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

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

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

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

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

Hugh Scantlebury

Published

22 July 2026

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

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

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

Chris Tredwell & Charis Thomas

Published

28 May 2026

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

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

    Dvir Hoffman

    Published

    27 April 2026

    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

    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

      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

      Todd Moore

      Published

      14 April 2026

      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

      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

      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

      Dan Brightmore

      Published

      24 March 2026

      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

      Martijn Gribnauis

      Published

      23 March 2026

      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

      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

      Dan Brightmore

      Published

      9 March 2026

      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

      Dan Brightmore

      Published

      4 March 2026

      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

      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

      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

      Adrian Wood

      Published

      19 February 2026

      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

      Kevin Janzen

      Published

      12 February 2026

      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

      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

      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

      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

      Antoni Vidiella

      Published

      26 January 2026

      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

      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

      15 January 2026

      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

      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

      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

      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

      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

      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

      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

      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

            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

            Dan Brightmore

            Published

            5 November 2025

            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

            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

            Dan Brightmore

            Published

            27 October 2025

            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

            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

            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

            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

            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

            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

            Nell Walker

            Published

            10 September 2025

            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

            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

            Nell Walker

            Published

            29 August 2025

            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

            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

            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

            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

            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

            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

            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

            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

            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

            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

            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

            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

            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

            Vikas Krishan

            Published

            28 April 2025

            Estimated Read time

            6Mins

            Vikas Krishan, Chief Digital Business Officer & Head of EMEA at Altimetrik, on the disruptive power of AI in FinTech

            AI is already disrupting every area of the Financial Services Industry, and is being included in almost every strategic conversation around technology-enabled transformation. This transformation is exemplified by industry leaders like JP Morgan Chase. CEO Jamie Dimon has championed a £12 billion annual investment in data and technology, overseeing over 400 AI use cases. These include fraud detection, customer service improvements and operational efficiencies across the bank. The core platforms underpinning the industry risk buckling under the weight of modernisation. AI is gradually loosening the components of legacy institutions and presenting fresh opportunities. These are scalable, resilient and adaptable to the agile needs of Financial Services. Through this reimagining of core platforms, those who choose to act now can expect to leapfrog their competition. Meanwhile, those who fail to act now risk obscurity, lack of productivity and being disregarded by their consumer base. 

            The transition to new architectures 

            For decades, banks have relied on legacy systems to power their core operations. These often ageing platforms are becoming increasingly difficult and expensive to maintain. They have been built both in languages not commonly used and architected with a different business reality in mind. Many frequently lack the flexibility required to meet the demands of today’s digital-first customers. They also struggle to integrate with modern financial technologies. A significant challenge facing organisations is the accumulation of technical debt. There is a cost to additional work or rework caused by choosing quick or limited solutions over more robust, maintainable approaches. Over time, this can lead to significant issues that compound the challenges of legacy systems.

            This lack of nimbleness is often the byproduct of a Frankenstein approach to architectural systems. Many financial institutions have traditionally built new features or attempted to fuse together two platforms. This is a delicate balancing act, requiring extensive planning and careful execution. If done with limited oversight, challenges can arise. These include operational disruptions, increased security risks and obvious incompatibility issues. The high risks and cost burdens associated with maintaining legacy platforms has led many banks to reconsider traditional merger approaches. Increasingly opting for modern, cloud-based microservices driven solutions that offer enhanced scalability, security and integration potential. 

            Meeting the challenge

            As the industry establishes governance around this necessary transition, core platforms are being replaced by newer, more adaptable microservice-based architectures. Navigating this evolution requires leveraging an industry partner with a deep understanding of the complexities and risks involved. There are challenges moving from monolithic core systems to flexible, modern frameworks. 

            If we think back five years or so, many players in the market were already aware of this critical shift. Companies like Misys and Avaloq were acquired by private equity firms and given substantial investment to advance digital initiatives, developing solution suites. The reason for this was clear, everyone understood the market was changing. However, the challenge still remains in managing the migration of large, complex platforms. The key question has always been how to de-risk these migrations when moving to newer architectures. This is an issue across organisations, and it is something that we at Altimetrik actively work with clients in financial services to address. 

            Data first with AI

            If we consider platforms such as core banking or payments systems, the data generated from these transactions should, in theory, hold value. However, gaining insights from legacy platforms is significantly more challenging and the cost of extracting and utilising that data is often prohibitive. It is here that a data-driven approach to AI must be agreed upon.  

            High-quality, accurate data lies at the core of every successful AI implementation. AI thrives on data; the more precise the data, the better the AI can learn and provide reliable insights. This fundamental truth highlights the importance of data integrity within the AI ecosystem. However, many financial institutions are struggling in this area, both in effectively using internal data and leveraging accurate, timely external data. As companies grow, their data environments become increasingly complex, adding to these challenges. 

            As financial services organisations expand, they often face the challenge of data silos, declining data quality and scattered, disconnected data repositories. This leads to a fragmented data ecosystem. It can limit AI’s potential to deliver meaningful insights and drive improvements. This transformation requires active leadership from the top. Successful digital transformation depends on executive-level commitment and understanding. Leaders like Charles Scharf of Wells Fargo demonstrates how CEO ownership of data and AI initiatives drives organisation-wide adoption and success. Their hands-on approach ensures these technologies aren’t just IT projects, but core business strategy enablers.

            A Single Source of Truth with AI

            To overcome this, financial institutions should establish a Single Source of Truth (SSOT) and in doing so move away from older, somewhat clumsy core platforms. An SSOT will provide a unified, consistent view of data across the organisation. This accelerates decision-making with greater confidence. As demonstrated by successful implementations across the industry. For exmple, Bank of America’s AI-powered virtual assistant Erica providing personalised financial advice to Wells Fargo’s modernised data infrastructure. This enables enhanced risk assessment and management. By centralising core data, an SSOT enables the identification of operational inefficiencies, better monitoring of customer behaviours and effective execution of strategies to foster growth. 

            The key question is how to successfully de-risk this transition from a fixed cost base to a more flexible, agile one. This transition is essential for becoming an outcomes-focused business with greater adaptability. So, how can technology help achieve this?  

            One approach involves what is often (unfortunately) referred to as a Strangler Pattern. Instead of a wholesale shift from one platform to another, this modulated approach guides clients on a journey that focuses on gradually moving specific functionalities. By decomposing the legacy system function by function, we rebuild each component within the new platform. This allows the old system to run in parallel until fully replaced. Thus shrinking the monolithic structure in a manageable, low-risk way. It is a method preferred by many large financial services players when they move to become digital businesses.

            By working within a digital business methodology that prioritises outcomes over technology, we gain significant advantages. The beauty of this function is its flexibility. When implementing a new function, the management of a FS firm may discover it isn’t meeting expectations or fulfilling business needs. And yet these clients still have the security of the old platform to fall back on and can easily revert back to the original system and refine the new function before trying again. This way of working ensures a safety net. It can reduce risk and enable iterative improvements without causing major disruptions to business operations. 

            The full picture  

            The transformation of core platforms through AI presents both immense opportunity and significant challenges. Those institutions willing to embrace this change, adopting data-first approaches and modern architectures, are poised to redefine the industry landscape. The transition, whilst complex, can be managed through measured strategies allowing for gradual, low-risk modernisation. As we move forward, the success of financial institutions will increasingly hinge on their ability to harness AI’s potential. They will need to create unified data ecosystems and adapt to the evolving needs of the digital age. Financial services businesses must embrace AI and modernise their core platforms or risk becoming as obsolete as a floppy disk.

            • Artificial Intelligence in FinTech

            Author

            Anish Kapoor

            Published

            4 April 2025

            Estimated Read time

            5Mins

            AccessPay CEO Anish Kapoor examines the positive impact of DORA on the digital payments industry

            The EU’s Digital Operational Resilience Act (DORA) is a positive step for the payments industry and will help boost the resilience of an ecosystem that has changed radically over the last twenty years. Even so, the implications of this landmark regulation for payment service providers (PSPs) are complex and far-reaching. It will require investment in processes and infrastructure, which must also factor in the ongoing shift to real-time payments.

            The technology backstory

            Two decades ago, payment technology predominantly referred to back-end systems used by banks and PSPs to process electronic transactions. Online banking was still in its infancy, the smartphone hadn’t yet been launched, and traditional payment methods such as cash and cheques were much more prevalent.  

            Today, it is a very different story. The number of electronic payments made via cards and digital wallets, credit transfers and direct debits has exploded. Technology is front and centre in payment service delivery, as individuals and businesses use online portals and mobile apps to manage accounts and initiate payments. While the rise of real-time payments, such as the EU’s SEPA Instant Credit Transfer (SCT Inst), means an increasing proportion of bank transfers are settled instantly rather than over several working days, which also means that anti-fraud measures and other compliance checks have to take place in real-time given the heightened fraud risk.

            So, if there is a technological failure at any point in this new world of payments, it can have immediate and considerable ramifications for individuals and businesses. The now-infamous CrowdStrike outage in July 2024 affected several sectors, including banking, with some PSPs unable to process payments. More recently, an hours-long glitch at Bank of Ireland in December 2024 caused delays in processing payroll transactions for some employers, while a two-day outage at Barclays in February 2025  left customers unable to make bank transfers and use their debit cards. To catch up, Barclays had to process payments over the weekend and extend call centre operating hours.  

