really adds the most value. So, we are constantly upgrading where we apply human interaction in a really forensic way to make sure that it’s optimised as much as possible.”

                                  Why is this an exciting time for the business?

                                  “It’s really exciting right now. We’ve been having some record months recently and broken some big milestones. We are now approving around 10,000 new business loans every month, which is huge. Our loan book across the UK is almost £1 billion. And then a bit closer to home, we’ve also just moved offices. We’ve got more space and we’re still able to attract exceptional talent into iwoca and it’s great to have a new home in central London to do that.”

                                  “Embedded finance is a big trend right now. It’s important for us to make sure that customers can access lending when and where they need it. We’re integrating lots of partners through our open API – around a third of our applications come through partner channels. So, that’s a very important trend and growing for us in the future. We’re also seeing a lot of hyper-personalisation. We know that customers want to be able to tailor loan products exactly to their needs, and we want our products to be able to provide that flexibility to them. We’re looking at increasing loan amounts, changing durations and offering different types of repayment schedules with interest only options. And that’s hugely exciting. And one of the big trends that I’ve heard about here at FTS, and which we are working on at iwoca, is how we leverage AI and what we might be able to do with AI to make us even more efficient, but still maintain an excellent customer service.”

                                  What pain points are your customers experiencing that you need to address? What are they asking you for help with? How are you meeting the challenge?

                                  “So, it’s important to remember that iwoca exists in order to solve pain points for customers because customers were just relying on traditional lenders. Those traditional lenders, the big banks, have much longer application processes, typically taking weeks and sometimes just aren’t able to lend to those customers at all because it’s not within their risk appetite. Whereas at iwoca you can get a loan within minutes. We can also lend to customers that banks couldn’t lend to because we’re able to use data and data science to be able to understand the risk level and different customers much better.”

                                  Tell us about a recent success story…

                                  “We are operational in the UK and Germany, and a success story for us is the fact that we are now working with a loan book of almost a £1 billion and we are profitable. And we have been for quite a while now, since early 2023. So, it’s a real success story for us that we’re able to use that profitability to fund our core business growth but also use it to invest in solving other pain points for customers beyond lending.”

                                  What’s next for iwoca? What future launches and initiatives are you particularly excited about?

                                  “Yeah, there’s a lot of things that we’re working on right now. I’m excited about some of the AI tools that we are trialling to make our service even more efficient. There’s a number of exciting applications out there, so there’s a lot of people at iwoca exploring and exploiting different AI technologies. It’s going to be very exciting to see how that rolls out across our business in the rest of this year. And then also looking at new ventures that are beyond lending, which we may be launching later this year or early next.”

                                  Why do you think the evolution of collaboration between banks and FinTechs is set to continue? What are you excited about?

                                  “Collaboration is hugely important to us and our business model. Traditional banks are able to access capital more cheaply than we can, but they’re able to provide us with access to their balance sheet so that they provide financing to us so that we can then lend to our customers. So, with their financing, we are able to use our data and our technology to reach customers that they wouldn’t be able to reach directly. At the moment, something like 80% of our funding comes from banks such as Barclays and Citi. So, they’re hugely important to us and we are continuously reviewing with them the performance of our own book and finding ways that we’d be able to lend to more of our customers.”

                                  Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for iwoca?

                                  “This is my first time at this event, and I’ve been really impressed. It’s been really well organised and the panels have been insightful with some great speakers. I’ve learned quite a lot. I’ve met some really interesting people and I’m really impressed by the diversity of people that are coming here. So, I was just on a panel with somebody from Zopa, which is where I used to work. I also met somebody in the audience who came from Lloyd’s, which is where I worked about 15 years ago. So, it’s great to see that this ecosystem being brought together at FTS.”

                                  Learn more at iwoca.co.uk

                                  About iwoca

                                  Fast, flexible finance empowers small businesses to manage their cash flow better and seize opportunities – making their business and the economy stronger as a whole. At iwoca, we do just that. We help businesses get the funds they need, when they need it, often within minutes. We’ve already made several billion pounds in funding available to over 100,000 businesses since we launched in 2012 and positioned ourselves as a leading Fintech in Europe. Our mission is to finance one million businesses. We’ll get there by continuing to make our finance ever more relevant and accessible to more businesses by combining cutting-edge technology, data science and a 5-star customer service.

                                  Author

                                  Dan Brightmore

                                  Published

                                  6 August 2025

                                  Estimated Read time

                                  6Mins

                                  Read in brochure format

                                  FinTech Strategy speaks with Jonas von Oldenskiöld, Head of Partnerships at Qover, about the future for the insurance industry

                                  Financial Transformation Summit 2025 EXCLUSIVE

                                  At Financial Transformation Summit, Jonas von Oldenskiöld, Head of Partnerships at Qover, spoke on a panel (alongside peers from Davies Group, Accenture, Superscript and YuLife) entitled ‘Bridging the Gap: How InsurTech is Reinventing Traditional Insurance Processes’.

                                  Following the panel, we spoke to Jonas to find out more…

                                  Hi Jonas, tell us about your role at Qover?

                                  “I’m the Head of Partnerships at Qover. We are focused on embedded insurance. We try to enable that for a lot of different players in the markets. Everything from motor insurance, SMEs, going the whole way down to simple things like classes[1]  such as travel, trying to be the enabler between the typical risk carrier and the distribution platform.”

                                  You spoke on a panel at the Summit about InsurTech innovation. Give us an overview of your thoughts…

                                  “It was a very interesting group of people on the panel coming from different angles across the industry. And the key things for me were around where InsurTech needs to go now and how it enables insurance companies at this point in time. The common understanding was that we, the InsurTechs, come from being disruptors to being more of a force into them where we can plug in and help them to change a little bit the behaviours that are currently going on. Being that catalyst in the organisation and helping them to drive innovation. Because I think a lot of large organisations have realized that innovation cannot be driven by a single hidden team somewhere, it needs to be driven from a business perspective.”

                                  Why is this an exciting time for Qover?

                                  “I think there are many reasons. Of course, you cannot be at an event like this without speaking about AI and the opportunity that gives to us. Also, we’re seeing a generational shift. The industry needs to get ready to service a completely different type of customers going forward and that will drive a lot of exchanges we’ll see in the next couple years.”

                                  “I think a key one is to be able to navigate the future role of AI regulation. That will be very interesting to see what opportunities are there and what opportunities would be possible to use. More importantly, I think it is taking data from something, using data from something that is good to have, to really put it in the forefront of the operation to start planning your business process from a data perspective. This is the data that we need to have in order to deliver a good product rather than having data as the outcome of the whole process. You have set up and try to do something from that perspective. So, we need to turn the table on that.”

                                  What other pain points your customers are experiencing that you need to address? What are they asking you for help with? How are you meeting the challenge?

                                  “They particularly need help with the UX and how to deliver the product. I think the underlying product itself doesn’t change so much, but it’s a lot about the delivery, making sure that it actually does get delivered at the point in time that we like to call events driven. So, for us it is distributing insurance when you have a life event, if that is having a child, buying a car, buying a house or whatever it might be, data can help us to drive that. So, for us it’s very much around the delivery rather than the product underneath.”

                                  Tell us about a recent success story…

                                  “We’re very proud that we now have several new motor programmes in place where we have been working with large motor organisations that have realized that they’re not only selling a car, they’re selling a means of transportation and convenience, which also then includes insurance across that whole journey. We recently announced partnerships with both Volvo and BMW. And we have more in the pipeline. So, I think that has been a great success where large established industries have realised they need to go further in order to have that UX design.”

                                  What’s next for Qover? What future launches and initiatives are you particularly excited about?

                                  “In 2025, our focus is on expanding into more new verticals. We are involved in driving that engagement to see where we can expand. We started traditionally with a lot of the travel organisation and bike providers. We’re now working with neobanks[2] , traditional banks and the motor industry. I also see more opportunities in areas like utilities, in SME supporting functions, everything from accountancy to data provision and being a software provider. These expansions will be the goal over the next 24 months.”

                                  Why do you think the evolution of collaboration between industries and InsurTechs is set to continue? What are you excited about?

                                  Partnerships is one of the key things changing the insurance industry. We still have some very large players around. They’re fulfilling their function, and they do it very well. But in order for them to adapt into the new situation, partnerships are important. You always need to be able to work at scale, which is important for them. Of course, with a partnership you lose a little bit of control compared to acquiring something or developing it yourself. But on the other hand you win on the speed to market and potentially also on the cost side. So, for me, the winners will be the ones that can handle partnerships in the right way. And at the end of the day, a partnership is a relationship. You can have as many contracts as you want, but it comes down to people.”

                                  Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for Qover?

                                  “We get a lot of good feedback and the great thing with events like this is that you have the chance to do networking both informal and formal. You’re having a formal agenda but also have a chance to rotate around. I always make sure to join the sessions and round tables. It has been interesting to speak to peers across the industry. It’s a good way of getting away from the desk and finding some new inspiration.”

                                  Learn more at qover.com

                                  About Qover

                                  Embedded insurance orchestrators… We’re creating a global safety net with insurance,

                                  empowering people to live life to the fullest.

                                  Qover was founded in 2016 by Quentin Colmant and Jean-Charles Velge. From the very beginning, our co-founders had a clear vision of the future of insurance: a simple, transparent and accessible service across borders.

                                  Through embedded insurance, we can create a global safety net that protects everyone, everywhere. To that end, our embedded insurance orchestration platform enables any company to harness the power of technology to embed insurance as a native component of or add-on to their core product or service.

                                  In doing so, embedded insurance becomes a powerful tool for businesses to enrich their value proposition, enable their success and care for their community.

                                  Author

                                  Dan Brightmore

                                  Published

                                  5 August 2025

                                  Estimated Read time

                                  6Mins

                                  Read in brochure format

                                  FinTech Strategy meets Vikki Allgood, Director of Technology Strategy at Fidelity, to discuss the fundamental importance of culture in driving a successful business transformation

                                  Financial Transformation Summit 2025 EXCLUSIVE

                                  At Financial Transformation Summit, Vikki Allgood, Director of Technology Strategy at Fidelity International, gave a keynote speech entitled ‘Psychological Safety – The Hidden Key to Transforming Your Business’. Following her appearance, we spoke to Vikki to learn more…

                                  Hi Vikki, tell us about your role at Fidelity?

                                  “I am Director of Technology Strategy for Fidelity. We’re looking at how we can ensure we can adapt our response to our business’ needs through our technology to meet whatever demand is coming over the horizons tomorrow. And in the years to come.”

                                  You spoke at this Summit about psychological safety driving business transformation. Tell us more…

                                  “At Fidelity, our strategy for our technology has culture as our foundational pillar. Talking with our leaders over the last 18 months, we looked to understand how we can create a brilliant culture, recognising that psychological safety is a fundamental element in that.

                                  “Transformations often stumble because the business plan forgets its most volatile, and most valuable component, the people asked to deliver it. Without psychological safety, even well‑funded and organised programmes stall. Teams focus more on protecting themselves instead of challenging ideas. That’s when the risks remain hidden until it’s costly, and the collective new ideas to solve the biggest challenges are never formed. That’s why we ask leaders to invest time and energy in building a culture where it’s safe to question, experiment, challenge the status quo and admit what’s not working. In that environment the behaviours every transformation depends on (curiosity, creativity, problem‑solving, healthy challenge) all naturally emerge.

                                  Psychological safety isn’t some new trendy HR slogan, it’s a timeless basic human need wired into our biology through millennia of evolution. When people sense social threat, the amygdala floods the body with cortisol and the prefrontal cortex (the part of our brain we rely on for reasoning, innovation, etc.) literally dims. Remove the threat, and the brain’s chemistry flips, dopamine and oxytocin rise, and teams move from cautious compliance to bold collaboration. Leaders must ask themselves if their teams can lean in and challenge effectively or if they are staying quiet to protect themselves. The hidden key is simple, but non‑negotiable, leaders must consciously, relentlessly and courageously build psychological safety through everything they do and say. If they do that, then your technology and transformation plans will have the human engine they need to succeed.”

                                  Why is this an exciting time for Fidelity?

                                  “I think that within the industry, all the opportunities that are coming along, and our ability to adapt to our customers’ needs, is what makes it exciting. We are all on an exponential curve of change. Technical possibilities, customer expectations, regulatory demand, industry landscapes, are all going to keep moving, with new challenges and opportunities presenting themselves. We are ensuring that we can meet those needs of our customers both today and tomorrow. Finding new ways to do that is pretty exciting.”

                                  “So, from a technology perspective, I would say that we are making sure that all our foundational elements are there so that we can respond and adapt. One of Fidelity’s differentiators is that we have historic long running relationships with our customers. We are reintegrating our data strategy to allow us to better leverage this, in addition to market data, allowing us to provide personalised solutions to our customers.

                                  “AI is absolutely generating a buzz for us right now as well, and not just Generative AI. We’re seeing a push towards Agentic AI and how we can look to provide faster, quicker, more cost-effective services for our business partners who can then provide better outcomes for our customers. This in combination with our long-standing history gives us a unique opportunity.”

                                  What pain points are your customers experiencing that you need to address? What are they asking you for help with? How are you meeting the challenge?

                                  “We need to understand the new generations entering the wealth space and what their expectations are and how they engage with us. We’re looking to ensure we can keep pace with their demands. For example, we’ve just launched Pay by Bank allowing our customers to pay money into their accounts in a faster more secure way. This feature leverages the Open Banking Technology that is now available to financial institutions.”

                                  Tell us about a recent success story for Fidelity…

                                  “Across the technology landscape, we have been amplifying our existing cloud strategy by removing complexity in our hybrid setup, reducing the number of dependencies back to on-premises. This is a well-known challenge for financial institutions who have regulatory reasons to have highly confidential systems in house. This will allow us to respond at pace to what customers need. Looking a couple of years down the line nobody can be sure what the next big opportunities are going to be, so ensuring we’re building that foundation to respond to what comes over the horizon is fundamental.”

                                  What’s next for Fidelity? What future launches and initiatives are you particularly excited about?

                                  “Security is incredibly important to us. With that in mind, we are exploring Quantum to understand both the opportunities and risks that it could present in the future and how we can stay at the forefront of it. Ensuring a secure and reliable service for our customers is an absolute non-negotiable part of our strategy.”

                                  Why do you think the evolution of collaboration between banks and FinTechs is set to continue? What are you excited about?

                                  “I think the reality is that we need the collective mindsets to come together to create the best outcomes. We’re never going to have all the answers all by ourselves. So, starting to engage and work with people and collaborate means that we get to have a better, wider perspective. Coming to events like this, we get to learn, understand what other industries are doing, what other areas are looking at, and it helps to widen our perspectives and have more opportunities to find those out of the box ideas that are going to then help our customers.”

                                  Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for Fidelity?

                                  “I was particularly keen to attend this conference because I think transformation and how we can do this successfully is so important at the moment. The reality is, sadly, and I covered this in my talk, a staggeringly large number of transformations miss the mark or fall short. And so, learning and embracing how you can ensure that you go after it and you get the value that you’re aiming for, that is for me what’s important. As I said, getting that learning, talking to each other, understanding what’s worked, what hasn’t worked and sharing tips and techniques is actually incredibly powerful and something you can then take back and use at your organisation.”

                                  Learn more at fidelity.co.uk

                                  About Fidelity

                                  It has been more than 50 years since we were founded. We’ve seen many market cycles – bull and bear, boom and bust. We have stayed the course through different investment environments regardless of market performance.

                                  The needs of our customers have always steered our decisions, which is why we’ve stuck to our core activity of investing. We believe this is what allows us to excel – and, even more importantly, to repay the trust placed in us by our customers.

                                  Whether you’re investing for the first time, or have a wealth of experience, it’s essential to be informed and to be comfortable with your decisions. Through Trustpilot, you can read up-to-the-minute, real-world reviews and see for yourself how Fidelity aims to put the customer first and make investing a bit easier.

                                  Our do-it-yourself online services give you 24/7 access to our investment guidance, handy tools, and range of accounts from your computer, tablet or phone. Transfer your existing investments to us, or open a new account online and begin investing in just a few steps.

                                  Author

                                  Dan Brightmore

                                  Published

                                  4 August 2025

                                  Estimated Read time

                                  8Mins

                                  Read in brochure format

                                  FinTech Strategy met with Standard Chartered’s Head of Digital Assets – Financing & Securities Services, Waqar Chaudry, at Money20/20 Europe to discuss how the bank is connecting traditional with digital, collaborating with FinTechs directly and via SC Ventures, and taking a measured approach to entering the crypto market

                                  Money20/20 Europe Exclusive

                                  There is a buzz in the air at Money20/20 Europe. Waqar Chaudry, Head of Digital Assets – Financing & Securities Services at Standard Chartered, has just spoken on Mastercard’s Horizon Stage about the great digital assets opportunity. We meet up with him at his bank’s stand in the heart of the action at the Amsterdam RAI Arena.

                                  Waqar works in custody to secure digital assets at Standard Chartered. It also has a fund accounting business and offers transfer agent services. “The financing in the Financing & Securities Services elements are in our FX Prime offering,” he explains. “At the moment my sole focus is on crypto custody, tokenisation and building an ecosystem around those products.”

                                  The Rise of Digital Assets

                                  It’s an exciting time for Standard Chartered with crypto custody and the rise of stablecoins and tokenisation… Whether the asset is Bitcoin, a tokenised money market, or anything tokenisable, there have been a lot of conversations with the bank’s partners in terms of the technology quest.

                                  “Most of the conversations historically have been led by the fact that technology does give you the capability to do 24/7 trading and settlement. Risk management from the technology side is much better. The blockchain dream is sold to everyone, which remains true,” notes Waqar. “The issue has been that on the business side, tackling the areas that actually can work with this technology. You have your near instant settlement availability on blockchains. On the other side you have a T+1 or T+3 cash settlement time – that doesn’t gel very well.

                                  “Entrenched in the day-to-day business of these really large institutions is to be able to inject a new piece of technology. And then suddenly say, hey, all these things are solved. For all the inefficiencies in the system it doesn’t work that quickly. We’re actually taking one step at a time. That’s why it’s exciting that we can see in five or ten years from now what the world will look like. Basically, in our vernacular that means we have near instant settlements and near instant international transfer of value. So, that’s the kind of stuff that we are really interested in for the future.”

                                  Meeting the Blockchain Challenge

                                  Waqar explains that when something like a blockchain comes into a traditional bank, and especially blockchains like the ones that support an asset like Bitcoin, you don’t know who the counterparties are (which are clear on the SWIFT network).

                                  “You have to build capability from a technology side, operations side, risk management side,” he continues. “You need to develop the governance of all those functions to be able to get the value of the asset in the ecosystem. And then be able to add value to that to transact on it. We don’t yet have those ingredients, so it becomes very challenging for us to accept the assets. A lot of the work that the bank has done over the past five years has been around embedding those elements into our day-to-day operations. It’s about understanding the risk profile of the coins and understanding the risk profile of the blockchains.”

                                  Waqar’s team works on how to protect the ecosystem from risks from both an AML and KYC point of view. “We’re also making sure that by doing that we don’t create such a burden to the client that the service becomes useless,” he adds. “We’re trying to balance that out and that’s where the challenges lie at the moment. The next stage is to also be able to integrate all of our traditional cash and assets rails into this. And that’s where the next level of risks will come in… Where people are not used to seeing things on the blockchain… They are used to seeing things on the SWIFT network or a CSD. But when the blockchains come in, profiles will change and that’s where we have to meet the challenges.”

                                  Traditional Meets Digital

                                  For an asset manager with a variety of equities and bonds, but keen to start in crypto and other digital assets, the rails are very different… “The liquidity venues and the way you settle the instrument are very different. And they don’t naturally talk to each other,” confirms Waqar. “It’s a big challenge. But to be able to go with the provider that has all the capabilities, which includes the cash side, the asset side, the crypto side and the blockchain side, is something people are looking for now. Without having the end-to-end picture, it would be very difficult for our clients to have an equitable strategy for their clients. We need to be able to service them appropriately based on the rails they operate in.”

                                  For Standard Chartered’s clients it’s increasingly important for payments to facilitate activity on-chain regardless of the use case of digital assets. “There is a key challenge with payments at the moment. If you do transfer value across geographies or between B2B and B2C, what do you do with that value afterwards?” asks Waqar.

                                  “Are you going to keep it on the books for your treasury or account purposes or are you going to find a way to liquidate the position to pay your employees or pay your service provider? Without the capability to store the asset appropriately and then convert it into a usable form, you can’t do much with it. The only thing you can do is actually transfer value. So, for us what’s important in payments is that we get the transfer value happening immediately. Or as quickly as possible. And then also connect our payment infrastructure and the banking behind. We aim to support the transfer of value from a digital asset into an actual cash asset.”

                                  Building on Success

                                  Standard Chartered’s work with OKX in Dubai has spurred demand the bank didn’t expect. “The key ingredient is that a really large crypto exchange has come together with a really large bank,” reasons Waqar. “When you combine the product features of a large bank like ours with the liquidity of OKX it creates a unique proposition in the market. The traditional players have started to show interest in that because now they can buy diverse assets, pledge them as collateral and start trading while the assets remain safe in a genuine large institutional bank. And at the same time, they also have access to a highly regarded institutional exchange. That story is for us quite important and we’re fostering these relationships more and more…”

                                  It’s been a real success story for Standard Chartered on the money market fund side which is also connected to what the bank is doing on the collateral side. “Money market funds are used to gain value and have an asset that does generate yield on the one side, but also the capability to use the asset as collateral is important,” adds Waqar.

                                  “The money market fund that we launched for China Asset Management in Hong Kong, albeit it’s a retail use case for a start, but then the ambitions are big. The next thing is how do we start using that same asset for pledging for trading purposes and then how do we inject that into a portfolio basket of assets that people buy? At Standard Chartered, we aim to create a supermarket of tokens in a centralised ecosystem. So, our collateral story and the tokenised money market funds is connected, and we want to continue building around it. We’re thinking about other assets now too… We’re looking at equities, bonds and enabling more cryptocurrencies in the same ecosystem as well. It’s just the start of all the things we need to build in the future.”

                                  Why Money20/20?

                                  “This is my first time coming to Money20/20 Europe. Digital asset companies are here alongside financial services and related FinTechs. It’s great that they’re able to talk to each other and it’s quite evident there are lots of great meetings happening. There are many companies here we are either supporting or we’re working with. We’ve also had meetings with UK government representatives geared to attracting talent into the country. They’re trying to make sure that their FinTech ecosystem grows quite significantly for us in the UK and for other footprint markets in Asia; Middle East and Africa are also quite important in how we do that and continue to grow.”

                                  The Evolution of Collaboration between Banks and FinTechs

                                  Standard Chartered is also working in harmony with its ventures partner SC Ventures. The bank is working closely with Libeara for tokenisation and with Zodia Custody as Saas. “Our core institutional bank and our Ventures business are quite tightly coupled from that point of view,” says Waqar. “And it’s quite obvious that the reason for that is how we’ve made significant investments into them. We’ve given part of our DNA into this ecosystem and now, at the bank, they’re building the ecosystem around these capabilities, so we’re keen to bring them in and use their solutions for our services as well.”

                                  Standard Chartered may be a traditional bank but it is a seasoned collaborator with innovative FinTechs. “They need traditional services too,” reasons Waqar. “Once they get to a critical mass, a FinTech may not have the bandwidth to manage certain client sizes. By partnering with some of the FinTechs, we’re seeing that once a certain size of a client comes in, they prefer to work with a large institution like ours. So, that partnership is proactively managed as well from our side. From our ventures side, bringing their innovative approach to product development and technology into the bank, building the ecosystem around risk management and governance from the bank side and then connecting into the FinTechs outside of that ecosystem is something I think is quite an interesting proposition for us. We’re going to keep building on top of that.”

                                  Standard Chartered – Financing & Securities Services

                                  Promoting your future in global securities

                                  We’re ready to help you flourish in emerging and frontier securities services markets

                                  In today’s fast-moving markets, especially  across Asia, Africa and Middle East, success isn’t just about the solutions you choose – it’s about the partnerships you build.

                                  Standard Chartered has been committed to these regions for decades. We understand both the promise and challenges. That’s why we go beyond delivering end-to-end custody, fund, and fiduciary  solutions – we actively help shape the markets themselves.

                                  By working with local governments and industry associations, we bring you early insights and access to new opportunities. Partnering with leading asset managers, fintechs, and infrastructure providers, we connect you to the best of the industry, via a single partner. Because in a world of complexity, collaboration is your greatest advantage.

                                  Learn more at sc.com/en/corporate-investment-banking/financial-markets/financing-and-securities-services/

                                  Author

                                  Dan Brightmore

                                  Published

                                  1 August 2025

                                  Estimated Read time

                                  5Mins

                                  Read in brochure format

                                  FinTech Strategy meets Ishtiaq M Ahmed, Senior Product Manager – Emerging Tech, Innovation & Ventures at HSBC, to learn more about the future of payments – real-time, cross-border and beyond

                                  Financial Transformation Summit 2025 EXCLUSIVE

                                  At the Financial Transformation Summit 2025, Ishtiaq M Ahmed, HSBC’s Senior Product Manager, for Emerging Technology, Innovation & Ventures, joined a panel with J.P. Morgan, Revolut, Lloyds and EY to explore how real-time payments, embedded finance and global collaboration are shaping the future of financial services. How are real-time payments reshaping banking infrastructure? What are the regulatory challenges for cross-border payments? How can banks compete with FinTechs in the rapidly evolving payments space? How are digital wallets and mobile payment platforms changing consumer spending behaviours?

                                  We spoke with Ishtiaq after the session to explore what drives HSBC’s approach to innovation, how customer expectations are evolving, and why trust remains at the core of transformation.

                                  Hi Ishtiaq, tell us about your role at HSBC?

                                  “I work on Global Product within HSBC’s Emerging Technology, Innovation & Ventures team. Our focus is to deliver next-generation propositions, particularly across payments, embedded finance and frontier technologies. We work on horizon 2 and 3 initiatives, with a view to turning emerging ideas into viable, scalable solutions. The goal isn’t just to experiment. It’s to test, validate and shape innovations that will help us serve customers better and redefine how financial services operate in the years ahead.”

                                  It’s a transformational time for payments with the rise of open banking and a national vision for the UK. Give us your overview…

                                  “Payments is possibly the most loved area by both FinTechs and banks. A lot of what is happening in payments, it’s where a lot of meaningful innovation is already landing. It’s no longer theory or ideation, its practical and accelerating. The UK’s National Payments Vision is ambitious, and rightly so. But ambition needs alignment. We need stronger collaboration between Banks, FinTechs, Regulators and infrastructure service providers. This journey will take time and coordination. It’s more a marathon than a sprint, and we’re only just getting started.”

                                  Why is this an exciting time for HSBC?

                                  “Simply because the way technology has penetrated our lives and the influence of technology on how banking is evolving are very closely knitted. Technology is no longer on the edges of banking; it’s embedded in every customer interaction.”

                                  “The shift towards alternative payment methods is one I feel strongly about. For decades, the path was linear: cash to cheque to card. Now, we’re entering a new chapter. Pay by Bank, or direct account-to-account payment, is gaining traction. Some regions have already scaled it. In the UK, it’s about to accelerate. This trend will unlock lower costs, faster movement of money and better control for users. It’s not just about technology. It’s about user experience and future-ready infrastructure.”

                                  What other pain points are your customers experiencing that you need to address? What are they asking you for help with? How are you meeting the challenge?

                                  “I think for customers it’s very simple. As a customer myself, I look for speed, ease, and simplicity in everything that I do. That’s universal. But what makes it complex today is the influence of AI, automation and data. People want innovation, but not at the expense of trust. So, while we innovate, we keep trust as the anchor. The real test is whether customers can do more, faster and easier, while still feeling their money is protected and their experience is safe. That’s the balance we aim to strike.”

                                  Tell us about a recent success story…

                                  “We’re particularly proud of the work we’re doing on embedded payments. The goal is to make payments feel invisible – integrated into the environment the customer is already in. Whether that’s a retail website, a social app or a business platform, customers shouldn’t have to toggle across apps to complete a payment. We have already launched products in this space, and we’re continuing to build. It’s about making banking ambient – present where the customer is, not where the bank wants them to be.”

                                  Why do you think the evolution of collaboration between banks and FinTechs is set to continue? What are you excited about?

                                  “FinTechs bring urgency and imagination. Banks bring trust, infrastructure and scale. The opportunity is not in competing, but in co-creating. We have seen some encouraging partnerships, and we’re still working at the surface level. There’s a much deeper layer of value if we can move beyond tactical deals into genuine joint innovation.”

                                  Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for HSBC?

                                  “Events like this are important because they bring together different voices with a shared interest in shaping the future. What stood out to me is how open the audience and panellists are to challenging ideas and exploring new perspectives. These are places where real conversations happen; where you meet regulators, banks, FinTechs and enablers all under one roof. It’s these intersections that move the industry forward.”

                                  Learn more at ventures.hsbc.com

                                  About HSBC Emerging Technology, Innovation & Ventures

                                  HSBC Emerging Technology, Innovation & Ventures team is a global group of technologists, data scientists and venture specialist dedicated to shaping the banks future capabilities. Our goal is to deliver world class digital-first banking across HSBC’s global footprint.

                                  Our mission is to drive meaningful innovation across the organisation by identifying and unlocking opportunities that enhance customer experience, improve operational efficiency and embrace disruptive technologies.

                                  Our approach is rooted in experimentation, rapid prototyping, continuous iteration. By working closely with both internal and internal partners and external collaborators, we test and refine new ideas, prioritising solution that are scalable, impactful and aligned with the needs of our customers.

                                  We actively partner with leading technology firms, FintTechs, academic institutions and policy makers to stay at the forefront of digital innovation and accelerate time to market.

                                  By combining the scale, trust and resilience of HSBC with agility and mindset of a tech start-up, we aim to nurture transformative ideas, drive strategic innovation and shape the future of banking.

                                  Author

                                  Dan Brightmore

                                  Published

                                  31 July 2025

                                  Estimated Read time

                                  7Mins

                                  Read in brochure format

                                  FinTech Strategy speaks with Matt Bazley, Account Executive at Hyland, to explore how the content intelligence and process automation specialists are helping to drive operational efficiencies for their financial services clients

                                  Financial Transformation Summit 2025 EXCLUSIVE

                                  Hyland empowers organisations with unified content, process and applications intelligence solutions, unlocking the profound insights that fuel innovation. The Hyland team was at Financial Transformation Summit to reveal the ways organisations can transform their processes with the Hyland Content Innovation Cloud™. By combining AI-powered automation with built-in integrations to productivity tools and business applications, Hyland streamlines workflows across multiple channels, accelerating response times, boosting productivity and improving customer satisfaction.

                                  At the event, Neil Rayment, Sales Solution Engineer, demonstrated the intuitive end-user experience and showed how easy it is to configure, tailor and deploy solutions that can empower key stakeholders across any business. We spoke to Hyland’s Matt Bazley, Account Executive for Financial Services, to find out more…

                                  Hi Matt, tell us about your role at Hyland?

                                  “I’m the Account Executive responsible for banking across the UK and Ireland. I’ve been with the company for just over 18 months. Across my career, I’ve been helping financial services institutions for over 15 years with digital transformations and various programmes.”

                                  What are the key digital transformation solutions Hyland offers Financial Services organisations? How are they making a difference? What are some of the use cases you’re exploring?

                                  “Hyland is at the cutting edge of the content space. We have what we call our Content Innovation Cloud, which is delivering content intelligence, process intelligence and application intelligence. What that means in reality is that we’re helping organisations get access to their content that they don’t currently have access to because it’s spread over many siloed systems and sat in an unstructured format. So, with our content and intelligence, we’re able to get access to that unstructured data, which is around about 80% of an organisation’s data in the financial services sector. And we’re able to then provide knowledge and insight on that content, which helps organisations to make better strategic decisions. Allied to that, with this process intelligence, we’re able to help automate processes across the business. Whether it be orchestrating use cases and workflows or integrating with other systems to deliver application intelligence, we’re able to manage that whole end-to-end life cycle of information across an organisation.”

                                  Why is this an exciting time for the business?

                                  “We’re excited because our strategy is really leading the way. We’re leveraging large language models (LLMs) and AI to be able to deliver these real-life use cases that solve actual challenges. A lot of the time AI projects fail because businesses are trying to implement AI that isn’t actually a solution solving a problem. Whereas the AI we’re using is to actually solve a real-life challenge that businesses face because they want to be hyper-personalised for customers and more customer-centric. And you can’t really do that if you’re only leveraging 20% of the data you hold about your customers. And that’s why getting access and insight around this unstructured data is really vital for financial services organisations right now. We are able to help them leverage that unstructured data and meet them where their data is at. So, it’s not a case of having to migrate all of that data into different platforms or into our platform. We confederate across your information wherever it’s held as a financial services organisation; and that’s really a game-changing position for us and for the industry.”

                                  “AI is the big one. Although it is a bit of a buzzword that everyone’s mentioning nowadays, we’re actually delivering AI solutions to solve problems that businesses face. And that’s one of the real trends in the industries. Most AI projects fail, and companies want AI projects that succeed and deliver real value. The other thing we’re seeing is the rise of hyper-personalisation as part of being really customer-focused and customer-centric. Again, by helping businesses leverage that 80% of information around their customers that they don’t currently have access to, and provide insights on that information, we’re helping those organisations to become really specific and personalised in their dealings with their customers.

                                  “The final piece is around data and governance. So, security around our data as customers, because we’re all consumers at heart and want to know that our information is secure. Using best-in-class processes around security and governance is what we’re really focused on. And that’s a real trend in the market as well. We’re making sure that while we’re leveraging that information about customers, we’re keeping it safe and only using it for what it’s intended for and making sure the processes and governance around that information are really robust.”

                                  What other pain points are clients in the FS space experiencing that you need to address? What are they asking you for help with? How are you meeting the challenge?

                                  “The one big one is the siloed information across multiple systems as part of digital transformation strategies. Over the years, I’ve seen many businesses implement point solutions. They might be best-in-class point solutions… But that means you end up with information and data and processes across 10, 15 or 20 systems. How do you then unify that data and leverage it to make the user journeys more effective? And also the customer journeys better, whatever channel those customers are using?