            DORA’s goals

            DORA aims to make the EU’s financial institutions (FIs) more resilient to information and communication technology (ICT) risks. It will minimise the potential for IT outages and require FIs to be back online as quickly as possible when they do occur. From a practical perspective, it will oblige them to create and implement ICT risk management frameworks. And meet new requirements for resilience testing, outage reporting, and information sharing.

            Of course, the advent of DORA adds to the compliance burden for FIs, who will partly be spurred to comply to avoid fines for non-compliance and the associated negative press. Still, its rollout should be seen as positive for the industry. It should help to improve resilience across the ecosystem and boost customer confidence in the sector.

            Improving infrastructure resilience with DORA

            One angle that is less widely discussed when it comes to DORA is its implications for a PSP’s infrastructure. Whether developed in-house or outsourced, payment systems will need to have the capacity to accommodate peak loads following any outage. This will require PSPs to scale by multiples of their standard throughput.

            For example, if a PSP’s average processing volume is 1,000 transactions per hour and its systems are down for three hours, it will need to have the capacity to process those 3,000 outstanding transactions once service resumes. And without impacting new transactions coming through the system. Additionally, if they are real-time payments, the delayed transactions must be settled as soon as possible. In this hypothetical example, such an outage would mean the system needs to handle 4,000 transactions in one hour, four times its usual capacity.

            This requirement to recover quickly from IT outages will necessitate additional investment in infrastructure and automation. Especially given the move towards real-time settlement. In particular, it will likely drive interest in cloud-native technology, which can scale more readily on demand.

            Third-party vendor relationships

            DORA will also significantly impact how PSPs manage third-party IT vendor relationships. This development has been driven by the growing complexity of the financial ecosystem in the wake of digitisation and the rise of open banking. Research from McKinsey Digital highlights how the growth in the number of apps and vendors has increased the complexity and pressure on IT leaders.  

            Under DORA, FIs are expected to monitor third-party providers, update supplier contracts to cover IT resilience, and establish an oversight framework for critical third-party providers. Consequently, conducting due diligence on third-party providers, particularly new vendors, and their approach to resilience is essential. Generally, we are likely to witness a flight to quality, with the providers that invest in controls and resilience set to fare best in the long term.

            Adjusting to DORA

            The arrival of DORA is a positive development for the payments industry. The sector has changed significantly in recent decades and relies heavily on technology for service delivery. Likewise, its customers depend on the PSPs to deliver their services so that they can conduct their business uninterrupted. However, the changes required by DORA are extensive and will require PSPs to invest in their infrastructure, processes and third-party relationships. As they adjust to the requirements of DORA, PSPs should ensure that infrastructure is resilient and flexible enough to handle surges in transaction flows. And factor in the shift to real-time settlement, which will only add to the demands made of payment systems.

            • Cybersecurity in FinTech
            • Digital Payments

            Author

            Arsalan Minhas

            Published

            3 April 2025

            Estimated Read time

            5Mins

            Arsalan Minhas, AVP Sales Engineering, EMEA & APAC, at Hyland, on how AI revolutionising financial services

            Artificial intelligence (AI) is revolutionising financial services, reshaping how institutions detect fraud, personalise customer experiences, and optimise investment strategies. From AI-powered chatbots assisting customers to machine learning models predicting market trends, the technology is driving unprecedented efficiency and insight.

            Yet, alongside these advancements come new challenges. AI-driven scams are evolving in sophistication, algorithmic biases raise ethical concerns, and regulatory scrutiny is increasing. As financial institutions accelerate AI adoption, they’re walking the fine line between harnessing its benefits and mitigating its risks. 

            AI in fraud detection and prevention – strengthening security measures

            One of the most critical areas where AI has transformed financial services is fraud detection and prevention.

            Traditional fraud prevention methods relied on static rule-based systems, which were often ineffective at identifying evolving threats. Such systems aren’t necessarily equipped to keep up with the sheer pace of financial service operations today, which has led to a surge of interest in automated alternatives.

            AI, particularly machine learning algorithms, offers a dynamic solution by analysing vast datasets in real time to identify anomalies and potential fraud. AI also enhances biometric authentication methods, such as voice and facial recognition. This can ensure secure access to accounts, reducing the reliance on passwords, which are vulnerable to breaches.

            According to a recent McKinsey report, AI-driven fraud detection systems can reduce financial fraud losses by up to 50%. Making them a crucial asset for financial institutions. These unprecedented levels of speed and versatility has made AI a priority for even the biggest players.

            Of course, fraud detection is not without its challenges. Criminals are also leveraging AI to create sophisticated scams, such as deepfake-based identity fraud. And the introduction of new technologies can challenge cybersecurity initiatives.

            With that in mind, financial institutions must constantly update their AI models to stay ahead of emerging threats. Regulatory compliance adds another layer of complexity, as AI’s decision-making much align with consumer protection laws and data privacy regulations like GDPR and CCPA.

            The future of Customer Experience

            On the customer-facing side of things, Artificial Intelligence is transforming the customer experience through hyper-personalised financial services. Gone are the days of generic banking interactions. AI now enables financial institutions to tailor services based on individual customer behaviours, preferences and financial goals.

            Leading UK banks like NatWest and Lloyds Bank have invested heavily in AI-powered virtual assistants. NatWest’s digital assistant, Cora, has handled millions of customer interactions, providing real-time financial insights, bill reminders, and even fraud detection alerts. Similarly, HSBC uses AI-driven tools to analyse spending patterns and offer personalised financial advice. The ability to assess transaction data allows banks to recommend budgeting strategies, suggest tailored loan offers, and predict future financial needs, making banking more intuitive and customer centric.

            AI-driven robo-advisors, such as those offered by Nutmeg and Moneyfarm, have revolutionised investment management by providing algorithm-based financial planning. These platforms leverage AI to assess risk tolerance, market trends, and historical data to offer personalised investment strategies with lower fees than traditional financial advisors. 

            While such tools can be incredibly effective, they do raise concerns about data privacy and algorithmic bias. The more AI knows about an individual’s financial habits, the greater the risk of data misuse or bias in lending and investment recommendations.

            Financial institutions must therefore ensure transparency and fairness in AI decision-making to build customer trust and meet regulatory regulations. The basis upon which customers share their personal data, and the protections that it is afforded, are a non-negotiable for any serious financial organisation.

            Redefining market strategies in trading and investment

            According to Deloitte, Artificial Intelligence is poised to be one of the most disruptive forces in investment management. High-frequency trading (HFT) firms now rely on AI algorithms to process vast amounts of market data within milliseconds. It also enables hedge funds and investment firms to predict market movements by analysing patterns from historical data, social media sentiment, and global economic indicators.

            Leading firms like Man Group and XTX Markets have harnessed AI to enhance their trading strategies and portfolio management. Man Group, managing $175 billion in assets, utilises machine learning tools to develop its platform, ManGPT, to analyse trades and optimise investment decisions.

            Similarly, XTX Markets, a London-based trading firm, employs advanced AI models to execute millions of trades daily, emphasising AI-driven strategies over sheer speed. Predictive analytics have become an indispensable tool in portfolio management, helping firms adjust their strategies based on real-time market fluctuations.

            Naturally, these automated tools require to-the-second oversight from the business itself. The 2010 Flash Crash, in which the stock market plunged nearly 1,000 points within minutes, was exacerbated by algorithmic trading. AI-driven trading models can react unpredictably in volatile markets, amplifying risks if not properly regulated. Humanised AI – the combination of human and AI working in concert, rather than automated systems working in isolation – is crucial.

            The future of AI in financial services

            As Artificial Intelligence continues to evolve, its integration within financial services will only deepen. Institutions that successfully integrate AI into their operations will gain a significant competitive advantage. Benefiting from enhanced fraud detection, superior customer experiences, and data-driven investment strategies.

            These businesses must also navigate the complexities of regulatory compliance, data privacy, and ethical AI deployment. The EU’s AI Act is one of many policies aiming to create the most robust governance structures for AI applications, and finance is no exception.

            Striking the right balance between innovation and regulation will be crucial to ensuring AI remains a force for positive transformation rather than disruption. Financial institutions must prioritise transparency, human oversight, and ethical considerations in deployment to fully realise its potential while maintaining consumer trust.

            The financial industry is on the brink of an AI-driven revolution. With careful implementation and responsible oversight, the technology has the power to make financial services more secure, efficient, and customer-friendly than ever before. Institutions that embrace this technology while addressing its challenges will shape the future of finance, redefining the way money is managed, invested, and protected in the years to come.

            • Artificial Intelligence in FinTech

            Author

            Scott Zoldi

            Published

            25 March 2025

            Estimated Read time

            4Mins

            Scott Zoldi, Chief Analytics Officer at FICO, explains why there should be no AI alone in decision making processes

            Many AI models are black boxes and developed without proper consideration for interpretability, ethics, or safety of outputs. To establish trust, organisations should leverage Responsible AI. This defines standards of robust AI, explainable AI, ethical AI, and auditable AI. Under Responsible AI, developers define the conditions that lead to some transactions having less human oversight and others having more. But can we take people out of the decision-making loop entirely? To answer that question, let’s look at some developments in Responsible AI.

            Trust in Developing AI Models

            One best practice that organisations can adopt is maintaining a corporate AI model development standard. This dictates appropriate AI algorithms and processes to enable roles that keep people in the loop. This will often include the use of interpretable AI, allowing humans to review and understand what AI has learned for palatability, bias, ethical use and safety. Auditable AI will then codify the human-in-the-loop decisions and monitoring guidelines for operational use of the AI.

            Responsible AI codifies all the essential human decisions that guide how AI will be built, used and progressed. This includes approving or declining the use of data, removing unethical relationships in data (i.e., illegal or unethical data proxies), and ensuring governance and regulation standards are met. Responsible AI leverages an immutable blockchain that dictates how to monitor the AI in operation. And the decision authority of human operators, which can include conditions where AI decisions are overruled, and operations move to a ‘humble AI model.’ AI Practitioners are keenly aware that even the highest performing AI models generate large number of false positives. So, every output needs to be treated with care and strategies defined to validate, counter, and support the AI.

            A Responsible AI framework

            There should be a well-defined process to overrule or reverse AI-driven decisions. If built in a Responsible AI framework, these decisions are codified into a crystal-clear set of operating AI blockchain frameworks well before the AI is in production. When there is a crisis you need clear preset guidance, not panicked decision making. This blockchain will define when humans can overrule the AI through alternate models, supporting data, or investigative processes. This AI operating framework is defined in coordination with the model developers, who understand the strengths and weaknesses of the AI. And when it may be operating in ways it wasn’t designed, ensuring there is no gap between development and operation. When auditable AI is employed, there are no nail-biting decisions in times of crisis. You can rely on a framework that pre-defines steps to make these human-driven decisions.

            Companies that utilise Responsible AI frameworks enforce usage adherence by auditable AI, which is the operating manual and monitoring system. Embracing Responsible AI standards can help business units attain huge value. At the same time they can appropriately define the criteria where the businesses balance business risks and regulation. Domain experts/analysts will be given a defined span of control on how to use their domain knowledge and the auditable AI will monitor the system to alert and circumvent AI as appropriate.

            Drawback prevention begins with transparency

            To prevent major pull-back in AI today, we must go beyond aspirational and boastful claims to honest discussions of the risks of this technology. We must define how involved humans need to be. Companies need to empower their data science leadership to define what is high-risk AI, and how they are prepared or not to meet responsible/trustworthy AI. This comes back to governance and AI regulation. Companies must focus on developing a Responsible AI programme, and boost practices that may have atrophied during the GenAI hype cycle. 

            They should start with a review of how AI regulation is developing, and whether they have the tools to appropriately address and pressure-test their AI applications. If they’re not prepared, they need to understand the business impacts of potentially having AI pulled from their repository of tools. And get prepared by defining AI development/operational corporate standards. 

            Companies should then determine and classify business problems best suited for traditional AI vs. generative AI. Traditional AI can be constructed and constrained to meet regulation using the right algorithms to meet business objectives. Finally, companies will want to adopt a humble AI approach to have hot backups for their AI deployments. And to tier down to safer tech when auditable AI indicates AI decisioning is not trustworthy.

            The vital role of the Data Scientist

            Too many organisations are driving AI strategy through business owners or software engineers who often have limited to no knowledge of the specifics of AI algorithms’ mathematics and risks. Stringing together AI is easy. Building AI that is responsible and safe and properly operationalised with controls is a much harder exercise requiring standards, maturity and commitment to responsible AI. Data scientists can help businesses find the right paths to adopt the right types of AI for different business applications, regulatory compliances, and optimal consumer outcomes. In a nutshell: AI + human is the strongest solution. There should be no AI alone in decision-making.

            • Artificial Intelligence in FinTech
            • Blockchain & Crypto

            Author

            Dan Brightmore

            Published

            13 March 2025

            Estimated Read time

            5Mins

            Fouzi Husaini, Chief Technology & AI Officer at Marqeta, answers our questions about Agentic AI and its applications for businesses

            Agentic AI is emerging as the leading AI trend of 2025. Industry figures are hailing Agentic AI as the broadly transformative next step in GenAI development. The year so far has seen multiple businesses release new tools for a wide array of applications. 

            The technology combines the next generation of AI tech like large language models (LLMs) with more traditional capabilities like machine learning, automation, and enterprise orchestration. The end result could lead to a more autonomous version of AI: Agents. These agents can set their own goals, analyse data sets, and act with less human oversight than previous tools. 

            We spoke to Fouzi Husaini, Chief Technology & AI Officer at Marqeta about what sets Agentic AI apart whether the technology really is a leap forward in terms of solving AI’s shortcomings, and how Agentic AI could solve business problems.

            1. What makes AI “agentic”? How is the technology different from something like Chat-GPT? 

            “Agentic refers to the type of Artificial Intelligence that can act as agents and on its own. Agentic AI leverages enhanced reasoning capabilities to solve problems without prompts or constant human supervision. It can carry out complex, multi-step tasks autonomously.

            “GenAI and by extension Large Language Models, the most famous example being ChatGPT, require human input to solve tasks. For instance, ChatGPT needs user prompts before it can generate content. Then, sers need to input subsequent commands to edit and refine this. Agentic AI has the capability to react and learn without human intervention as it processes data and solves problems. This enables it to adapt and learn much faster than GenAI.”

            2. Chat-GPT and other LLMs frequently produce results filled with factual errors, misrepresentations, and “hallucinations”, making them pretty unsuited to working without human supervision – let alone orchestrating important financial deals. What makes Agentic AI any better or more trustworthy? 

            “All types of AI have the possibility to ‘hallucinate’ and produce factually incorrect information. That being said, Agentic AI is usually less likely to suffer from significant hallucinations in comparison to GenAI. 

            “Agentic AI’s focus is specifically engineered to operate within clearly defined parameters and follow explicit workflows, making it particularly well-suited for having guardrails in place to keep it on task and from making errors. Its learning capabilities also allow it to recognise and adapt to its mistakes, ensuring it is unlikely to hallucinate multiple times.”

            “On the other hand, GenAI occasionally generates factually incorrect content due to the quality of data provided, and sometimes because of mistakes in pattern recognition.”

            “In fintech, Agentic AI technology can make it possible to analyse consumer spending data and learn from it, allowing for highly tailored financial offers and services that are more accurate and help to create a personalised finance experience for consumers.” 

            3. How could agentic AI deployments affect the relationship between financial services companies and their customers? What about their employees? 

            “The integration of Agentic AI into financial services benefits multiple parties. First, 

            integrating Agentic AI into their offerings allows financial service companies to provide their customers with bespoke tools and features. For instance, AI can be used to develop ‘predictive cards’. These cards can anticipate a consumer’s spending requirements based on their past behaviour. This means AI can adjust credit limits and offer tailored rewards automatically, creating a personalised experience for each individual.

            “The status quo’s days are numbered as consumers crave tailor-made financial experiences. Agentic AI can allow fintechs to provide personalised financial services that help consumers and businesses make their money work better for them. With Agentic AI technology, fintechs can analyse consumer spending data and learn from it. This allows for more tailored financial offers and services.   

            “As for employees, Agentic AI gives them the ability to focus on more creative and interesting tasks. Agentic AI can handle more routine roles such as data entry and monitoring for fraud, automating repetitive tasks and autonomous decision making based on data. This helps to reduce human error and enables employees to focus more time and energy on the creative and strategic aspects of their roles while allowing AI to focus on more administrative tasks.”

            4. How would agentic AI make financial services safer? 

            “Agentic AI has the capability to make financial services more secure for financial institutions and consumers alike, by bringing consistency and tireless vigilance to critical financial processes. With its ability to analyse vast strings of information, it can rapidly identify anomalies in spending data that indicate potential instances of fraud and can use its enhanced reasoning and ability to act without human prompts to quickly react to suspicious activity. 

            “While a human operator will be susceptible to decision fatigue, an AI agent could always be vigilant and maintain the same high level of precision and alertness 24/7. This is vital for fields like fraud detection, where a single missed signal could lead to significant consequences.

            “Furthermore, its capability to learn without human interaction means that it can improve its ability to detect fraud over time. This gives it the ability to learn how to identify new types of fraud, helping it to adapt as schemes become more sophisticated over time.” 

            5. What kind of trajectory do you see the technology having over the next year to eighteen months?

            “In fintech, Agentic AI integration will likely begin in the operations space. These areas manage complex, but well-defined, processes and are perfect for intelligent automation. For instance, customer call centres where human agents usually follow set standard operating procedures (SOPs) that can be fed into an AI system, which makes automation easier and faster than before.

            “In the more distant future, I believe we will see Agentic AI integrated into automated workflows that span entire value chains, including tasks such as risk assessment, customer onboarding and account management.” 

            • Artificial Intelligence in FinTech

            Author

            Dan Brightmore

            Published

            25 February 2025

            Estimated Read time

            5Mins

            Brendan Thorpe, Customer Success Manager at Auriga, on how banks can gain valuable insights from ATM data

            Everyday customer interactions with ATMs or ASSTs to withdraw cash or check their account means these touchpoints emit hundreds of thousands of data points per day. This data holds the answers to how customers interact with those end points and how they are performing. However, currently this data is not being fully analysed or harnessed at all.

            Data Analytics

            This is surprising when you consider how better data analytics is widely understood to be crucial to enable banks to stay ahead of the competition. Indeed, one major study found that nearly half (48 percent) of banking executives globally agreed on this. However, many do little with it. The data which is harvested from the self-service banking network, including ATMs and ASSTs, is a critical way for banks to lower their operational costs. At the same time it can improve their offerings and increase their bottom line.

            Real-time data collection and analysis is more than just critical for managing operational costs. It also plays a significant role in how banks realise their omnichannel ambitions to improve customer engagement and experience. For this to be successful, banks must leverage tools which provide actionable insights into performance across a number of channels including in-person services, ATMs, online and apps. The insights which are collected on these channels provide a complete and integrated picture of banking performance across all touchpoints.