                                  “What we see is that while trying to be omnichannel for their customers, organisations end up with multiple solutions. One for their mobile app, a solution for their website, a solution for in-branch banking… So, you end up with omnichannel processes that are actually siloed processes. What we are trying to help businesses do is to unify those processes. We can break down those silos and make it a really seamless, integrated journey internally and externally for colleagues and customers.”

                                  Tell us about a recent success story …

                                  “A great example is our work with ABN AMRO – a bank that is one of our longstanding and valued customers. They were looking for a solution because of this very challenge. The bank had multiple siloed systems holding a lot of information and a very complex architecture. They went to market and Hyland was able to prove our solution was able to manage the sheer volume and complexity of the information and content that they had. And most importantly we were able to help them integrate with their line-of-business systems very easily to create that seamless internal/external journey for both users and customers.”

                                  What’s next for Hyland? What future launches and initiatives are you particularly excited about?

                                  “It’s all about continuing to grow for us. With the Content Innovation Cloud, the reception we’ve received from the market, from our customers, has been absolutely tremendous. Businesses are so excited to see the ability and capability of what we’re able to do. And what we’re able to deliver for them in terms of real value through the Content Innovation Cloud. We’ve got customers onboarded already. It’s now about expanding that list of customers who are going to see real value from leveraging the cloud, our AI solutions and driving efficiencies with our content process and application intelligence across their businesses.”

                                  Why do you think the evolution of collaboration between banks and FinTechs is set to continue? What are you excited about?

                                  “Across the market over the last 15-20 years the banks are starting to see FinTechs more as allies than competitors. And they’re leveraging these technologies rather than trying to challenge them. I think that’s going to continue because FinTechs are far more agile. And as customer expectations continue to evolve and become more demanding, banks need to evolve and deal with these demands more effectively and more fluidly. And that’s why leveraging FinTechs is going to be a key differentiator over the next 10 years. That trend is going to continue where banks and FinTechs work together and collaborate rather than challenge each other.”

                                  Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for Hyland?

                                  “It’s my fourth year coming here with a couple of different companies and I always find this event really valuable. Not only to obviously promote our products and our brand… But to speak to key decision-makers and peers across financial services. We aim to learn from them about whether the challenges we perceive as a vendor are seen by them as a customer. We will continue to learn and evolve our business around key market challenges. Hyland can then focus our solutions around the real-world problems our peers are seeing across financial services. Coming to this event is a great way to meet as many people as possible. And just really enjoy having those meaningful conversations with leaders in the financial services sector.”

                                  Learn more at hyland.com

                                  About Hyland

                                  Hyland puts your content to work, making it smarter and more accessible in the moment of need.

                                  Hyland’s content, process and application intelligence solutions empower customers to deliver exceptional experiences to those they serve. The solutions capture, process and manage high volumes of diverse content, helping you improve, accelerate and automate operational decisions and workflows.

                                  3 Core enterprise content management solutions

                                  20+ Distinct product offerings

                                  1,000s of ways to transform the way you work

                                  Author

                                  Dan Brightmore

                                  Published

                                  Estimated Read time

                                  4Mins

                                  Read in brochure format

                                  Our cover star Rebecca Fitzgerald, Director of Data & AI at Yorkshire Building Society, reveals a digital transformation journey meeting…

                                  Our cover star Rebecca Fitzgerald, Director of Data & AI at Yorkshire Building Society, reveals a digital transformation journey meeting customers, wherever they are.

                                  Read the latest issue of FinTech Strategy here

                                  Yorkshire Building Society: Data, AI & Inclusive Leadership

                                  Our cover story focuses on the data revolution taking place at Yorkshire Building Society (YBS)… Navigating this journey of change is Director of Data and AI, Rebecca Fitzgerald. Her ambitious vision is to transform the 160-year-old mutual through ethical, human-centred data strategies and AI innovation. In a rapidly evolving digital landscape, she aims to ensure YBS does not just keep up but leads from the front.

                                  “I’m accountable for developing and implementing strategies to enhance data-centricity and drive value from data and AI for our customers and colleagues,” Rebecca states. This directive is grounded in strong governance, positive data culture, and the empowerment of people through data literacy and technological upskilling.”

                                  Tyme Group: Scalable Global Digital Banking

                                  Dietmar Bohmer, Chief Analytics Officer at Tyme Group, on operationalising innovation, cultivating a culture of empowerment and driving transformation from the inside out…

                                  “It’s been wild ride from a technology point of view,” admits Dietmar… Today, that foresight is paying off. The cloud-native architecture has provided Tyme with the elasticity, resilience, and speed it needs to support its rapid growth across emerging markets. “With each new deployment, the organisation has evolved and refined its technological foundation,” notes Dietmar. “When the time came to launch GoTyme Bank in the Philippines, lessons learned from the rollout of TymeBank in South Africa enabled the team to rethink and redesign their stack, optimising for scale, performance, and localised feature delivery.”

                                  ČSOB: A Digital Transformation Journey

                                  ČSOB Slovakia is undergoing a major transformation aimed at future-proofing its technology, enhancing customer experience, and reinforcing its leadership in digital banking. Under the stewardship of its CIO Ludek Slegr, the bank’s IT team is navigating a major upgrade of its responsibility, overhauling core IT systems and implementing agile methodologies to meet its strategic goals. At the heart of this transformation is a focus on delivering value through technology, supporting people development, and fostering sustainable innovation.

                                  “The next step for digital-first is continuous improvement of straight-through processing ratio, i.e. reducing involvement of manual work in our processes.”

                                  Money20/20 Europe

                                  FinTech Strategy also reports from the conference floor at Money20/20 Europe in Amsterdam. Bringing together the world’s leading innovators, institutions, investors, and influencers from across the FinTech and financial services spectrum, more than 8,000 delegates from over 2,300 companies were in attendance… We sat down with Standard Chartered’s Head of Digital Assets – Financing & Securities Services, Waqar Chaudry, to discuss how the bank is connecting traditional with digital, collaborating with FinTechs and taking a measured approach to entering the crypto market. And we spoke with Veritran’s CMO, Jorge Sanchez Barcelo, to find out more about the tech firm’s partnership with Manchester City which is reimagining CX to create a frictionless digital experience for fans.

                                  Financial Transformation Summit

                                  The Financial Transformation Summit at London’s ExCel is one of the most immersive and interactive events in the financial services calendar. As a media partner, FinTech Strategy took the temperature of industry innovation at our stand with on camera hot takes from the tech leaders pushing the boundaries at Hyland, Fidelity, HSBC, Citigroup and more…

                                  Also in this issue, we keep you up to date with the key FinTech events across the globe; and read on for more insights from InsurTech disruptors Qover, lending innovators iwoca and investment experts Eastern Horizon…

                                  Read the latest issue of FinTech Strategy here

                                  • Artificial Intelligence in FinTech
                                  • Blockchain & Crypto
                                  • Cybersecurity in FinTech
                                  • Digital Payments
                                  • Embedded Finance
                                  • InsurTech
                                  • Neobanking

                                  Author

                                  Jess Shanahan

                                  Published

                                  29 July 2025

                                  Estimated Read time

                                  7Mins

                                  AI’s rapid evolution is creating both opportunity and urgency. AlixPartners lays out what needs to change — and why risk-takers will lead the way.

                                  The use of artificial intelligence (AI) in procurement is gaining traction with many organisations already looking at how the technology can improve processes. However, there’s scope to go beyond efficiency and instead focus on transforming value delivery. 

                                  At DPW New York, we spoke to Amit Mahajan and Aaron Addicoat from AlixPartners, a management consultancy firm doing things a little differently. The organisation is advising its clients on how to implement AI to drive value, but it’s also using AI internally, too. 

                                  “AlixPartners has a unique business model,” explains Addicoat. “We have a very senior model, very few junior resources. So now you imagine taking people with 10 or 15 years experience and now you equip them with AI… for us, it’s a huge unlock.”

                                  This is about more than just productivity gains. AlixPartners focuses on using AI to transform the way procurement teams work, while crucially, maintaining the human touch.

                                  How procurement professionals are using AI

                                  With the support of technology, it’s possible to shift procurement from a cost-saving exercise to a potential revenue driver. Procurement teams are already looking for these opportunities, as Mahajan explains. “They’re starting to think about new ways of doing things,” he says. “It’s not just automation, but asking how do I leapfrog and do something differently?”

                                  There are plenty of use cases where AI is helping with automation. This is a great place to start as it frees up human workers to do more valuable jobs that need a personal touch. “I have a client who’s using AI every day,” says Addicoat. “This allows them to review documents and contracts rapidly, to find key clauses and termination dates. They’re also using it in spend control processes to identify which things need to be reviewed more thoroughly.”

                                  Many organisations are also using AI agentically to create their own bots. This gives teams a more accessible way to review information. “One example is a client who’s using AI for their business to help with acronyms,” says Addicoat. “They built it as an acronym tool to help break down the language barrier between different functions using different terms. This led to better engagement.”

                                  This empowers employees across an organisation to be more autonomous while still getting the full picture. Agentic AI, especially, allows them to interact with information in a way that previously would’ve required specialist technical knowledge. Now, it’s possible to query information within a contract directly. 

                                  “It’s about using agents and AI to look at anomalies within your procurement contracts,” explains Mahajan, “and be able to help the category analysts, the category specialists, and others to get more of those insights.”

                                  While generative AI might be a hot topic, it’s not the only way to use the technology. In combining several sources of data and using AI to spot trends, it’s possible to create workflows tailored to the current environment. Addicoat explains: “We take a series of data inputs, such as weather patterns, lead times, contractual terms, inventory, and forecast. Then the AI generates the purchase order, queues it for review, and upon approval, places the order.”

                                  This can help an organisation to place orders with the right supplier in the most timely fashion to avoid delays, and optimise for cost, for example. This fully automates the end-to-end process, using AI to interpret those important data signals.

                                  While this is useful for procurement teams, it’s only the start. “Using AI in this way is really cool,” says Addicoat, “but what I found most fascinating is that you’re building a data model, and with AI layered into it, that over time can tell you how to optimise itself.”

                                  This has huge implications for procurement teams looking to save money and drive revenue. “For example, it could tell us the commodity price at a certain point in time was low,” says Addicoat, “but because inventory capacity to hold resin was maxed out the client could only buy so much at that low price. So now investing in a new storage unit at a cost of a few hundred thousand dollars could, under the same scenario in the future, save millions of dollars..Data quality challenges

                                  A roadblock that can stop procurement teams from fully embracing AI is a lack of quality data. With so many sources of information, often including paper-based documents, some might think it’s difficult to get the data AI needs to be truly useful.

                                  “Don’t wait for everything to be perfect before you get started,” says Addicoat. 

                                  This is a sentiment echoed by Mahajan: “Use AI to solve your data problem before solving your business problems.”

                                  This requires a mindset shift. While AI can help cleanse, enrich, and structure existing unstructured data, it’s important to take the right approach. Shift from asking ‘what can we do with our data?’ to ‘what value do we need to create?’ and work backwards from there.

                                  With this approach, the questions are less about the data and more about the business problem. This then allows you to use AI to work with the information you have to help answer those questions.

                                  “Start with the value proposition in mind and work backwards,” explains Addicoat. “You can get data from anywhere — it has to serve a purpose.”

                                  Bringing back the human touch

                                  AI can free up procurement teams to focus on tasks that need more nuance and expertise. Using technology to automate workflows and make information more accessible has a huge impact on employee productivity. “It’s fundamentally transforming the way they work, the amount of work they can do, and the type of work they’re able to do,” says Addicoat.

                                  There’s always the worry that with any new technology, the human element will be forgotten. “With every new advancement that comes in,” says Mahajan, “whether that was a steam engine or when computers came along, everybody wondered what they were going to do. But as humans, we always find ways to start doing higher-level work.”

                                  This means that many professionals will find new ways of doing things. “Imagine all the mundane tasks you have to do in your daily job now,” Addicoat continues. “With these new ways of working, imagine the speed with which you can turn an idea into something real. All that time you free up allows you to go talk to people and build relationships that mean something.”

                                  On the other side of things, the sheer volume of AI-generated content out there is going to drive people towards those more meaningful interactions. “You don’t know what to trust and what to believe anymore,” Addicoat says. “That’s going to lead to a resurgence in face-to-face content, being at the office, and being at events.”

                                  AI’s impact on procurement talent

                                  The talent landscape is changing. With technology playing a larger part than ever before, organisations don’t just need procurement professionals, they need adaptable, tech-savvy people. The nature of the job means that those in procurement need a wide range of skills. 

                                  “We do everything,” says Addicoat, “legal, operations, supply chain, negotiation, analytics. Procurement professionals are generalists.” 

                                  Tech plays into every element of that skillset, which means tech skills are becoming even more important for candidates applying for procurement roles. “Nobody goes to college thinking they’ll be a procurement professional,” says Mahajan, “but with AI and tech, that’s changing.”

                                  With procurement often seen as a proving ground for leadership, embedding these tech-minded generalists could have a huge impact on the future. “We have a shortage of talent,” explains Addicoat. “But with more and more CEOs and COOs coming from procurement, that speaks volumes to what procurement does and the value it brings, as well as what the future holds.”

                                  At AlixPartners, the passion for procurement is very clear with Addicoat saying: “There are only two kinds of people in the world: those who love procurement and those who don’t know it yet.”

                                  Change is coming

                                  With AI of all forms steadily gaining traction, procurement could change dramatically in the coming years. It’s the organisations that are willing to take risks and embrace change that will come out on top.

                                  “AI has the potential to disrupt the whole management consulting world,” says Mahajan. “Firms focused on transformation will thrive.” 

                                  With AI’s capabilities increasing rapidly, it’s difficult to predict what comes next. However, adaptability is key. “Hold onto your hat. In a year and a half, the world’s going to look very different,” concludes Addicoat.

                                  Author

                                  Jess Shanahan

                                  Published

                                  Estimated Read time

                                  7Mins

                                  We spoke to Chief Product Officer Prerna Dhawan about what it takes to move from experimentation to execution.

                                  As AI continues to dominate conference stages and boardroom discussions, the pressure to use it is everywhere. As this technology becomes further embedded in enterprise strategy, many organisations are still grappling with how to apply it in a way that delivers real, measurable value.

                                  Rather than focusing on AI for the sake of innovation, the question is how to align new tools with real business problems. That means looking beyond dashboards and pilots to deploy AI where it can simplify decision-making and improve processes.

                                  At Beroe, this principle is central to how AI solutions are developed, deployed, and scaled. As the company behind the world’s leading procurement intelligence platform, Beroe provides real-time market data, cost analysis, and supplier risk assessments, empowering thousands of organisations globally to streamline operations and mitigate risks. Its latest advances in autonomous negotiation, supplier discovery, and predictive analytics show what it means to align AI with business objectives.

                                  We spoke with Prerna Dhawan, Chief Product Officer at Beroe, during this year’s DPW New York conference. The discussion explored how procurement leaders can move beyond hype and start unlocking the full potential of AI.

                                  Misalignment with business needs

                                  There are plenty of real-world examples of how AI can improve efficiency within a business, from automating manual tasks like invoice processing to identifying new suppliers based on complex sourcing criteria. Accessing this technology is easier than ever with a wide range of tools available to procurement professionals. It can be tempting to jump on the bandwagon and integrate AI across every area of an organisation, but success requires a more nuanced approach.

                                  The key is to ask the right questions, Dhawan explains: “We talk about all the latest and greatest technology out there, but what does it mean in practical terms? We need to ask, ‘How can I apply it today in the work I am doing as a head of product or as a procurement professional?’”

                                  The allure of generative AI is especially strong, but business leaders should ask whether that’s the right solution for their needs. As with any decision, it’s important to consider the business problem. “It starts with a little bit of knowledge about what you’re looking for,” says Dhawan. “What are some of your biggest challenges, and which of those challenges could AI technology solve?”

                                  Matching the right tool to the job

                                  Once an organisation has identified a specific problem, it’s possible to find the AI solution that fits. While generative AI gets a lot of attention, other AI technologies and machine learning based systems might be more appropriate. 

                                  In some cases, prescriptive, rule-based, or predictive AI could be a better choice to solve a problem without the need for a large language model. For example, forecasting commodity prices doesn’t require generative AI, just strong, contextual machine learning. 

                                  “We are looking at AI across two dimensions,” says Dhawan. “Firstly, what is our offering to customers, in terms of procurement intelligence and autonomous negotiation technology. Second, we are looking at AI internally. Let’s say in product development, how do we use the latest AI solutions to accelerate our product development cycles so we can release new modules and capabilities more quickly.”

                                  Regardless of the type of tool chosen, it should cover a high-impact use case. Integrating AI to solve a problem that only surfaces for a small group of people a couple of times a year won’t have a great return on investment. Instead, look for regularly occurring problems that, if fixed, could have a huge impact on productivity or quality. 

                                  Reducing the cognitive load

                                  We’re already bombarded by information, and the use of AI to add to this doesn’t make sense. “I don’t need another dashboard in my life,” says Dhawan. 

                                  When implemented correctly, AI can make data more accessible while reducing cognitive load for users. The result is increased productivity and faster decision-making. 

                                  “I think the power of AI is to simplify access to data. This is why ChatGPT has been a success: it democratises access to information. That’s what our B2B technology world is waiting for. It gives me something simple that allows me to talk to my data. Then I can focus on what insights I need to make a decision or take action.”

                                  For most B2B users, the key is intelligent simplification. Look for ways to simplify access to data through agent AI tools and conversational interfaces. This brings the focus back to action rather than dashboards.

                                  Inside Beroe

                                  While many procurement teams are still exploring AI’s potential, Beroe has already embedded it across both its platform and internal operations. The company, founded in 2006, provides procurement intelligence to thousands of organisations worldwide. Its platform delivers the critical data that professionals need to make informed sourcing decisions, from commodity prices and risk indicators to ESG scores and supplier intelligence.

                                  “We provide all data that procurement needs for decision making, whether it’s cost data, risk data, ESG data or price data,” says Dhawan. “Our reimagination of the future is not just giving access to more data but creating that layer of recommendations that help you make decisions at speed and scale.”

                                  One of the clearest examples of this in action is Beroe’s new ‘autonomous negotiations’ platform resulting from its recent acquisition of negotiation technology business, nnamu.  Delivering a significant evolution in the procurement technology landscape the platform enhances the foundational elements of AI and game theory with Beroe’s industry-leading market intelligence and, according to Dhawan, it’s being deployed successfully in live sourcing scenarios.

                                  “This is a technology that is being used for multilateral negotiations,” Dhawan explained. “It’s no longer just a POC or prototype, it’s live and being used at scale.” These new tools reflect Beroe’s core mission: to help procurement professionals minimise surprises and maximise margins. 

                                  Crucially, Beroe isn’t waiting for perfect data to apply these technologies. Instead, the company is using AI to work with what’s available — cleansing, interpreting, and extracting value from both structured and unstructured sources.

                                  “You can use AI for cleansing data – even paper contracts,” Dhawan says. “Historically, we thought data had to be structured. But now, with vision models and image analytics, that’s no longer the case.”

                                  Rather than striving for 100% accuracy before taking action, Beroe embraces a more agile mindset that balances speed and precision. 

                                  Is mindset holding procurement back?

                                  The technology is ready. The use cases are proven. So why do so many procurement teams still hesitate to embrace AI? “There’s this subconscious fear that I think is a barrier to adoption,” she said. “And to some extent, it’s to do with our friends in Hollywood.”

                                  There’s the myth that AI is a job-threatening black box, especially in industries where trust and experience are the backbone of good decision-making. For procurement, where professional judgement and business context are critical, the idea of handing over tasks to AI can feel risky.

                                  But Dhawan believes this fear is misplaced. At Beroe, AI isn’t replacing procurement professionals, it’s augmenting them. Whether it’s surfacing new suppliers, automating elements of negotiation, or flagging risks earlier in the sourcing cycle, the aim is to enhance human decision-making. She says: “I think with the new kinds of AI technology that’s available to us, it is an opportunity for us in B2B tech to embrace more human-centred design with higher focus on UX.”

                                  Looking ahead

                                  Looking ahead to 2026 and beyond, Dhawan sees procurement evolving into a more personalised and responsive function – one where AI plays a critical role in both strategy and execution.

                                  “We see hyper-personalisation coming, both in supplier relationships and internal stakeholder engagement,” she explains. “AI will be at the centre of that.”

                                  Rather than one-size-fits-all sourcing strategies, AI will enable procurement teams to tailor their approaches to specific business units, categories, or even individual suppliers. This means smarter segmentation, more relevant insights, and stronger commercial outcomes.

                                  Another key shift is the growing ability to connect macro events, such as geopolitical shocks or regulatory changes, with micro actions inside the business. AI can help procurement teams identify these signals earlier, respond faster, and still align with long-term goals such as cost efficiency or sustainability.

                                  “It’s about balancing your fire-fighting reactions to market events with your long term goals and strategy,” says Dhawan. “Procurement needs visibility and flexibility at the same time.”

                                  Beroe is already moving in this direction. Alongside its growing AI capabilities, the company is refining how it delivers intelligence, building agents and recommendation layers. These not only inform decisions, but also help teams take action on them. Whether that means automating routine negotiations or proactively flagging supply risks, Beroe is evolving to meet the needs of a procurement function that’s more dynamic than ever.

                                  As Dhawan points out, the goal isn’t to overwhelm teams with more tools, it’s to make their lives easier. “It’s about reducing complexity and giving procurement professionals confidence in what to do next,” she concludes.

                                  For many procurement leaders, AI still feels like a long-term ambition. But the solutions are already here, and through companies like Beroe, they’re already in use. The challenge now is not whether AI can deliver value. It’s whether teams are ready to adopt the mindset and cultural shift that will allow them to unlock that value.

                                  Author

                                  Dan Brightmore

                                  Published

                                  28 July 2025

                                  Estimated Read time

                                  3Mins

                                  Read in brochure format

                                  Our cover story charts the rise of RAKBANK in the UAE driven by agile practices and a people-first culture delivering…

                                  Our cover story charts the rise of RAKBANK in the UAE driven by agile practices and a people-first culture delivering banking with a human touch.

                                  Read the latest issue of FinTech Strategy here

                                  RAKBANK: A Banking Transformation in the UAE

                                  Our cover story explores the digital transformation journey of RAKBANK in the UAE. Head of Digital Transformation, Antony Burrows, reveals the agile practices, enterprise-wide enablement and people-first culture delivering digital banking with a human touch.

                                  “Culture is the cornerstone,” Antony stresses. RAKBANK codifies this into its Four Cs Framework – Connect, Communicate, Collaborate and Celebrate. “Here in the UAE, banks are pivoting from a model of ‘we know everything’ to recognising that one of the best ways to deliver continuous change and value to customers is through partnerships with startups and FinTechs. It’s no longer banks versus startups – it’s banks and startups, working together for the customer. This shift is especially meaningful as banks expand beyond traditional services to focus on customers’ broader financial lives.”

                                  MTN MoMo: Empowering Africa Through FinTech

                                  Hermann Tischendorf, Chief Information & Technology Officer at MTN MoMo (the telco’s mobile money division) reveals a bold roadmap for leveraging FinTech to drive financial inclusion across the African continent.

                                  “MoMo is comparable in monthly active users to some of the top ten FinTechs globally. We’re playing in the same league as Revolut or Nubank – but in much more complex markets,” notes Hermann. “Access to financial services is fundamental. Without it, people are excluded from the global economy. Our services are the equaliser allowing individuals in frontier markets to participate in trade, store value, and ultimately improve their quality of life.”

                                  Republic Bank: Building a Digital Bank

                                  Republic Bank has been serving customers via its branches for over 185 years and now serves 16 different countries across the Caribbean and beyond. It’s “a regional bank with a growing global reach,” explains Group Chief Information & Digital Transformation Officer, Houston Ross.

                                  His team is building a digital bank during a Year of Delivery and Accountability (YODA). “When we talk about digitalisation it’s a journey that never ends. And product is the vehicle to make sure we’re continuously improving.This is our digital pathway and we have to change minds in terms of going beyond the challenges to achieve what’s possible with the right frameworks, tools and processes for our people to serve our customers.”

                                  Also in this issue, we keep you up to date with the key FinTech events across the calendar and read on for insights from Lloyds Banking Group, Recorded Future, AAZZUR, Ayre Group, Marqeta, SCOR and TerraPay.

                                  Read the latest issue of FinTech Strategy here

                                  • Artificial Intelligence in FinTech
                                  • Blockchain & Crypto
                                  • Cybersecurity in FinTech
                                  • Digital Payments
                                  • Embedded Finance
                                  • InsurTech
                                  • Neobanking

                                  Author

                                  Nell Walker

                                  Published

                                  Estimated Read time

                                  6Mins

                                  We caught up with Valdera’s Co-Founders to find out why chemical procurement comes with its own challenges.

                                  Chemical procurement is one of the most complex and overlooked categories in the supply chain. Between navigating regulatory constraints, aligning on technical specifications, and finding qualified suppliers, even the most experienced procurement teams face major hurdles. That’s exactly the gap Valdera was built to solve.

                                  Founded by sister-brother duo Sruti Arulmani (CEO) and Dheev Arulmani (COO), Valdera is an AI-native sourcing platform purpose-built for chemicals and raw materials. Rather than applying generic technology to a specialised industry, the team set out to reimagine chemical procurement from the ground up.

                                  “Chemicals are one of the most complex sourcing categories,” says Dheev. “In order for a company to gain leverage from AI in this space, it must build the data infrastructure and the AI specific to this industry. That was the inspiration behind Valdera. Our vision was to partner directly with procurement organisations and help digitise that entire sourcing workflow all the way from supplier discovery to market intelligence to qualification.”

                                  “Direct procurement is really at the core of your product’s margin,” adds Sruti. “In today’s economy, business leaders are focused on staying profitable, and that starts with ensuring the materials behind your products deliver on both margin and performance. Most of the physical products we touch and interact with every day come down to what they’re made of. That’s why we’re so passionate about chemicals and raw materials.”

                                  The power of vertical AI models

                                  While general-purpose LLMs are powerful, they fall short when it comes to industries like chemical procurement where context, precision, and deep domain expertise are crucial. Valdera has taken a different approach: building vertical AI specifically trained to understand the language, data, and complexity of chemicals and raw materials. 

                                  “In procurement, especially for chemicals, one-size-fits-all AI doesn’t cut it,” says Sruti. “You need models that can interpret highly technical specifications, normalize data across formats and suppliers, and understand the nuances that determine whether a supplier can actually meet a request.”

                                  That’s exactly what Valdera has built. “We will continue to layer the specificity of the chemical industry on top of an LLM that’s already good at structuring information and returning information in a useful way,” Sruti adds.

                                  Dheev continues: “If you look at the generic LLMs available today, the challenge with these is that they fundamentally don’t work in this industry. The reason for that is that there are no LLMs that are trained on chemical specs. So what we’ve done is take those models and fine-tune them using our own proprietary dataset of chemical specs and properties, built over the last five years. That’s what positions us to drive real value for our users.”

                                  Prioritising privacy

                                  In the chemicals industry, data is sensitive. Trust is everything. Buyers are protective of their proprietary formulations, and understandably do not want their data used to train models that could benefit competitors. On the other side, suppliers are cautious about publicly listing their full product catalogs, especially when it comes to custom or high-value materials. Valdera was built with these realities in mind, and its platform is designed to protect both sides.

                                  “In chemicals, suppliers are very protective of their proprietary catalogs,” Dheev adds. “And buyers are equally cautious about sharing proprietary formulations that go into their products. So there needs to be an independent third party that both sides can trust—someone who can facilitate discovery and sourcing without compromising confidentiality.”

                                  “For us, it’s about protecting the interests of both buyers and suppliers,” Sruti explains. “We only use customer data to drive outcomes for that customer. We’re not here to train on anyone’s inputs or share information across the ecosystem. We’re here to help our customers get the best results for their business. That’s core to how we think about data privacy and partnership.”

                                  The humanity of procurement

                                  Even as AI becomes more powerful, procurement remains deeply human. Trust, context, and judgement are critical to strong buyer-supplier relationships, and no model can replace that. Instead, AI can enable teams to work faster, focus on strategy, and unlock new value across the supply chain. 

                                  “Procurement is a human business,” says Sruti. “At the end of the day, it’s two people coming together and making an agreement. We believe that’s never going to change.”

                                  Rather than add complexity or replace roles, Valdera’s AI helps teams do more with the resources they already have. That means less time spent on manual tasks like gathering supplier documentation or comparing specs and more time spent on strategic decision-making, relationship-building, and growing the business.

                                  “Our customers don’t want to be buried in paperwork. They want to focus on the work that actually drives outcomes,” Sruti adds. “We’re here to take the most repetitive parts of the job off their plate so they can do that.”

                                  “The chemicals industry is inherently relationship-driven,” says Dheev. “But today’s procurement teams are stretched thin. With Valdera, one person can now manage a broader scope: sourcing faster, accessing a wider network of qualified suppliers, and making smarter decisions in less time. That’s what’s getting our customers excited.”

                                  Driving impact beyond cost

                                  In chemical procurement, cost will always matter but it’s only part of the equation. The organizations leading the way are the ones thinking strategically: securing supply, expanding their supplier base, improving agility, and driving long-term value. That’s why more teams are turning to Valdera not just to cut costs, but to unlock a new level of visibility, access, and control.

                                  “Our vision is to enable procurement professionals to leverage this data in order to give them market intelligence, expand their supplier network, and enable margin expansion,” Dheev concludes. “If you ask any of our customers, they’ll tell you savings are just table stakes when using Valdera. The real impact comes from levers like security of supply, innovation and sustainability. Those levers are harder to quantify, but they’re critical to the long-term success of the business.”

                                  Implementing an outcome-based approach

                                  In a crowded and fast-evolving tech landscape, it’s easy to get distracted by the promise of sweeping, all-in-one solutions. But the most effective procurement teams stay focused, starting with a clear understanding of their business goals and choosing technology that’s purpose-built to achieve them.

                                  “Success starts with knowing the outcomes you’re trying to drive,” says Sruti. “Whether it’s sourcing the right chemicals, improving security of supply, unlocking savings, or advancing sustainability and innovation. Being clear about those goals is what helps you identify the right tools and partners to get there.”

                                  That kind of clarity leads to faster wins and less wasted effort. “We always encourage customers to start where the impact matters most,” Dheev adds. “Don’t spread yourself too thin. Be specific about the problem you’re solving, define the KPI that matters, and test any solution against that. Just because a tool is popular doesn’t mean it’s the right fit. The best results come from targeted solutions that align with your most pressing priorities.”

                                  Author

                                  Dan Brightmore

                                  Published

                                  25 July 2025

                                  Estimated Read time

                                  4Mins

                                  In today’s digital economy, finance is no longer confined to banks. Thanks to embedded finance, financial services are being integrated…

                                  In today’s digital economy, finance is no longer confined to banks. Thanks to embedded finance, financial services are being integrated directly into non-financial platforms. This allows customers pay, borrow, insure, or invest without ever leaving the app they’re using. For businesses, embedded finance unlocks new revenue streams and deeper customer engagement. In 2025, here are five of the top FinTech solutions leading this revolution.

                                  1. Stripe Connect – Embedded Payments Infrastructure

                                  Stripe has become synonymous with online payments, but Stripe Connect takes it further… It enables platforms like marketplaces, SaaS apps, or gig platforms to onboard sellers, manage payouts, and handle compliance seamlessly. Its APIs offer modular, customisable solutions for embedding payment flows, KYC, tax reporting, and global transfers.

                                  Why it leads: Stripe Connect simplifies complex financial operations. It gives platforms the ability to become payment facilitators without becoming regulated entities themselves.

                                  2. Railsr (formerly Railsbank) – Full-Stack Embedded Finance

                                  Railsr provides a modular platform that allows brands to embed banking, payments, and credit products into their own apps. Whether it’s issuing branded debit cards, offering BNPL, or enabling in-app bank accounts, Railsr acts as the financial layer beneath consumer-facing businesses.

                                  Key strength: It provides a single, developer-friendly API to access multiple financial services. This speeds up time-to-market, reducing infrastructure complexity.

                                  3. Unit – Embedded Banking-as-a-Service (BaaS)

                                  Unit is a US-focused BaaS provider that helps FinTechs and software companies embed features. These include checking accounts, cards, ACH payments, and lending directly into apps. Its toolkit includes compliance workflows, ledgering, and integrations with banking partners.

                                  Why it stands out: Unit’s out-of-the-box functionality allows tech companies to go from idea to launch in weeks, not months. Furthermore, staying compliant with US banking regulations.

                                  4. UpLift – Embedded BNPL for Travel and Lifestyle

                                  UpLift is a niche embedded finance provider focused on travel, hospitality, and lifestyle experiences. Its BNPL tool is integrated directly into checkout pages for airlines, cruise lines, and vacation providers. This allows consumers to split costs into manageable monthly payments.

                                  Unique angle: By focusing on high-ticket discretionary purchases, UpLift helps merchants increase conversions and average order value. Moreover, giving consumers more flexible options.

                                  5. Qover – Embedded Insurance for Digital Platforms

                                  Qover is a leading embedded insurance provider that enables companies to integrate customised, white-labelled insurance directly into their apps or services. From gig platforms and neobanks to mobility and travel apps, Qover supports multiple insurance lines. These include motor, health, cyber, and income protection—across more than 30 countries in Europe.

                                  What sets it apart: Qover’s modular APIs let businesses plug insurance into user journeys with minimal friction. It also handles underwriting partnerships, multilingual customer service, and real-time claims dashboards, offering full-stack support.

                                  Why it matters: Qover empowers platforms like Revolut and Deliveroo to offer relevant protection at scale. Moreover, boosting user trust, engagement, and retention without building insurance infrastructure from scratch.

                                  Embedded finance is transforming how financial products are delivered… Moving from standalone services to contextual, on-demand experiences. Tools like Stripe Connect, Railsr, Unit, UpLift, and Cover Genius empower companies to embed finance where it adds the most value: at the point of need. For FinTechs, retailers, travel firms, and SaaS platforms, these tools represent the future of customer-centric finance—convenient, invisible, and deeply integrated.

                                  • Embedded Finance

                                  Author

                                  Jess Shanahan

                                  Published

                                  Estimated Read time

                                  6Mins

                                  Candex exists to solve tail spend by removing friction and giving procurement leaders time to focus on what truly drives value.

                                  Candex isn’t chasing trends for the sake of innovation. Instead, the company is focused on solving one of the oldest and most persistent challenges in enterprise procurement: getting rid of the noise. 

                                  Most in procurement will be familiar with Candex. Co-founded by Shani Vaza, Chief R&D Officer, and Jeremy Lappin, CEO, Candex is a technology-based master vendor that simplifies onboarding and payments to small and one-time vendors. It delivers a fast, compliant, and easy buying experience for requisitioners, while procurement gains automation, visibility, and control, reducing the vendor master by up to 80%.

                                  For years, procurement teams have battled fragmented data, manual onboarding processes, and administrative bottlenecks. This results in time and resources spent on tasks that add little value, while strategic initiatives suffer from a lack of focus. 

                                  For many organisations, 70% of vendors account for just 5% of spend. With Candex, procurement can manage that long tail of spend without adding operational burden. This frees up teams to focus on strategic priorities, redirect spend to preferred suppliers, and drive more value across the business. At this year’s DPW New York conference, Jeremy Lappin and Chief Customer Officer Danielle McQuiston shared how their platform is helping procurement evolve beyond compliance and cost savings into something far more valuable: clarity.

                                  Addressing the core problem

                                  While many conversations at the event kept coming back to the use of AI, Candex is doing things differently. “AI will transform procurement by uncovering better, more innovative vendors,” says Lappin. “But every new vendor comes with the burden of onboarding and compliance. That’s where Candex makes a real difference—we streamline that process by enabling fast, compliant purchasing without the heavy lift of onboarding. As companies adopt AI, they’ll need a system like ours to truly benefit from what it reveals.”

                                  It’s about bringing the conversation back to the core problem. Lappin continues: “Candex makes it possible to onboard and pay new vendors in minutes, and without setup delays, while keeping procurement firmly in control. That’s where we unlock both agility and compliance.”

                                  Solving procurement’s data problem

                                  After speaking to many procurement leaders at events such as DPW New York 2025, one topic of conversation stood out: that messy data can be a major hurdle to overcome before successful AI adoption can occur. Companies dealing with multiple affiliates for a single vendor can find their data ends up split, duplicated, and difficult to work with at scale. 

                                  “The fragmentation of data is a very old problem,” says Lappin. “One of the reasons it occurs is because the data is organised by affiliates and isn’t aggregated properly. This creates enormous processes.”

                                  A dedicated platform can take on the heavy lifting of sorting through this data, without the use of complex AI models. Lappin continues: “One thing that Candex does to help this problem with smaller vendors is auto-aggregating affiliates under one corporate umbrella. It’s going to massively reduce the data problem by directing that small spend through us.”

                                  McQuiston adds: “Data is the foundation of all the decisions that procurement makes. And the fact that they can consolidate that data within Candex, and look at it only when it’s relevant to what actions they have to take, is a huge contribution to the space for procurement.”

                                  The right data at the right time

                                  Candex isn’t trying to flood procurement teams with dashboards. Instead, it delivers data when and where it’s needed, stripping away the noise to surface what’s important.

                                  “Our customers tell us we filter out 95% of the noise and highlight just the actions that matter. It’s not just visibility, it’s visibility at the right moment,” says McQuiston. “We have amazing reporting that has hundreds of lines of precision data in there, but it’s also aggregated in a way that it calls out to the things that need attention rather than being bogged down with the rest.”

                                  “Oftentimes the stuff that goes through us is the stuff that procurement doesn’t have the time to give its attention to,” explains Lappin. “I think one of the most powerful things we do is get rid of the things they shouldn’t care about so that it’s very easy to see what they should.”

                                  Simplicity wins

                                  Some procurement tools are complex, slow to adopt, and full of friction, but Candex takes a different approach. “The users just want to be able to operate and do the work that they need to do to serve their objectives,” says McQuiston. “Procurement doesn’t have enough resources to deal with all of the small things.”

                                  Bringing the focus back to the core function of procurement simplifies processes and reduces noise. When working with a lot of small vendors, procurement teams can get bogged down with admin and data. This is where Candex takes on the weight of that burden, and allows the business to move forward.

                                  “At the end of the day, Candex is a tool that is so simple from a user perspective, but still has the confidence of the procurement organisation,” McQuiston continues. “It also shines a better light on the procurement function, which often gets a black eye for being in the way of things.”

                                  Real people

                                  For Lappin, the hype around AI isn’t what makes a product great; it’s real-world validation from customers. “There’s only one way to get through the hype,” he says, “and that’s to find other companies that are using the products and loving them. I think that’s one of the things that has made us successful.”

                                  It’s one of the strengths of DPW; these events showcase real use cases, not just demonstrations. This enables attendees to see the impact of new technologies for themselves, and connect with the people behind them. “DPW has the ambition to use real use cases rather than just relying on demos,” says McQuiston. “That’s what’s a little bit different about DPW compared to some other conferences; the proof is in the pudding.”

                                  Lappin and McQuiston also highlighted the importance of customer-led innovation through Candex Connects – roundtables all over the world that allow procurement peers to meet, discuss the challenges affecting them, and learn from one another, as well as sharing their own inspirational use cases. “We’re not just providing a solution. We’re providing a space where our customers get together, discuss best practices,” McQuiston adds. “And I think we’ve done that really well.”

                                  Procurement, repositioned

                                  Ultimately, Candex is about more than just a tool. It’s about reshaping the perception and potential of procurement teams, giving them the freedom and focus to lead strategically. By removing some of the friction of dealing with myriad small vendors, procurement teams are empowered to drive deeper value.

                                  “Our whole business is focused on agility and value creation,” says McQuiston. “We have to be compliant because our customers demand it, but it’s not really about cost savings when you talk about tail spend. Procurement has always been in a position where they believe they can squeeze something out of every purchase. We’ve gotten to a point in the evolution of the function where they realise there’s a portion they can’t squeeze anything out of. It’s powerful to be able to let that go.”

                                  “Procurement needs to be involved in decisions around spend,” adds Lappin. “They help negotiate. They figure out the right vendors. They really are needed in this process, which is why it exists.”

                                  Candex isn’t just solving tail spend, it’s redefining how procurement operates at scale. With built-in controls, full audit trails, and seamless integration with existing systems, Candex empowers procurement to lead strategically, reduce supplier bloat, and stay agile in a complex world.

                                  Candex is proving that the biggest transformation comes from helping procurement teams reduce the noise and get back to the work that matters. 

                                  Author

                                  Lisa Miles Heal

                                  Published

                                  24 July 2025

                                  Estimated Read time

                                  5Mins

                                  Silverfin’s CEO, Lisa Miles Heal, on how the accountancy industry must innovate with technology to evolve

                                  The accountancy industry is at a crossroads. With rapid technological advancements, accountants are balancing the demand for more efficient compliance and an increased emphasis on value-added advisory services.

                                  Meeting the Challenges

                                  Inflation and the unstable economic outlook are also having a serious impact on all sectors. The UK has been through a tumultuous few years, and the combined effects of Brexit, the COVID-19 pandemic, and high inflation are only gradually receding. Growth remains meagre across the economy as a whole.

                                  At the same time, the global geopolitical situation remains unpredictable, threatening to upset the applecart again at any moment. Alongside this, the possibility of high trade tariffs coming into force in the US in 2025 brings a whole host of conceivable challenges, including spiralling goods costs suppressing growth across a host of industries, with knock-on effects across the services sector. All of this impacts accountants directly, as businesses lean on them for guidance through economic uncertainty.

                                  But it’s not all doom and gloom. Innovations  like automation and AI can help accountants navigate through the volatility and focus on the higher value tasks. But we know that this isn’t an easy one and done. Firms purchasing fintech technology are on an education journey, requiring a cultural shift to overcome resistance and replace fear with an understanding of how machine learning and analytics drive growth, not replace staff. As firms embrace this shift, 2025 could see accountancy transformed into even more of a more strategic, data-led profession. 

                                  As a result, 2025 is set to be a year of rapid change, of challenge and opportunity. Two key areas will continue to impact the sector – inflation, and further consolidation through mergers and acquisitions (M&A). Let’s explore in more detail how these two issues will shape 2025 for accountancy firms and their clients, as well as looking at the way professionals’ roles are likely to evolve in response.

                                  Automation Will Transform the Way Accountants Respond to Inflation

                                  Inflation remains a significant dynamic that accountancy firms must navigate carefully in 2025. It impacts everything – from wages and employee culture through to supply costs and cash flow. As inflation stabilises, it’s crucial for accountancy firms to reflect on how they handled recent high inflation periods, and adapt their strategies for a lower-inflation environment.

                                  Using technology and data insights can help firms remain competitive and navigate this new economic phase. A data-led approach is crucial given the complexity of the factors that feed into the inflationary landscape, and the myriad ways it can affect business. Reacting based on intuition won’t cut it. Accountants need to base their strategic decisions on insights derived from rich data, in as close to real time as possible.

                                  This approach has two critical advantages. First, it allows firms to act proactively, leveraging advanced analytics to anticipate trends and outcomes before they occur.. Second, it allows for greater agility, enabling firms  to gain deeper insights  into how  rapid market changes are affecting  their business, and to adjust their strategies swiftly in response.

                                  Mergers & Acquisitions Will Ramp Up

                                  The accounting sector is set for more consolidation as firms face high numbers of partner retirements, due to an ageing workforce. This consolidation is an opportunity for both large and specialised practices – if they can pivot in the right way. 

                                  Larger firms have the potential to dominate, leveraging scale to process work more efficiently across different markets. On the opposite end of the scale, smaller, niche firms can shift to offer highly personalised services. It’s the middle ground that’s at risk. Mid-sized firms that don’t evolve will either be absorbed by larger entities or see talent move towards more specialised practices. 

                                  Private equity is also playing a part in this M&A trend. Investors see opportunities to modernise firms and extract value through efficiency gains and technology adoption. Fintech tools, such as cloud-based financial reporting and compliance platforms, present a low-risk avenue to drive long-term value for pension funds and other stakeholders, especially during the current volatile environment. These trends signal an era of structural evolution within the sector, driven by innovation and investment.

                                  Accountants Will Grow Their Strategic Role

                                  Finally, amid all this change, accountants will need to redefine their role. By automating routine tasks, accountants can reclaim valuable time to focus on higher-value work, such as compliance and providing fiscal and legal advisory services. Firms that adapt to this shift will thrive, while those clinging to traditional models risk losing relevance or being absorbed by larger, more agile competitors.

                                  In 2025, the widening availability of next-gen, AI-enabled technology will make success dependent on firms that fully  integrate their operations. These firms will harness  insights and expertise from all areas of the business  to inform decision-making. Accountants have a crucial role to play in providing these insights based on the financial status of their clients – a role they can only play if they’re freed up from repetitive, low-value tasks. Technology holds the key to the evolution of the sector – 2025 is the time to take that next step.

                                  About Silverfin

                                  It all started with two founders and a big idea… to create an innovate cloud platform to make accountants more successful.​ These are exciting times for accountants.

                                  Technology has changed bookkeeping forever. While bookkeeping has been transformed, the day-to-day life of the accountant has yet to see the same change. Until now.

                                  Silverfin was founded by an accountant frustrated by how he had to work and a software architect looking for a tough problem the cloud could crack. 

                                  So they turned their thinking to how data, and the cloud, could make life easier for accountants, make their businesses better, and at the same time unlock new opportunities for revenue streams from value-added client advisory services.

                                  We give accountants the technology and tools they need to be more successful. For themselves. For their clients. We improve the efficiency, competitiveness and profitability of compliance and reporting services. We make this work faster, easier and better. Plus we power the development and delivery of new advisory services.

                                  • Artificial Intelligence in FinTech
                                  • Neobanking

                                  Author

                                  Published

                                  Estimated Read time

                                  5Mins

                                  Read in brochure format

                                  This month’s cover story features SSEN Transmission’s journey to build a digitally-enabled, AI-ready energy business to meet the country’s clean power, energy security and net zero goals.

                                  Welcome to the latest issue of Interface magazine!

                                  Click here to read the latest edition!

                                  SSEN Transmission: Digitally Enabling the Grid of the Future

                                  James McLean is the Chief Information Officer (CIO) of SSEN Transmission, a growing Business Unit of SSE Plc. In our lead feature this month, he charts the company’s journey to build a leadership team for IT capable of meeting Transmission’s goals, while facing the daily challenges of operations and programme delivery, allied with focusing on the drive for cyber-readiness, architecture expansion and the growing need for data and analytics.

                                  “The business case was to stand up core systems to deliver foundational technologies capable of driving efficiencies across an expanding enterprise,” he explains. “During my first few months I dialled into how SSEN Transmission operates and considered staffing plans. What does my organisation look like? At this point there were just seven people on the IT team and as T1 was ending we had some deliverables to do in preparation to ramp up for T2.”

                                  “It’s been a unique and interesting challenge leading a constantly growing organisation,” reflects James. “The majority of our people have never worked for SSEN Transmission before, and they’ve come from other industries. We’ve been fortunate in the fact that our business sector is attracting strong talent keen to be part of our energy security and net zero ambition as we work towards that goal.”

                                  Craig Thomas, CIO at the Merit Systems Protection Board.
                                  Craig Thomas, CIO at the Merit Systems Protection Board.

                                  The Merit Systems Protection Board: Championing Public Sector Change

                                  Digital transformation on a public sector budget is no mean feat, and the operational requirements of a government agency compounds the challenge.

                                  Craig Thomas, CIO at the Merit Systems Protection Board, met with Interface to explain how he and his team overhauled each of MSPB’s legacy systems one-by-one.

                                  “The digital transformation has been critical to MSPB operations because the agency can absorb much more organisational change without having to spend time and money retrofitting IT systems. The environment that we’re in now requires the ability to move very quickly and to change direction with minimal effort.”

                                  Carnival Corporation: Maturing Cybersecurity Across Global Operations

                                  Carnival Corporation’s CISO, Margarita Rivera. With two decades’ experience in the cybersecurity space, she has witnessed immense change both in the fabric of the industry and in its growing importance in increasingly complex and risk-prone digital environments.

                                  With a wealth of multi-industry experience, deeply transferable qualifications, and a front-row seat to the profound changes seen in cybersecurity over the past 20 years, Rivera is ideally placed to lead the ongoing process of securing the company’s digital and data environments.

                                  “People saw cyber as just an IT or tech problem, and I think today folks realise that cybersecurity is much more than that,” says Rivera. “We’re much more involved with many other stakeholders, ingrained in other parts of the business, helping to drive change in a positive fashion and providing guardrails for faster innovation that’s accelerating the way the business can operate.”

                                  “When I first started, there weren’t a lot of women in the tech and cybersecurity space,” she says. “I was one of the first. I remember going to conferences and being the only woman in the room. Now, thankfully there’s been a lot of change. 

                                  “I recently met with a partner that’s helping us with a project here, and I looked around the room to see it’s probably sixty-forty, with the sixty in favour of having more women-representative engineers and founders. That’s quite exciting. I think there’s a special skillset that women possess that they bring to the table in terms of creativity and collaboration.”

                                  Appian: Redefining Enterprise Transformation With AI

                                  Gregg Aldana, VP, Head of Global Solutions Consulting, shares what CIOs are really asking for in 2025 and beyond, how Appian is answering that call like no other platform, and why he believes the most progressive and impactful approach to AI is by embedding it inside the most critical processes.

                                  Gregg Aldana, VP, Head of Global Solutions Consulting, shares what CIOs are really asking for in 2025 and beyond, how Appian is answering that call like no other platform, and why he believes the most progressive and impactful approach to AI is by embedding it inside the most critical processes.

                                  “When I first came to Appian a little under a year ago, one of the first things that came up was the need to spend time with customers,” says Aldana. “If you really want to learn what’s driving and going on in the industry, you’re not going to find out from just reading analyst reports or looking online. You’ve got to go out and physically meet with and talk to people that are leading these changes. Meeting with 200+ CIOs and CTOs a year gives you a front seat to reality.”

                                  Click here to read the latest issue!

                                  • Digital Strategy
                                  • Events

                                  Author

                                  Dan Brightmore

                                  Published

                                  Estimated Read time

                                  2Mins

                                  Read in brochure format

                                  Accenture is helping SSEN Transmission manage hundreds of infrastructure projects vital to achieving the UK’s Net Zero ambition. Effective delivery…

                                  Accenture is helping SSEN Transmission manage hundreds of infrastructure projects vital to achieving the UK’s Net Zero ambition. Effective delivery required addressing fragmented data and disconnected tools that can slow the flow of information between systems. SSEN Transmission sought a partner to help reshape its approach for data-driven execution on capital projects.

                                  Meeting the Digital Challenge with Accenture

                                  SSEN Transmission partnered with Accenture to embrace automation and digitisation in response to increasing project demands, a challenge reflected across the wider Capital Projects sector. Through the adoption of BIM (Building Information Modelling) and the implementation of Integrated Project Management (IPM), which was developed with Oracle and Microsoft, this collaboration laid the groundwork for more connected ways of working and continues to promote transformation across the organisation.

                                  Key Benefits Delivered

                                  Accenture supported with IPM (Integrated Project Management) and Building Information Modelling (BIM) customised to meet specific needs and achieve key goals: 

                                  • Digitise processes for a single unified environment
                                  • Unify data for a standardised and trusted source of truth
                                  • Create a scalable platform for delivering capital projects

                                  “With a unified real-time view of project data, SSEN Transmission has improved efficiency and strengthened collaboration across internal teams and with external partners. This allows for more time focused on higher value insight-led work, supporting better outcomes, faster decisions and much more agile delivery”

                                  Huda As’ad, Managing Director, Capital Projects & Infrastructure, UKI

                                  Building for the Future

                                  More than a solutions provider, Accenture helps with strategy and issupporting SSEN Transmission’s continued focus on refining best practice for smooth project delivery. The partnership is helping to evolve ways of working and strengthening the digital foundation for future readiness.

                                  “Our collaboration is built on a strong digital foundation that can scale with SSEN Transmission’s growing needs. By unifying systems, data, and process, we are enabling the faster adoption of new capabilities and supporting the shift towards a fully data-driven capital project delivery”

                                  Nithin Vijay, Managing Director, Industry X – Capital Projects & Infrastructure

                                  Accenture: A Partner for the Journey

                                  Transformation is a journey that begins with the right foundation across people, data and process. It also requires a digital partner that brings together the best of industry experience, process excellence and technology to:

                                  • Develop a clear, actionable strategy for digital and data transformation
                                  • Embed industry best practices to optimise processes and drive continuous improvement
                                  • Enable smarter, more consistent delivery aligned to a long-term vision, from strategy through to execution

                                  And that’s where Accenture makes its mark, helping clients navigate the journey with confidence.

                                  Learn more about how Accenture is supporting SSEN Transmission on its digitisation journey with Huda As’ad, Managing Director, Capital Projects & Infrastructure, UKI and Nithin Vijay, Managing Director, Industry X – Capital Projects & Infrastructure

                                  • Digital Strategy
                                  • Infrastructure & Cloud
                                  • Sustainability Technology

                                  Author

                                  Akbar Hussain

                                  Published

                                  23 July 2025

                                  Estimated Read time

                                  5Mins

                                  Akbar Hussain, Co-founder and Chief Legal & Compliance Officer at TerraPay, on cross-border payment innovation

                                  Every transaction tells a story. Most pass by unnoticed: familial remittances, a gift, a balance topped up. But behind the scenes, every transfer or cross-border payment sets off a chain reaction of checks, rules, and decisions. Signals are assessed. Contexts are weighed. Trust is verified.

                                  Cross-border payments don’t operate in a vacuum. They move through regulatory frameworks and risk assessments, often in milliseconds. And as more and more transfers pass through this complex system, there is a growing need for infrastructure that knows not just how to move money effectively but how to govern its movement wisely.

                                  Small Transactions, Big Stakes

                                  There’s a myth in the payments world that small transactions carry small risk. That compliance obligations only apply at scale. Or that low-value payments fly under the regulatory radar. But in a globally connected system, nothing operates in isolation.

                                  Small transactions power financial inclusion: school fees, emergency loans, micro-business payments. They are frequent, personal, and essential. And when repeated millions of times across loosely monitored corridors, they can create risk patterns with system-wide consequences.

                                  When oversight is thin, even a modest flow of funds can be exploited for money laundering, fraud, or sanctions evasion. The notion that scale is only measured by individual ticket size ignores how quickly volume and velocity can multiply exposure. The risk isn’t always in the size of a transaction, it’s in how little is known about it.

                                  Risk also doesn’t scale linearly. A seemingly harmless payments corridor can, over time, become a blind spot for illicit flows if the right compliance checks aren’t embedded. That’s why building safeguards into the infrastructure, not just the interface, of any payments system is critical.

                                  Ultimately, there’s no such thing as a low-value transaction when the cost of failure is measured in trust.

                                  Innovation vs Regulation

                                  In much of the FinTech world, there’s still a belief that building effective cross-border payment systems means choosing between two paths: innovate fast or regulate carefully, as if the two can’t coexist. But this is a false choice. There is no sustainable growth in cross-border finance without regulatory credibility. Any system built to avoid or defer oversight will ultimately collapse, hollowed out by its own shortcuts.

                                  In reality, we shouldn’t think of compliance as a barrier to scale but rather as a condition of scale. It’s what unlocks markets, builds durable infrastructure, and earns the trust of partners, governments, and users. Trust isn’t a switch that flips at go-to-market; it’s something built transaction by transaction, jurisdiction by jurisdiction.

                                  That means licensing, yes. But it also means culture. It means embedding compliance into the architecture of your systems, the rhythms of your operations, and the priorities of your leadership. When regulatory design is built in from the start—rather than patched on later—it helps power growth.

                                  Systemic Risk Has No Borders

                                  One of the defining features of modern financial infrastructure is its interdependence. There are no isolated risks anymore. A lapse in one system—a poorly monitored corridor, a flawed due diligence model, an unvetted partner—doesn’t stay local. It echoes outward. Financial crime doesn’t respect borders. Neither does reputational damage.

                                  This is particularly true in high-risk markets, where traditional institutions are limited or absent, and the appetite for speed often overshadows prudence.

                                  These are also the places where financial inclusion efforts matter most—and where failure risks cutting people off entirely. Getting it wrong in these contexts risks shutting out the unbanked and underbanked from the systems designed to serve them, reinforcing the very barriers this industry claims to dismantle.

                                  Financial institutions that choose to operate in these environments must do so with heightened accountability. The organizations that lead with integrity understand this and act accordingly: investing in real-time monitoring, adapting to regulatory shifts, and holding their partners to the same standard.

                                  Building for the Future with Cross-Border Payments

                                  There’s an understandable appeal to silver-bullet solutions: AI for fraud detection, blockchain for traceability, real-time everything. These technologies are powerful, and when applied with care, they can significantly enhance the robustness of compliance systems. But they’re not infallible. When adopted without scrutiny, they risk masking deeper structural weaknesses beneath a surface-level sense of control.

                                  The more sustainable approach is rarely the flashiest. It’s incremental, data-driven, and adaptive. It prioritizes experimentation over assumption and refinement over scale for scale’s sake. Using anonymised data to test systems, deploying AI to extend—rather than replace—human oversight, and continuously evolving alongside the regulatory environments these systems must serve: this is where long-term resilience is built.

                                  Trust, in Practice

                                  To design for trust is to design for complexity. It means making peace with the regulatory landscape and recognizing that compliance isn’t a one-off exercise but a constant, evolving discipline that must move in step with innovation—not trail behind it.

                                  It may not be the flashiest part of the story, or the one that makes the headlines, but any serious player in the cross-border economy must learn to balance the urgency of go-to-market with a deep, operational understanding of compliance and security. Regulation isn’t something to be welded on later. It’s something to be baked in from the start.

                                  • Digital Payments

                                  Author

                                  Nell Walker

                                  Published

                                  Estimated Read time

                                  6Mins

                                  The proof, as they say, is in the pudding – and the evidence of TealBook’s increasingly-successful evolution lies in its client relationships.

                                  We talked endlessly about data and AI at DPW New York 2025. A universal truth is that the successful implementation of AI requires clean data. It doesn’t have to be perfect, but businesses certainly need to have a decent handle on their data before adopting AI tools successfully. 

                                  To help make this a reality, North American data and software company TealBook has recently announced a legal entity-based data model. It’s designed to resolve supplier records to the correct legal entities, map parent-child relationships, and enrich profiles with verifiable attributes, enabling accurate supplier data to flow seamlessly into procurement systems and AI applications. “This is part of a 12-year journey for TealBook,” says Stephany Lapierre, the company’s Founder and CEO. “Our vision has always been to build a way to enable procurement organisations to have high quality data with a lot of integrity. That way, you give them the trust they need to put data directly into their systems. 

                                  “Twelve years ago, we underestimated the complexity of getting large enterprises to trust a third-party data solution. As part of our journey, we started using AI early on to find information where it exists on supplier websites and databases. We also started creating digital profiles in a structured way for procurement to access it, match it to their vendor master, and use it.”

                                  TealBook’s data evolution

                                  But, again, at the beginning, TealBook couldn’t be sure whether the data was high enough quality. In 2017, the company was primarily known as a supplier discovery application. It was positioned as a pre-sourcing engine to help procurement teams identify alternative suppliers. At the time, TealBook’s data and models enabled it to determine which companies were similar to others. This meant users could search and find comparable suppliers to expand their sourcing options.

                                  “But that was just a way for us to deliver something that was underserved in the market,” Lapierre continues. “Then our customers started asking for certificates, which are hard to collect and match. They needed cleaner data. They felt they were under-reporting. So in 2018, we started to see whether our technology could refine the data more. We focused on certificates and supplier diversity. We collected great use cases along this journey, and the vision never wavered.

                                  “Just last year we released a new technology – completely different, really sophisticated – allowing us to pull from a lot more data sources. We have provenance so our customers can actually verify where the data’s coming from. We can match it to vendor masters. And now, we also have this new model that includes 230 million verifiable global legal entities from across 145 countries’ registries. We marry this with global parent and child hierarchy, which is really hard for our customers to match themselves.”

                                  Partnership with Kraft Heinz

                                  Now, after 12 years of that vision, TealBook is deeply proud of what it’s achieved. Part of its ability to get to this point is due to early adoption from key customers. Kraft Heinz is a business which Lapierre describes as a “co-innovation partner”, and has been invaluable in helping TealBook achieve its recent goals.

                                  From the perspective of Stefanie Fink, Head of Global Data and Digital Procurement at Kraft Heinz, the partnership has been an immediately valuable one. “It really started with having a visionary, like-minded relationship,” she says. “That’s an important piece of it, because my vision for procurement is that we are partners in our enterprise. 

                                  “In order for us to do our jobs, we have to bring in the right data for use. This is where Stephany’s partnership and vision really resonated. We were really looking for diversity and we could make things easier for our partners, while making sure we had the right people in our ecosystem. We also had to lift up the hood and see what was underneath everything we’ve got. Stephany brought our vision to life. TealBook has evolved too, as we’ve seen; it’s more about orchestration and software-as-a-service. It has been a partnership of need and we cannot continue to do other things without this kind of partnership around data.”

                                  When initially dabbling with this relationship, Fink was clear that Kraft Heinz had no desire to be taking care of more stuff. What she wanted from TealBook was a strong focus on good quality data. After last year’s product release from TealBook, Kraft Heinz already saw its data enriched by 25%. The recently-announced new data model gives the business and TealBook’s other customers the right structure tied to a legal entity, which is a highly credible anchor. “We’re able to do entity resolution – all automated – remove all the duplicates, and then you start with a clean, digitised vendor master,” says Lapierre. “That’s what brings further enrichment.”

                                  The challenge of assessing data quality

                                  Assessing its data before involving TealBook was important for Kraft Heinz, but challenging for such a large organisation. “We had to fail first and fail fast,” says Fink. “We tried some AI around fixing things early, but that didn’t work for us. It was a real eye-opener, realising where this next evolution could take us. Particularly regarding focusing on AI and agents for the right things, not the meaningless things. Before, we were asking agents to tell us if things were duplicates, when we should have been asking: what do these suppliers offer? Where is the innovation? Where is the value?”

                                  What surprised Fink most when looking under Kraft Heinz’s hood was the lack of attention that was being paid to what the business was doing. “It was amazing that nobody had questioned it sooner,” she says. “So I said, let’s take this as a crawl, walk, run approach. I have a wonderful CPO who really understands where we want procurement to go as a function. She was excited about us just getting it done and getting people involved, and that’s what it takes: real pride in ownership of the data.”

                                  Getting engrossed in GenAI

                                  True partnership and an all-in approach has enabled Kraft Heinz to work successfully with AI. This is something some businesses are struggling with as the conversation around artificial intelligence grows louder. For Lapierre, as the CEO of a tech company, adopting AI successfully has meant trying and failing and being fully entrenched in AI as it has evolved.

                                  “We’ve been using AI in our technology since 2016,” she states. “We’re an early adopter. We’d be talking about scraping data, and data in the cloud, and AI models, and our customers’ pupils would widen in surprise. We’ve come a long way and the market has come a long way. 

                                  “The technology we deliver today wouldn’t be possible without the AI tools now at our disposal. We used to build models; we don’t do that anymore. We spend a lot of time investing in engineers to build and test models. That’s made us so much more efficient. I use GenAI every day for so many things now. I’m encouraging my team to be so involved in AI. That’s how you build expertise. You need really strong expertise to use GenAI well. 

                                  “Getting good with AI is about taking risks and having a leadership team that pushes for new things. Suddenly, the successful use of AI becomes a habit.”

                                  Author

                                  Nell Walker

                                  Published

                                  Estimated Read time

                                  7Mins

                                  Why businesses should prepare themselves for AI by not getting lost in the whirlwind of hype and focusing only on what works for their needs.

                                  With AI being the topic of conversation for procurement professionals right now, it’s easy to get lost in the maze of conflicting information. Vroozi is a procure-to-pay platform powered by robust AI capabilities to deliver meaningful use cases. CEO and Co-Founder, Shaz Khan, takes approaching AI the right way very seriously. 

                                  For Vroozi, the use of AI is a two-sided coin. It’s an organisation that talks about AI both in production and consumption. AI is a tool that has been a game-changer, because it has enabled Vroozi’s software and technology engineers to be able to rapidly prototype and develop code. And that code is beneficial for creating feature sets and capabilities that the company wants to introduce to the market.

                                  “Similarly, we take steps to look at how a customer interacts with our software for the first time,” Khan explains. “The implementation process is also ripe for consuming and producing great results with AI. Imagine you go through some type of interview wizard where you prompt the system based on your region and industry. The system will self-configure according to your business unit. This is real intelligence that understands your business at a different level, as well as the competitive landscape, and brings in best practices to deliver incredible results.”

                                  Getting the approach right

                                  Having said that, Khan freely admits that we’re in the early innings of AI adoption. For him, leaders should adopt a multi-pronged approach to implement AI. The first move is to assemble a team. “One key area with AI is that a lot of companies are relying on outside experts that don’t know the business and the goals that they’re trying to achieve,” he explains. 

                                  “You should invest in your own people before you invite outside parties in. Bring that education and assemble a use case, before assessing the problems you’re trying to solve and determining whether AI is a good tool set or capability to solve the problem. If these things match up, execute the game plan, bring in the right technologies and the right expertise, and only then bring AI capabilities into your workforce.”

                                  The challenges

                                  With this being the “early innings”, there are also barriers and challenges. The main issue, from Khan’s perspective, is security. “There’s a trust aspect that has to be looked at,” he explains. “There’s also an ethics aspect. Are you delivering the right results? And how much autonomy are you giving AI and its agents to go out and deliver those results for you without any human interaction? I think the companies that get it right will strike a balance between the trifecta of automation, really great AI technologies, and a balance of human interaction to create an overall output.”

                                  There’s also the question of data. If the data isn’t clean, output will be compromised and lead to poor results. We haven’t seen the worst of what can happen, Khan believes, and AI has the potential to create scenarios that are hard to recover from, if used poorly. “We need to prepare ourselves now to prevent those types of potential calamities from happening,” says Khan. Which is the entire point of DPW: for procurement and technology leaders to educate and learn about best AI practice. 

                                  This allows people to cut through the, as Khan puts it, “hysteria” around AI that can cause problems for businesses. They’re rushing to solve problems, and while leveraging AI can be a component of a complete holistic toolkit, it can’t be the only answer. “A lot of companies today still struggle with getting their businesses off spreadsheets,” he states. “AI should be an equaliser and enabler to get it right.”

                                  Structuring unstructured data

                                  For Khan, in order to ready themselves for AI, procurement professionals and practitioners need to be absolutely committed to data management and governance. “What companies often forget is that much of today’s data is unstructured. It’s not neatly stored in databases – it might be a chat, an image of a spec sheet, or a contract never digitised. This unstructured data often can’t be used by AI models today, so companies risk only addressing a small part of the challenge. Data governance has to be an ongoing exercise.”

                                  Having said that, Khan is keen to differentiate between clean data and perfect data. In fact, many procurement professionals we spoke to at DPW New York 2025 said the same. The message is: don’t wait around for everything to be perfect, or you’ll never start.

                                  “Good enough data is just fine,” Khan says. “But if you’re going to continue to feed your AI engines and algorithms bad data, your outputs will be compromised. Companies need to have data governance strategies and upfront policies in place so that they can manage this, independent of the people that offer them.”

                                  AI creating a complete picture

                                  While treading carefully is important, Khan is equally keen to extoll the many virtues of AI for procurement professionals. There are many incredible use cases already, and AI tool sets and algorithms can effectively interrogate a company’s data and give them the answers they require. AI enables these users to have a complete picture of their buying cycle, and allows them to get additional information for where they can pivot.

                                  “This is where the true power of agentic AI will come into play,” says Khan. “When you can fully trust the system inputs, AI will be able to orchestrate those processes autonomously, and present that information to an end user for final decision.”