            Actionable Insights from Data

            No matter how a customer interacts with the bank, every touchpoint provides large amounts of data which can be collected, sorted, and analysed for actionable insights. However, taking this information from raw data and transforming it into valuable insights is a challenge for many financial services organisations.

            To do this, it involves strong data management and analytics processes and end-to-end mapping of all self-service banking channels, in-person and online. Real-time insights are also key to understanding how the network is performing and how customers are interacting with the endpoints. Importantly, this information must be easily accessed throughout the organisation. Doing this will enable the bank to identify if there are any inefficiencies or issues throughout the network which can be fixed swiftly, with minimal disruption to services.

            Significantly, with real-time monitoring, banks can see any attacks on their services or endpoints from threat actors. The sensors are not only on the ATM. Those around the machines will be able to collect any interactions with the endpoints and in the surrounding area. For the most part, the sensors will pick up harmless interactions, but other times this may be an indicator that a threat actor was trying to take money out of the machine. As such, collecting, sorting, and analysing real-time data from the sensors can protect the bank and their customers and mitigate any harmful threats.

            Furthermore, predictive analytics and continuous monitoring will enable banks to forecast the future performance of each touchpoint. Banks are able to apply specific parameters. Depending on their current business objectives they can better understand how each service channel is forecasted to perform in a specific situation.

            How advanced analytics is transforming banking

            As budgets tighten with rising costs, banks need to approach their ATM networks in a smarter way to optimise cash management and data forecasting. Real-time data tracking gives banks a greater understanding into customer behaviour. This is key to service performance improvements, including knowing in real time whether the ATM self-service interface is working or not. However, banks must get their data right, before they lean on the insights.

            From real-time monitoring of customer interactions, financial services institutions can collect data based on the transaction flow, which can indicate if there is a better way for customers to complete their transaction. This will allow banks to see where network inefficiencies lie and then drive a culture of continuous improvement. The ATM is a vital touchpoint for a full omnichannel service, so banks leveraging data in the right way will ensure that the endpoint and the network are more user friendly.

            Moreover, real-time tracking will also enable banks to predict when cash cartridges need to be replenished. As such, this will ensure there is enough cash in the machines for customers, and be able to better forecast how much cash the endpoint will need. This creates efficiencies around how banks deliver cash to the machines that need it. It reduces their Cash-In-Transit (CIT), security, interest and insurance costs.

            Digital Transformation

            To make sure that banks are making the most out of the data, they should leverage a dynamic, industry-specific banking business analytics platform. This should be available to all in the business and be able to seamlessly integrate into their current systems. The platform must collect and analyse the data in real-time from all key touchpoints in a bank’s network. Importantly, this data should be converted into usable insights for customer behaviour and performance metrics for the ATM. This will enable banks to adapt their offerings to changes in customer needs and market conditions. This will place banks on the front foot so they can focus investment in the up-and-coming areas.

            The banking industry shows no signs of slowing down when it comes to digital transformation and development. The key here is to understand how all service channels, in-person and online, are performing to ensure customer demands are met. The way to do this is through leveraging real-time insights and data analytics. Financial services organisations must transform their approach to self-service banking strategies as data analytics is not only a driver of competitiveness, but also of long-term success.

            Learn more at https://www.aurigaspa.com/en/

            • Neobanking

            Author

            Dan Brightmore

            Published

            12 February 2025

            Estimated Read time

            1Min

            Aviva, one of the UK’s leading insurance, wealth and retirement businesses, has chosen AutoRek, a leader in automated reconciliations, as its…

            Aviva, one of the UK’s leading insurance, wealth and retirement businesses, has chosen AutoRek, a leader in automated reconciliations, as its reconciliation and CASS tool.

            The collaboration will ensure greater efficiency and compliance through automation. Aviva will leverage AutoRek’s end-to-end platform to implement a fully audited, rules-driven reconciliation process, ensuring complete transparency for CASS auditors and internal stakeholders.

            With AutoRek, Aviva will gain an improved automated solution for client money and regulatory reporting, reducing the manual effort and inherent risk associated with manual processing.

            This new capability will enable Aviva to reduce operational inefficiencies, streamline compliance, and enhance overall financial control.

            “Aviva is dedicated to investing in technology to further our growth strategy. Following an extensive tender process, we were highly impressed with the quality of the AutoRek tool. The implementation of the AutoRek solution will streamline our processes and allows us to confidently address future scalability and volume requirements.”

            Chris Golland, Head of CASS & Middle Office, Aviva

            “We’re thrilled to onboard Aviva as a client to the AutoRek platform, empowering them to achieve greater efficiency and accuracy in their operations. Together, we’re driving innovation and setting new benchmarks for financial excellence.”

            Jack Niven, VP Sales, AutoRek

            • InsurTech

            Author

            Stuart Cheetham

            Published

            22 January 2025

            Estimated Read time

            4Mins

            Stuart Cheetham, CEO at MPowered Mortgages, on how AI-powered technology allows mortgage lenders to fully underwrite loan applications in minutes

            AI technologies are about to have a huge impact on the mortgage market… In November last year the founders of Revolut announced plans to launch a “fully digital, instant” mortgage in Lithuania and Ireland in 2025. Details were sketchy but the company said that mortgages will be part of a “comprehensive credit offering” it intends to build.

            Neobanking progress with AI

            Digital only banks, like Revolut and Monzo, are renowned for using the power of technology and data science to create efficiencies and improve customer experience. The reason neobanks have been so successful is because they provide a modern, convenient and cost-effective alternative to traditional banking. This is done a transparent way, through fast onboarding, 24/7 app access and instant notifications. All with a user-friendly interface.

            While many financial services sectors have embraced financial technology in the way Revolut and Monzo have for the retail banking sector, the mortgage sector has struggled to make a real breakthrough here. Why hasn’t the mortgage industry caught up one might ask? Mortgages are complex financial products, existing at the intersection of justifiably stringent regulation. They represent the single biggest financial commitment people make in their lifetimes. Financial advisors who source mortgages on behalf of borrowers are hindered at every stage by outdated systems and inadequate or commoditised product offerings.

            Disrupting the Mortgage Market

            The mortgage industry is one financial services sector that has been yearning to be shaken up by the FinTech industry for some time. While it’s encouraging to see a successful brand like Revolut enter this market, what is less known is that huge progress is being made already by smaller and less well known FinTech disruptors.

            For example, the mortgage technology company MQube has developed a “new fast way” of delivering mortgage offers using the cutting edge of AI technology and data science. Today, it still typically takes several weeks to get a confirmed mortgage offer. This is one of the major reasons the homebuying process can be so time consuming and stressful for brokers and borrowers. The mortgage process is characterised by bureaucracy, paperwork, delays and often frustratingly opaque decision-making by lenders. This leads to stress and uncertainty for consumers, and their advisors. And at a time when they have plenty of other property-purchase related challenges to contend with.

            Our proprietary research shows us, and this will come as no surprise, that the biggest pain point for borrowers and brokers about the mortgage process is that it is time consuming, paperwork heavy and stressful. Imagine a world where getting a mortgage is as quick and as easy as getting car insurance. This is MQube’s vision.

            MQube – AI-powered Mortgages

            MQube‘s AI-powered mortgage origination platform allows mortgage lenders to fully underwrite loan applications in minutes. MPowered Mortgages is MQube’s lending arm and competes for residential business alongside the big banks. It uses MQube’s AI-driven mortgage origination platform and is now able to offer a lending decision within one working day to 96% of completed applications.

            The platform leverages state-of-the-art artificial intelligence and machine learning to assess around 20,000 data points in real-time. This enables lenders to process mortgage applications in minutes, transforming the industry standard of days or weeks. It automates the entire underwriting journey, from application to completion. This helps to provide a faster service, reduce costs, mitigate risks, and to make strategic adjustments quickly and effectively. By assessing documents and data in real-time during the application, it is able to build a clearer and deeper understanding of a consumers’ circumstances and specific needs. Applicants are never asked questions when MQube can independently source and verify that data, leading to a streamlined and paperless experience. Furthermore, this whole process reduces dependency on human intervention.

            The benefits of AI

            More and more lenders are seeing the benefits AI and financial technology can bring to their business. They are beginning to adopt such AI-driven financial systems which are scalable and serve to address systemic problems in this industry. The mortgage industry is still some way behind the neobanks, but what’s hugely exciting to see is the progress that has been made so far. Moreover, if FinTechs continue to innovate this sector and if lenders continue to embrace financial technology and use at scale, then getting a mortgage could genuinely become a quick, easy and stress free process. At this point, the mortgage industry could begin to see a shift in consumer perception and change in consumer behaviour. A new frontier for the mortgage industry is upon us.

            • Artificial Intelligence in FinTech
            • Neobanking

            Author

            Dan Brightmore

            Published

            3 January 2025

            Estimated Read time

            4Mins

            Glenn Fratangelo, Head of Fraud Product Marketing & Strategy at NICE Actimize, on financial services fraud prevention in 2025.

            2024 marked a turning point in financial crime management with the advent of Generative AI (GenAI). McKinsey estimates GenAI could add a staggering $200-340 billion in annual value to the global banking sector. A potential revenue boost of 2.8 to 4.7%. This underscores the transformative potential of GenAI. IT IS rapidly evolving from a futuristic concept to a powerful tool in the fight against financial crime. However, 2024 was just the prelude. 2025 promises to be the year GenAI truly comes into its own. Unlocking transformative capabilities in combating increasingly sophisticated threats. 

            This evolution is not merely desirable, it is essential. The Office of National Statistics (ONS) reported a concerning 19% year-over-year increase in UK consumer and retail fraud incidents in 2024, reaching approximately 3.6 million. This stark reality underscores the urgent need for financial institutions (FIs) and banks to bolster their defences against financial crime. In 2025, leveraging the power of GenAI is no longer a luxury, but a necessity for protecting customers and safeguarding the financial ecosystem. 

            The evolving GenAI-powered fraud landscape

            Fraudsters have embraced GenAI as a potent weapon in their arsenal. This technology’s ability to create realistic fakes, automate attacks and mimic customers creates a significant threat to the financial landscape.

            Deepfake technology has become a particularly insidious tool. By generating highly realistic voice and facial fakes, fraudsters can bypass remote verification processes with ease. This opens doors to unauthorised access to sensitive information, enabling account takeovers and other fraudulent activities.  

            In addition, the rise of synthetic identities further complicates the challenge. By blending real and fabricated data, fraudsters can create personas that seamlessly infiltrate legitimate customer profiles. These synthetic identities are extremely difficult to detect, as they appear indistinguishable from genuine customers. Making it challenging for institutions to differentiate between legitimate and fraudulent activities.

            Phishing scams have also undergone a dramatic evolution, becoming more sophisticated and personalised. AI-driven techniques allow fraudsters to craft personalised, convincing emails that mimic legitimate communications, resulting in significant data breaches.

            Harnessing GenAI

            GenAI is being used by criminals – presenting a significant challenge in the realm of fraud. It requires advanced AI capabilities such as real-time behavior analytics that use machine learning to continuously analyse all entity interaction and transaction patterns. This can identify subtle deviations from a customer’s typical behaviour. It allows for initiative-taking and the flagging of suspicious activity before any damage occurs. Moreover, providing a significant advantage over traditional, rigid rule-based systems that often fail to detect nuanced threats.

            Fraud simulation and stress testing using GenAI can also empower institutions to proactively assess the resilience of their systems. By simulating potential fraud scenarios, financial institutions can identify vulnerabilities and train detection models to recognise emerging tactics. Furthermore, this proactive preparation ensures that defences remain ahead of fraudsters’ evolving methods, creating a more robust and adaptable security infrastructure.

            Low volume high value fraud, such as BEC or other large value account to account transfers usually lack the quantity of data needed to optimise models. GenAI can address this by creating synthetic data that mimics real-world scenarios. This approach significantly improves the accuracy and robustness of detection models, making them more effective against new and unforeseen threats.

            GenAI has the potential to transform the investigation process by automating tasks such as generating alerts and case summaries, as well as SAR narratives. This automation not only minimises errors but also frees analysts from mundane tasks, allowing them to focus on higher-value activities. The result is a significantly accelerated financial crime investigation process, enabling institutions to respond to threats with greater speed and efficiency.

            The battle against fraud in 2025 and beyond

            The battle against financial fraud in 2025 and beyond is an undeniable arms race. Fraudsters, wielding generative AI as their weapon, will relentlessly seek to exploit vulnerabilities. To counter this evolving threat, financial institutions must embrace AI to outmanoeuvre fraudsters and proactively protect their customers.

            The future of fraud and financial crime prevention hinges on our ability to innovate and adapt. Institutions that view GenAI not just as a challenge, but as an opportunity, will emerge as leaders in this fight. AI is a force multiplier for institutions striving to combat fraud and financial crime, empowering them with smarter, faster, and more adaptive defences, we can create a more secure and trustworthy financial ecosystem. The choice to innovate in the face of adversity will define the path forward and shape the future.

            • Artificial Intelligence in FinTech

            Author

            Paul O'Sullivan

            Published

            11 December 2024

            Estimated Read time

            4Mins

            Paul O’Sullivan, Global Head of Banking and Lending at Aryza, on the rise of AI in banking

            The banking sector stands at the crossroads of technological innovation and operational transformation. AI is taking centre stage in reshaping how financial institutions operate. The banking sector is beginning to recognise AI’s potential. It can address challenges, enhance operational efficiency, and deliver more personalised customer experiences.

            The Current State of AI in Banking

            Research reveals that while a number of banking organisations have yet to fully integrate AI into their operations, key areas such as debt recovery are leading the charge. The slower pace of adoption can be attributed to the highly regulated environment of banking. Because transparency, compliance, and customer trust are non-negotiable. However, despite this cautious approach, banks that have implemented artificial intelligence are already seeing significant benefits, particularly in risk management.

            AI’s Role in Risk Management

            Effective risk management is a cornerstone of the banking sector. AI is proving to be a powerful tool in this area. By analysing vast amounts of data and providing predictive insights, AI enables banks to mitigate risks early. They can strengthen customer portfolio stability, and make data-driven lending decisions. These capabilities are essential in a landscape where financial risks can escalate rapidly.

            Beyond the expected benefits, banks have also reported enhanced customer insights as an unexpected advantage. By leveraging AI to analyse customer behaviours and preferences, banks can tailor their products and services more effectively. Furthermore, they can improve customer satisfaction and experience, whilst fostering long-term loyalty.

            Challenges to Adoption

            Although organisations are experiencing a multitude of advantages, the integration of AI in banking is not without its hurdles. Legacy IT systems, stringent regulatory requirements, and concerns around data privacy pose significant challenges to widespread adoption. Banks must ensure AI-driven decision-making processes are effective. Moreover, they must also be fully transparent and compliant with industry regulations. Further highlighting the importance of a gradual, strategic approach to AI implementation.

            Opportunities Ahead

            The potential for AI in banking extends far beyond risk management. From streamlining operational workflows to enhancing customer personalisation and improving decision-making. AI is set to drive innovation across the sector. For example, AI-powered chatbots and virtual assistants transform customer service by providing instant, 24/7 support. They can handle complex interactions, enhancing customer satisfaction. At the same time, advanced analytics enable banks to analyse behaviour patterns, predict trends, and personalise product offerings. Furthermore. enhancing cross-selling opportunities and driving deeper customer engagement. These tools are becoming strategic enablers for innovation in the financial landscape.

            A Call to Action

            For banks to fully realise the benefits of AI, they must address the digital transformation gap, modernising outdated infrastructures and fostering a culture of innovation. This includes investing in technologies that align with their strategic goals, ensuring robust data security measures alongside maintaining compliance with evolving regulations.

            As the banking sector continues its journey towards digital maturity, AI will play a pivotal role in defining its future. By overcoming current barriers and embracing AI-driven solutions, banks can not only enhance operational efficiency but also deliver the seamless, personalised experiences that customers now expect in an increasingly digital world.

            About Aryza

            At Aryza know that in today’s highly regulated world, there is huge value in quickly guiding your customers through the product that best fit their immediate needs, through a seamless journey that is tailored to their specific circumstances.

            We created smart platforms, responsible and compliant products, and a unique system of companies and capabilities so that businesses can optimise their customers’ journey through the right product at the right time.

            For our teams across the globe, the growth of Aryza is a good news story and a testament to our clear vision and goals as an international business.

            And also front of mind as we build a global footprint is our impact on the environment. Aryza is committed to reducing its carbon impact through the choices it makes and we are pleased to say that we follow an active roadmap.

            • Artificial Intelligence in FinTech

            Author

            Dan Brightmore

            Published

            8 November 2024

            Estimated Read time

            2Mins

            additiv, a global leader in fintech and digital transformation, has announced the launch of an InsurTech solution with AXA Switzerland

            AXA Switzerland has successfully launched its addProtect bancassurance offering, powered by additiv’s technology platform. Furthermore, this innovative InsurTech solution allows banks to directly protect their mortgage customers against key risks with a simple plug-and-play solution.

            addProtect InsurTech solution from additiv

            As a seamless plug-and-play solution, addProtect gives banks direct access to the platform without the need for additional integration with existing IT systems. Its user-friendly and intuitive design allows banks to effortlessly integrate the platform into their day-to-day business operations. With the death and payment protection insurance, bank advisors have easy-to-understand products at their disposal. These offer added value to customers beyond the existing offering. The addProtect platform is now available for banks, and an initial pilot will be launched in collaboration with PostFinance.

            Samuel Peter, Head of Partnerships at AXA Switzerland, stated:

            “With addProtect, AXA is responding to the growing need of customers and banks for appropriate insurance solutions where and when they are needed. The solution creates additional advisory potential and better protection for the customers of our partners’ banks. We look forward to making the solution available to other partners.”

            Dieter Lützelschwab, General Manager Switzerland at additiv, added:  

            “When developing addProtect, we focused on the user experience for the customer and the bank advisor. In addition, our platform provides an easily configurable, modular insurance solution that covers the entire value chain from quotation to claims processing.”

            About additiv

            additiv empowers the world’s leading financial institutions and brands to create new business models and transform existing ones. additiv’s API-first cloud platform is one of the world’s most powerful solutions for wealth management, banking, credit, and insurance. The InsurTech technology, together with the global ecosystem of regulated financial services providers, opens up new opportunities for banks, insurance companies, asset managers, IFAs and consumer brands to quickly and flexibly offer their own and third-party financial solutions through existing or new customer channels.