                                  Khan is very excited about what Vroozi is doing within its own AI layer. The business looks at AI and intelligence as a pervasive thread across its entire tech stack. Every aspect of its platform has some kind of AI enablement, although it’s not an AI-first company. 

                                  “We follow three distinct areas where we are thriving on the AI front,” says Khan. “First is intelligent document processing. Can we take structured and unstructured data such as contracts, quotes, work orders, and invoices, and populate them automatically onto a screen without any human touch? Processing invoices might require an army of people typing in data, and they might not capture it all. But an AI toolset can take millions of records and process them simultaneously. That’s the power of AI.”

                                  The power of hyper-personalisation

                                  The second area is what Vroozi calls hyper-personalisation, where it intensely personalises the platform to meet a company’s preferences and needs. It’s about how AI can find trends and not only predict the user’s needs, but also help take the next steps. This includes finding suppliers and ordering things that are needed, so that workflows aren’t disrupted.

                                  “Then we also have what we call the push economy,” says Khan. “AI’s power is in pushing and giving people head starts. So when you talk about AI algorithms and look at analytics, it’s about how AI can present to companies in the procurement space when they need to lock in favourable pricing on products and services, and predict when you are seeing potential fraud scenarios based on trends and patterns. You need a lot of data for those AI models to train on, which is why I say we’re in the early innings. It takes time, but it’s incredibly powerful when you get to that point.”

                                  The benefits ahead

                                  At such an exciting time for procurement, 2025 and 2026 look bright for leaders in this space. Not only procurement, but also supply chain and FinTech, are set to benefit from what AI can do with data. 

                                  “There’s going to be a focus on how to capture and harness data, and feed it into AI in a way that produces results,” says Khan. “What we’ll see in the next two years is that AI has now learned from the data that’s been fed into it. You’re going to see higher-quality results and better outcomes. Again, I would caution companies to define the problem first. Then determine if AI is an absolute enabler and game changer. We believe AI can be an influencer and supercharger in terms of productivity. However, there needs to be specific use cases that make sense for corporations. 

                                  “In 2025 and beyond, you’re going to see great technologies embedded into organisations that really work.”

                                  Author

                                  Dan Brightmore

                                  Published

                                  22 July 2025

                                  Estimated Read time

                                  4Mins

                                  The insurance industry, long known for its complex processes and legacy systems, is undergoing a dramatic transformation. At the heart…

                                  The insurance industry, long known for its complex processes and legacy systems, is undergoing a dramatic transformation. At the heart of this shift is InsurTech – the fusion of insurance and technology – bringing faster claims, personalised policies and more efficient operations. In 2025, several tools are leading the charge. Here are five of the top InsurTech solutions reshaping the sector.

                                  1. Tractable – AI-Powered Claims Automation

                                  Tractable uses computer vision and artificial intelligence to assess vehicle and property damage in real time. With just a few photos uploaded by the policyholder, the tool can evaluate damage and generate repair estimates instantly. This significantly shortens claims processing times from days or weeks to mere hours. Tractable is already used by global insurers like GEICO and Covéa and is expanding into home insurance applications as well.

                                  Why it’s a game changer: It replaces manual claims inspection with automated, objective AI assessments – cutting costs and improving customer satisfaction.

                                  2. Shift Technology – Fraud Detection Engine

                                  Shift Technology offers an advanced AI platform specifically trained to detect insurance fraud. Using machine learning, it analyses claims data, historical fraud patterns, and external sources to flag suspicious activities. Its algorithms adapt over time, improving their detection accuracy.

                                  Key advantage: It empowers insurers to prevent millions in fraudulent claims annually, without sacrificing the customer experience for legitimate policyholders.

                                  3. Zego – On-Demand Insurance for the Gig Economy

                                  Zego offers usage-based insurance tailored to gig workers, delivery drivers, and small businesses. Its app-based platform integrates with telematics, ride-hailing apps, and work schedules to offer dynamic, pay-as-you-go coverage. This flexibility makes it ideal for freelancers and platforms like Uber or Deliveroo.

                                  Innovation point: Zego rewrites traditional insurance models by aligning premiums with real-time usage and risk levels – ideal for the on-demand economy.

                                  4. Cover Genius – Embedded Insurance API

                                  Cover Genius provides APIs that allow digital businesses to offer embedded insurance directly within their platforms. For example, a travel booking site can offer flight cancellation protection at checkout, or an e-commerce retailer can embed product warranty options. Cover Genius handles everything – from pricing and underwriting to claims and global compliance.

                                  Impact: It brings insurance directly to the customer at the point of need, improving uptake and customer convenience while opening new distribution channels.

                                  5. Sprout.ai – Intelligent Claims Triage

                                  Sprout.ai combines NLP (natural language processing) and data enrichment to automate the first notice of loss (FNOL) and claims triage process. It can pull insights from emails, documents, and databases to provide context-rich claim summaries, which are then used to assign the right workflows or handlers.

                                  Business benefit: Sprout.ai reduces administrative overhead and speeds up claim resolution by up to 70%, while maintaining transparency and fairness.

                                  Insurtech tools like Tractable, Shift, Zego, Cover Genius, and Sprout.ai are not just digitising insurance, they’re reimagining it. With AI, APIs, and real-time analytics at their core, these platforms are improving efficiency, reducing fraud, and delivering a customer-first experience. As insurers adopt these innovations, expect faster, smarter, and more responsive insurance services for the modern age.

                                  • InsurTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  18 July 2025

                                  Estimated Read time

                                  5Mins

                                  Morne Rossouw, Chief AI Officer at Kyriba, on leveraging AI skills to enhance decision-making and compliance in financial services

                                  At the intersection of innovation and responsibility, the finance sector faces a pivotal challenge… The ‘trust gap’ in AI adoption. CFOs and treasury leaders are aiming to safeguard their organisations’ financial health. The promise of AI’s transformative power is often tempered by concerns around security, transparency and regulatory compliance. Yet, as the latest IDC InfoBrief and Kyriba CFO survey reveal, there is a clear path forward. It is one that requires essential AI foundation skills and a thoughtful approach to AI solutions.

                                  Understanding the Trust Gap

                                  The potential for AI in treasury and finance is compelling. Over 84% of treasury professionals agree Generative AI will significantly impact treasury processes within the next 24 months. However, the journey to widespread adoption is hindered by what many see as a  ‘trust gap’. There is a divide between transformative promise and concerns about security and privacy risks.

                                  These real concerns cover several aspects, first and foremost: risk aversion. Many finance professionals by training are inherently compelled to act with a risk mitigation mindset. By extension, many are cautious about the ‘black box’ nature of artificial intelligence and its role in decision-making. They prefer systems where they can better understand and interpret outcomes. Another layer is the pressure to adhere to the industry’s strict and evolving compliance requirements. These are now expanding to cover legal and industry standards around adoption, such as the EU AI Act.

                                  Data quality and security further complicate the picture. Financial data is highly sensitive, and organisations must address issues of accuracy, bias, and privacy when integrating AI solutions. In addition, there is a skills gap to overcome. Many finance professionals may lack the newly emerging need for expertise to leverage these tools effectively and securely in a financial context, making the development of new competencies essential for successful adoption.

                                  Building a Culture of Trust for AI

                                  Despite concerns, the interest in and potential value of artificial intelligence to streamline and optimise treasury operations are clear. In fact, the latest studies show:

                                  • 44% of treasury professionals see immediate value in AI-enhanced cash management
                                  • 50% prioritise AI for financial fraud detection
                                  • 46% focus on risk management applications¹

                                  Achieving success with artificial intelligence requires more than simply adopting new technologies. It demands a broader cultural transformation. Structured training programs are critical for helping finance teams develop confidence and competence in using AI. And gaining hands-on experience with AI tools in real-world scenarios allows professionals to apply their knowledge and adapt to evolving capabilities.

                                  As one CFO noted: “AI is redefining the CFO’s mandate as we speak. With the right foundation and skills, I don’t believe AI widens the trust gap; it closes it.”

                                  Essential Foundational Skills to Bridge the Trust Gap

                                  Narrowing the trust gap between the immense opportunities of AI with the real potential risk requires organisations to develop three critical foundation capabilities. The first is communication and interaction. Finance professionals should learn how to engage in clear dialogue with AI systems by asking effective questions, refining requests, and understanding how to guide AI tools to support financial reporting and analysis.

                                  The second foundational skill is data storytelling. Transforming complex AI outputs into clear, actionable insights helps make financial data more accessible and meaningful to stakeholders. This means not only interpreting results but also presenting them through compelling narratives and visualisations.

                                  As a final safeguard, teams should develop a systematic approach to validating AI-generated insights to ensure that outputs align with regulatory requirements and business logic. This process is crucial for maintaining compliance standards and fostering confidence in AI-driven decisions.

                                  Trusted AI requires a Trusted Platform

                                  Organisations can build trust in AI adoption by prioritising security and transparency in their technology choices. Selecting tools and platforms that provide enterprise-grade security and offer explainable insights is vital. Equally important is ensuring that customer data remains private and is not used to train external models, as is the use of built-in validation tools to support compliance.

                                  Trust is further built by user-led design. Intuitive interfaces make it easier for finance teams to interact effectively with new technologies. Leveraging visual analytics and dashboards enhances the ability to tell stories with data, while comprehensive validation frameworks help support regulatory and business frameworks.

                                  Establishing a trusted platform foundation is the final piece. Building on robust data infrastructure allows organisations to define key AI foundation skills. Investment in training and certification programs helps finance professionals stay up to date with best practices, while real-time validation and oversight of AI-driven decisions further reinforces organisational trust.

                                  The Path Forward

                                  The potential impact of increased AI skills, in tandem with secure solutions, is immense. Enhanced decision-making becomes possible through improved cash visibility and forecasting, while compliance is strengthened through systematic validation and fraud detection. Efficiency gains are realised via optimised AI/Human collaboration, and more accurate and insightful financial reporting is achieved through advanced data storytelling. Organisations also benefit from reduced processing time thanks to intelligent automation.

                                  In an era where trust underpins financial and broader business leadership, success depends on developing strong foundational capabilities alongside robust solutions. Responsible AI – such as Kyriba’s Trusted AI portfolio – emerges as a strategic partner for CFOs and treasury teams, providing not just the technology but also the framework for skill development essential to closing the gap.

                                  Through this comprehensive approach – combining foundation skills and trusted solutions-organisations can confidently embrace AI’s transformative potential while maintaining the security, compliance, and transparency essential to modern financial operations. The result is a future where skilled professionals leverage AI to drive data-driven business decision making that can unlock unprecedented levels of financial performance and agility.

                                  • Artificial Intelligence in FinTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  17 July 2025

                                  Estimated Read time

                                  2Mins

                                  Join industry leaders and innovators at the 5th Annual Digital Banking Summit

                                  Digital Banking Summit is a premiere event designed to explore the most transformative trends shaping the banking sector in the modern era. This two-day conference will delve into critical topics such as AI-driven banking, open finance, financial inclusion, and the future of digital identity. Discover how cutting-edge technologies like edge computing, hyper-personalisation, and APIs are redefining corporate and retail banking. Engage in discussions about legacy system modernisation, sustainability through ESG initiatives, and the regulatory landscape, including DORA and GDPR. Book your place here.

                                  Gain Expert Insights

                                  With sessions led by top executives from global financial institutions, attendees will gain actionable insights… Learn more about leveraging innovation to streamline operations, enhance customer experience and build resilient financial ecosystems. Speakers include thought leaders representing Wells Fargo, Revolut, Wise, Standard Chartered, Lloyds and more…

                                  Take advantage of networking opportunities and 1:1 meetings to connect with senior leaders and experts. Don’t miss this opportunity to be part of the conversation shaping the future of banking.


                                  DAY 1 @ Digital Banking Summit

                                  • Revolutionising Banking in the Digital Era
                                  • Open Banking and Open Finance
                                  • Financial Inclusion in Banking
                                  • Digital Identity: Onboarding, Compliance and Embedded Finance
                                  • Cross-Industry Collaboration in Banking
                                  • Banking for a Digital Workforce
                                  • Hyper-Personalisation in Wealth Management
                                  • Edge Computing
                                  • The Role of APIs in Transforming Corporate Banking
                                  • Digital Resilience
                                  • Legacy Systems vs Modernisation
                                  • AI in Banking

                                  DAY 2 @ Digital Banking Summit

                                  • Automation and Cloud Banking
                                  • Data Monetisation: Ethics and Opportunities
                                  • Digital Marketing in Banking
                                  • Central Bank Digital Currencies
                                  • Sustainable Banking Future with ESG
                                  • Navigating DORA, GDPR and Beyond
                                  • Wallets
                                  • Mobile Banking
                                  • Crypto, Instant Transfers and Banking
                                  • AI-Driven Fraud
                                  • Customer-Centric Innovation
                                  • Cybersecurity: Deepfakes, AI Attacks and Quantum Risks

                                  Book your ticket here.

                                  Author

                                  Dan Brightmore

                                  Published

                                  15 July 2025

                                  Estimated Read time

                                  4Mins

                                  The FinTech industry, sitting at the nexus of finance and technology, is a prime target for cybercriminals. With the growing…

                                  The FinTech industry, sitting at the nexus of finance and technology, is a prime target for cybercriminals. With the growing prevalence of digital banking, mobile payments, and crypto-assets, cybersecurity has become a non-negotiable priority. In response, a new generation of tools has emerged to help FinTech companies stay ahead of threats. Here are the top five cybersecurity tools safeguarding the sector in 2025:

                                  1. CrowdStrike Falcon – Endpoint Protection Powerhouse

                                  CrowdStrike Falcon has become a leading choice for FinTech companies due to its advanced endpoint detection and response (EDR) capabilities. Powered by AI and cloud-native architecture, Falcon provides real-time monitoring and threat intelligence across endpoints, detecting suspicious behavior before it escalates. Its lightweight agent and scalable design make it ideal for rapidly evolving digital infrastructures.

                                  2. Snyk – Securing FinTech DevOps

                                  FinTech’s embrace of continuous development and integration demands security solutions built for speed. Snyk focuses on developer-first security, helping teams identify and remediate vulnerabilities in open-source dependencies, containers, and infrastructure as code. It integrates directly with GitHub, GitLab, and CI/CD pipelines, ensuring vulnerabilities are caught early—without slowing down development.

                                  3. Fortinet FortiWeb – Web Application Firewall (WAF)

                                  Web applications are the backbone of many FinTech platforms, and FortiWeb provides critical protection. This intelligent WAF defends against OWASP Top 10 threats, including SQL injection and cross-site scripting, while leveraging machine learning to tailor protections in real-time. FinTech platforms using APIs heavily benefit from FortiWeb’s deep learning inspection and bot mitigation features.

                                  4. IBM Security QRadar – SIEM Intelligence

                                  QRadar continues to lead as a top-tier Security Information and Event Management (SIEM) solution. It aggregates and analyzes data from across an organization’s digital ecosystem, detecting threats and providing actionable insights. FinTech firms rely on QRadar for compliance with financial regulations and for its ability to deliver fast, context-rich threat detection and response capabilities.

                                  5. Auth0 – Identity and Access Management (IAM)

                                  Auth0, a standout solution in identity and access management. In FinTech, controlling user access with precision is crucial. Auth0 provides secure, scalable authentication for apps and APIs, offering features like single sign-on (SSO), multi-factor authentication (MFA), and adaptive access policies. With rising threats targeting user credentials, IAM is no longer a back-office function—it’s frontline security.

                                  Cybersecurity in FinTech requires agility, intelligence, and regulatory alignment. Tools like CrowdStrike Falcon, Snyk, Fortinet FortiWeb, IBM QRadar, and Auth) are not just protecting infrastructure. They’re enabling innovation in one of the world’s most dynamic industries. As threats grow more sophisticated, these platforms will continue to shape the future of secure financial technology.

                                  • Cybersecurity in FinTech

                                  Author

                                  Philipp Buschmann

                                  Published

                                  11 July 2025

                                  Estimated Read time

                                  5Mins

                                  Philipp Buschmann, co-founder and CEO of AAZZUR – a one-stop-shop for smart embedded finance experience – on business transformation

                                  Business spending used to be a mess. Think mountains of receipts, last-minute expense reports, and a constant guessing game about where the money actually went. For many companies, especially those growing fast or juggling lots of moving parts, keeping tabs on spending felt like trying to plug holes in a sinking ship. Even with spreadsheets and corporate cards, it was hard to get real visibility or control.

                                  But something is changing. Behind the scenes, a quiet shift is taking place. It’s called embedded finance andwhile the name might sound technical, the impact is very real and surprisingly simple: it’s giving businesses more control over how they spend money, without adding complexity.

                                  At its core, Embedded Finance means putting financial tools directly inside the platforms businesses already use. So instead of switching between software to pay bills, issue cards, or track expenses, those features are built right into the systems companies rely on every day, like accounting tools, logistics platforms, or even team management apps.

                                  It’s like turning on the lights in a dark room. Suddenly, business leaders can *see* where the money is going, in real time. They can set rules. They can act faster. And best of all, they don’t need a finance degree to understand what’s happening.

                                  Goodbye Expense Reports with Embedded Finance

                                  This will be music to your ears. One of the most obvious and painful examples of messy spending is employee expenses. Traditionally, employees pay out of pocket, save their receipts, and submit reports at the end of the month. Finance teams then spend days chasing missing documentation and trying to figure out whether each purchase was actually necessary. The entire process is slow, frustrating, and ripe for errors.

                                  With embedded finance, that whole routine gets flipped. Now, companies can issue virtual cards with built-in controls, like daily limits, merchant restrictions, or even time-based rules. Employees use the cards directly from their phones, receipts are uploaded instantly, and managers can see every transaction as it happens. No more end-of-month surprises and best of all, nomore chaos.

                                  Real-Time Visibility, Real-Time Decisions

                                  Having a hard time making quick decisions? When spending is scattered across departments, locations, or tools, it’s hard to have a coherent plan. Business leaders often operate with outdated information, relying on month-end reports to spot issues that have already happened. That lag can be costly, especially in a fast-moving economy.

                                  Embedded Finance changes that by connecting spending directly to data. Whether it’s a construction company managing field purchases or an e-commerce brand scaling its supply chain, having real-time visibility into expenses means leaders can make smarter decisions, faster. If costs spike in one area, they can spot it and adjust instantly. If a new supplier overcharges, they’ll know right away.

                                  It’s not just about seeing the numbers—it’s about being able to act on them in the moment.

                                  Fewer Tools, Less Friction with Embedded Finance

                                  A big source of business friction comes from too many disconnected systems. You might have one platform for payroll, another for invoicing, and yet another for managing employee cards. Every tool means another login, another source of truth, and more opportunities for things to slip through the cracks.

                                  Embedded Finance simplifies the stack. Instead of stitching together a patchwork of tools, companies can use one unified system where spending and financial controls are already built in. For employees, that means fewer steps to get what they need. For finance teams, it means fewer errors to clean up. And for leadership, it means clearer insight into how money is being used to drive the business forward.

                                  Solaris is making waves in the circular economy by teaming up with Grover to allow people to subscribe to tech devices monthly instead of purchasing them. Due to stringent rules, they needed a product they could integrate to enable customers full control and increase loyalty. They succeeded by launching the Grover Card to boost engagement and retention and make payments borderless and hassle-free.

                                  Empowering Teams Without Losing Control

                                  One of the biggest tensions in company spending is the balance between trust and control. You want teams to move fast and make smart decisions, but you also need to avoid waste and fraud. Too much freedom, and things go off the rails. Too much control, and progress stalls.

                                  Embedded finance helps solve that tension. Because financial tools are built into the workflow, companies can set smart rules from the start. Maybe the marketing team can spend up to a certain limit on campaigns, but anything over, needs approval. Maybe contractors can only use their cards during work hours. These aren’t rigid roadblocks—they’re flexible guardrails that keep spending aligned with company goals.

                                  At the same time, employees feel more trusted. They don’t have to front their own money or wait for approvals. They can focus on doing their jobs, knowing they have the tools they need.

                                  Final Thoughts

                                  Embedded Finance isn’t about adding more technology for the sake of it. It’s about making finance work better, smarter, faster, and with less hassle. For businesses that have struggled with messy, unpredictable spending, it’s a breath of fresh air.

                                  The companies embracing these tools aren’t just getting more efficient, they’re unlocking new levels of clarity and confidence. And in today’s unpredictable business environment, that’s not just a nice-to-have – it’s a competitive advantage that will pay back in spades.

                                  • Embedded Finance

                                  Author

                                  Lysan Drabon

                                  Published

                                  10 July 2025

                                  Estimated Read time

                                  5Mins

                                  Lysan Drabon, Managing Director at the Project Management Institute (PMI), on the critical role of project management in successfully integrating Artificial Intelligence (AI) as a tool for driving sustainability initiatives within FinTech and financial services

                                  The financial services sector, traditionally associated with spreadsheets and skyscrapers, is undergoing a green transformation. FinTech, at the forefront of this evolution, is increasingly leveraging Artificial Intelligence (AI) to drive sustainability initiatives. However, the path to a greener financial future isn’t paved with algorithms alone. Effective project management is the crucial compass, guiding these AI-powered initiatives towards tangible and lasting impact.

                                  The potential for genuine progress hinges on a structured, project-based approach. Without it, AI risks becoming a costly distraction. Failing to deliver on its promise of a more sustainable financial ecosystem.

                                  The challenge is significant. Financial institutions face growing pressure from investors, regulators, and customers to demonstrate their commitment to ESG principles. AI offers powerful tools for achieving these goals. From optimising energy consumption in data centres to identifying and mitigating climate-related financial risks. Yet, as Project Management Institute’s (PMI) recent research reveals, success is far from guaranteed.

                                  The findings highlight a clear disparity between organisations that strategically integrate AI into their sustainability efforts and those that treat them as separate endeavours. Those with a robust project management framework, capable of balancing these complex initiatives, are far more likely to achieve meaningful results.

                                  So, how can FinTech companies and financial institutions effectively harness the power of AI to drive sustainability? The answer lies in prioritising three key elements within a project management framework: data readiness, leadership preparedness, and strategic alignment.

                                  Data Readiness: The Foundation for Sustainability in Finance Using AI

                                  AI algorithms are only as good as the data they consume. In the context of FinTech and financial services, this means establishing robust data collection, management, and utilisation processes. These must capture a wide range of sustainability-related metrics.

                                  This includes data on energy consumption, carbon emissions, investment portfolios, and supply chain practices. Project managers must champion data readiness as a fundamental project requirement, ensuring that data is accurate, consistent, and readily accessible.

                                  Imagine trying to assess the ESG performance of an investment portfolio when data on the environmental impact of underlying assets is incomplete or unreliable. A “single source of truth” for sustainability data is essential. It provides a reliable foundation for AI models to accurately assess risks, identify opportunities, and track progress towards sustainability goals.

                                  This also means addressing the ethical considerations around data. Financial data is highly sensitive, and project managers must ensure that AI systems are used responsibly and ethically, protecting data privacy and preventing bias.

                                  Leadership Preparedness: Building Sustainability-Savvy AI Teams

                                  The successful integration of AI for sustainability in fintech demands a new breed of leader. Project managers must not only possess the traditional skills of planning and execution but also cultivate a deep understanding of both AI technologies and the nuances of sustainable finance. This requires a proactive approach to talent development, fostering a culture of continuous learning and experimentation.

                                  Building successful teams means bridging the gap between data scientists, financial analysts, sustainability experts, and regulatory compliance officers. Project managers must act as translators, delivering effective communication and collaboration across these diverse disciplines. They need to be adept at identifying and nurturing talent. Whether through upskilling existing employees or recruiting individuals with specialised expertise.

                                  Moreover, leadership preparedness extends to the ability to navigate the ethical complexities of AI in finance. Project managers must be equipped to address potential biases in algorithms, ensure data privacy, and promote transparency and accountability in AI-driven decision-making. This requires a strong commitment to responsible innovation and a willingness to challenge conventional thinking.

                                  Strategic Alignment: Embedding Sustainability into FinTech’s DNA

                                  AI-driven sustainability initiatives must be aligned with broader organisational objectives. Project managers must ensure sustainability is embedded into the project’s core strategy. Every stage of a project must be evaluated for its environmental and social impact.

                                  This requires buy-in from senior management and establishing clear metrics for measuring sustainability performance. Additionally, it means developing frameworks for reinvesting AI-driven sustainability gains into further initiatives. This creates a virtuous cycle of continuous improvement.

                                  Consider a FinTech company developing an AI-powered platform for lending. Without strategic alignment, the project might focus solely on optimising loan approvals, potentially overlooking the social and environmental impact of lending decisions. Project managers must work with stakeholders to define clear sustainability goals. And also establish measurable metrics, and ensure that these are integrated into the project’s overall objectives.

                                  Beyond Efficiency: A Holistic Vision for Sustainable Fintech

                                  AI offers immense potential for automating tasks and optimising processes. Moreover, it’s crucial to remember that sustainability is about more than just efficiency. Fintech companies and financial institutions must adopt a holistic approach that considers the environmental, social, and economic impacts of their operations.

                                  Project managers play a vital role in ensuring that AI is used responsibly and ethically, with a focus on transparency, accountability, and fairness. This includes addressing potential biases in AI algorithms and protecting data privacy. Furthermore, it also means ensuring AI systems are aligned with human values. They must contribute to a more equitable and sustainable financial system.

                                  By embracing a structured, project-based approach, FinTech companies and financial institutions can unlock the full potential of AI to drive genuine and lasting sustainability improvements. Project management is not just a supporting function; it’s the linchpin for success in the age of AI-driven sustainability. It’s about building the right foundations, equipping the right teams, and aligning projects with the right strategic objectives.

                                  • Artificial Intelligence in FinTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  9 July 2025

                                  Estimated Read time

                                  3Mins

                                  Solidarités International goes live with FinScan to strengthen AML compliance in global humanitarian operations

                                  Solidarités International, a French-based humanitarian aid organisation, has gone live with FinScan. The Innovative Systems solution comes from a leading provider of advanced anti-money laundering (AML) compliance solutions. This will enhance screening processes across its global operations in a cloud-based environment.

                                  As a nonprofit committed to providing life-saving assistance in areas affected by conflict and natural disasters, Solidarités International faces increasing regulatory expectations from public donors. These include the United Nations, the US Bureau for Humanitarian Assistance (BHA), and European funding bodies. These expectations include rigorous AML screening of suppliers, staff, and local partners to ensure accountability and transparency.

                                  FinScan for AML

                                  Solidarités International’s decision to adopt FinScan followed a thorough selection process involving external advisors and peer recommendations from within the NGO community. Criteria such as workflow flexibility, user delegation, audit history, and alignment with data privacy standards were central to the evaluation. FinScan is now fully operational at Solidarités International’s headquarters.

                                  “With FinScan, we’re able to delegate screening responsibilities across field missions while maintaining centralised oversight and data privacy. The responsiveness of the FinScan team and the tool’s intuitiveness and configurability have been key positives,” said Pierre DeSoil, IT Project Lead at Solidarités International. “Our users picked up the system quickly and are more confident with the process.”

                                  Designed to support complex compliance needs, FinScan helps organisations like Solidarités International meet donor due diligence requirements. It does this through customisable workflows, robust matching algorithms, and scalable deployment.

                                  “We’re proud to support the mission of Solidarités International with a powerful, cloud-based AML solution that helps protect humanitarian aid from financial crime risk,” said Steve Maul, Chief Customer Officer at Innovative Systems. “Their dedication to both compliance and the communities they serve exemplifies how technology and purpose can align.”

                                  About Solidarités International

                                  Founded in 1980 and headquartered in Clichy, France, Solidarités International provides urgent humanitarian aid in conflict zones and disaster-stricken areas. Its core mission is to meet the vital needs of vulnerable populations—providing water, food, and shelter in life-threatening conditions. Learn more at https://www.solidarites.org/en/.

                                  About FinScan

                                  Trusted by hundreds of organisations worldwide, Innovative Systems, Inc.’s FinScan® offers advanced Anti-Money Laundering (AML) compliance technology and consulting solutions. Built on decades of experience in data management and proprietary matching technologies, FinScan provides a data-first, risk-based approach to ensure unparalleled accuracy and efficiency in identifying and reducing risk, accelerating AML compliance workflows, and optimising team productivity. FinScan’s comprehensive, integrated platform includes Know Your Customer (KYC), unparalleled sanctions screening, risk scoring, data quality, and advisory services for implementing a holistic compliance program. FinScan offers flexible deployment including SaaS, on-premise, and hybrid options. FinScan’s SaaS clients are screening more than 300 billion names a year. Learn more at www.finscan.com and follow us on LinkedIn.  

                                  • Cybersecurity in FinTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  8 July 2025

                                  Estimated Read time

                                  3Mins

                                  As of 2025, artificial intelligence (AI) tools are revolutionising the financial industry by enhancing efficiency, accuracy, and decision-making across various…

                                  As of 2025, artificial intelligence (AI) tools are revolutionising the financial industry by enhancing efficiency, accuracy, and decision-making across various domains. Here are five leading AI platforms making significant impacts in finance:

                                  1. JPMorgan’s Coach AI & GenAI Toolkit

                                  JPMorgan Chase has integrated AI tools like Coach AI and a comprehensive GenAI toolkit to enhance client services and operational efficiency. Coach AI assists advisors in swiftly retrieving research and anticipating client inquiries. This has led to a 95% reduction in information retrieval time. The GenAI toolkit, utilised by over half of JPMorgan’s 200,000 employees, has contributed to nearly $1.5 billion in savings. The company has seen improvements in fraud prevention, trading, and credit decisions.

                                  2. BlackRock’s Asimov

                                  BlackRock has developed Asimov, an AI platform capable of autonomous actions such as analyzing documents and providing real-time portfolio insights. This tool enables portfolio managers to maintain situational awareness and make more informed decisions continuously, enhancing the firm’s investment processes.

                                  3. Hebbia

                                  Hebbia is an AI platform designed to perform complex, multi-step tasks autonomously, effectively functioning like a high-capability intern. It can handle tasks such as analysing financial filings, building valuation models, and drafting memos. Major financial institutions like BlackRock and KKR utilise Hebbia to streamline operations and free professionals to focus on strategic work.

                                  4. Datarails FP&A Genius

                                  Datarails offers an AI-powered Financial Planning and Analysis (FP&A) platform that automates data consolidation and financial reporting. It provides workflows, templates, and data visualisation tools to facilitate budgeting, forecasting, scenario modelling, and financial analysis. These enhance the speed and accuracy of financial decision-making.

                                  5. Feedzai

                                  Feedzai is a data science company that develops real-time machine learning tools. These identify fraudulent payment transactions and minimise risk in the financial services industry. Its AI-based applications are used for fraud detection, risk assessment, and regulatory compliance. They are helping organisations manage and mitigate financial crime risks effectively.

                                  These AI tools exemplify the transformative impact of artificial intelligence in finance. Offering solutions that enhance operational efficiency, risk management, and strategic decision-making.

                                  • Artificial Intelligence in FinTech

                                  Author

                                  Kenan Maciel

                                  Published

                                  7 July 2025

                                  Estimated Read time

                                  5Mins

                                  Kenan Maciel, Director of Strategy at Lab49, on the future for cross-border payments in the global push for instant settlement

                                  Cross-Border payments are the unseen infrastructure powering global commerce. A multinational corporation settling international invoices, a small business sourcing products overseas, or a family transferring remittances across continents… The global economy has relied on the seamless movement of money across borders for decades. Now, with the total value of cross-border payments estimated to increase from almost $150 trillion in 2017 to over $250 trillion in 2027, it’s clear just how fundamental they are to the future of the global economy.

                                  However, despite their scale and importance, cross-border payments remain plagued by inefficiencies and high costs. High transaction fees, slow settlement times and a persistent lack of transparency have consistently challenged businesses and consumers. The Financial Stability Board, responsible for the G20 Roadmap for Enhancing cross-border payments, has acknowledged that “significant progress will be needed to meet the targets” this year. This statement highlights the reality of the industry as it stands. While the need for better infrastructure is widely recognised, the pace of change is unsteady.

                                  A Landscape of Legacy

                                  For decades, cross-border payments have relied on an established set of mechanisms: banks, credit card networks and money transfer operators. Traditionally, the biggest facilitators of cross-border payments have been the platforms established by major banks and governments like SWIFT, SEPA and CHIPS. These systems have served their purpose but are increasingly ill-suited to the demands of modern commerce. More recently, traditional card networks such as Visa, Mastercard and American Express have expanded their role in this space, capturing an ever-growing share of the cross-border market by offering relatively faster and more integrated solutions than conventional bank transfers.

                                  In recent years, the emergence of new technologies has begun to reshape the landscape, helping to expand the growth of cross-border payments. DLTs, stablecoins and CBDCs offer the promise of faster, more secure, transparent and cost-effective payments compared to traditional methods. While the overall volume of cross-border payments handled on blockchain is still a fraction of the global market, its growth trajectory is significant. BVNK, for example, estimates that stablecoin payments alone could represent a $60 trillion opportunity in the next five years.

                                  The Problems Persist

                                  Still, challenges persist. The cross-border payment model is weighed down by high fees from traditional facilitators often driven by currency conversion charges, intermediary bank costs and compliance related expenses form different regulatory jurisdictions. Often, a single payment is subject to multiple checks and validation, each requiring different sets of data, which not only slows down processing times but also increases operational complexity. FX risks and associated high funding costs further complicate the picture. Banks are often required to pre-fund transactions in destination currencies to enable timely settlement, resulting in high funding costs and the need to hold capital that could be more productively deployed elsewhere.

                                  A lack of transparency further compounds these issues. For many businesses, understanding the total cost of a transaction, and tracking its progress, remains frustratingly difficult. Information about fees, exchange rates and settlement times is often fragmented and inconsistent, further increasing uncertainty and risk.

                                  What’s Changing?

                                  Nevertheless, meaningful change is underway. One area seeing rapid development is FX hedging. Companies are increasingly making use of forward contracts and options to manage currency risk, while fintechs are leveraging smart contracts and decentralised finance platforms to automate FX conversion, improving both cost efficiency and predictability. The introduction of ISO 20022 and the looming November deadline, means that a global standard for financial messaging is inching closer. By standardising electronic data interchange between financial institutions, it promises to reduce friction and facilitate faster, more accurate payments.