            Headquartered in Switzerland, with regional offices in Singapore, UAE, Germany, and the UK, and more than 250 employees, additiv serves over 400 financial institutions (banks, insurers, asset managers, pension providers, IFAs, etc.) and brands worldwide.

            • InsurTech

            Author

            Scott Zoldi

            Published

            31 October 2024

            Estimated Read time

            4Mins

            Scott Zoldi, Chief Analytics Officer at FICO considers whether the current AI bubble is set to burst, the potential repercussions of such an event, and how businesses can prepare

            Since artificial intelligence emerged more than fifty years ago, it has experienced cycles of peaks and troughs. Periods of hype, quickly followed by unmet expectations that lead to bleak periods of AI-winter as users and investment pull back. We are currently in the biggest period of hype yet. Does that mean we are setting ourselves up for the biggest, most catastrophic fall to date?

            AI drawback

            There is a significant chance of such a drawback occurring in the near future. So, the growing number of businesses relying on AI must take steps to prepare and mitigate the impact a drawback or complete collapse could have. Research from Lloyds recently found adoption has doubled in the last year, with 63% of firms now investing in AI, compared to 32% in 2023. In addition, the same study found 81% of financial institutions now view it as a business opportunity, up from 56% in 2023.

            This hype has led organisations to explore AI use for the first time. Often with little understanding of the algorithms’ core limitations. According to Gartner, in 2023 less than 10% of organisations were capable of operationalising AI to enable meaningful execution. This could be leading to the ‘unmet expectations’ stage of the damaging hype/drawback cycle. The all-encompassing FOMO of repeating the narrative of the incredible value of AI does not align with organisations’ ability to scale, manage huge risks, or derive real sustained business value.

            Regulatory pressures for AI

            There has been a lack of trust in AI by consumers and businsses alike. It has resulted in new AI regulations specifying strong responsibility and transparency requirements for applications. The vast majority of organisations are unable to meet these in traditional AI, let alone newer GenAI applications. Large language models (LLMs) were prematurely released to the public. The resulting succession of fails fuelled substantial pressure on companies to pull back from using such solutions other than for internal applications. It has been reported that 60% of banking businesses are actively limiting AI usage. This shows that the drawback has already begun. Organisations that have gone all-in on GenAI – especially those early adopters – will be the ones to pull back the most, and the fastest.

            In financial services, where AI use has matured over decades, analytic technologies exist today that can withstand regulatory scrutiny. Forward-looking companies are ensuring they are prepared. They are moving to interpretable AI and backup traditional analytics on hand while they explore newer technologies with appropriate caution. This is in line with proper business accountability, vs the ‘build fast, break it’, mentality of the hype spinners.

            Customer trust with AI

            Customer trust has been violated by repeated failures in AI, and a lack of businesses taking customer safety seriously. A pull-back will assuage inherent mistrust in companies’ use of artificial intelligence in customer facing applications and repeated harmful outcomes.

            Businesses who want their AI usage to survive the impending winter need to establish corporate standards for building safe, transparent, trustworthy Responsible AI models that focus on the tenets of robust, interpretable, ethical and auditable AI. Concurrently, these practices will demonstrate that regulations are being adhered to – and that their customers can trust AI. Organisations will move from the constant broadcast of a dizzying array of possible applications, to a few well-structured, accountable and meaningful applications that provide value to consumers, built responsibly. Regulation will be the catalyst.

            Preparing for the worst

            Too many organisations are driving AI strategy through business owners or software engineers who often have limited to no knowledge of the specifics of algorithm mathematics and the very signifiicant risk in using the technology.

            Stringing together AI is easy. Building AI that is responsible and safe is a much harder and exhausting exercise requiring model development and deployment corporate standards. Businesses need to start now to define standards for adopting the right types of AI for appropriate business applications, meet regulatory compliances, and achieve optimal consumer outcomes.

            Companies need to show true data science leadership by developing a Responsible AI programme or boosting practices that have atrophied during the GenAI hype cycle which for many threw standards to the wind. They should start with a review of how regulation is developing, and whether they have the standards, data science staff and algorithm experience to appropriately address and pressure-test their applications and to establish trust in AI usage. If they’re not prepared, they need to understand the business impacts of potentially having artificial intelligence pulled from their repository of tools.

            Next, these companies must determine where to use traditional AI and where they use GenAI, and ensure this is not driven by marketing narrative but meeting both regulation and real business objectives safely. Finally, companies will want to adopt a humble approach to back up their deployments, to tier down to safer tech when the model indicates its decisioning is not trustworthy.

            Now is the time to go beyond aspirational and boastful claims, to have honest discussions around the risks of this technology, and to define what mature and immature AI look like. This will help prevent a major drawback.

            • Artificial Intelligence in FinTech

            Author

            Dan Brightmore

            Published

            24 October 2024

            Estimated Read time

            4Mins

            Fred Fuller, Global Head of Banking at Endava, on how banks can effectively communicate AI advancements and demonstrate ROI to investors

            There is no single solution, AI or otherwise, that can prepare financial institutions for the modern world. To build a bank capable of successfully navigating the challenges of the future, a long-term digital transformation strategy is required. Especially relevant in the wake of recent IT outages,

            At present, according to Endava’s Retail Banking Report 2024, 67% of banks are still heavily reliant on legacy systems. This leads to wasted budget and decreased efficiency. With limited resources available to modernise their tech stack, company leaders are often forced to choose which technology-type to prioritise. When doing this, 50% have chosen artificial intelligence (AI).

            Is AI alone enough?

            Can AI overhaul archaic processes or are there too many hurdles in the way? The first hurdle to successful digital transformation in financial services is overcoming the employees’ perception of the process. Time and time again, corporations have failed in the goal to integrate solutions that successfully feed into a long-term tech strategy. Often, this is due to wide-spread change fatigue. When exhausted by continuous efforts to change their day-to-day, workers become resistant to transformation. The best way to overcome change fatigue, and drive digital transformation in financial institutions, is through overhauling legacy systems. And adopting solutions that will stand the test of time.

            Legacy Systems

            Across the world, outdated legacy systems are holding financial institutions back and costing them billions. From 2022 to 2028, this expense is expected to grow at a rate of 7.8%. Not only do these archaic processes cost money, but they force banks to contend with a multitude of siloes. From departments to data. We live in a world where neobanks are growing in popularity. They are able to provide a frictionless customer experience using their modern tech stack. Traditional organisations must rid themselves of siloes to enable all areas of the business to leverage AI. In turn, this will provide them with strong data collection and support from departments who are agreed on next steps.

            At present, three quarters of financial institutions feel they need to modernise their core. Without this change, they lack the secure, data-driven foundation necessary to utilise AI and see return on their technical investments.

            The benefits of AI integration

            Once a strong foundation has been laid, it becomes easier to see the practical benefits of integrating AI. For example, when data is no longer siloed by legacy systems, using chat bots to support customers with simple queries creates an efficient consumer experience. There are internal benefits too. AI can spot potentially suspicious activity, flagging it before it is too late. Or analysing data to ensure risk management and process automation. Despite its wide-reaching capabilities, AI alone is not the only option for financial institutions…

            Routes to the future

            Endava’s Retail Banking Report also showcased the variety of solutions that banks are using to improve their tech stack. 45% of respondents recognised data analytics, in and of themselves, as a top area for investment. Meanwhile 30% flagged IoT, and 14% the Metaverse.

            There’s a reason for the emphasis on strong data. It not only supports the integration and use of AI-fuelled capabilities, but it is the driving force behind numerous functions of the bank itself. Of those surveyed, 37% aimed to use data to improve customer service. 34% to strengthen security, and 33% to personalise products and improve the customer experience.

            As well as attracting and retaining consumers, business leaders can benefit from their access to strong data by attracting and retaining talent. With 39% of failed digital transformations viewing lack of employee buy-in as a factor, financial institutions are encouraged to educate workers on their technology integration plans, and ensure solutions are user-friendly. Fortunately, looking ahead, 20% of banks surveyed seek to use data to improve the workplace.  

            A bank’s priority – looking ahead

            More than ever, banks are reliant on data to keep operations running smoothly. From providing customers with a personalised experience to improving the workplace in the competition for talent, there are a multitude of reasons to ensure the foundations of your tech stack are strong.

            Doing so makes integration of new technology a smoother experience for all. To this end, it’s no shock that 50% of banks are keen to embrace AI, using it to benefit customers and speed up processes. However, with many hampered by the legacy technology and the ever-looming threat of change fatigue, integration of any technology should be carefully planned, customer focused and data led.

            • Artificial Intelligence in FinTech

            Author

            Gabe Hopkins

            Published

            16 October 2024

            Estimated Read time

            6Mins

            Gabe Hopkins, Chief Product Officer at Ripjar, on how GenAI can transform compliance

            Generative AI (GenAI) has proven to be a transformational technology for many global industries. Particularly those sectors looking to boost their operational efficiency and drive innovation. Furthermore, GenAI has a range of use cases, and many organisations are using it to create new, creative content on demand – such as imagery, music, text, and video. Others are using the new tools at their disposal to perform tasks and process data. This makes previously tedious activities much more manageable, saving considerable time, effort, and finances in the process.

            However, compliance as a sector has traditionally shown hesitancy when it comes to implementing new technologies. Taking longer to implement new tools due to natural caution about perceived risks. As a result, many compliance teams will not be using any AI, let alone GenAI. This hesitancy means these teams are missing out on significant benefits. Especially at a time when other less risk-averse industries are experiencing the upside of implementing this technology across their systems.

            To avoid falling behind other diverse industries and competitors, it’s time for compliance teams to seriously consider AI. They need to understand the ways the technology – specifically GenAI – can be utilised in safe and tested ways. And without introducing any unnecessary risk. Doing so will revolutionise their internal processes, save work hours and keep budgets down accordingly.

            Understanding and overcoming GenAI barriers

            GenAI is a new and rapidly developing technology. Therefore, it’s natural compliance teams may have reservations surrounding how it can be applied safely. Particularly, teams tend to worry about sharing data, which may then be used in its training and become embedded into future models. Moreover, it’s also unlikely most organisations would want to share data across the internet. Strict privacy and security measures would first need to be established.

            When thinking about the options for running models securely or locally, teams are likely also worried about the costs of GenAI. Much of the public discussion of the topic has focussed on the immense budget required for preparing the foundation models.

            Additionally, model governance teams within organisations will worry about the black box nature of AI models. This puts a focus on the possibility for models to embed biases towards specific groups, which can be difficult to identify.

            However, the good news is that there are ways to use GenAI to overcome these concerns. This can be done by choosing the right models which provide the necessary security and privacy. Fine-tuning the models within a strong statistical framework can reduce biases.

            In doing so, organisations must find the right resources. Data scientists, or qualified vendors, can support them in that work, which may also be challenging.

            Overcoming the challenges of compliance with AI

            Despite initial hesitancy, analysts and other compliance professionals are positioned to gain massively by implementing GenAI. For example, teams in regulated industries – like banks, fintechs and large organisations – are often met with massive workloads and resource limits. Depending on which industry, teams may be held responsible for identifying a range of risks. These include sanctioned individuals and entities, adapting to new regulatory obligations and managing huge amounts of data – or all three.

            The process of reviewing huge quantities of potential matches can be incredibly repetitive and prone to error. If teams make mistakes and miss risks, the potential impact for firms can be significant. Both in terms of financial and reputational consequences.

            In addition, false positives – where systems or teams incorrectly flag risks and false negatives – where we miss risks that should be flagged, may come from human error and inaccurate systems. They are hugely exacerbated by challenges such as name matching, risk identification, and quantification.

            As a result, organisations within the industry quite often struggle to hire and retain staff. Moreover, this leads to a serious skills shortage amongst compliance professionals. Therefore, despite initial hesitancy, analysts and other compliance professionals stand to gain massively by implementing GenAI without needing to sacrifice accuracy.

            Generative AI – welcome support for compliance teams

            There are numerous useful ways to implemented GenAI and improve compliance processes. The most obvious is in Suspicious Activity Report (SAR) narrative commentary. Compliance analysts must write a summary of why a specific transaction or set of transactions is deemed suitable in a SAR. Long before the arrival of ChatGPT, forward thinking compliance teams were using technology based on its ancestor technology to semi-automate the writing of narratives. It is a task that newer models excel at, particularly with human oversight.

            Producing summarised data can also be useful when tackling tasks such as Politically Exposed Persons (PEP) or Adverse Media screenings. This involves compliance teams performing reviews or research on a client to check for potential negative news and data sources. These screenings allow companies to spot potential risks. It can prevent them from becoming implicated in any negative relationships or reputational damage.

            By correctly deploying summary technology, analysts can review match information far more effectively and efficiently. However, like with any technological operation, it is essential to consider which tool is right for which activity. AI is no different. Combining GenAI with other machine learning (ML) and AI techniques can provide a real step change. This means blending both generalised and deductive capabilities from GenAI with highly measurable and comprehensive results available in well-known ML models.

            Profiling efficiency with AI

            For example, traditional AI can be used to create profiles, differentiating large quantities of organisations and individuals separating out distinct identities. The new approach moves past the historical hit and miss where analysts execute manual searches limiting results by arbitrary numeric limits.

            Once these profiles are available, GenAI can help analysts to be even more efficient. The results from the latest innovations already show GenAI-powered virtual analysts can achieve, or even surpass, human accuracy across a range of measures.

            Concerns about accuracy will still likely impact the rate of GenAI adoption. However, it is clear that future compliance teams will significantly benefit from these breakthroughs. This will enable significant improvements in speed, effectiveness and the ability to respond to new risks or constraints.

            Ripjar is a global company of talented technologists, data scientists and analysts designing products that will change the way criminal activities are detected and prevented. Our founders are experienced technologists & leaders from the heart of the UK security and intelligence community all previously working at the British Government Communications Headquarters (GCHQ). We understand how to build products that scale, work seamlessly with the user and enhance analysis through machine learning and artificial intelligence. We believe that through this augmented analysis we can protect global companies and governments from the ever-present threat of money laundering, fraud, cyber-crime and terrorism.

            • Artificial Intelligence in FinTech
            • Cybersecurity in FinTech

            Author

            Dan Brightmore

            Published

            Estimated Read time

            2Mins

            The AXA Group aims to protect over 20 million customers through inclusive insurance globally by 2026

            AXA Egypt and Post for Investment (PFI), the investment arm of Egypt Post, are establishing the first micro-insurance company in Egypt. This strategic collaboration is made possible by leveraging the new insurance law and aims to revolutionise the insurance landscape in the country.

            Financial Inclusion

            This initiative is fully aligned with AXA´s conviction that postal networks play a crucial role in global financial inclusion. Over a quarter of the world’s adult population accesses formal financial services through their post office. AXA notably signed a partnership with the Universal Postal Union (UPU) in May 2024. Moreover, this collaboration with UPU includes a research program. It will showcase successful postal insurance models and the establishment of the Postal Insurance Technical Assistance Facility (PITAF). This will promote financial inclusion and risk mitigation among underserved populations. Through this partnership, AXA is pushing the boundaries of insurance to better protect all. Solidifying its dedication to inclusive insurance practices worldwide.

            The Egypt Post, who will be the main distribution channel of this JV, is a well-respected organisation. It has a strong nationwide presence, renowned for its last mile distribution capabilities and robust brand credibility. Additionally, with over 4000 branches, kiosks, and mobile trucks across all governorates, Egypt Post is an integral part of the country’s infrastructure. It caters to the population with unparalleled reach.

            “We believe in the power of collaboration to create lasting change, and this joint venture is a testament to our commitment to inclusive insurance. Together, we are revolutionising the insurance landscape in Egypt to better protect and empower communities, setting new benchmarks for millions seeking reliable and accessible insurance protection.”

            Garance Wattez-Richard

            Micro-insurance from AXA

            The product categories will include both retail and group offerings. Embedded and voluntary options will cater to diverse needs. The range of products will cover various areas. These include hospital cash, personal accident, term life, payment protection, credit life, livestock, and group protection, ensuring comprehensive coverage for the customers.

            The ambitious goal is to reach 12 million customers within the first decade of operation. Therefore, underlining the commitment to making a significant impact on the lives of Egyptians through tailored insurance solutions.

            This collaboration between AXA EssentiALL, AXA Egypt and PFI/Egypt Post marks a significant milestone in the local insurance industry. It paves the way for inclusive and impactful micro-insurance offerings that have the potential to transform the socio-economic landscape of Egypt. As the first of its kind, this micro-insurance company is poised to set new benchmarks. Furthermore, it can become a beacon of hope for millions of Egyptians seeking reliable and accessible insurance protection.

            • InsurTech

            Author

            Dan Brightmore

            Published

            4 October 2024

            Estimated Read time

            7Mins

            FinTech Strategy spoke with Ryan O’Holleran, Head of Sales, Enterprise, EMEA at Airwallex, to learn about the global payments and financial infrastructure provider

            Airwallex, a financial infrastructure provider, offers a range of services. Including multicurrency accounts, payment acceptance card issuing, foreign exchange (FX) payouts, treasury and expense management. In addition to supporting small and medium-sized businesses, the company also provides APIs and a software layer for direct access to enterprise businesses. As well as an enterprise platform product called Scale. Airwallex has found success working across various industries. It works with the likes of Bird (formerly MessageBird) to handle global accounts and backend treasury, and partners with Qantas to offer financial tools for their business partners.

            The company also enables faster and more efficient payments through its patchwork network of financial partnerships and licenses. Airwallex has experienced significant growth even during economic downturns. As of August this year, Airwallex globally processed transactions worth more than $100 billion annually and saw a 73 percent year-on-year increase. It is now focused on embedded finance solutions and global expansion.

            At Money20/20 Europe, FinTech Strategy spoke with Airwallex’s Head of Sales, Enterprise, EMEA, Ryan O’Holleran, to find out more…

            Tell us about the genesis of Airwallex?

            “Our co-founder, Jack Zhang, started a coffee company in Melbourne, Australia, which is still around today, with a few friends from university. And while they were building out this coffee shop, they were buying beans from abroad, along with supplies and packaging. They found how hard it was to actually pay for services, send funds abroad and deal with multiple currencies. So, they saw an opportunity to help streamline the financial infrastructure for small businesses. That’s when Jack and his co-founders put Airwallex together and built out an initial SME’s use case to allow multicurrency accounts and FX payouts. Since then, the business has really expanded…

            Today, Airwallex provides a set of APIs – we’re really providing financial infrastructure to move money globally. On those APIs, we also have a layer of software that we can offer direct access to enterprise businesses. The third part of this, which is kind of the new product over the last three years, is our enterprise platform product called Scale. Scale allows you to embed those financial services into a product as well as a platform or marketplace. So, you kind of think about it as a direct treasury product, APIs and a platform product.”

            Tell us about your role at Airwallex?