                                  Another encouraging development is the expansion of central banks’ instant payment infrastructures. For example, Fed Now in the US, Faster Payments in the UK, and SEPA Instant in the EU operate around the clock, offering real-time, 24/7 settlement. These developments mark a significant departure from traditional systems like standard SEPA which typically settle over two business days and only during working hours. While the cost of using these instant infrastructures is often higher, the benefits in terms of speed, transparency and availability offer a compelling improvement. Their growing presence is helping to set new expectations for what’s possible in domestic and cross-border payments.

                                  With DLTs and stablecoins also gaining traction as credible alternatives to traditional methods, the industry is also moving closer to near instant global settlement and the ability to operate 24/7. A significant improvement over the lengthy settlement times and limited operating hours of legacy systems. Although, mainstream adoption still faces hurdles, with one of the primary challenges being convenience and usability. For many uses, managing digital wallets and understanding decentralised systems remains unintuitive, limiting adoption outside of extremely digital literate circles.

                                  Who’s Leading the Charge?

                                  Importantly, it is no longer just FinTechs and startups leading the charge. Traditional financial institutions are actively investing in digital asset infrastructure. Visa’s tokenised asset platform and the Bank of America’s plans for a proprietary stablecoin are prime examples of how legacy players are adapting. Institutions like these are often helping to define the future of cross-border payments.

                                  The industry stands at a turning point, on the cusp of achieving the required speed, cost, transparency and access for the global economic future. With ongoing technological innovation and evolving regulatory frameworks, the path is becoming clearer. However, the nature of global finance means that no single approach will dominate. Different payment models require different tools, and the most effective solutions will be those tailored to specific needs and truly fit for the modern financial ecosystem.

                                  • Digital Payments

                                  Author

                                  Dan Brightmore

                                  Published

                                  4 July 2025

                                  Estimated Read time

                                  4Mins

                                  As cryptocurrency continues its march toward mainstream adoption in 2025, selecting a reliable, high-performing exchange has never been more critical….

                                  As cryptocurrency continues its march toward mainstream adoption in 2025, selecting a reliable, high-performing exchange has never been more critical. With factors like security, liquidity, user experience, and range of offerings playing a pivotal role, here are the top five crypto exchanges currently leading the industry.

                                  1. Binance

                                  Overview: Still the largest exchange globally by trading volume, Binance offers a comprehensive platform that serves both retail and institutional traders.

                                  Key Features:

                                  • Over 600 cryptocurrencies supported.
                                  • Advanced trading tools including spot, margin, and futures trading.
                                  • Binance Earn, Launchpad, and Staking features for passive income.
                                  • Highly competitive fees, starting at 0.1%.

                                  Security & Regulation:
                                  Binance has faced regulatory scrutiny in various countries but continues to work toward greater transparency and compliance. It holds licenses in several jurisdictions and maintains a robust SAFU (Secure Asset Fund for Users) for emergencies.

                                  2. Coinbase

                                  Overview: Widely regarded as the go-to platform for beginners, Coinbase maintains its stronghold in North America with a user-friendly interface and strong regulatory standing.

                                  Key Features:

                                  • Offers 150+ digital assets.
                                  • Integrated with Coinbase Wallet for decentralised applications.
                                  • Recurring buys, portfolio tracking, and robust mobile apps.
                                  • Listed on NASDAQ, ensuring public transparency.

                                  Security & Regulation:
                                  Coinbase is regulated by U.S. authorities and is one of the few exchanges with full AML/KYC compliance. It employs best-in-class security practices, including cold storage for over 98% of customer funds.

                                  3. Kraken

                                  Overview: Kraken is a favorite among institutional and advanced traders thanks to its robust features and reputation for security.

                                  Key Features:

                                  • Supports over 200 cryptocurrencies.
                                  • Offers spot, futures, and margin trading.
                                  • Kraken Pro for enhanced charting and order types.
                                  • Kraken Staking with competitive yields.

                                  Security & Regulation:
                                  One of the oldest operating exchanges (since 2011), Kraken has never suffered a major hack. It is regulated in the U.S. and holds a Special Purpose Depository Institution (SPDI) charter in Wyoming.

                                  4. Bybit

                                  Overview: Bybit has risen quickly by offering cutting-edge features tailored to derivatives traders, along with a fast and intuitive UI.

                                  Key Features:

                                  • Specializes in crypto derivatives, with high leverage options.
                                  • Also supports spot trading, launchpad tokens, and NFT markets.
                                  • Popular for its trading competitions and rewards system.

                                  Security & Regulation:
                                  Bybit prioritises fund security with cold wallets and real-time risk audits. It has begun increasing compliance in jurisdictions where regulation is tightening.

                                  5. OKX

                                  Overview: OKX has emerged as a comprehensive crypto ecosystem, offering far more than just a trading platform.

                                  Key Features:

                                  • Over 300 cryptocurrencies and DeFi integration.
                                  • Powerful tools for copy trading, bot trading, and options.
                                  • Active ecosystem for NFTs, DApps, and Web3 tools via OKX Wallet.

                                  Security & Regulation:
                                  OKX publishes monthly proof-of-reserves and maintains robust risk controls. It’s actively pursuing compliance in key regions including Hong Kong and the EU.

                                  Conclusion

                                  While the crypto landscape remains dynamic and subject to regulatory evolution, these five exchanges have proven resilient, innovative, and trustworthy. Whether you’re a newcomer or seasoned trader, choosing the right exchange depends on your specific needs. Be they security, advanced tools, or ease of use. Always consider using multiple platforms to diversify risk and maximise opportunities.

                                  • Blockchain & Crypto

                                  Author

                                  Peter Curk

                                  Published

                                  3 July 2025

                                  Estimated Read time

                                  6Mins

                                  Peter Curk, CEO of ICONOMI, a leading platform in digital asset management explores the EU’s MiCA regulation and what it means for holders of crypto assets in the UK

                                  Launched between June 2023 and December 2024, the European Union’s (EU) Markets in Crypto-Assets (MiCA) regulation was the first of its kind. It introduced a need for compliance into a space that had previously been beyond the remit of any governmental oversight. It was an exercise that could only be contentious. So, it’s hardly surprising that it’s been met by scrutiny and criticism. But while MiCA is a cause for concern to many within the EU, for the UK it could potentially be beneficial.

                                  Why the EU is struggling with MiCA

                                  The MiCA regulation has drawn significant criticism from both industry insiders and analysts, with concerns broadly converging around five main issues. Chief among them is the glaring omission of stablecoins from MiCA’s scope. Given that the digital currency is seen as one of the riskiest crypto assets due to its systemic volatility, as well as its potential to destabilise not only the crypto markets but the broader financial system, this exclusion has raised multiple eyebrows. So, the EU’s decision to regulate the rest of the crypto space while leaving stablecoins unregulated is widely regarded as both bizarre and problematic. It also undermines the perceived effectiveness of MiCA. This makes its more stringent provisions seem almost futile, while stablecoins are left unfettered.

                                  On the other hand, in the areas MiCA does cover, there are growing fears that the regulation could stifle the innovation that has been central to the crypto sector’s rapid progression. Breakthrough technologies, such as blockchain, tokenised assets, and decentralised finance, have all emerged from the crypto space.  But now, with compliance costs climbing, smaller companies and startups – the traditional drivers of innovation – are being pushed out of the EU’s crypto market. This risks stagnating growth across the industry.

                                  Compounding the issue is MiCA’s apparent lack of futureproofing. Despite its rigid framework, it appears to hold no contingencies for future technological developments or emerging threats. This could potentially leave loopholes for fraudulent activity and other bad actors.

                                  Additionally, there remain concerns regarding the cost of compliance. With this likely to be passed on to consumers, it holds the potential to raise barriers to entry while driving investors toward more affordable, less regulated markets – potentially including the UK.

                                  Lastly, the delayed release of MiCA’s regulatory technical standards (RTS) – which were not made available until more than 18 months after the legislation began to come into play – created prolonged uncertainty during implementation. Uncertainty that could have been avoided. It may also have helped resolve other concerns if addressed earlier.

                                  Collectively, these issues have cast a shadow over what could have been a positive move for the crypto space, bringing authenticity, accountability, and stability. The question is, how could MiCA’s failure to do all this help the UK’s crypto space?

                                  MiCA’s impact on the UK

                                  If the UK is clever, there are two ways in which it could use the problems with MiCA to its own advantage.

                                  Better Regulation

                                  With the EU was the first territory to roll out crypto regulation, it won’t be a lone player for long. The UK is currently in the process of preparing its own version of MiCA. The Financial Conduct Authority (FCA) is suggesting 2026 implementation. MiCA can provide the learning experience that the EU lacked. It doesn’t just offer a potential framework – it shows why the traditional financial regulatory framework, adopted by MiCA, is unsuited to the crypto space. It provides clear, working examples of what not to do. But it also provides points of success that the UK can build upon – because despite the detractors, there are many good things about MiCA. The FCA can use all of this information to build a better regulatory infrastructure that limits the potential for fraud and dishonest behaviours, while helping to foster future growth and innovation – something that the crypto space has long been crying out for.

                                  If the UK does well with this, it could set the global standard for crypto regulation, raising its status in an area where it has previously been lacking.

                                  Market growth

                                  Before we get to regulation, however, there is also the potential for the UK market to benefit from the EU’s troubles. Right now, the EU’s crypto investors and startups are unhappy and looking for alternative places to put their money. The UK could be one of those places. 

                                  The UK has has only really ever dabbled in crypto. After more than 15 years, there are only around 40 registered crypto businesses in the UK, compared to more than 2,000 in the EU, and 4,852 in America. This could be the time for the UK to grow. The US is currently in a state of political and financial turmoil, making many investors wary. By contrast, the UK is a friendly near-neighbour, with a near-universal language. It won’t take much to tempt European investors and startups across – something that could be sustainable, if the FCA makes the right regulatory decisions.

                                  ICONOMI – Growing the UK Crypto Market

                                  ICONOMI is in the process of doing this. We’re officially licensed in the UK and preparing to enter the EU market under a MiCA license. This means, we’ll shortly have the ability to passport our license in other EU member states. This means the ability to attract customers from other territories across the EU. If other UK crypto businesses follow suit, there is significant potential to generate growth for the UK crypto market. For the short and longer term. 

                                  Cryptocurrency was never intended to go mainstream. When Satoshi Nakamoto launched Bitcoin, they had a vision of a currency that could operate outside of traditional financial institutions and regulation. Meanwhile, providing transparency and trust through technology. But the space evolved beyond expectation, creating more than 25,000 other cryptocurrencies in the process. They are worth literally billions of pounds, and millions of people have a stake in the market. If the crypto market crashes, it could significantly impact the wider economic ecosystem globally. So, no one is arguing against the fact that the crypto space needs regulation. Only that it needs to be regulated properly. And the UK could be the country to do that.

                                  Peter Curk is the CEO of ICONOMI, a leading platform in digital asset management. With a background in finance and blockchain, Peter is passionate about making crypto investing accessible and easy for everyone. Under his leadership, ICONOMI has grown into a trusted name in the industry, offering innovative solutions for individuals and institutions alike.

                                  • Blockchain & Crypto

                                  Author

                                  Kristian Torode

                                  Published

                                  2 July 2025

                                  Estimated Read time

                                  3Mins

                                  Kristian Torode, Director & Co-Founder at Crystaline, on Closing the gap between digital convenience and regulatory compliance

                                  As financial firms adopt more digital tools – from instant messaging to video calls – the challenge of capturing, storing and monitoring every conversation in line with regulatory expectations for comms has grown exponentially.

                                  With regulators demanding stricter oversight of all business comms, financial firms must now rethink how they manage messaging across every level of the organisation. Unifiesd Communications (UC) software can help financial service providers remain compliant.

                                  A recent Theta Lake survey revealed that over 70 firms were fined in 2024 for failing to comply with communications regulations. What is more, almost two-thirds of financial firms anticipate even more regulatory requirements on communications in the coming years.

                                  Consequences of Non-Compliance

                                  While fines for failure to comply with comms regulations are more prevalent in the US, there have been several cases affecting financial services firms in the UK.

                                  In August 2023, Morgan Stanley was fined £5.4 million by Ofgem, the UK’s energy regulator, after the bank’s traders discussed wholesale energy prices over WhatsApp on private devices. Use of the platform does not meet regulatory standards for data retention and monitoring, as financial service providers are unable to record these messages concerning energy trading.

                                  Despite industry speculation, the UK Financial Conduct Authority (FCA) has chosen not to implement an outright ban on WhatsApp for business use. Instead, the FCA expects firms to implement policies and monitoring tools to ensure compliance when using such platforms. While this provides some flexibility, it puts the onus on firms to maintain secure and auditable communication records across emerging technologies.

                                  Balancing security and convenience

                                  For financial businesses, the challenge lies in finding a comms solution that is both secure and convenient. WhatsApp appeals to many due to its familiarity and features like group chats, voice calls and file sharing. However, while convenient, it presents serious risks in data privacy, security and compliance, making it unsuitable as a primary communication platform for highly regulated industries like finance.

                                  To address these concerns, many firms are turning to UC platforms that integrate multiple communication tools. These include voice, video, instant messaging and file sharing across a single, secure interface. These platforms provide the convenience of more familiar tools such as WhatsApp while addressing compliance concerns.

                                  Several UC providers now offer platforms tailored to highly regulated industries like finance. Many include security features such as end-to-end encryption, centralised access management and real-time monitoring. This can detect potential compliance breaches, offer built-in archiving for regulatory adherence and consent management to meet data protection requirements.

                                  Digital business communications will continue to play a key role in the financial services sector, but not at the expense of traceability and data security. Unified Communications offers a secure, compliant platform for financial services without sacrificing convenience.    

                                  If your organisation is reassessing its communications strategy in light of evolving compliance demands, Crystaline can provide guidance on navigating the shift to unified communications.

                                  • Cybersecurity in FinTech

                                  Author

                                  Anshul Srivastav

                                  Published

                                  1 July 2025

                                  Estimated Read time

                                  5Mins

                                  Anshul Srivastav, Senior Vice President and Head – Europe for Zensar Technologies on securing AI with blockchain

                                  Artificial Intelligence (AI) is rapidly transforming financial services. According to The Bank of England, 75% of financial services firms are already using AI. A further 10% are planning to use it in the next three years.

                                  Firms are deploying AI because of the benefits it can bring. These include enhanced data and analytical insights, improved anti-money laundering (AML) and fraud detection and efficiencies in cybersecurity practices. As well as providing customers with better, more personalised services.

                                  While the wide-scale deployment of AI brings a range of benefits for the financial services sector, it’s also creating additional risks. Especially when the AI systems used to make trusted decisions are becoming a prime target for cyber-attacks.

                                  Attacking AI

                                  Bad actors can manipulate AI systems to make them malfunction or operate in ways that weren’t intended. This can have potentially severe consequences.

                                  Using what’s known as data poisoning attack, threat actors can intentionally compromise or alter datasets used by AI to influence the outcomes of the model for their own malicious ends.

                                  For example, an attacker trying to bypass the AI-powered fraud detection systems of a bank could attempt to inject false data into the system during a data training cycle the intention would be to manipulate the system into believing certain false transactions are legitimate. Ultimately this enables the threat actor to steal money or sensitive data without being noticed.

                                  AI systems can also result in additional threats to data privacy. Like many workers, financial service professionals can use Large Language Models (LLMs) like ChatGPT to aid with queries and tasks.

                                  However, this brings the risk that sensitive information could get uploaded to the model if the employee inputs certain data, such as contracts or confidential reports. This data might be saved by the model, opening businesses up to data leaks. Because with the correct prompts, it’s possible for a user from outside the company to tease out this confidential information from the LLM.

                                  These privacy concerns can be exacerbated by the black box nature of AI. Often, it isn’t publicly detailed how the algorithms and the decision-making process behind them operate. This lack of transparency can lead to mistrust among users and stakeholders. As well as potential issues with regulatory compliance. For example, the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

                                  All of this means that the use of AI in financial services, while beneficial, is creating new security challenges which need to be addressed. The solution to this is the integration of blockchain technology to create a secure, transparent, and trustworthy AI ecosystem. And by leveraging blockchain’s inherent security features, vulnerabilities in AI systems can be countered.

                                  Blockchain Explained

                                  Blockchain consists of a chain of blocks, each containing a list of transactions. Each block is linked to the previous one, forming a secure chain. This structure ensures that once data is recorded, it cannot be altered without changing all subsequent blocks. These mechanisms ensure that all participants agree on the state of the blockchain. Therefore preventing fraud and enhancing security.

                                  This is achieved through three key pillars. The first is data immutability, which ensures it can’t be altered or deleted once recorded on the blockchain. Guaranteeing that the data remains consistent and trustworthy over time, ensuring its integrity.

                                  The second pillar is decentralisation, based on how blockchain functions through a network of independent nodes. Unlike centralised systems, where a single point of failure can compromise the entire network, decentralisation distributes control and data across many nodes. This reduces the risk of system failures, as no single target point exists, meaning decentralisation enhances security and resilience.

                                  Cryptographic security is the third pillar. Blockchain uses a system of public and private keys to secure transactions and control access. The public key is visible to anyone, while the private key is a secret code known only to the authorised party.

                                  These fundamentals of blockchain, combined with the transparency and security it offers, can help financial services organisations address the security challenges they’re being faced with by the rapid deployment of AI.

                                  Combining Blockchain with AI for Improved Data Security

                                  Integrating blockchain with AI can massively aid with securing data integrity. For example, through creating tamper-proof records. By making immutable records of AI training data and model updates, complete with timestamps and links to previous entries, this ensures a tamper-proof history of the data. Enabling stakeholders at financial services companies to verify the integrity of the data used in AI models. Therefore improving security of the whole system and protecting it against attacks.

                                  Combining AI with blockchain can also help to counter potential data privacy implications introduced by the deployment of AI in financial services. Blockchain techniques like zero-knowledge proofs allow the data to be verified without revealing the actual data. This can help financial services firms to verify the data they’re using is correct. While also still maintaining the required data privacy and regulatory compliance.

                                  In addition to this, implementing AI with blockchain technology can aid with building trust and transparency in how AI systems work and what they’re used for. By providing a transparent record of AI decision-making processes, the blockchain allows stakeholders to review and verify the process. All the while ensuring there’s accountability of who made changes and when. This arrangement could therefore help financial services providers prevent data poisoning and other attacks targeting their AI systems.

                                  Building a Secure, Transparent, and Trustworthy AI Ecosystem

                                  The rapid adoption of AI is changing the financial services industry. However, according to The Bank of England’s survey, only 34% of financial services firms said they have ‘complete understanding’ of the AI technologies they use.

                                  Much of this can be attributed to how the technology is new, but also how the algorithms which power AI technology are often mysterious in their nature. This results in risks around malicious attacks and data privacy issues. However, by combining AI frameworks with blockchain technology, these security issues can be addressed.

                                  By taking these steps, stakeholders can collectively contribute to building a secure, transparent, and trustworthy AI ecosystem. An ecosytem that leverages the strengths of blockchain technology to address current and future challenges.

                                  • Artificial Intelligence in FinTech
                                  • Blockchain & Crypto

                                  Author

                                  Sejal Mehta & Wendy Di Blasio

                                  Published

                                  30 June 2025

                                  Estimated Read time

                                  4Mins

                                  Sejal Mehta and Wendy Di Blasio from Odgers Berndtson’s Global FinTech and Financial Services Practices, discuss new leadership demands in a rapidly evolving cross-border payments space

                                  The global landscape for cross-border payments is at an inflection point. It is driven by rapid technological advances, evolving regulatory frameworks, and shifting consumer expectations. With Asia emerging as a hub of growth, particularly in countries like China, India, and Singapore, the industry is projected to soar to $23.8 trillion by 2032. This represents over one-third of global transactions.

                                  Yet, this significant growth introduces complexity. Challenges in interoperability, regulatory divergence, and varying regional consumer behaviours make effective leadership indispensable. In such an environment, strong executive leadership not only manages but proactively shapes these transformations.

                                  A New Leadership Mandate in Cross-Border Payments

                                  Today, successful leadership in cross-border payments requires much more than operational effectiveness or market penetration. Modern executives must adeptly manage uncertainty, anticipate disruption, and drive transformation at scale.

                                  Visionary leadership is paramount. Executives need to foresee industry trends, understanding key initiatives such as Project Nexus. This aims to integrate real-time payment systems across Asia, enhancing transaction speed and seamlessness.

                                  Strategic agility is equally critical. Given volatile geopolitical dynamics and fluctuating financial flows, leaders must skilfully balance immediate demands with long-term goals. The capacity to make informed, data-driven decisions amid complexity is now a hallmark of effective leadership.

                                  Cultural competence also defines leadership excellence. Executives must nurture inclusive, agile teams that can navigate diverse cultural contexts and regional expectations. Emotional intelligence, cultural sensitivity, and the ability to effectively lead multicultural, distributed teams are no longer optional. These are essential leadership competencies.

                                  Navigating Regulatory Complexity with Strategic Foresight

                                  Managing regulatory fragmentation across jurisdictions is a significant challenge for leadership in the cross-border payments space. Countries continue to implement and update localised rules around data protection, anti-money laundering (AML), and financial compliance. Executives are under increasing pressure to ensure both global consistency and local compliance.

                                  This environment calls for a nuanced understanding of international law, regional policy developments, and collaborative regulatory frameworks. Successful leaders are those who build strong regulatory partnerships, anticipate changes in legal landscapes, and embed compliance into the strategic DNA of their organisations.

                                  For instance, responding to initiatives like ISO 20022, which standardises financial messaging formats, requires more than technical adaptation. It demands coordinated leadership across compliance, operations, and technology functions. By staying ahead of these shifts, executives not only minimise risk but can unlock new efficiencies and competitive advantages.

                                  Several emerging trends are reshaping leadership in cross-border payments, significantly influencing how companies approach talent development and executive roles.

                                  Intergenerational leadership has become a priority as Millennials and Gen Z increasingly dominate the workforce. These groups value purpose, flexibility, and impactful work, disrupting traditional loyalty structures. Today’s leaders must actively foster collaboration and unity across diverse age groups, aligning teams around shared ambitions like innovation, sustainability, and inclusivity.

                                  The fluidity between traditional financial institutions (TradFi) and FinTech organisations is increasing noticeably. Executives moving between these spheres bring invaluable cross-sector expertise, methodologies, and perspectives. This intersection demands leaders who can seamlessly bridge legacy systems with innovative technologies, balancing stability with innovation.

                                  Consequently, organisations are more frequently leveraging tools like psychometric assessments to identify crucial leadership attributes such as adaptability, resilience, and learning agility. These assessments are increasingly applied not only to senior executives but also to non-executive board members, helping firms strategically future-proof their leadership capabilities.

                                  Cultivating Leadership Through Development and Succession Planning

                                  Effective leadership development strategies have become critical as companies scale operations and navigate ongoing technological and geopolitical changes. Organisations cannot solely rely on external hires. They must cultivate internal talent pools prepared to address future challenges.

                                  Forward-thinking companies are investing in targeted leadership programmes, mentorship opportunities, and rotational assignments designed to expose emerging leaders to diverse operational complexities. These practices strengthen organisational resilience, encourage internal innovation, and foster adaptability among leadership ranks.

                                  Strategic succession planning further enhances organisational robustness. Rather than responding reactively to sudden leadership gaps, high-performing companies proactively identify, and nurture promising talent. This approach requires upskilling the leaders by providing them with a strategic understanding of newer technologies, data models and associated risks.

                                  Leadership as the Cornerstone of Competitive Advantage

                                  In the rapidly evolving cross-border payments landscape, leadership quality will ultimately distinguish market leaders from followers. As regulatory pressures intensify and technological advancements continue to reshape the industry, effective leadership is pivotal to turning complexity into growth opportunities.

                                  By committing to leadership development, strategic executive recruitment, and aligning talent management with overarching business objectives, organisations position themselves for resilience and sustained success. The right leaders will not only navigate challenges but leverage them into lasting competitive advantages, transforming vision into tangible market value.

                                  • Digital Payments

                                  Author

                                  Galeal Zino

                                  Published

                                  27 June 2025

                                  Estimated Read time

                                  4Mins

                                  With the right approach, cybersecurity can be contagious argues Galeal Zino, Founder & CEO at NetFoundry – a provider of zero-trust connectivity solutions and originator of the open source tool OpenZiti

                                  Modern financial services are composed of a digitally integrated secure ecosystem – networked together and codependent on ecosystem APIs, microservices and shared data. Complexity and ambiguity are high.

                                  Sir Alex Younger, former head of the British Intelligence Service MI6 said recently that the job of the intelligence service is to dispel complexity and ambiguity.That would make a fine mission statement for the heads of information security in the financial sector.

                                  Meeting a Complex Security Challenge

                                  Most banks leverage core banking systems (CBS) from providers like Temenos, FIS and Finastra. This makes security complex. Connections are needed between the bank’s network and its CBS provider’s network. Traditionally, this necessitates nailing up VPNs. And managing permitted IP addresses in firewall ACLs, MPLS or dedicated circuit-based extranets. Also required are pre-shared certificates, shipping hardware, VDI and/or leaking routes. All of which have multiplied in complexity during digital transformation. And are about to multiply again with AI.

                                  A different approach is secure-by-design. Rather than bolt-on the infrastructure described above, each session is strongly identified, authenticated and authorised. All before it is granted a virtual circuit on a network. This is similar to what the banks do internally with solutions for zero trust, but it is borderless. It works across their digital supply chains, including with their core banking platform and software providers.

                                  One CBS leader, Euronet Worldwide, uses a third-party secure-by-design platform to enable their financial institution customers to connect to its core banking software. This is a great example of the supplier being proactive about their role in security. We’ll see this happen more as new legislation takes effect, the EU CRA. The Euronet example shows that it’s possible to remove some of the ambiguity from shared responsibility. Euronet’s secure-by-design system doesn’t just protect itself but makes every interaction with supply chain partners more secure.

                                  Security designed-in for Financial Services

                                  The same principles apply across financial services. Companies like Euronet can deploy their own zero trust supply chain connections, rather than putting the burden on their finance sector customers to figure it out. In large supply chain scenarios like CBS, this helps everyone. The reality now is that if the VPN of any one financial institution is compromised, then potentially all the banks who connect to the same CBS providers can be exploited. By removing complexity and ambiguity, Euronet is simplifying and securing the entire supply chain.

                                  The big picture is that the WAN/SASE/firewall model is struggling in the post digital transformation, hyperconnected, soon to be AI- powered world. That model was built to secure the WAN. However, new workflows such as the financial supply chain are outside the borders of any single WAN. So, the precious SASE WAN gets connected to the internet via open firewall ports (ACLs) and vulnerable VPNs so the business can connect to supply chain partners. It’s like building a strong boat and then punching holes in it to get a better look at the water. 

                                  AI is the nail in the WAN coffin because AI multiplies and accelerates these workflows. They have at least one leg outside the WAN and it makes them less predictable and more dynamic. More complexity and ambiguity. Good luck connecting AI agents via VPNs and firewall ACLs.

                                  Secure-by-Design Supply Chain

                                  So, what does a secure-by-design supply chain look like and how can financial services identify viable migration paths?

                                  The main characteristics are:

                                  • Close all inbound “listening” ports on all network firewalls and servers to make your DMZ unreachable from the underlay networks.  Eliminate the reachable firewalls and VPN servers.  No more holes beneath the waterline!
                                  • End-to-end zero trust between supply chain participants, meaning least-privileged access not just to the network or firewall, but all the way through to applications, APIs, servers and devices. Nothing can connect to anything else without strong identity, authentication and authorisation. This includes end-to end-encryption – no sharing of encryption keys with cloud security providers (which also helps ensure data sovereignty).
                                  • Microsegmentation, the ability to define in granular detail who or what has access to which applications, and to limit lateral movement in the event of a breach. In effect, every application session becomes a private network-of-one, and it is quarantined by design.

                                  Find out more at https://netfoundry.io/

                                  • Cybersecurity in FinTech

                                  Author

                                  Alexandra Mousavizadeh

                                  Published

                                  26 June 2025

                                  Estimated Read time

                                  4Mins

                                  Alexandra Mousavizadeh, Co-Founder & CEO at Evident, on the rise of Agentic AI in financial services

                                  Agentic AI is no longer the preserve of the distant future. Agents are already here, embedded in the day-to-day operations of businesses. As well as answering questions and crunching numbers, they’re making decisions, taking action, and learning on the fly. They can handle customer queries, tap into APIs, and even rewrite their own instructions.

                                  It’s a big shift from traditional AI, which stayed firmly in the realm of prediction and recommendation. Agentic systems are very dynamic in comparison, and involve more acting and doing, which fundamentally changes the risk landscape.

                                  For banks looking to capitalise on agentic, the implications are especially consequential. This is a highly sensitive sector where trust, compliance and control are existential issues. That is why Responsible AI (RAI) has quickly moved from being a nice-to-have to a critical foundation. It can balance the need for controls with the promise of innovation.

                                  In our latest Responsible AI in Banking report at Evident, we found a clear upweighting of RAI priorities. More banks are appointing RAI leads. More are publishing principles. And more are thinking hard about how to scale those capabilities across the business.

                                  But Agentic AI is a different challenge. It pushes past the limits of old governance models and forces a rethink of how we manage risk, maintain oversight, and build trust. 

                                  Here’s why a rethink is needed…

                                  Static Governance Doesn’t Work for Dynamic Systems

                                  Most current AI oversight models are built for systems that behave predictably. They assume models will be trained, validated, deployed, and then monitored using relatively fixed parameters. This is no longer the case.

                                  Agentic AI systems learn and act independently. They are decision-making agents as well as tools. That makes governance more complicated.

                                  Banks need oversight models that can keep pace in real time. That includes enterprise-wide assurance platforms that can help to spot unexpected behaviour, adjust on the fly, and give leaders a clear view of what’s happening across the organisation.

                                  Building the right tooling in this way is essential. What’s harder is laying out an agentic AI strategy and ensuring it’s being applied across teams, with clear direction on where agents will be used and the governance guiding decisions.

                                  Having these failsafes in place is an approach that allows for continued innovation without running an unacceptable level of risk.

                                  We’re Seeing a Regulatory Shift – from Theory to Evidence

                                  AI regulation is morphing over time, moving gradually from high-level principles to concrete requirements that need to be backed up by evidence. The EU AI Act, NIST frameworks and ISO standards all suggest that financial institutions will need to demonstrate not just model performance, but responsible use.

                                  This creates new compliance needs. Banks will need to show how decisions are made, how risks are mitigated, and how safeguards perform under pressure. As one senior executive told us during our research, “AI risk is no longer model risk. It’s also architectural.”

                                  All of this means that keeping reliable documentation and maintaining end-to-end system visibility is becoming a baseline expectation. Banks will need explainability mechanisms that can keep up with increasingly complex AI systems. Pressure for more transparency on agentic AI use and human in the loop is likely to follow too.

                                  Responsible AI is a Strategic Capability

                                  Responsible AI has often been framed as a brake on progress – important for safety and reputation, but ultimately slowing things down. In practice, we’ve seen the opposite. The banks leading the charge on effective AI adoption know that RAI is a strategic enabler. That means that in addition to developing more use cases, scaling faster across business lines and hiring more talent, they are also ahead of the curve when it comes to RAI.

                                  They also earn more trust, whether from customers, regulators or from their own leadership. That trust will grow more important as agentic systems begin to underpin services ranging from credit assessment to wealth management.

                                  In this environment, responsibility is not a constraint. It is a foundation that allows banks to push further with AI, including finding new applications for agentic tools, while keeping risk in check.

                                  ____________________________________________________________________________________________________________________________________________________

                                  The banking industry has made huge strides on the road towards AI adoption, and the arrival of Agentic AI – while creating new compliance and safety challenges – is nevertheless an opportunity that the leading AI-first banks will be keen to embrace.

                                  Banks have already made significant investments in AI governance. What Agentic AI does is raise the bar, requiring them to ensure they’re able to demonstrate a deeper institutional understanding of autonomy, intent, and accountability – in essence, what the AI agent is doing and why.

                                  The decisions being made today about AI governance will shape the next generation of financial services. Forward-thinking institutions are already preparing for that future. JPMorgan, Citigroup, Wells Fargo, UBS and Capital One have quietly assembled specialist teams focused on agentic AI. Others are hoping their existing frameworks will stretch far enough.

                                  Opting for the latter approach is a big risk to take. Agentic AI is arriving faster than many expect. The challenges are real and so is the opportunity, but only for those who have already laid the groundwork via an RAI structure that lets them reap the benefits while maintaining trust, transparency and control.

                                  • Artificial Intelligence in FinTech

                                  Author

                                  María Ávila Silván

                                  Published

                                  23 June 2025

                                  Estimated Read time

                                  4Mins

                                  María Ávila Silván, CRO at PagoNxt Payments, on the future of B2B payments and why digital-first providers are best positioned to lead

                                  Despite long-standing claims that ‘cash is dead’, it continues to solve three distinct business problems for payments – immediacy, certainty, and accessibility.

                                  Now, however, with the European Parliament’s decision to cap cash transactions at €10,000 by 2027, businesses who rely on these attributes are facing a turning point. Where cash is no longer a viable foundation for business operations, and digital is no longer optional. While positioned as an anti-money laundering measure, this regulation’s most profound impact will be catalysing the final stage of payment digitisation across European commerce. For banks and payment providers, this represents a compliance challenge. And a strategic opportunity to extend digital payment ecosystems.

                                  The benefits of this acceleration towards digital payments are substantial. Over the past two decades, we’ve seen digital transactions offer enhanced traceability, providing both compliance benefits and improved financial visibility. In addition, they reduce the security risks and insurance costs associated with holding and transporting physical currency. Automated reconciliation capabilities eliminate countless hours of manual processing, while the granular data available from digital transactions generates treasury insights once thought impossible in the cash era. The operational efficiency gains alone can transform finance functions into the strategic enablers of enterprises.

                                  That said, the shift isn’t straightforward. Those serving cash-intensive sectors must develop solutions that deliver the same immediacy businesses expect. This requires reimagining payment workflows entirely.

                                  Human-Centric Design

                                  The €10,000 cash limitation creates distinct challenges for businesses. Consider construction companies paying contractors on completion, or wholesale distributors accepting immediate payment upon delivery. Both will face disruption to established operational rhythms. Neither are inconveniences, but touch core business relationships where immediate exchange has built trust and operational predictability.

                                  The digital alternatives now mandated by the EU must address human factors alongside technical capabilities. The reluctance to entirely abandon cash often stems from well-grounded concerns about digital payment accessibility, complexity, and reliability. Systems requiring multiple authentication steps, specialised hardware, or stable internet connectivity create friction points that cash simply doesn’t have. Any viable alternative to cash must address these barriers through education, simplified experiences, and demonstrable security. This is on all of us to address, and doing so must be viewed as a transformation journey rather than a compliance exercise. This means engaging clients early and understanding their specific operational concerns. We need to develop tailored pathways that address both the technical and cultural dimensions of payments change.

                                  Matching the Core Strengths of Cash

                                  As stated earlier, the greatest virtue of cash has always been its immediacy. You hand over notes, you receive goods or services. This represents a real-time transaction with instant settlement certainty.

                                  Digital payment systems have historically struggled to match this attribute, introducing settlement delays and reconciliation challenges that create operational friction. That is, until now. The SEPA Instant initiative addresses this gap directly, enabling settlement within seconds rather than days. Yet despite these benefits, adoption remains inconsistent, with fewer than 5% of European banks currently maintaining the robust infrastructure needed to fully support these capabilities. The cash cap now creates a powerful incentive to hasten the speed, particularly for institutions serving affected sectors.

                                  Real-time payment infrastructure delivers the immediacy businesses need. When combined with enhanced data capabilities, it creates a far superior experience to cash across multiple dimensions. A contractor receiving instant payment via their smartphone gains the same immediacy as cash while obtaining automatic documentation, tax records, and payment history. A wholesaler accepting immediate settlement receives not only funds but also structured invoice data that automates reconciliation and inventory updates. The possibilities are limitless.

                                  Building these capabilities requires substantial investment and specialisation. Institutions must manage increasing compliance demands while simultaneously accelerating their technical capabilities. This is a challenging combination even for well-resourced organisations.

                                  Scalable Solutions for a Complex Payments Transition

                                  The complexity of replacing cash transactions varies significantly across different business contexts and sectors. A unified, scalable approach becomes essential for financial institutions serving diverse client bases. Payment-as-a-Service (PaaS) models excel in this environment by providing configurable solutions that can adapt to sector-specific requirements while maintaining consistent compliance frameworks.

                                  Modern PaaS platforms deliver orchestration capabilities that manage the entire transaction lifecycle from initiation through compliance screening to settlement and reconciliation. This approach meets evolving AML requirements while delivering the real-time payment capabilities businesses require. Such a combination addresses both sides of the cash replacement equation – meeting regulatory demands while maintaining operational efficiency for end users

                                  The EU’s €10,000 cash transaction limitation marks a defining moment in European payment evolution. It creates both challenges and opportunities – forcing reconsideration of established approaches while enabling enhanced capabilities. Financial institutions have a unique opportunity to deliver solutions that preserve cash’s operational benefits while introducing new dimensions of intelligence, integration, and experience. In turn, there is a golden opportunity to create payment ecosystems that are more transparent, efficient, and valuable for all European businesses.

                                  • Digital Payments

                                  Author

                                  Radi El Haj

                                  Published

                                  16 June 2025

                                  Estimated Read time

                                  5Mins

                                  Radi El Haj, CEO and Executive Director at RS2 – a leading global provider of payment technology solutions and processing services, on a unified approach to managing payments with AI

                                  Do you build, buy or partner? When you need payment solutions it would seem that you only have three options. You can build a new system in-house, buy a solution outright or partner with a payments provider. All have advantages and disadvantages. Heres how AI can change that…

                                  Building, rather obviously, requires having the capacity to build in-house. Few payments companies are going to need to develop world-class coding expertise in their IT departments. Buying is increasingly impossible – nearly everything works on a software-as-a-service model. Partnering is by far the most common approach to extending a company’s capacities. Working alongside an established provider of payments technology to integrate their solutions into your existing technology.

                                  A staggering 70 cents in every dollar of a bank IT budget is spent on patching up old systems, and whether you build, buy or partner the aim is almost always to patch old systems rather than ‘rip and replace’. There is simply too much risk when completely overhauling legacy systems. So unless financial services companies are starting from scratch (like neobanks) then they will have a patchwork of modern and legacy systems gradually modernising over time.

                                  But what if these aren’t the only ways to build new capacities and capabilities in payments? What if AI-enabled orchestration layers could offer a pragmatic, risk-mitigated and cost-effective fourth option? According to RS2’s latest research, this is not only possible, it’s already happening. And it’s driving measurable improvements in transaction success rates, fraud reduction and customer insights across global banking operations.

                                  What is payment orchestration?

                                  A payment isn’t a simple case of sending a fixed sum from one bank to another. There is a multi-part, often multi-national process to every payment that has to take place within fractions of a second, involving multiple companies and systems, some of them AI-based.

                                  Just as each musician in an orchestra knows their individual part to play but needs a conductor to become a unified whole, a payment orchestrator makes sure each element in the payments chain works harmoniously. In practice, this means determining the optimal route for each transaction based on the payment itself: one particular payment might have more chance of being accepted going down one route than another, particularly when payments are being made across national borders. It means that merchants can connect with a single payment orchestrator and from there access an entire world of payments companies, each suitable for a certain part of certain payments. These transaction chains are also made to be compliant with regulations in whatever jurisdictions that they take place in.

                                  One under-appreciated part of payment orchestration is the top-down view it gives over a merchant’s payments, and from there how it can be analysed to improve payments and the merchant’s operations as a whole. It can give merchants insight into payment trends, customer behavior, performance and fraud, and if these aspects of payments can be optimized then there is potential for significant cost savings.

                                  This is key: the ultimate outcome of payment orchestration is reduced costs for merchants and their customers. Whether it is through reducing the cost of each payment through the most efficient processors or allowing data analysis to find ways in which to optimize payments, the ultimate outcome is always going to be cost savings.

                                  Enter AI

                                  Artificial intelligence has been a major news story for the past three years, but the real picture of what is happening and what could be happening in the space is much more complex and interesting.

                                  Almost all of the press attention on artificial intelligence over the last years has been toward Large Language Models (LLMs) like ChatGPT. These can produce convincing bodies of text but this has little utility in payments beyond being a cheap alternative to customer-service agents. The real use of AI in payments has a longer history and is much more useful, especially when combined with the influx of data that can come from payment orchestration.

                                  So, what can AI be used for in payments? Merchants and payments providers produce incredible amounts of data, much of which goes unanalyzed and sits inert in cloud storage, becoming a cost rather than a source of revenue. Machine-learning algorithms have shown an incredible ability to sort through this information and provide insights that no human could come up with. These insights can inform top-level decision-making (‘our customers are moving toward alternative payment methods’) or micro-scale adjustments (‘using payment service provider A instead of payment service provider B at weekends gives a 0.043% increase in acceptance rates’).

                                  AI-enabled orchestration layers take this a step further. They connect all banking platforms—card management, UX, third-party services, ledgers, reconciliation, interchange, and more—into a central intelligence hub. The result is dynamic optimization of transaction routing, cost reduction in acquiring and FX, and a dramatic reduction in fraud and transaction failure​.

                                  The AI Orchestration Layer

                                  Imagine that you have an orchestra with both veteran (perhaps even past their prime) musicians and enthusiastic newcomers. Hypothetically they can play the sheet music in front of them, but what they need is a conductor to bring it all together.

                                  This is the AI orchestration layer. Instead of building, buying or partnering to upgrade individual services, an AI system can ensure that all of the existing parts of a company’s payments ecosystem are working as a unified, insight-driven whole.

                                  With real-time fraud detection, transaction risk scoring, and automated escalation steps (like biometric authentication), AI orchestration layers significantly reduce chargebacks and improve compliance. Smart decline recovery techniques—such as real-time retries or alternative payment prompts—directly increase revenue and improve customer satisfaction​.

                                  AI also simplifies regulatory compliance. With built-in AML and KYC checks, suspicious activity monitoring, and auto-generated reporting, banks can meet growing compliance demands with fewer human resources and less manual intervention​.

                                  Beyond Build, Buy, or Partner

                                  This isn’t just a new tool—it’s a new model. RS2’s white paper describes AI orchestration as the “fourth path” beyond build, buy or partner. Rather than risky system replacements, banks can phase in AI capabilities without ever compromising core operations. By implementing self-hosted AI within secure Virtual Private Clouds, RS2 ensures full control over sensitive financial data while delivering full interoperability with ISO 20022 messaging frameworks​.

                                  The result? Lower fraud, higher conversion rates, smarter compliance, and a customer experience that feels truly modern—all achieved without the disruption of traditional overhaul strategies.

                                  Banks don’t need to choose between building from scratch, outsourcing, or stitching together third-party solutions. AI-enabled orchestration offers a more elegant, efficient, and secure way forward—and it’s available today.

                                  • Artificial Intelligence in FinTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  13 June 2025

                                  Estimated Read time

                                  3Mins

                                  Building on a long-term partnership, Klarna will leverage Marqeta’s platform and the Visa Flexible Credential to expand payment options for Klarna’s new debit card 

                                  Marqeta, the global modern card issuing platform enabling embedded finance solutions, has announced it is working with Klarna. It will enable the global digital bank and flexible payments provider’s new debit card. The debit card is powered by Visa Flexible Credential (VFC) that allows access to built-in flexible payment options.  

                                  Klarna powered by Visa Flexible Credential and Marqeta

                                  In July 2024, Marqeta became the first issuer processor in the US certified for Visa Flexible Credential. With VFC, Marqeta will enable Klarna customers to pay immediately or pay later when needed, all on the same card. This milestone builds on years of collaboration between Marqeta and Klarna. Including powering Klarna’s virtual cards in the US since 2018. The card is currently in a trial phase in the US, with a broader rollout expected later this year. 

                                  “The future of payments is flexible. We’re proud to enable this new offering together with Visa,” said Rahul Shah, Chief Product and Engineering Officer at Marqeta. “Our ongoing partnership with Klarna is a true testament to what’s possible with Marqeta’s platform. And how we enable our customers to grow and innovate at global scale.”  

                                  With its flexible card issuing platform, Marqeta makes it possible for global leaders like Klarna to expand to new markets. And offer innovative payment options tailored to evolving customer needs. Marqeta currently supports Klarna in six countries, helping to drive global growth and deliver seamless, consumer-first experiences.  

                                  “Through our continued partnership with Marqeta and Visa, we’re evolving the Klarna Card into a truly dynamic and versatile payment experience,” said David Sandström, Chief Marketing Officer, Klarna. “We’re excited to continue innovating alongside Marqeta as we scale the Klarna Card to provide smart, seamless payments that empower smarter, more informed shoppers everywhere.” 

                                  About Marqeta 

                                  Marqeta makes it possible for companies to build and embed financial services into their branded experience. And unlock new ways to grow their business and delight users. The Marqeta platform puts businesses in control of building financial solutions, enabling them to turn real-time data into personalized, optimized solutions for everything from consumer loyalty to capital efficiency. With compliance and security built-in, Marqeta’s platform has been proven at scale, processing nearly $300 billion in annual payments volume in 2024. Marqeta is certified to operate in more than 40 countries worldwide. Visit www.marqeta.com to learn more. 

                                  About Klarna 

                                  Klarna is a global digital bank and flexible payments provider. With over 100 million global active Klarna users and 2.9 million transactions per day, Klarna’s AI-powered payments and commerce network is empowering people to pay smarter with a mission to be available everywhere for everything. Consumers can pay with Klarna online, in-store and through Apple Pay in the U.S., UK and Canada. More than 724,000 retailers trust Klarna’s innovative solutions to drive growth and loyalty, including Uber, H&M, Saks, Sephora, Macy’s, Ikea, Expedia Group, Nike and Airbnb. For more information, visit Klarna.com. 

                                  About Visa 

                                  Visa (NYSE: V) is a world leader in digital payments, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories. Our mission is to connect the world through the most innovative, convenient, reliable and secure payments network, enabling individuals, businesses and economies to thrive. We believe that economies that include everyone everywhere, uplift everyone everywhere and see access as foundational to the future of money movement. Learn more at  Visa.com. 

                                  • Digital Payments
                                  • Neobanking

                                  Author

                                  Rob Meakin

                                  Published

                                  12 June 2025

                                  Estimated Read time

                                  5Mins

                                  Rob Meakin, Director of Fraud & Identity at Creditinfo, on leveraging tech to tackle fraud

                                  Financial fraud is increasing around the world, putting both mature and emerging digital economies at risk. The overall global economic impact of financial crime has been estimated to be $5 trillion. Furthermore, according to the 2024 Nasdaq global financial crime report, fraud losses totalled $485.6 billion worldwide. This from fraud scams and bank fraud schemes alone. As such, organisations face a series of challenges, from eroding profit margins to reputational risks to data breaches.

                                  Many factors contribute to this growing wave of fraud. For example, digitisation in banking has created new opportunities for bad actors. With more identity data existing online, attack surfaces have expanded. Hackers now have more possible entry points to exploit vulnerabilities.

                                  At the same time, new technologies, like machine learning (ML), artificial intelligence (AI), and automation are enabling bad actors to innovate faster and evade detection more effectively. AI, in particular, is a double-edged sword. While many businesses use the technology to improve efficiency and decision-making, it also gives bad actors a helping hand. Deepfakes and social engineering, for example, enable them to impersonate individuals with uncanny realism.

                                  Additionally, cybercrime – especially financial crime – is becoming more sophisticated. Today, over two-thirds of financial institutions admitting they’re unprepared to defend against the rising wave of attacks.

                                  Counting the many costs of fraud

                                  Rising fraud creates challenges at local, national, and global levels. Financial loss is, obviously, a primary concern. But financial loss is only part of the total cost of cybercrime. Fraud also brings reputational damage, increased risk of data breaches, and potential legal consequences.

                                  As organisations devise new strategies to tackle rising fraud, they must also heed regulatory requirements. Namely, Anti-Money Laundering (AML) registration, as well as other standards for privacy and consent. These regulations create further challenges for organisations as they aim to uphold rigorous compliance requirements without impacting sales, operating costs, or the customer experience.

                                  It’s time for a different approach to fraud detection

                                  On both local and global levels, mounting fraud threatens economic growth. In its Plan for Change, the UK government has recognised global co-operation will be necessary to tackle fraudsters. However, existing security strategies are too fragmented to suit the needs of diverse markets.

                                  Emerging economies, for example, often lack mature controls, making them inherently vulnerable to hackers. Yet, with smaller digital infrastructures, they’re also less attractive targets for financial crime.

                                  In contrast, more mature economies usually have stronger security defences. However, their larger digital ecosystems make them perhaps even more vulnerable to bad actors’ advances. After all, the more digital an economy becomes, the more fragmented and complex an individual’s identity and the more opportunities for bad actors to exploit or impersonate it.

                                  Combatting fraud at a global scale requires going local

                                  Considering the scale and sophistication of cybercrimes, combatting global fraud will require organisations to turn to localised data for more precise identity verification.

                                  By integrating data from diverse, localised sources and tailoring fraud prevention strategies to market-specific risks, organisations can better detect fraud and establish identity trust. And in a way that both upholds the customer experience and promotes financial inclusion.

                                  Combine credit, government, and digital data to enhance intelligence

                                  Thwarting fraudsters begins with building intelligence to establish trust and verify presented identities. This is where localised data can help. By combining credit bureau data with government registries and digital signals, organisations can find a correlation across multiple digital identity attributes and digital risk signals to assess risk and enable real-time identity trust.

                                  Credit bureau data associated with the presented identity can be used to determine risk and trust based on four vectors:

                                  • The bureau footprint: information comprising records from multiple contributing organisations
                                  • Activity history: evidence of recent and consistent payment activity
                                  • Data consistency: personal data stability
                                  • Application velocity: recent application history

                                  Meanwhile, government information services and other registries can be incorporated to further cross-check the presented identity and strengthen verification.

                                  By leveraging such a wide range of independent, localised data sources and correlating them with the presented identity attributes, organisations can significantly enhance intelligence to detect fraud without compromising the customer experience.

                                  Tailor strategies to specific markets to support compliance and accessibility

                                  It’s also important that organisations tailor their security and identity-verification strategies to the unique needs and maturity levels of specific markets. For example, in emerging economies, many people struggle to access financial services. This is often due to a lack of a formal credit history or other recognised financial records. Without this information, it can be a challenge for organisations to verify identity and reach trust decisions without inadvertently excluding legitimate users.

                                  But by using localised data sources and market-specific strategies, organisations can make more informed decisions to bring more traditionally excluded parties into the financial system and promote broader financial inclusion without increasing risk or compromising security.

                                  These targeted, market-specific fraud prevention strategies also help organisations with regulatory compliance. For example, for AML compliance, organisations must “identify, assess, and understand the money laundering and terrorist financing risk to which they are exposed.” Using localised data and market-specific strategies can help organisations meet this expectation by aligning fraud detection controls with region-specific threat intelligence.

                                  Conclusion

                                  Global financial crime continues to ramp up, creating new challenges for organisations to detect fraud, verify identities, and comply with regulations. But finding strategies to beat bad actors is made even more difficult by markets’ varying needs, maturity levels, and digital infrastructures.

                                  To combat fraud and cyberthreats on a global scale, organisations should pivot to a localised approach. By combining credit, government, and digital data and tailoring fraud-prevention strategies to specific markets, they can enhance intelligence, maintain compliance, and better manage risk. In doing so, they can not only strengthen security but facilitate access to financial products and services for broader financial inclusion, worldwide.

                                  • Cybersecurity in FinTech

                                  Author

                                  Nick Saywell

                                  Published

                                  11 June 2025

                                  Estimated Read time

                                  4Mins

                                  Nick Saywell, Senior Manager at PSE Consulting, on the rise of account-to-account payment

                                  With Apple and Android both unlocked, can account-to-account payment finally rival cards at the checkout?

                                  For years, account-to-account (A2A) payment providers have dreamed of bringing their low-cost, real-time model to the in-store experience. But one key problem kept getting in the way: the experience wasn’t seamless enough to challenge the tap-and-go ease of cards. That may be about to change.

                                  In mid-2024, the European Commission struck a landmark deal with Apple, forcing it to open access to the iPhone’s NFC chip to third-party payment providers. With both Android and iOS now unlocked, a door has opened that could finally give A2A wallets a shot at real parity with cards — and give merchants and consumers a meaningful alternative to the traditional payment rails.

                                  The race is on. But can A2A deliver?

                                  A2A Payments, Rebooted

                                  A2A in-store payments have technically been possible for some time. But the experience has often fallen short, marred by clunky QR codes, awkward authentication flows, and too many screens. Consumers, spoiled by contactless cards and mobile wallets, weren’t interested in waiting even a few extra seconds.

                                  Now, with tap-to-pay functionality available on all major devices, A2A apps can finally offer what was missing: frictionless in-store payments that rival the card experience. And with that, the real advantages of A2A — faster settlement, lower fees, and direct-to-bank transfers — are no longer hidden behind usability issues.

                                  The question is no longer “can they?” It’s “how far can they go?”

                                  The Contactless Advantage

                                  In-store, speed is everything. In markets like the UK, where 93% of card payments are contactless, expectations are sky-high. For A2A wallets to compete, tap-to-pay is the bare minimum – and until now, it simply wasn’t available on iOS.

                                  That changed in December 2024, when Vipps MobilePay launched the first-ever A2A tap-to-pay solution on iPhone, enabling “Tap with Vipps” at stores across Norway. With expansion plans underway for Denmark, Finland, and Sweden, the Nordic region is quickly becoming a proving ground for A2A in-store dominance.

                                  Other markets are following – and fast. Sweden’s Swish has moved from Bluetooth to NFC for Android tap-to-pay. Bizum, used by over half of Spain’s population, is rolling out “Bizum Pay”, enabling A2A and card-linked tap payments later in 2025. In Poland, where Blik already dominates eCommerce, the company is planning iOS tap-to-pay integration this year.  

                                  Crucially, these aren’t just tests or pilots — they’re market-ready rollouts. And they show that the A2A space is no longer content to sit in the shadow of cards.

                                  The Big Economies Lag Behind

                                  However, not everyone is moving at the same speed.

                                  Despite the momentum in Scandinavia, Spain, and Poland, Europe’s biggest economies have been slower to act. The UK has yet to see a major A2A wallet gain traction in-store. In Germany and France, legacy infrastructure and conservative adoption curves are proving hard to shake.

                                  Even Wero, the pan-European A2A wallet backed by the European Payments Initiative, won’t have an in-store solution ready until 2026. That delay risks leaving Europe’s largest markets outpaced by smaller, more agile neighbours — at a time when merchants and consumers alike are increasingly open to change.

                                  For now, it’s the early movers who are defining the space — and setting expectations.

                                  The Cross-Border Payment Battle

                                  While domestic progress is promising, cross-border A2A remains the next big challenge. Regional alliances are forming — including:

                                  • EuroPA: A partnership between Spain’s Bizum, Italy’s Bancomat Pay, and Portugal’s MB Way, which completed its first cross-border transaction in late 2024.
                                  • EMPSA: An alliance including Bancomat Pay, Switzerland’s Twint, and Austria’s Bluecode, focused on cross-border interoperability.

                                  But the road ahead is bumpy. Without a unified European solution, A2A risks becoming fragmented — more complicated for consumers, and harder to scale. Some argue that Wero offers the long-term answer. But in the short term, it’s up to these alliances to prove cross-border A2A is more than a theory.

                                  The pressure is on to prove that A2A can work as well across borders as it does at home — without sacrificing simplicity or reliability.

                                  The Moment of Truth for A2A Wallets

                                  This isn’t just a technical breakthrough — it’s a power shift. For the first time, A2A wallets are competing with cards on the one thing that mattered most: convenience. With NFC access now universal, and major players moving fast, the old excuses no longer apply.

                                  Whether A2A becomes the new default or remains a challenger brand depends on what happens next. Can providers scale fast enough? Can they deliver the reliability, UX, and trust that card payments have built over decades?

                                  One thing’s clear – 2025 will be a crucial year in the battle to redefine Europe’s payment scene, and a new offensive to win in-store transactions is just starting.

                                  About PSE Consulting

                                  PSE Consulting is a leading global provider of payment advisory services to players across the payments landscape. PSE’s expertise has enabled it to deliver actionable market insights and operational optimisation to senior payments leaders for over 30 years. Find out more here.

                                  • Digital Payments

                                  Author

                                  Dan Brightmore

                                  Published

                                  10 June 2025

                                  Estimated Read time

                                  4Mins

                                  Russell Gammon, Chief Solutions Officer at Tax Systems, on the benefits of AI in automating routine processes to make time for higher level strategic tasks

                                  In the past two and a half years since the launch of ChatGPT – and the likes of Copilot – the world has been gripped with generative AI fever. However, after the initial rush of enthusiasm, many businesses today are taking a more cautious approach. Trying to identify tangible benefits and use cases that can prove its worth before making costly investments.

                                  One industry where the use cases are becoming more evident day by day is Financial Services. Repetitive and time-consuming tasks, traditionally completed manually with all the risk of human error that entails, can now be automated. Capabilities such as machine learning, generative AI, and advanced data analytics algorithms are being used to help ensure organisations remain compliant through delivering accurate, timely calculations, tax filings and reports. And creating clearer visibility.

                                  AI Revolution

                                  By automating routine processes, such as data analysis and reconciliation, finance executives can spend more time on higher level strategic tasks. AI can also provide insights beyond the capacity of humans thanks to its ability to crunch vast volumes of data, It can uncover trends that might otherwise go unnoticed. This enables real-time reporting and analysis with AI insight forming the basis of smarter decision-making.

                                  For finance, this is just the beginning of the AI revolution. Look deeper into any finance sector and a huge variety of more specialised applications are revealed. Take the tax industry, for example, where a sizeable cohort of professionals still spend a considerable amount of time checking long lists of numbers on invoices or using spreadsheets to track spending. Not only is this work frustratingly boring, it is also prone to human error. AI has the potential, at a single stroke, to handle such tasks.

                                  Navigating Choppy Regulatory Waters

                                  Staying in the tax-related field, AI can also play a pivotal role in handling incoming regulations, such as Pillar Two. Multinational corporations are grappling with the complexities of this legislation. AI is emerging as a game changing tool in compliance management, transforming tax reporting, risk mitigation, and regulatory adaptation.

                                  AI is being used to automate compliance and reporting processes. It can streamline data aggregation, ensure accurate reporting, and adapt to evolving regulations. AI-powered compliance tools optimise the evaluation, monitoring, and reporting of Pillar Two obligations. This can reduce complexity and improve precision. They can also integrate and standardise financial data across jurisdictions, improving consistency in tax computations.

                                  These solutions seamlessly connect disparate systems, extracting and harmonising data from multiple sources regardless of format. By normalising and processing this information in line with BEPS regulations, AI can swiftly identify potential compliance risks. Advanced algorithms can flag irregular transactions between related entities and pinpoint inconsistencies in transfer pricing. This helps to detect possible profit-shifting activities before they become regulatory concerns. AI thus has the potential to change compliance management from a costly obligation to a strategic advantage.

                                  Be Wary of AI’s Limitations

                                  So, there is clearly a lot of potential for AI to transform financial services in terms of daily operations and compliance. However, it is important to remain wary of its limitations. Chief amongst them, is AI’s propensity to ‘hallucinate’ or make information up if it can’t find the right answer. That casts a shadow over the accuracy of all of its output. And underlines the importance of professional gatekeepers who can verify AI content and ensure it is correct.

                                  AI also currently lacks the ability to interpret subtle context, which humans can more easily respond to. This can feed into spurious responses and misinterpreted data. However, with the right training, monitoring and oversight, AI tools can overcome such weaknesses.

                                  Supporting, Not Replacing, the Human Touch

                                  Understandably, given AI’s potential, many are concerned about the impact on jobs. If AI can digest thousands of lines of data and spit out a report in seconds, what do we need interns for? But it’s important to see AI as an augmentation of existing human talent, not a replacement for it.

                                  As noted above, the possibility of hallucination means that qualified professionals will always have a role to play in quality checking output. So, what we are seeing is the development of a symbiotic relationship wherein professionals are freed from the drudgery of repetitive grunt work. They can focus on more strategic objectives, while AI handles it under their careful eye.

                                  For the tech-savvy Gen-Z entering the workplace today, this is a hugely positive change. The finance and tax industries have become a less attractive career option for this generation, due to the traditional processes and lack of technological innovation. What graduate wants to spend their days entering data after years of studying their chosen subject? With AI ready as a helping hand, they can enter the workplace and use their skills and knowledge to assess the technology’s output, rather than spending hours manually doing it themselves. The finance industry is now in a position to embrace this opportunity that AI has presented. And encourage new talent into the industry.   

                                  Given the financial services sector is plagued with skills shortages, and ever-growing workloads, employers can now offer more attractive career opportunities. Furthermore, striking the right balance to drive improved efficiency, productivity and performance and reap the rewards of an AI-enabled future. 

                                  • Artificial Intelligence in FinTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  9 June 2025

                                  Estimated Read time

                                  3Mins

                                  Jonathan Brander, COO at Upvest, on best practice for trading platform infrastructure

                                  In the early hours of market turbulence, when retail investors are scrambling to respond, it’s not volatility that fails them: it’s infrastructure. In the past, we’ve repeatedly seen investing technologies buckle under pressure during moments of peak market stress. 

                                  During times of high demand, many platforms might struggle to maintain uptime. In recent weeks, as Trump’s tariffs announcements saw retail trading volumes surge, some of the world’s biggest trading platforms went dark. These responses to market volatility are not outliers: they are predictablestress tests. Market volatility correlates strongly with spikes in trading volume. A study by the European Central Bank found that liquidity shocks consistently drive increases in trading activity, especially in frequently traded assets. Platforms should expect and be designed for these surges. 

                                  Yet time and again, outages occur at precisely the moments when retail investors and advisers need control. In these moments, investors don’t merely lose access, they lose confidence.

                                  Trading Platform Infrastructure

                                  A 2024 poll found that 30% of UK banking customers would consider switching providers following a technology failure. Among 25-34 year olds, this figure jumps to 57%. For trading platforms (and their technology providers) trust is hard-won and easily lost. Operating in a financial market characterised by risk, investment infrastructure resilience is no longer a “nice-to-have”. It is a strategic necessity. 

                                  According to McKinsey, global assets under management in private markets grew to $13.1 trillion in 2023. In the UK, over a third (39%) of adults are actively investing and the number is growing, thanks in part to government-led market reforms. As trading volumes increase, retail investors need infrastructure that doesn’t flinch under pressure. So what does this look like in practice?

                                  First, elasticity is essential. Systems must be able to scale to meet demand spikes. When trading activity spiked following Trump’s tariff announcement, Upvest experienced the highest trading volumes in our history. Our platform scaled exactly as it was designed to do, enabling millions of Europeans to seamlessly trade and invest in thousands of instruments with zero downtime. At times of volatility, “stability as a service” emerges as a key competitive differentiator. 

                                  Second, build for failure. The leading question in our conversations with clients is no longer “can you add this feature?”, it’s “can you guarantee uptime under pressure?” Financial institutions need to know that trading can continue in volatile conditions. Infrastructure providers must build with this in mind and leverage modular systems – where trading, settlement, and custody run independently – to reduce the risk that a single point of failure cascades across an entire platform. Decentralised services improve incident isolation and, in a digital-first financial ecosystem, reliable infrastructure that remains operational even when pressure peaks is the foundation of investor empowerment.

                                  Observability is also key. Real-time monitoring allows operations and tech teams to anticipate issues before they become outages. This means constantly tracking latency, error rates, and system health, as well as regularly simulating and stress-testing for high volume scenarios to ensure systems can perform under extreme load. These synthetic tests mimic real-world event spikes and ensure you can deliver under pressure.

                                  Finally, communicate transparently. When issues arise, investors deserve clear metrics on uptime and response windows. Public dashboards and incident post-mortems are no longer optional, they’re foundational to trust. At Upvest, for example, API Status is always available online so our clients can see whether we’re experiencing any issues.

                                  Future Resilience

                                  These steps are no longer operational best practice: they’re a necessity. The investment industry must move beyond treating volatility as an edge case and start building resilience into platforms as a priority. Retail investors don’t judge their investment providers during periods of calm, they judge them in crisis. When the market wobbles, infrastructure is the differentiator. That’s when confidence is earned and financial empowerment starts to happen.

                                  • Blockchain & Crypto
                                  • Digital Payments

                                  Author

                                  Mark Andreev

                                  Published

                                  6 June 2025

                                  Estimated Read time

                                  4Mins

                                  Mark Andreev, COO at Exactly, presents a practical guide to tackling e-commerce fraud with payment tokenisation

                                  Tokenisation can solve a big problem… e-commerce fraud is a growing threat that continues to impact online businesses worldwide. According to recent figures from Statista (2025), global e-commerce losses due to online payment fraud are projected to exceed $100 billion by 2029. As fraudsters increasingly exploit IT vulnerabilities, it is imperative for online and brick-and-mortar businesses to fortify their cybersecurity posture.

                                  Amidst the current security challenges, payment tokenisation emerges as a technology to future-proof business operations and is projected to reach USD 28.97 billion worth by 2033.

                                  This guide explores the concept of payment tokenisation, emphasising its value and role in ensuring credit card payment processing standards for merchants.

                                  What is Payment Tokenisation?

                                  Tokenisation is the process of substituting sensitive data with non-sensitive values – tokens. It works as a key layer of protection for stored data by replacing card numbers with illegible, surrogate values.

                                  During a transaction, payment details are securely transmitted to a trusted payment provider via hosted payment page or through direct API integration.

                                  In the hosted payment page flow, the customer is redirected to a secure payment page operated by the payment provider. Here they can enter their payment information. The provider handles data collection, encryption, and transaction authorisation, keeping sensitive information off the merchant’s servers.

                                  In the API integration flow, the merchant’s website collects payment details using secure client-side tools. In this case, the merchant is responsible for ensuring full PCI DSS compliance, as sensitive data passes through their systems.

                                  Following a transaction, sensitive card data is substituted by a special character sequence. The translation of characters into randomised values refers to the tokenisation process.

                                  For merchants who are not PCI DSS compliant, storing sensitive information on their side is not allowed. In these cases, the third-party payment provider retains the sensitive data and the tokens for future use, while merchants don’t retain any sensitive information.

                                  This method is one of the key cybersecurity best practices to ensure payment providers remain compliant with PCI DSS and is also crucial for merchants using API integration to store sensitive data.

                                  Different Types of Tokens

                                  There are different types of tokens available to merchants, offering different levels of complexity and security. Simple tokens refer to randomised reference numbers that are unidentifiable and unrelated to customer data. They provide a high level of security when implemented correctly by a reputable payment provider.

                                  On the other hand, token vaults represent a more complex system of payment security and data handling. Essentially, token vaults are encrypted repositories of original payment data associated with tokens from each customer transaction. Depending on the type of payment gateway integration, either the merchant or the payment provider may retrieve the payment information as needed. Token vaults can also be deployed in cloud environments, mitigating the need for extensive infrastructure.

                                  The Value of Tokens

                                  In an era where cybersecurity is paramount, failing to secure customer data can come at significant costs. Recently, the IT systems of the UK’s most prominent retailers suffered significant downtime following a series of cyberattacks. They were prevented from serving their customers as a result. As the consequences of these attacks continue to linger, affected UK retailers are working overtime to get back on track. In these situations, the use of tokenisation payment security has partly helped prevent what could have been a catastrophic breach. Reducing the risk of a lateral exploitation of customer data. In fact, using payment tokens, retailers avoid the need to encrypt and retain sensitive payment details. This lowers the risk of attacks, breaches, and noncompliance with ever-changing payment processing and data security policies.

                                  Tokenisation also enables seamless customer experiences, addressing a crucial customer demand – convenience. In fact, with tokenisation enabling one-click checkouts, customers avoid re-entering card details and access a seamless shopping experience, meeting an important need for comfort and familiarity for consumers.

                                  Finally, from a regulatory perspective, compliance with PCI DSS is mandatory for payment providers and merchants specifically using API integration within payment gateways to store sensitive information. In this regulatory context, tokenisation becomes a straightforward strategy to meet fundamental data handling legal requirements. In an era of rising cyber threats and increasing customer expectations, tokenisation offers merchants a scalable, effective, and future-ready approach to safeguarding sensitive data, building trust, and preserving business integrity.

                                  • Cybersecurity in FinTech
                                  • Digital Payments

                                  Author

                                  Dan Brightmore

                                  Published

                                  5 June 2025

                                  Estimated Read time

                                  3Mins

                                  The final day at Money20/20 Europe 2025 was packed with more insights on the future of FinTech, from banks to borderless innovation.

                                  Money20/20 Conference Themes & Tracks

                                  Money20/20 Europe 2025 is structured around four thematic content tracks:

                                  • Digital DNA – Exploring core infrastructure, platform strategies, and foundational technologies.
                                  • Embedded Intelligence – AI, machine learning, data strategies, and real-time analytics.
                                  • Beyond Fintech – Partnerships between fintechs and other sectors like retail, health, and climate.
                                  • Governance 2.0 – Regulation, digital identity, privacy, and ESG compliance.

                                  Day three featured more impactful sessions across all four pillars, offering attendees more valuable insights and strategies for innovation.

                                  Highlights from Key Sessions at Money20/20 Europe:

                                  How to Create and Leverage FinBank Partnerships

                                  The discussion focused on the evolution and success of FinTech partnerships with banks. Key points included the shift from transactional partnerships to more collaborative, value-driven relationships, emphasizing joint KPIs and product creation. 

                                  Alex Johnson, Chief Payments Officer, Nium

                                  “You really have to differentiate. You really have to stand out for a bank to say, ‘Yeah, I like what you offer enough to go through, six months of onboarding.’ Dare I say, maybe more.”

                                  John Power, SVP, Head of JVs & AQaaS, Fiserv

                                  “The legacy system, it’s a fact of life. They’re there. They’re pervasive. They’re going to be here for a long time, and banks historically have made huge investments in those platforms and systems. So I think both the challenge for the for the bank and the opportunity for the FinTech is, how do you at the front end of those legacy systems develop new products that can scale and that you can bring cross border easily and readily.”

                                  Cecilia Tamez, Chief Strategy Officer, Dandelion Payments

                                   “It really is cutting the line to be able to deliver opportunity for customers and to be able to expand propositions for new customers.”

                                  “The economic development supply chains shifting to low to middle income countries are incredibly important right now, and cross border payment rails have not been good in low middle income countries.”

                                  Where Fintech goes Next: Tapping into Platforms and Verticals 

                                  The discussion centred on the democratisation of financial services through embedded finance. The panel emphasised the importance of data quality, personalisation, and strategic partnerships in delivering seamless financial experiences – ultimately enhancing customer satisfaction and improving business efficiency.

                                  Hiba Chamas, Growth Strategy Consultant – Independent

                                  “Embedded finance is going to be defined by region and use cases.”

                                  Amy Loh, Chief Marketing Officer – Pipe

                                  “Small businesses don’t want to manage their business through a bunch of different tools that are stitched together. They’re looking to platforms to do everything for them and keep high end services.”

                                  Zack Powers, VP Commercial & Operations – Mangopay

                                  “Most platforms or merchants out there trying to diversify revenue, and they will get auxiliary revenue, or maybe get primary revenue through FinTech activity.”

                                  The Neobanks Strike Back

                                  ​​In a dynamic exploration of neobanking’s evolution, Ali Niknam revealed bunq’s remarkable journey from a tech-driven startup to a sustainably profitable digital bank. By leveraging AI across every aspect of their operations, bunq has transformed traditional banking, reducing support times to mere seconds and creating a hyper-personalised user experience. Niknam emphasised the power of user-centricity, showing how innovative features like simple stock trading and multi-language support can democratise financial services.

                                  The bank’s strategic approach – focusing on user needs rather than investor expectations – has enabled them to expand thoughtfully, with plans to enter the UK and US markets. By embracing technological change and maintaining a relentless commitment to solving real customer problems, bunq exemplifies the next generation of banking.

                                  Ali Niknam, Founder & CEO, bunq


                                  “Somewhere in the 70s, we let go of the gold standard, and now currencies are basically floating. The only reason why a dollar or a euro is worth what it’s worth is because of trust and perception. Philosophically, it’s very logical that we have found another abstraction layer by introducing stablecoin, which is not much else than a byte number that has a denomination currency as a backing asset that itself doesn’t have anything as a backing asset. A lot of people might ask, ‘Why would you need a stablecoin? We have euros. I go get a coffee, pay with Apple Pay or cash.’ But there are many countries on this planet where the local currency is not stable. If your country has an inflation rate of 30,000% like Zimbabwe, you would really love to use a different currency. The US dollar has been the currency of choice, but as a normal person, you cannot access the US dollar. A US dollar stablecoin that you can access by simply having a mobile phone – that’s going to be transformational for large groups of people.”

                                  Innovating When Regulation Can’t Keep Up: Lessons from NASA 

                                  Lisa Valencia covered an array of topics, from her 35 year career at NASA and Guinness World Record to the rise of private entities like SpaceX, which has launched 180 missions this year, and the increasing role of public-private partnerships in space exploration. The speaker also touched on international collaborations, particularly with the European Space Agency and the Italian Space Agency, and the potential for space tourism and colonization of the moon.

                                  Lisa Valencia, Programme Manager/Electrical Engineer – Pioneering Space, LC (ex NASA)

                                  “Back in the day, NASA got 4% of the national budget. Now it’s down to just 0.1%, so we’ve had to get creative with private partnerships. SpaceX is the perfect success story. They came to us in 2007 needing money after some rocket mishaps, and look at them now! From my balcony, I see their launches every other day. They’re planning 180 launches this year alone.Talk about a return on investment!” 

                                  “We’re planning to colonise the South Pole on the moon. The idea is to extract water and hydrogen from the regolith—both for living there and for fuel.”

                                  Scaling Internationally in 2025: Funding, Innovating, and Breaking into New Markets

                                  The conversation focused on the growth and strategy of fintech companies, particularly those with a strong presence in Europe and the US. The panel featured Ingo Uytdehaage, CEO and co-founder of Adyen, and Alexandre Prot, CEO of Qonto. Both leaders expressed a preference for organic growth over acquisitions, emphasizing the importance of scaling efficiently before pursuing an IPO.

                                  Ingo Uytdehaage, CEO and co-founder of Adyen

                                  “I think an important part of scaling a company is not just thinking about your product, but also considering the markets you want to address, and how you ensure you become local in each country.”

                                  “We realised over time that if we really want to bring the customers, we need to have the best licenses to operate. A banking license gives you a lot of flexibility.” 

                                  “Being independent from other companies, other financial institutions, that gives you flexibility to build what your customers really want.”

                                  “I think it’s very important, also in Europe, that we continue to be competitive. If you think about regulations and AI, we shouldn’t try to do things completely differently compared to the US.”

                                  Alexandre Prot, CEO of Qonto

                                  “We need to be very strict about tech integration and avoiding legacy which slows us down.”

                                  “We still need to scale a lot before we have a successful IPO. A few team members are working on it and getting the company ready for it. But, the most important thing is just scaling efficiently in the business, and maybe an IPO would be welcome in a couple of years.”

                                  Putting The F in Fintech

                                  The panel discussion focused on the role of women in FinTech based on personal experiences.

                                  Iana Dimitrova, CEO, OpenPayd

                                  “At times, being underestimated is helpful, because if you’re seen as the competition, driving an agenda is becoming more difficult. So what I found, actually, over a period, is that bringing your emotional intelligence, leaving the ego outside of the outside of the room, and just focusing on execution is is incredibly helpful.” 

                                  Megan Cooper, CEO & Founder, Caywood

                                  “The moment we start defining ourselves as like a female leader or a female entrepreneur, you almost kind of put yourself in a bit of a box. And so I think just seeing yourself on an equal playing field and then operating it on an equal playing field and interacting in that way is quite advantageous.”

                                  “We can’t just want diversity and hope it happens. We actually have to be intentional about creating it.”

                                  Valerie Kontor, Founder, Black in Fintech

                                  “Black women make up 1.6% over the FinTech workforce, but when we look at the financial reality of black women by the age of 60, only 53% of black women have enough money in their bank account to retire. We need to start marrying people in FinTech and the people that we need to serve.”

                                  Money20/20 Europe 2025 closed its doors but the next edition of the conference will return to Amsterdam from June 2–4, 2026, promising to continue the tradition of shaping the future of financial services…

                                  • Artificial Intelligence in FinTech
                                  • Blockchain & Crypto
                                  • Cybersecurity in FinTech
                                  • Digital Payments
                                  • Embedded Finance
                                  • Host Perspectives
                                  • InsurTech
                                  • Neobanking

                                  Author

                                  Dan Brightmore

                                  Published

                                  4 June 2025

                                  Estimated Read time

                                  4Mins

                                  Day two of Money20/20 Europe 2025 at RAI Amsterdam continued the momentum with a focus on digital assets, stablecoins, and…

                                  Day two of Money20/20 Europe 2025 at RAI Amsterdam continued the momentum with a focus on digital assets, stablecoins, and the evolving regulatory landscape. The event attracts over 8,000 attendees, including FinTech leaders, investors, and policymakers, all eager to explore the future of finance.

                                  Money20/20 Conference Themes & Tracks

                                  Money20/20 Europe 2025 is structured around four thematic content tracks:

                                  • Digital DNA – Exploring core infrastructure, platform strategies, and foundational technologies.
                                  • Embedded Intelligence – AI, machine learning, data strategies, and real-time analytics.
                                  • Beyond Fintech – Partnerships between fintechs and other sectors like retail, health, and climate.
                                  • Governance 2.0 – Regulation, digital identity, privacy, and ESG compliance.

                                  Day two featured more impactful sessions across all four pillars, offering attendees further valuable insights and strategies for innovation.

                                  Highlights from Key Sessions at Money20/20 Europe:

                                  Digital Wallets and Co-opetition

                                  A standout session featured industry leaders from Fluency, Curve, PayPal, and BLIK discussing the competitive yet collaborative nature of Europe’s digital wallet ecosystem. The panel delved into how traditional financial institutions and FinTech startups are navigating partnerships and competition to enhance user experiences and expand market reach.

                                  Africa’s Fintech Innovation

                                  Another significant discussion spotlighted Africa’s role in global fintech innovation. Representatives from 500 Global, Tech Safari, and Moniepoint highlighted how African startups are leveraging technology to drive financial inclusion and create scalable solutions that could influence global markets.

                                  Digital Assets

                                  A standout session featured Waqar Chaudry, Head of Digital Assets for Financing & Securities Services at Standard Chartered. In a fireside chat titled “The Digital Assets Opportunity: How Banks Can Win at Web3,” Chaudry, alongside Sygnum Bank’s Aliya Das Gupta, delved into the evolving landscape of digital assets.

                                  Chaudry highlighted Standard Chartered’s initiatives in digital asset custody, tokenisation, and the launch of tokenised money market funds. Furthermore, he discussed the development of stablecoin solutions aimed at improving liquidity and settlement times. Chaudry underscored the importance of banks adopting robust digital asset strategies to meet growing client demands and navigate the complex regulatory environment. Drawing from his regulatory background at the Abu Dhabi Global Market, Chaudry provided a unique perspective on balancing innovation with compliance.

                                  WealthTech Evolution

                                  Leaders from Raisin, Upvest, and PensionBee explored the transformation of wealth management through AI and APIs. The panel emphasised the importance of personalised financial services and the integration of technology to meet the evolving needs of consumers.

                                  Central Bank Digital Currencies (CBDCs)

                                  A fireside chat with officials from the European Central Bank and the Bank of England provided insights into the development of the digital euro and pound. The discussion covered technical challenges, regulatory considerations, and the potential impact of CBDCs on the financial ecosystem.

                                  Navigating the Evolving Cyber Threat Landscape

                                  The financial services sector faces an unprecedented convergence of threats with sophisticated cyber attacks and the rise of new technologies… Recorded Future CEO Christopher Ahlberg assessed the evolving threat landscape and strategies for building secure digital ecosytems. He was joined by In Security CEO Jane Frankland and Mastercard EVP Johan Gerber

                                  Networking, Partnerships, and Brand Activations at Money20/20

                                  Notable Announcements:

                                  • Money20/20 and FXC Intelligence Report: A collaborative report titled “How Will Europe’s Money Move in the Future?” was released, offering insights into the future of European cross-border payments and the impact of emerging technologies.
                                  • Policy Exchange Roundtables: Money20/20 introduced focused roundtable discussions involving central banks, regulators, and industry leaders to address critical regulatory challenges in the digital financial landscape

                                  Day two of Money20/20 Europe 2025 underscored the dynamic interplay between traditional financial institutions and emerging FinTech innovations. Discussions on digital assets, stablecoins, and regulatory frameworks highlighted the industry’s commitment to embracing change while ensuring stability and compliance. The second day underscored the event’s role as a catalyst for innovation, collaboration, and growth within the fintech industry. As the conference progresses, stakeholders remain focused on shaping a resilient and inclusive financial future.

                                  • Artificial Intelligence in FinTech
                                  • Digital Payments
                                  • Embedded Finance
                                  • Host Perspectives
                                  • Neobanking

                                  Author

                                  Dan Brightmore

                                  Published

                                  3 June 2025

                                  Estimated Read time

                                  6Mins

                                  Money20/20 Europe 2025 opened its doors to a full-capacity audience at the RAI Convention Centre in Amsterdam. Bringing together the…

                                  Money20/20 Europe 2025 opened its doors to a full-capacity audience at the RAI Convention Centre in Amsterdam. Bringing together the world’s leading innovators, institutions, investors, and influencers from across the fintech and financial services spectrum. With more than 8,000 delegates from over 2,300 companies in attendance, the opening day set a high-energy, insight-rich tone for the rest of the week.

                                  “Money Morning Live”

                                  The day kicked off with “Money Morning Live”. A signature fast-paced keynote session hosted by Tracey Davies (President of Money20/20), Scarlett Sieber, and Zachary Anderson Pettet. The morning show served as a pulse check for the industry. Combining thought leadership with entertainment to engage both newcomers and veterans.

                                  Rahul Patil, CTO of Stripe, delivered a keynote on AI’s role in payments infrastructure. Highlighting how machine learning is now essential for fraud detection, customer service, and onboarding. He emphasised AI should not merely be viewed as an efficiency tool, but as a strategic pillar to create personalised user experiences. And deliver scalable innovation across markets.

                                  David Sandstrom, CMO at Klarna, reflected on the Swedish FinTech giant’s evolution, particularly its use of generative AI for customer engagement and internal operations. Sandstrom noted Klarna’s AI assistant, which now handles two-thirds of its customer queries globally, has dramatically improved both customer satisfaction and cost efficiency.

                                  Money20/20 Conference Themes & Tracks

                                  Money20/20 Europe 2025 is structured around four thematic content tracks:

                                  • Digital DNA – Exploring core infrastructure, platform strategies, and foundational technologies.
                                  • Embedded Intelligence – AI, machine learning, data strategies, and real-time analytics.
                                  • Beyond Fintech – Partnerships between fintechs and other sectors like retail, health, and climate.
                                  • Governance 2.0 – Regulation, digital identity, privacy, and ESG compliance.

                                  Day one featured impactful sessions across all four pillars, offering attendees valuable insights and strategic foresight.

                                  Highlights from Key Sessions at Money20/20 Europe:

                                  Open Banking & Payment Rails

                                  “Putting the Bank Back in Open Banking Payments”, saw speakers from Token.io, Santander, and BNP Paribas examine how banks are reclaiming relevance in the open banking conversation. While FinTechs initially led the charge, the panel noted banks now play a crucial role in building trusted, interoperability, and high-volume “pay by bank” solutions. The debate touched on customer adoption hurdles, PSD3’s role in shaping future APIs, and the monetisation challenges still plaguing the open banking model.

                                  Card Issuance at Scale

                                  In a fireside chat led by Thredd’s President Jim McCarthy, representatives from Railsr, Worldpay, Flagship Advisory, and Caxton discussed the complexities of issuing card programs globally. The group addressed fragmentation across regulatory environments. Especially in regions like LATAM and Asia-Pacific. They urged the need for programmatic flexibility, local compliance, and better BIN management. The panel agreed that the future of card issuing lies in seamless orchestration between platforms, banks, and third-party fintechs.

                                  Agentic AI: Ready for Prime Time?

                                  A standout session focused on the concept of Agentic AI — autonomous agents capable of completing financial tasks without manual prompts. Industry leaders from NVIDIA, bunq, and Visa debated how ready the financial services sector truly is for deploying such systems. While the technology is progressing rapidly, concerns around regulatory clarity, model interpretability, and risk frameworks remain.

                                  NVIDIA’s Head of Financia Technology, Jochen Papenbrock, stressed the need to democratise access to compute infrastructure. And bunq’s AI evangelist, Ali El Hassouni, showcased how the challenger bank is testing semi-autonomous agents in customer support workflows. Meanwhile, Visa SVP for Products & Solutions, Mathieu Altwegg, emphasised the importance of embedding guardrails in agentic systems to ensure ethical AI practices. Especially in credit scoring and wealth advisory roles.

                                  Scaling AI Across the Enterprise

                                  A collaborative session featuring leaders from Stripe, Starling Bank, AWS, and Swift delved into the challenges of scaling AI initiatives beyond prototypes. The discussion spotlighted the importance of clean, real-time data pipelines, strong governance structures, and cross-functional collaboration between engineering, data science, and compliance teams.

                                  Networking, Partnerships, and Brand Activations at Money20/20

                                  Notable announcements:

                                  Beyond the conference rooms, the exhibition floors buzzed with product demos, startup pitches, and impromptu huddles among VC firms, banks, and emerging FinTechs. Exhibitors such as Plaid, Adyen, Marqeta, and Fireblocks showcased new tools for embedded finance, real-time treasury management, and blockchain settlement.

                                  • Wise teased a new enterprise FX tool tailored for SMEs.
                                  • Checkout.com introduced an AI-enhanced fraud prevention dashboard.
                                  • Avalanche Foundation launched an initiative to bring blockchain-based micro-insurance products to underserved markets in Eastern Europe.

                                  Stablecoin News: Institutional Interest Accelerates

                                  A particularly significant development emerged around stablecoins, with clear signals that regulated, bank-issued digital currencies are entering a new phase of maturity:

                                  • U.S. Megabanks Signal Joint Stablecoin Initiative
                                    Executives from JPMorgan Chase, Wells Fargo, Bank of America, and Citigroup confirmed that initial groundwork has begun on a joint U.S. dollar-denominated stablecoin, subject to the passage of the pending GENIUS Act (Guiding and Establishing National Innovation for U.S. Stablecoins).
                                    The stablecoin aims to offer faster, cheaper cross-border settlement and programmable liquidity for enterprise clients. Bank leaders emphasized that this would complement, not replace, traditional banking rails.
                                  • Ripple Expands in the UAE
                                    In a regional announcement, Zand Bank and fintech firm Mamo revealed a partnership with Ripple, using its blockchain infrastructure to enable real-time, low-cost cross-border remittances. This move, anchored in the UAE’s pro-digital asset stance, aligns with broader ambitions to make the country a hub for regulated digital currencies.
                                  • Institutional Stablecoin Custody
                                    Panels featuring speakers from Fireblocks, Anchorage Digital, and Circle addressed the evolving role of stablecoins in treasury operations and FX management. There was widespread agreement that tokenised cash equivalents, including USDC and EURC, are increasingly being used for short-term settlement and yield farming, particularly in Asia and Europe.

                                  These discussions signalled a broader institutional acceptance of stablecoins, with an emphasis on compliance, transparency, and integration into traditional finance rather than bypassing it.

                                  Day one of Money20/20 Europe 2025 delivered on its promise of convening the brightest minds to create the future of finance. From headline-grabbing keynotes and deep-dive panels to global product launches and off-stage networking, the conference created a rich mix of thought leadership, practical innovation, and human connection.

                                  Whether it was the evolution of AI in banking, the future of programmable money, or the balance between innovation and regulation, the discussions revealed a clear consensus: collaboration will define the next chapter of FinTech. Day two at Money20/20 promises even more, with upcoming sessions on decentralised finance, digital identity, and CBDCs.

                                  • Artificial Intelligence in FinTech
                                  • Digital Payments
                                  • Embedded Finance
                                  • Host Perspectives
                                  • Neobanking

                                  Author

                                  Dave Murphy

                                  Published

                                  2 June 2025

                                  Estimated Read time

                                  4Mins

                                  Dave Murphy, Head of Financial Services EMEA & APAC at Publicis Sapient, on unlocking data to unleash the intelligence with AI

                                  In today’s financial services landscape, the promise of artificial intelligence is everywhere… Hyper personalisation, intelligent automation, real-time insights, and AI-assisted customer experiences. But here’s the truth: AI doesn’t run on ambition. It runs on data.

                                  If your customer and transactional data remains locked inside monolithic core systems, even the most sophisticated AI will underdeliver. The most effective path to AI-powered transformation isn’t a complete rebuild of your core – it’s strategic decomposition. By making high-quality data available in near real-time to your channels and platforms, banks can unlock AI’s full potential without overhauling their entire architecture.

                                  At Publicis Sapient, we believe unlocking your data is the critical enabler for harnessing the full value of AI across the financial enterprise. It is no longer necessary to completely rebuild your core infrastructure. Instead, what’s required is strategic decomposition of monolithic systems to ensure near real-time data availability to your channels and AI applications.

                                  The Data Access Conundrum

                                  Banks are acutely aware that their legacy systems create data silos. Research reveals that 70% of banks’ IT budgets are still spent on maintaining legacy systems. Moreover, more than half cite the limitations of their core as the primary barrier to transformation.

                                  Despite a shared recognition of the need to change, many institutions remain hesitant, concerned by the perceived complexity, cost and risk of restructuring their data architecture and overhauling foundational platforms. But this hesitation comes at a cost. As customers demand more personalised and seamless experiences, and digital challengers launch AI-enabled services at speed, traditional institutions risk falling behind.

                                  Why Data Accessibility Unlocks AI’s Potential

                                  The simple truth is: AI cannot thrive in isolation. It needs high-quality, accessible, and timely data. It needs customer and transactional information that’s available near real-time. And it needs a composable, event-driven architecture where data can flow freely across customer journeys and operational workflows.

                                  Decomposing monolithic core banking systems enables all of this. By creating strategic APIs and data layers, banks can liberate critical information from legacy platforms and make it available to AI-powered services without the need for complete core replacement. In our work with leading banks globally, we’ve seen accessible data unlock:

                                  • 1:1 personalisation at scale
                                  • Real-time fraud detection and risk modelling
                                  • AI-assisted customer onboarding and service
                                  • Automation across lending, compliance and operations

                                  This is not theoretical. It’s already happening. In one engagement, we helped a regional bank transform its operating model via a phased core modernisation programme – delivering a one-to-one return on investment over five years by shifting from reactive IT spend to proactive value creation through accessible data.

                                  Progressive, Not Paralysing

                                  One of the biggest myths around core modernisation is that it requires a disruptive, ‘big bang’ transformation. That’s no longer the case. Advances in architecture, engineering tools, and AI-powered development platforms – such as our own Sapient Slingshot – now make it possible to modernise progressively and liberate critical data, rather than rebuilding everything from scratch.

                                  Techniques like multi-core routing, event-driven orchestration and domain-driven design allow banks to gradually make customer and transactional data available near real-time to channels and AI applications – all without jeopardising day-to-day operations or requiring full core replacement.

                                  Reorienting Around Data and People

                                  Technology alone is not enough. Successful transformation requires a cultural shift – one that reorients the organisation around data, agility, and human outcomes. The future-ready bank is not only AI-enabled but data-led and human-centric.

                                  By unlocking and democratising data through modern architecture, banks can power everything from predictive decision-making to better colleague collaboration. We are already seeing leading firms embed AI into their customer and employee journeys. Not as add-ons, but as integral parts of reimagined experiences built on liberated data.

                                  The Future Belongs to the AI-Enabled

                                  As AI capabilities continue to evolve, the divide between data-rich and data-poor, and AI-enabled and AI-limited institutions will widen. The leaders will be those that treat transformation not just as a technical challenge, but as a strategic imperative – reshaping how they operate, compete and serve.

                                  Now is the time to act. Unlocking your data through strategic core modernisation is no longer a question of ‘if’, but ‘how’. Because in the age of AI, the intelligence of your bank will only ever be as strong as the data it can access and learn from, and ultimately the systems that underpin it.

                                  Find out more from Publicis Sapient about core modernisation here

                                  • Artificial Intelligence in FinTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  29 May 2025

                                  Estimated Read time

                                  4Mins

                                  Recorded Future’s CISO, Jason Steer, looks at how FinTechs can advance the maturity of threat intelligence programmes to strengthen the resilience of cybersecurity and deliver tangible ROI

                                  Data from the UK government’s Cybersecurity breaches survey for 2025 paints a stark picture for FinTechs. 48% of finance or insurance businesses identified a cybersecurity breach or attack in the last 12 months. Similar numbers have been reported by Mastercard. A survey of 5,000 small and medium-sized businesses across four continents revealing that 46% have suffered a cyberattack. It’s increasingly becoming clear that it’s a case of ‘when’ and not ‘if’ a business will be targeted by cybercriminals.

                                  The growing urgency surrounding cyberattacks is helping drive a strategic shift in how organisations approach threat intelligence. When everything becomes urgent, it becomes increasingly complex to determine what is and isn’t a priority. Taking decisive and impactful action can be challenging. Threat intelligence is helping to solve this problem. With the right intelligence provider, people and processes, threat intelligence can prove a crucial part of a cybersecurity programme. It enables FinTechs to create an understanding of the who, what, how, when and why of security risks. This is pivotal for managing, accepting and reducing risk, and delivering wider ROI.

                                  Automated Intelligence for Cybersecurity

                                  The effectiveness of a Cybersecurity programme ultimately depends on a combination of people, processes, products and policies. Threat intelligence can add value in each of these areas. Identifying and prioritising the threats which matter most to an organisation. Not all threats carry the same level of risk. By narrowing focus to the most relevant and probable attacks, FinTechs can strengthen their overall preparedness and resilience.

                                  Threat intelligence can provide actionable insights to better anticipate potential attacks and address vulnerabilities. This can help to prevent a security breach, minimise the possible impact of an attack and improve overall responsiveness. It’s for these reasons that threat intelligence can deliver tangible ROI, in both the short and long term.

                                  Without automated threat intelligence and context, Cybersecurity teams can be swamped with time-consuming manual workflows required to gather and analyse data. Alongside this, manual alert triage, investigation and response processes can prove time and resource intensive, as well as being slow. A recent report by Recorded Future shows how automated threat intelligence can overcome these challenges. Cybersecurity teams can save nearly 11 hours each week by streamlining threat detection. They can then move straight to responding to relevant alerts more quickly. A similar amount of time per week was also saved through more efficient threat analysis, hunting and reporting. This enables valuable security resources to shift to other meaningful tasks that expand and grow their skills. Moreover, improving the overall security posture of their organisation.  

                                  Further findings from the report show examples of businesses automating 70% of manual security workflows, cutting investigation times by 50% and driving a 30% reduction in response times. Teams can work more efficiently and effectively to minimise downtime. Average billion-dollar businesses investing in threat intelligence recovered over $19,000 per month in revenue. This was due to reduced downtime, according to the Recorded Future report. That figure doesn’t account for the additional impacts of downtime, such as erosion of customer trust, productivity losses, and recovery expenses.

                                  Protecting Brand Reputations

                                  Threat intelligence also had a marked impact on cyber insurance costs, with organisations reporting reduced premiums of nearly $30,000 a year. Further ROI can be experienced through the mitigation of risks on brand reputation – something that’s particularly important in financial services, where customers want to be confident that their money and financial interests are being placed in safe hands. People need to be able to trust the FinTechs they do business with, and typosquats – illegitimate but similar-looking web domains – can quickly erode this trust.  

                                  Typosquats can be quickly identified, whether it’s company logos or brands being abused, and removed through the comprehensive understanding of digital footprints provided by threat intelligence. This can prove crucial in minimising the risks of phishing and safeguarding customers from inadvertently disclosing personal information to cybercriminals. 

                                  Cybersecurity Resilience

                                  Cybersecurity resilience powered by threat intelligence can deliver cross-functional value across a whole organisation. It can help FinTechs to align their organisations and customers with real risks, rather than hypothetical ones, to effectively manage and mitigate the growing issue of cyberattacks. This starts by defining an organisation’s security priorities and assessing threats in the context of risk to the FinTech. It’s an important first step to determining that not all vulnerabilities will be exploited, and not all threat actors pose an immediate risk, creating opportunity to focus on addressing the actual issues that are genuinely urgent and could actually harm people, assets and business.

                                  To find out more about how advanced threat intelligence solutions can deliver team productivity improvements and business and brand risk reduction impact, download Recorded Future’s ROI for Cybersecurity Teams report.

                                  • Cybersecurity in FinTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  Estimated Read time

                                  3Mins

                                  Read in brochure format

                                  Our cover story spotlights the US Department of Homeland Security and the people power driving its evolution with technology.

                                  Our cover story explores a technological integration journey at the US Department of Homeland Security

                                  Welcome to the latest issue of Interface magazine!

                                  Read the latest issue here!

                                  US Department of Homeland Security: Integrating with the Intelligence Community

                                  Zeke Maldonado, CIO at the US Department of Homeland Security (DHS) is tasked with integrating the Department with the intelligence community. During times of change, governments need innovative, strategic leadership more than ever. And that’s where inspirational figure like Maldonado come into play.

                                  “I remain committed to the DHS mission and want to take it to the next level. Many of the services we provide require substantial improvements, and I am eager to see how our modernisation efforts can help achieve the desired objectives. We play a crucial role in automating and enhancing the vetting process for non-US citizens, making it significantly more efficient.”

                                  Cotality: The AI-powered Property Platform

                                  Cotality, the AI-powered property and location intelligence platform, is making the real estate industry more efficient, smarter, and more resilient against climate change by leveraging the Google Cloud Platform.

                                  Chief Data and Analytics Officer, John Rogers, explains how… “Buying a home is the biggest purchase in most people’s lives, so we’re passionate about making sure the system works for them.”

                                  Nemko Digital: Pioneering Trustworthy AI

                                  Nemko boasts more than 90 years of building trust in physical products, Today, Nemko’s digital division is leading the way in defining that trust in an increasingly complex and connected world with its pioneering approach to trustworthy AI reveals Managing Director, Dr Shahram Maralani.

                                  “We want to be one of the top five players in this space. Our goal is to make the world a safer place.”

                                  Read the latest issue here!

                                  Author

                                  Dan Brightmore

                                  Published

                                  28 May 2025

                                  Estimated Read time

                                  2Mins

                                  Join 6,000+ attendees at Javits Center, New York June 4-5 for InsurTech Insights USA

                                  More than 6,000 of the world’s leading executives, entrepreneurs and investors will gather for the fastest-growing InsurTech conference. Improve your knowledge on challenging and strategic issues relevant to any organisation. Stay on top of future trends and seize new opportunities. Expand your toolbox and effectively solve the challenges of today and tomorrow. Join the decision makers and gain new insights from over 400 expert speakers, including representatives from AXA, MetLife, Munich Re, Gallagher and more.

                                  Join the InsurTech Revolution

                                  The insurance industry, no stranger to gauging risk, is facing one of the most disruptive periods in its history. Artificial intelligence, Machine Learning, Internet of Things, Blockchain, Data & Analytics, and other emerging technologies are enabling many startups to chip away at incumbent businesses. How can you transform, disrupt, and compete in the age of InsurTech?

                                  Join 6,000 attendees – from Insurers, InsurTechs and Investors – taking a strategic approach in a competitive landscape.

                                  Insurtechs

                                  • Understand the market and problems you are challenged with solving
                                  • Sharpen your proposition and identify what parts of the insurance value chain are ripe for innovation
                                  • Build awareness by networking with investors and insurance executives

                                  Insurers

                                  • Forge commercial partnerships and explore new ways of doing business
                                  • Learn how InsurTech fits in with your innovation agenda
                                  • Find where to gain competitive edge and find opportunities for growth in 2025
                                  • Discover how to adopt a culture that embraces innovation from the top down

                                  Investors

                                  • Meet the entrepreneurs shaping the future of insurance
                                  • Develop partnerships with insurance companies
                                  • Take the right approach in an increasingly strategic and competitive landscape
                                  • See where the money is going in 2025

                                  Book your tickets here.

                                  Author

                                  Dan Brightmore

                                  Published

                                  27 May 2025

                                  Estimated Read time

                                  5Mins

                                  Leading US banks, including JPMorgan Chase, Bank of America, Citigroup, and Wells Fargo, are in preliminary discussions to launch a…

                                  Leading US banks, including JPMorgan Chase, Bank of America, Citigroup, and Wells Fargo, are in preliminary discussions to launch a joint stablecoin. This initiative aims to provide a regulated alternative to existing cryptocurrencies, facilitating faster cross-border transactions and enhancing liquidity in digital markets.

                                  The project is contingent on the passage of the Guiding and Establishing National Innovation for US Stablecoins Act (GENIUS Act), which seeks to establish a regulatory framework for stablecoin issuance by banks and nonbanks.

                                  Stablecoin Growth

                                  The stablecoin industry could reach a $2.5 trillion market cap by 2030, according to one estimate, up from the current $248 billion.

                                  New legislation that aims to regulate stablecoins, a type of cryptocurrency whose value is pegged to another asset, is on its way to a vote in the US Senate, reports MarketWatch.

                                  Should the bill become law, crypto bulls see potential for it to drive wider adoption of dollar-linked stablecoins, and possibly to strengthen the battered U.S. dollar. Cryptocurrencies also may end up playing a much bigger role in the broader financial system, analysts said.

                                  The bill, called the Guiding and Establishing National Innovation for US Stablecoins Act – or Genius Act – aims to provide a regulatory framework for stablecoins and their issuers. If enacted, it would be the first legislation in the US regulating the $248 billion stablecoin market. 

                                  Stablecoins could play a more important role in financial markets down the road because they can serve as a bridge between traditional finance and the $3.3 trillion crypto market. Furthermore, they can facilitate trading, borrowing and lending in the crypto ecosystem. Currently, 83% of stablecoins are denominated in U.S. dollars, according to a recent note from Deutsche Bank.

                                  Here are four ways the proposed bill could change the stablecoin market:

                                  More stablecoin issuance

                                  If the Genius Act becomes law, it could greatly lower the regulatory risks for issuers of stablecoins and provide a much clearer path for legal compliance in terms of product design, the Cato Institute’s Schulp said in a phone interview. 

                                  While there are already hundreds of stablecoin issuers, the market is dominated by two stablecoins: One is known as USDT, which is issued by Tether, and another is USDC, a dollar-backed stablecoin developed by Circle. USDT and USDC account for 61% and 24%, respectively, of the market share in terms of market capitalization, according to data from CoinMarketCap. As of February, Tether was the 21st-largest foreign holder of US Treasurys, after the United Arab Emirates and Germany, according to Deutsche Bank. Meanwhile, Circle filed for an initial public offering last month.

                                  If the Genius Act clears up regulatory uncertainty, more companies that have been on the sidelines are likely to launch their own stablecoins, according to Thomas Cowan, head of tokenisation at Galaxy Digital, a crypto financial services firm. He expects stablecoin issuance from traditional payments institutions to pick up if the bill becomes law, given that companies would “have the rules of the road,” he said in a phone interview. He also thinks the technology could help more companies transform their back-end systems.

                                  On that front, Bank of America Chief Executive Brian Moynihan said in February that the bank was likely to issue a stablecoin once legislation was passed. Fidelity also said its digital-assets arm has been testing a stablecoin. 

                                  More tokenised products 

                                  Cowan said he also expected to see more tokenised financial assets, such as bonds or equities, being launched in the next 18 months if the Genius Act becomes law. Tokenization refers to the digital representation of assets on a blockchain. 

                                  Stablecoins are the “bedrock” of tokenisation, as a dollar-backed stablecoin is essentially a tokenised dollar, Cowan said. “If stablecoins are increasingly looked at as a default, we’ll see the rest of the industry begin to go up on the risk curve and begin to monetise other financial assets” such as stocks and bonds.

                                  Wall Street heavyweights BlackRock and Franklin Templeton launched tokenised money-market funds in 2024 and 2021, respectively.

                                  Wider crypto adoption

                                  If the stablecoin bill gets passed, it could increase the adoption of digital assets in general, noted Gannon at Davis Wright Tremaine. He expects the stablecoin market cap to reach $2 trillion to $2.5 trillion by 2030.

                                  Traders often park their assets in stablecoins instead of fiat currencies when trading crypto to enable faster transactions. Stablecoins also already play a significant role in decentralised finance, supporting crypto lending and borrowing. Decentralised finance refers to financial activities that happen on blockchains and that are executed without middlemen.

                                  As more people adopt stablecoins, there “will be more opportunities to use stablecoins in new or better blockchain-based products — to self custody, make purchases, send money, use DeFi [decentralised finance] and more,” Sam Broner, a partner at venture-capital fund a16z crypto, wrote in a recent note. 

                                  Support for the dollar

                                  The rise of stablecoins may amplify the dominance of the US dollar, noted Jim Reid, head of global macro and thematic research at Deutsche Bank. The greenback’s status as a reliable safe haven was tarnished amid the extreme market volatility earlier this year as Trump aggressively rolled out his tariff agenda.

                                  “Essentially, stablecoin providers are acting like money-market funds supporting US short-term debt markets and driving currently non-USD liquidity holdings into USD,” Reid wrote in a recent client note.

                                  If the Genius Act becomes law, people in other countries might have more trust in dollar-denominated stablecoins issued by U.S. companies as a way to gain exposure to the greenback and US Treasurys, because reserves of the coins will be attested, noted Dea Markova, director of policy at crypto infrastructure firm Fireblocks.

                                  • Blockchain & Crypto

                                  Author

                                  Dan Brightmore

                                  Published

                                  23 May 2025

                                  Estimated Read time

                                  4Mins

                                  Intergiro’s CEO, Nick Root, on how payments providers can meet the challenges for cybersecurity in the war on fraud

                                  We operate in the trenches of FinTech – real-time, full-stack and fully exposed to the relentless tide of digital fraud. As an embedded payments provider across the EU, Intergiro lives at the bleeding edge where innovation meets exploitation. And let me be clear: fraud isn’t a back-office nuisance anymore. It’s an existential threat. One that every modern financial company, especially those bootstrapped like ours, must treat as core business, not a support function.

                                  Right now, 30% of our headcount is dedicated to fraud prevention, compliance and cybersecurity. That’s not a vanity metric – that’s the reality of staying alive in a hostile digital environment. We spend millions annually not just on tooling and infrastructure, but on reimbursing innocent victims. For a company building its future on resilience, programmatic control, and capital efficiency, these costs are brutal. But necessary.

                                  The Scamdemic is Here

                                  Fraud is no longer a sideshow; it’s the main event. In the past 18–24 months, we’ve seen a sharp escalation. Sweden’s financial police reported an 80% spike in investment fraud between 2022 and 2023. Our internal metrics tell the same story. Spiking fraud attempts, more advanced attack vectors and a user base under siege.

                                  And this isn’t abstract. It’s personal. For example, I got hit by a fake Uniqlo storefront. Nearly lost money. Only Intergiro’s own controls saved me. It was a sobering moment: even a FinTech founder can fall victim. For digital natives, that’s embarrassing. For the less tech-savvy – think your parents’ generation – it’s a nightmare. My own father won’t use Uber unless one of us physically adds his card to the app.

                                  Understanding the Threat Landscape

                                  To address this epidemic, we first need to clarify the categories of fraud. Payment fraud and ID theft are mostly on us – as FinTechs. If a system fails, or a tool is exploited, we own that and cover the loss. But social engineering and investment fraud? They’re tougher. These rely on psychological manipulation – human vulnerabilities we can’t patch with software updates. Still, that doesn’t mean we’re powerless. We just need to shift our lens.

                                  Upstream, Not Downstream…Fighting social engineering with regulation is like mopping up the floor while the roof’s still leaking. Necessary, but ultimately reactive. We need to move upstream. Way upstream.

                                  Social Media: The Root of the Fraud Problem

                                  Over 75% of fraud starts on social platforms. That’s the front door. If we don’t lock it, we’re just chasing shadows. Meta’s FIRE partnership with UK banks is a baby step in the right direction. But let’s be honest – it shifts responsibility onto banks to clean up the mess, while platforms avoid real-time accountability.

                                  What we need is a pan-European version of FIRE, backed by the teeth of the Digital Services Act and centralised enforcement. FinTech alone can’t drive this. We need regulators, platforms and providers rowing in the same direction.

                                  Public Awareness: Borrowing the Pandemic Playbook

                                  Think about this: between 2020–2022, fraud cost the EU €157 billion. That’s not far off the public health spend from COVID. And fraud doesn’t recede – it compounds.

                                  In a pandemic, we responded with mass public education: masks, distancing, handwashing. We need the same for digital fraud. A real, coordinated public awareness campaign built around these pillars:

                                  • Basic operational security –  Email is not secure. Banks don’t ask for details over email. Wire transfers aren’t reversible like card transactions.

                                  • Social media hygiene –  If it smells like a scam; even from a verified blue tick – assume it is. “Stop. Think. Click.”

                                  • AI as defence –  The same AI used to create scams can help spot them. Let’s teach users how to turn the tools around – scan that investment pitch, audit that wallet address.

                                  Delivery matters here. Dry leaflets won’t cut it. Interactive quizzes, short-form video explainers, browser plug-ins – a toolkit that reaches people where the scams do: in-feed and in-app.

                                  Collective Action Against Fraud: Collaboration Over Competition

                                  FinTech has a reputation for speed, innovation and competition. But when it comes to fraud, isolation is the enemy. No single firm can win this war alone.

                                  We need a secure, privacy-conscious layer for FinTech collaboration. A shared fraud intelligence layer that goes beyond blacklists and blocked BINs. We’re not talking about turning FinTechs into police forces, but enabling programmatic detection through pooled data, shared signals and joint tooling.

                                  At Intergiro, we’re already piloting private data-sharing models with other European players. It’s early – but promising.

                                  Final Word: It Takes a Village

                                  This war against fraud won’t be won in the back office of your local neobank. It needs a whole-of-society effort. Platforms must step up. Regulators must align. And consumers must be trained – not blamed.

                                  Fraud isn’t going away. As AI evolves, so will the threat. But so will we – if we move fast, stay dynamic, and invest in people, tools, and partnerships. Not just for ROI – but for resilience.

                                  At Intergiro, we’re all in. But we can’t do it alone. If FinTech is the infrastructure of modern commerce, fraud is the fault line beneath it. And we can’t build the future on a fault line.

                                  • Cybersecurity in FinTech

                                  Author

                                  Dan Brightmore

                                  Published

                                  Estimated Read time

                                  10Mins

                                  Security, AI, and Digital Resilience: A look inside Visions CIO + CISO 

                                  The cybersecurity landscape has never been so fast-moving or complex. The stakes have never been higher. A worsening geopolitical reality and increasingly sophisticated cyber threats mean that the role of security leaders is more pivotal than ever as devastating cyber breaches become a matter of “when,” not “if.” It’s a time for information and skill sharing, networking, and collective action in an industry facing a more challenging future than ever. 

                                  Visions CIO + CISO Summit brings together executive security and technology leaders and experts from the largest organisations in multiple industries to network and learn from the people driving innovation in the IT and cyber spaces. This year’s event took place between April 28-30, and featured 8 tentpole sessions, over 30 presentations from key industry figures, and more than 30 speakers across the various panels, fire-side chats and peer-to-peer round tables that comprise the rest of the event. Speakers and solutions providers at this year’s event included Illumio, Threatlocker, LastPass, Claranet, Okta, Covertswarm, Intruder, and Ripjar RPC Services. Also in attendance were IT and security professionals from large scale enterprises, including Currys, Astley Digital, 24/7 Home Rescue, H&M Group, IBM, MUFG (Mitsubishi Financial Group), Federated Hermes, Deliveroo, Experian, Saint-Gobain, and Nordea GSK.

                                  At the event, and afterwards, we were lucky enough to catch up with some of the leaders speaking at Visions and get their perspectives on key trends affecting the IT space — from the ever-relevant issue of security to AI and digital resilience.  

                                  Natwest

                                  Ramit Sharma — Vice President & Lead Engineer

                                  1. What’s the general outlook for the IT and fintech sectors right now? Is this a scary time? An exciting one?

                                  “It’s an exciting time, particularly within the UK banking sector, where we’re seeing a real shift toward customer-centric innovation. Financial institutions are working hard to deliver seamless, secure, and personalised experiences—often by leveraging cloud, AI, and advanced analytics.” 

                                  “There’s a strong emphasis on modernising legacy systems, improving digital onboarding, and enhancing fraud prevention without compromising user experience. This push for technology-driven customer satisfaction is creating space for smarter, faster, and more agile solutions—making it a great time to be contributing to the evolution of digital trust and transformation in financial services.”

                                  2. What are some of the challenges organisations are facing that you can help them with? What problems are they asking you to solve?

                                  “Many organisations are grappling with how to secure cloud environments at scale without slowing down innovation. Key challenges include visibility across hybrid or multi-cloud setups, managing identity and access with precision, and operationalising zero trust.” 

                                  “There’s also a strong demand for integrating security earlier in the development lifecycle—what we often refer to as shifting security left. People are asking how to reduce complexity, automate controls, and move away from reactive postures to proactive, real-time risk mitigation.”

                                  Federated Hermes 

                                  Enis​​​​ Sahin — Head of Information Security

                                  1. What kind of outlook does an organisation like Federated Hermes have right now towards the industry? Is this a scary time? An exciting one?

                                  2025 is shaping up to be a very dynamic year for the markets at large. There are rapid developments, from geopolitics to booming technology innovation with AI, that are impacting how the markets move as well changing the environment we operate in as a business. As a global asset manager, Federated Hermes is staying abreast of these changes to ensure we can be where the markets are, whilst maintaining efficiency in our operations for strong profitability. 

                                  2. What problems are people asking you to solve right now?

                                  The ever changing world of cyber has historically been difficult for businesses to decipher. In the last few years, it has become even more difficult to keep up, with the advent of AI and how it is changing the technology landscape. Whilst businesses are trying to understand this new technology and embed it into their products and operations, cyber-criminal enterprises are leaping ahead in innovation and starting to leverage it in novel ways. The challenge this brings is two-fold.”

                                  “On one hand, businesses are trying to find the right use cases for AI to get their return on investment at every level. This applies to core business functions, as well as Technology departments and the Security organisations. As cyber strategists we are now being forced to be innovators ourselves and not just passive consumers of the latest products and market trends. This brings a new perspective to how we design controls, build our roadmaps and prioritize our budget items. Boards and executive teams are looking for Security teams who are embracing AI and maximizing the effectiveness and efficiency of their programmes.” 

                                  “The second challenge is on the defensive side. The average person, as well as the average corporate employee, is lagging behind in understanding what the latest AI models are capable of, let alone understanding how they can be used to conduct cybercrime. Working in security, we find ourselves in a situation where we both need to find ways to keep up with cyber criminals to defend our enterprises, as well as keep educating our staff and management teams so that we can bring them on this journey.” 

                                  Astley Digital 

                                  Martin Astley — Chief Information Security Officer

                                  1. Would you say this is an exciting time for Astley Digital?

                                  “Astley Digital is at a pivotal point in its journey, experiencing remarkable growth and expanding our service offerings. We’re actively exploring partnerships with innovative cybersecurity companies like ThreatLocker, enabling us to provide even more robust endpoint security solutions for our clients.” 

                                  “Additionally, the evolving landscape of cybersecurity is presenting us with unique opportunities to leverage AI for predictive threat analysis, streamline incident response, and enhance our managed security services. This moment is particularly exciting as we are positioning ourselves not just as a service provider but as a thought leader in cybersecurity strategy, risk management, and digital transformation for businesses across various sectors.”

                                  2.  What are some of the key challenges organisations are facing that you can help them with? What problems are they asking you to solve?

                                  “Organisations today are grappling with a rapidly changing threat landscape, and one of the most significant challenges is maintaining a strong cybersecurity posture amidst evolving threats. At Astley Digital, we address critical issues such as:

                                  “Endpoint Security: Many organisations struggle with managing endpoint security across remote and hybrid workforces. We provide comprehensive solutions that restrict unauthorised software and applications, preventing potential breaches and maintaining data integrity.”

                                  “Third-Party Risk Management: Ensuring third-party vendors maintain security standards is another pressing concern. We work closely with our clients to assess, monitor, and mitigate third-party risks to prevent supply chain attacks.”

                                  “Incident Response and Recovery: Companies are seeking rapid and effective incident response strategies. We offer real-time monitoring, response planning, and post-incident analysis to minimise business disruptions.”

                                  “Regulatory Compliance: Compliance is a growing concern, especially in highly regulated industries. Our team assists with implementing frameworks that align with industry standards, ensuring data protection and reducing legal risks.”

                                  S&W 

                                  Mark Hendry — Partner

                                  1. Why is this an exciting time for your company?

                                  “We are really fortunate to have reach and presence with clients across different sectors. We have professional service specialisms that respond to many of the trickiest and most important strategy and skill challenges that clients face; technology, cyber security, AI, data, and digital regulations to name a few. Not only is it a great time to be helping clients with those issues and helping them make their businesses more capable, effective, successful and resilient, from a selfish perspective it’s an incredible privilege for our people to be trusted by clients to help with these super interesting initiatives.”

                                  2. What are some of the key challenges organisations are facing that you can help them with? What problems are they asking you to solve?

                                  “We help clients with everything from assessing and improving their resilience positions, to complying with the intersections of a range of existing regulations, frameworks and standards, through to future gazing and thinking about what’s possible through challenging the status-quo.”

                                  “Lately that has included a lot of work on things like AI readiness, development of use cases, working on AI explainability and the human element of potential resistance to the kinds of change that AI and other emerging tech are delivering.” 

                                  “Of course an evergreen core of our work is digital resilience, including cyber security, so we do a lot on ensuring that new technology adoptions including those with AI sprinkled throughout them, are digitally and operationally resilient by design.” 

                                  Deliveroo

                                  Oliver Jenkins — IT Audit  Senior Manager

                                  1. Why is this an exciting time for Deliveroo?

                                  “We’re at a turning point where AI is no longer a side conversation—it’s embedded in the way Deliveroo operates. That shift brings real momentum and urgency to the work we do in securing AI adoption and protecting digital environments.”

                                  2. What are some of the key challenges organisations are facing that you can help them with? What problems are they asking you to solve?

                                  “The main concern is how to adopt AI without opening the door to unmanaged risk. Businesses know they can’t sit this one out, but they’re looking for help building the right guardrails to manage risk; especially with evolving regulation and the rise of AI-powered threats like deepfake vishing and advanced phishing.”

                                  Bilfinger

                                  Nnamdi Ozonma — Information Security Officer UK & Nordic Regions

                                  1. What are you here at Visions to discuss with your peers in the cybersecurity and IT space? 

                                  “The first panel I was part of was the Threat Detection & AI Panel Discussion. We were looking at establishing trust, mitigating risks, and safeguarding security in the age of AI. I focused on how to balance the benefits of AI with the challenges of building trust, managing risks, and ensuring security.”

                                  “Then, I had a deep dive into looking at an age where individuals don’t verify, they just take information, no longer researching to see if the information is correct.”

                                  “I always remain sceptical, whilst understanding the value of efficiency. AI is now embedded in so many tools, but now the main concern is the people within the organisation. Monitoring and education are essential. People will often try to find a shortcut and the easy way to go about things. Until training, governance and understanding is at a level where there can be trust, I suggest turning it off.”

                                  Ripjar

                                  Nick Cooper — Vice President, Information Security

                                  1. These are challenging times for cybersecurity teams. How has 2025 been going for you and Ripjar? 

                                  “Ripjar utilises new and emerging technology to solve customer problems in cyber threat investigations and anti-financial crime compliance. We’ve been able to help organisations achieve record results – identifying connections, anomalies and potential risks, while reducing false positives and increasing true positives – leading to best-in-class results in many industries. We’re excited to be sharing that technology, alongside further innovations, with other organisations as we expand our global coverage.”

                                  “The advent of generative AI creates vast risks and opportunities. It also shifts perspectives on existing machine learning and artificial intelligence technologies. It has been exciting to see how the newest AI can be combined with non-generative AI and other technologies to create new solutions to the problems that keep our customers awake at night.”

                                  2. What are some of the challenges organisations are facing that you can help them with? 

                                  “Ripjar serves customers in several areas. Our anti-financial crime customers are trying to make sense of the ever-expanding business risks presented by their customers and counterparties in a tumultuous world. We’re able to help them in that journey, whether it’s responding to changing Russian or Middle East sanctions or aligning with the massive political changes that have impacted PEP (politically exposed persons) regimes all around the world.”

                                  “Using foundational AI, we find broad risks in the media – which is often referred to as negative news or adverse media. That means reading through millions of daily news articles to identify risk signals which are important to those handling the world’s global payments or trading internationally. Agility is a key requirement for our customers, and machine learning and AI make it possible to make sense of huge quantities of structured and unstructured data quickly and accurately.”

                                  “Our cyber customers are sophisticated threat investigators working in complex environments, including a number of MSSPs. They rely on our data fusion and investigations software to identify potential threats to their data and ultimately their businesses.”

                                  Looking at the future

                                  The shadows of GenAI, looming threats, and a shifting regulatory landscape loom over the global cybersecurity and IT communities, but the tone is also optimistic. While every leader we spoke to at Visions CIO + CISO acknowledged the threat posed by emerging technologies, many were also excited by the potential of GenAI tools to detect threats and help strengthen cybersecurity defenses.

                                  Given how quickly the circumstances surrounding cybersecurity have changed in just a few short years, it’s almost impossible to predict where we’ll be by the end of the decade. However, the experts we spoke to at Visions are approaching the future with both eyes open — watchful for new risks, and determined to capitalise on new opportunities. 

                                  The next Visions CIO + CISO Summit (Autumn, UK) is taking place at the Allianz Stadium in London on 13 – 15 October, 2025. Learn more and register to attend here.

                                  Author

                                  Liselotte Munk

                                  Published

                                  22 May 2025

                                  Estimated Read time

                                  6Mins

                                  Liselotte Munk, CEO at core insurance solution provider Fadata, on the benefits of InsurTech digitalisation

                                  Unpredictable market shifts, weather crises, and increasingly digital-only policy holders are all putting demands on the insurance industry and their ability to deliver a modern, efficient service. Insurers recognise that they need to become more agile and digital. Time-to- market is crucial. What better way to tackle these challenges, than revitalising internal IT departments and empowering them to manage digital transformation?    

                                  Insurance Going Digital

                                  Insurance digitalisation has been ongoing longer than we have seen in other industries. Thanks to a shift in mindsets, new tech talent, and a wealth of emerging technologies, digital transformation is ramping up. Insurers reclaiming control of their IT strategy, infrastructure, and execution is fuelling the InsurTech surge. The decisive step to nurture and utilise internal IT skills to enhance digital capabilities is solving many pain points. The challenges associated with traditional external implementation are being overcome. Insurers are becoming empowered with agility, reduced infrastructure expenses, and future proofing. All of which is essential to ensuring competitiveness amid the fast-paced evolution of the insurance market.

                                  Redefining IT’s Role in the Insurance Value Chain

                                  The move to strategic internalisation for digital transformation is as much a strategic and cultural decision as a technical one. Working closely with underwriting, claims, marketing, and distribution to embed digital capabilities across the entire value chain, internal IT departments foster cross-functional collaboration, turning the IT function from a support function into a business enabler. No longer operational backwaters, internal IT departments are central to business strategy. This is why insurers are recognising the need to continually enhance their IT skills to secure future-proofed technology.

                                  Insurance Chief Digital Officers (CDOs) are making strong business cases for high level in-house IT capabilities. They argue internalisation of digital transformation is essential for long term success. It ensures critical knowledge is kept within the business, processes are significantly more efficient, and that the cost savings are unquestionable. On top of that, it should be much easier for insurers to attract, recruit and maintain top tech talent when more engaging and strategic career paths are on offer. Ultimately, this also improves retention.

                                  IT transformation is also changing the nature of vendor partnerships. Instead of traditional “implementation projects,” insurers are now “onboarding” platforms, and internal teams are taking charge of leading configuration and long-term evolution. Shifting focus from one-off rollouts to continuous collaboration, insurers are teaming up with external partners that provide scalable platforms and expert guidance.

                                  IT and Vendor Marriage

                                  Insurers are adept at building substantial internal IT organisations. The complexity and regulation-intensive nature of insurance demands deep integration between technology and business processes. The appointment of high-level roles like CDOs underscores just how imperatively strategic IT is to the industry.

                                  At its core, the decision to internalise control of digital transformation stems from a need for greater influence over platforms that support local regulatory requirements, customer behaviour, and product innovation. The long-term partnership between insurer and core vendor flourishes when it fully incorporates an internal IT team. Insurers are turning to the core platform vendors such as Fadata, that come hand-in-hand with dedicated expert teams, promise collaboration, share KPIs, and deliver the granular understanding required to reflect insurance market-specific nuances. Outsourced executors are being phased out so that insurers can avoid inefficiencies and slow rollouts. These are among the intrinsic problems developed from reliance on a third party with a culture of locking out IT departments or building overly generic solutions that require excessive, often complicated and costly customisation.

                                  Maximise Scalability, Minimise Customisation

                                  Insurers increasingly realise that all important scalability and agility come from adhering closely to out-of-the-box solutions. The trend toward minimal customisation not only simplifies future upgrades but also accelerates implementation timelines. This positions internal teams to rapidly launch new products and respond to market changes without the delays of extensive code rewrites or vendor negotiations. In times of shifting regulatory compliance – DORA being a great example – a standardised system providing the ability to upgrade swiftly is a high priority. And a major driver for internal IT. Insurers need to feel confident that any updates in order to comply can be made fuss-free.

                                  Fadata has already responded to this shift by supporting clients in regaining control of their technology environments. Rather than acting solely as an external implementation partner, Fadata is supporting its clients to create ‘centres of excellence’. These bolster an IT department’s understanding of its core solution, INSIS, to promote independence. The IT departments we work with are already able to seamlessly replicate product in new geographies, and up to 85% of out-of-the-box INSIS features are being copied with the click of a button. 

                                  SaaS Pizzazz – The Digital Future of Insurance IT

                                  The industry-wide shift to SaaS models shines a spotlight on the pivotal role IT plays in digitalisation and business strategy. With infrastructure responsibilities managed externally, internal IT resources can focus on strategic application of technology and drive insurance innovation from within. Inherently upgrade-friendly cloud-based solutions make this much simpler and more viable. These deliver ongoing automatic platform enhancements and maintenance without disruptive overhauls. Which also eliminates scope creep or unexpected integration issues, and helps to avoid IT resource bottlenecks.

                                  Next Generation Digital Mindset

                                  With focus being put on fulfilling the modern expectations of policy holders, which undoubtedly is driven first and foremost by speed and simplicity, insurers are shifting their mindset to a more customer-centric insurance business. Insurers are ready to embrace agile methodologies. These create the seamless digital journeys across mobile, web, and emerging channels that modern customers expect. To be digitally successful, insurers understand that a more hands-on strategy is key. And is why a natural understanding of modern technology is becoming increasingly relevant. IT departments are 100 percent best positioned to manage long-term digital strategy. This highlights the importance of nurturing a skilled IT team that can secure future-proofed technology.

                                  The fast-paced, changeable insurance market calls for faster iteration and product launches with continuous deployment. Insurers are becoming much more open to SaaS platforms, APIs and ecosystems. They recognise that the partnerships which have typically been seen as a threat to internal teams, are conducive with accelerating transformation. These digital trends, which lead to the faster decision making and improved responsiveness that can define success, are challenging legacy processes and partnerships that slow innovation. Insurers looking for competitive advantage are also increasingly turning to data. Greater emphasis is being put on real-time data and analytics. Prioritising the creation of customer data platforms, automated insights, and AI-driven decision-making, all of which require digital backing. Ultimately, internalising IT offers insurers the flexibility, security, and agility they need to thrive in a competitive landscape. With trusted platforms and collaborative partners, insurance companies are becoming better positioned to shape their digital futures – on their own terms.

                                  • InsurTech

                                  Author

                                  Paul O'Sullivan

                                  Published

                                  20 May 2025

                                  Estimated Read time

                                  6Mins

                                  Paul O’Sullivan, Global Head of Banking & Lending at Aryza, on how Open Banking is reshaping the financial ecosystem

                                  As Open Banking continues to gain momentum, it is poised to fundamentally reshape the financial ecosystem. Not only regarding how institutions operate but also in how individuals understand, manage, and trust their money. With secure data sharing at its core, Open Banking represents more than just a technological shift. It signals a transformation in the relationship between people and their finances.

                                  This piece explores five key areas where Open Banking is set to make its mark in the years to come…

                                  Transforming Society’s Relationship with Money

                                  Open Banking has the opportunity to reshape society’s relationship with money by providing greater transparency and enabling a more comprehensive view of personal finances. This heightened visibility is made possible by securely sharing financial data with trusted third-party providers. And empowering individuals to monitor spending habits, track expenses, and compare financial products and services more easily.

                                  Providing greater transparency and access to financial data will improve financial education for all by enabling a deeper analysis of trends across various activities. As a result, consumers can make more informed decisions. This can improve overall financial education and help to foster a healthier, more sustainable relationship with money.

                                  Additionally, Open Banking paves the way for more personalised financial solutions, as institutions compete to offer tailored services that meet the unique needs of customers. This increased choice not only boosts consumer confidence in managing their finances but also catalyses innovation within the financial sector. Ultimately, the shift toward Open Banking is poised to create a more dynamic, customer-centric financial services landscape. Moreover, one that will significantly enhance how individuals and businesses manage their money.

                                  The Convergence of Open Banking and AI

                                  The data provided by Open Banking should work hand in hand with AI to offer consumers advice on managing their finances. Whether that means making changes to their habits or finding more affordable products, in turn transforming financial guidance and creating a more personalised and efficient financial ecosystem.

                                  By enabling the secure sharing of consumer data, Open Banking provides the foundation for AI-driven solutions to analyse real-time information and offer tailored recommendations. This coule be suggesting improvements to spending habits or automating routine processes. Such AI-enabled tools will empower individuals to make more informed, data-driven decisions about their money.

                                  This synergy will go beyond surface-level insights, delivering hyper-personalised services that address each customer’s unique financial needs and preferences. The resulting efficiencies, such as automated account management, transaction processing, and even customer support, free human resources to focus on more complex issues. Ultimately, this combination of Open Banking and AI promises to enhance the overall customer experience. It can provide actionable, real-time support that helps individuals navigate their financial journeys more confidently and effectively.

                                  Evolving the Role of Traditional Banks

                                  While it’s still early to say for certain, traditional banks could indeed evolve into more utility-like services in an Open Banking world. We’re already seeing indications of this shift, with more consumers increasingly switching their banking services and using multiple accounts. Open Banking is a disruptive force that fosters greater competition and choice, enabling consumers to pick and choose the financial solutions that best meet their needs.

                                  To remain relevant, traditional banks are urged to embrace Open Banking rather than resist it. By securely leveraging customer data and collaborating with FinTechs and other third-party providers, they can create more specialised, value-added products and services. In doing so, banks can move beyond mere utility status. They can position themselves at the forefront of innovation while enhancing the overall customer experience in an increasingly competitive landscape.

                                  Redefining Financial Trust and Identity

                                  Open Banking is not only transforming technology infrastructure; it’s also redefining core principles such as trust, identity, and control. It will increase transparency by giving individuals a holistic view of their financial data. In turn, empowering them to track spending patterns, compare financial products, and make more informed decisions. Secondly, it enhances consumer control over personal data, as customers can grant or revoke access to trusted third-party providers. Therefore strengthening accountability and fostering greater confidence in the system.

                                  Furthermore, digital identity solutions replace traditional verification processes, enabling expanded access to financial services. This will ensure more people can participate in the banking system with ease. Underpinning these developments are trust frameworks, which establish standardised measures for data sharing, allowing banks, FinTechs and other providers to collaborate while maintaining consistent protection for users.

                                  A key emerging factor is the use of advanced cryptography and multi-factor authentication so that both individuals and financial institutions can operate confidently in a secure environment. This heightened focus on security and privacy can help mitigate concerns around data breaches and identity theft. Further strengthening consumer trust.

                                  By introducing new layers of transparency, giving consumers control over their data, and leveraging digital identity and robust security measures, Open Banking shifts our collective understanding of financial trust and identity. It moves us toward a future where trust is shared among various stakeholders. Security is paramount and individuals play a more active role in shaping their financial journeys.

                                  Harnessing Open Banking Data for Monetary Policy

                                  While often discussed through the lens of consumer empowerment, Open Banking may also prove to be instrumental in supporting smarter economic decision-making at a national level. Financial data through open banking could play a significant role in creating new tools for monetary policy. Particularly as the global financial system becomes increasingly interconnected. By providing governments and regulators with real-time insights into consumer spending patterns and business creditworthiness, Open Banking allows for more precise and targeted policy interventions. This data-driven approach can enable policymakers to respond swiftly to economic shifts. They could tailor interest rates, liquidity measures, and other monetary policy tools to specific sectors or demographics.

                                  Having access to comprehensive, standardised data can enhance the accuracy of economic forecasts and models. This leads to more informed decisions that can foster stability and growth in the economy. However, implementing these advanced tools requires robust data protection measures and regulatory frameworks to ensure the privacy and security of financial information. When managed responsibly, the fusion of Open Banking data and monetary policymaking promises to bolster both economic resilience and consumer trust.

                                  Charting the Path Ahead for Financial Innovation

                                  Open Banking is not just a new chapter in financial services, it’s a complete rewrite of how we engage with money, institutions, and technology. From personalised advice and AI integration to regulatory impact and redefined trust, the changes ahead are both profound and far-reaching. The next decade will be shaped by how institutions adapt, how consumers respond, and how effectively we harness data to deliver meaningful, secure, and transparent financial experiences.

                                  • Embedded Finance
                                  • Neobanking

                                  Author

                                  Dan Brightmore

                                  Published

                                  16 May 2025

                                  Estimated Read time

                                  2Mins

                                  Join 25,000 attendees for Seamless FinTech, the Middle East’s biggest FinTech event, at the Dubai World Trade Centre May 20-22

                                  FinTech Strategy is proud to be a media partner for Seamless FinTech 2025.

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                                  Furthermore, whether you’re presenting your latest payment innovations or showcasing impactful demos, this is your opportunity to foster connections and accelerate business growth. Join 25,000 attendees and 800 exhibitors gaining insights from a stellar line up of 750+ expert speakers from the likes of Revolut, J.P. Morgan, Monzo, Citi and more.

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                                  This event is perfect for those looking to forge new partnerships, gain valuable insights from industry trailblazers and drive innovation to stay ahead in the ever-evolving digital landscape. Moreover, whether you’re a startup, an established player, or an SME, Seamless Digital Commerce is designed to push the industry forward.

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                                  • Artificial Intelligence in FinTech
                                  • Digital Payments
                                  • Event Newsroom
                                  • Neobanking

                                  Author

                                  Dan Brightmore

                                  Published

                                  15 May 2025

                                  Estimated Read time

                                  5Mins

                                  Join FinTech’s greatest event when Money20/20 Europe returns to Amsterdam’s RAI Arena June 3-5