            “I’m originally from San Francisco, grew up in the Bay area, started in tech, did a couple of startups, and I actually got into payments via Stripe. I joined Stripe back when there were about 200 employees in San Francisco. Spent some time in Chicago and then moved to the UK initially with Stripe. I was there for about five and a half years, as we went from 200 staff to 6,000. At that point, I wanted to get back to something a little bit different. To help more cross-functioning with product and help scale businesses. The opportunity with Airwallex came along where I saw the company addressing many things my customers at Stripe were asking for.

            So, the FX piece, mass payouts, treasury, all complimented what Stripe is doing with acquiring. Since I joined the team three years ago, we’ve been scaling across EMEA. We now have offices in London, Amsterdam, Vilnius and just last year launched our office in Tel Aviv to cover Israel. And we have teams in the Americas and APAC where Airwallex was founded.”

            What are some of the key challenges financial institutions are facing that you can help them with? What problems are companies asking you to solve? In doing so, what are the challenges for Airwallex?

            “We work in different areas. This is where I think we have differentiated the business and also where I see the industry moving. If you look back over the last five, 10 years, there was this approach where you had Stripe and all the major players coming in and saying, we can do things and we can do it really well and you only need to use us, you don’t need to use a patchwork of providers. I think that is starting to shift. You see this with orchestration layers like Primer or Gravy, allowing people to be agnostic on PSPs. And then you’re seeing people think about redundancy. So, the heads of payments we’re talking to this week are looking at two or three providers because they need redundancy or want to use the best provider in each region. They don’t want to be siloed.

            Airwallex can be used in a segmented approach. So, if you just need us for payouts, you can do that. If you just need us for FX, you can do that. If you just need us for acquiring, you can do that. Or we could do that globally and you can adjust as you see fit. So, the flexibility of Airwallex I think is one of our superpowers.”

            Tell us about some of the successful partnerships Airwallex has been involved in…

            “The interesting thing about Airwallex is that since we’re providing financial infrastructure, there’s a huge variety of customers we work with. One of the local ones is Bird (a cloud communications platform that connects enterprises to their global customers). Using our software product they are creating global accounts, handling backend treasury, payroll, suppliers and more. We’ve also worked with Qantas to build out an SMB solution embedding all of the Airwallex financial services and they call it Qantas Business Money.          

            Elsewhere, Brex in the US were looking for a provider to help with their payout rails. One of the things Airwallex has done is rebuilt the Swift network via local rails. So, we have a patchwork network of financial partnerships and licences where if you are located, let’s say in the US, but you want to pay somebody out in the UK, you get access to faster payment rails having never set foot in the UK or separate rails via Europe having never set foot in the EU. So, you get this mass payoff solution of local rails, which is faster, cheaper, and more efficient than using something like Swift.”

            “I think where we’re seeing a lot of opportunities, in EMEA specifically, in B2B, vertical, SaaS, travel and marketplaces, is this embedded finance solution. It was kind of a buzzword a few years ago and now we’re actually starting to see it develop. I view it as actually embedding all of these financial services – whether it be a wallet, issued cards, or local multi-currency accounts – and being able to monetize that. So, we’re seeing this with a lot of our customers actually wanting to white label our products, embed that and bring payments on platform.”

            And what’s next for Airwallex? What future launches and initiatives are you particularly excited about?

            “The growth of Airwallex, specifically on a global scale, over the last few years is one thing I’m very proud of because it’s happened during one of the worst economic downturns we’ve experienced. FinTech was almost retracting in terms of budgets and investments. You’re starting to see the tide turn, but we were able to grow over 100 percent year on year, through some of the toughest times for business. And now we’re really starting to see that pick up because the businesses, who actually decided this is going to be a building year for us now, they’re going live, they’re accelerating, they’re growing.

            And so we’re seeing the ROI of that investment. It’s a testament to the global financial infrastructure we’ve built. Meanwhile, Airwallex became cash flow positive in 2023. It now processes more than $100 billion in annualised transaction volume. The company now employs over 1,500 people worldwide working across 23 international offices.”

            Why Money20/20? What is it about this particular event that makes it the perfect place to showcase what you do? How has the response been to Airwallex?

            “The great thing about Money20/20, here in Europe, and in Asia and the US, is the good division between buyers and sellers. So, you have all these service providers like Airwallex, Amex, etc… And then you have the Heads of Payments from companies like Booking.com, Vinted and SumUp who are coming here with their teams to meet with providers. If you think about that from a sales perspective, those meetings are very hard to get outside of this environment. But over a week you get 15 different meetings each day that would normally take months to arrange. So, the ROI from this week is really powerful just from being able to have these conversations. Three years ago, we first came to suss out the event and as we’ve grown the response has grown. People are being proactive and keen to engage with us which is exciting to see.”

            Author

            Hugo Farinha

            Published

            2 October 2024

            Estimated Read time

            5Mins

            Hugo Farinha, Co-founder and CTO at Virtuoso QA on why AI is driving organisational change across financial services

            We’ve seen an enormous amount of discussion concerning all aspects of AI since the emergence of Chat GPT made it headline news. However, most articles and conversations focusing on its business impact seem to wilfully ignore the ‘elephant in the room’. Namely, the inevitable organisational change AI will usher in, especially for employees.

            AI technology driving change

            To ignore change is folly, and likely to have the exact opposite effect that businesses and AI technology vendors want. We can’t pretend workforces won’t be disrupted by such a seismic technological advance. Certain job roles will become obsolete. Business leaders can’t run the risk of creating a culture of fear and uncertainty among employees who are unlikely to be fooled.

            It’s true AI could lead to leaner operations, particularly in insurance and finance companies, with fewer employees needed for routine tasks, but only half the story. Smart businesses will almost certainly reinvest cost savings into new growth areas that require specific human talent. Companies that maintain a strong human element in customer service and personalised offerings will differentiate themselves in a crowded market. The rise in AI-driven, agile companies will create faster market shifts and greater competition.

            While AI has the potential for productivity and efficiency gains, and even to do the same with less if needed, I actually don’t predict major job culls in the next few years. AI is particularly good at data processing and data analytics, in insurance for example. So, when more data can be processed and analysed, human intervention can make better informed decisions as a result. In the short to medium term, data analysis and decision making will remain firmly in the human realm. But powered by AI.

            The Future for Artificial Intelligence

            Meanwhile, the technology is still evolving, and organisations need to build a model that layers over the top of AI – powered by it, rather than replaced by it. Despite the hype, we are still a long way from AI becoming an entity that can lead, implement and operate itself to a purposeful end. But it will increasingly power applications overlaid by strategic, human-led frameworks.

            To achieve this, leaders must bring their teams with them on the journey. In the field of testing for example, developers have traditionally written code as part of their role. This is a very time consuming and laborious task. Historically skills gaps have led to delays in progress. But the ability to ‘outsource’ to AI has freed up the time of those developers to focus on the purpose of that code in relation to the product. And, ultimately, the customer. Similarly, leaders in all fields need a broader understanding of AI use cases such as these to make effective strategic decisions. For example, on hiring. Understanding when to bring in more people and when to bring in new technology to complement the skills of your existing team means understanding AI’s strategic implications, technical capabilities and limitations.

            An Evolving Job Market

            From the perspective of the employee, the job market will continuously evolve alongside AI advancements. It will require ongoing adaptation and learning to stay relevant. Skills such as empathy, communication, and negotiation will remain vital. These are differentiators and difficult for AI to replicate. Understanding AI tools and data analysis will be increasingly important, even for non-technical roles. The ability to adapt to new technologies and continuously learn will be essential. Moreover, as AI becomes more integrated, the need for professionals who understand the ethical implications and regulatory requirements will grow exponentially.

            Driving growth and job creation in this new world will require a different mindset to the current received wisdom. From both employees and leaders. In addition to the advances and changes already discussed, AI also has the potential to level the playing field, enabling smaller or newer companies to compete more effectively with, and even seriously threaten, established players. With many traditional barriers to entry such as burdensome start-up costs removed, new business models are likely to emerge. In much the same way as they did in the early days of the internet. Investors will be on the lookout for the next ‘giant killer’.

            This will create opportunities for those with the foresight to upskill, as well as for those looking to start their careers. Although those opportunities and the jobs of tomorrow may not yet be completely clear. What is clear, however, is that established businesses cannot afford to be complacent. Change is inevitable and empires can be toppled overnight by technology as disruptive as AI. By embracing it early, leaders in those businesses will have the opportunity to spot and fix the gaps and redundancies in their business models that the technology and its capabilities exposes before the market does so more painfully and publicly.

            Our mission is to enable and lead the world’s quality-first revolution. QA tools haven’t kept up with the demands of the testing world. Virtuoso is here to deliver with AI-powered, low-code/no-code test automation to support the modern business.

            “Virtuoso technology represents the foundation for software quality in the digital world, and we are proud to be a critical, guiding force in the era of AI.”

            Darren Nisbet, CEO, Virtuoso

            • Artificial Intelligence in FinTech

            Author

            Cullen Zandstra

            Published

            27 September 2024

            Estimated Read time

            4Mins

            Cullen Zandstra, CTO at FloQast on mitigating the risks of AI to deliver benefits to financial services

            There’s a lot of buzz around Generative AI (GenAI). What’s not always heard beneath the noise are the very real and serious risks of this fast-developing AI tech. Let alone ways to mitigate these emerging threats.

            Currently, one quarter (26%) of accounting and bookkeeping practices in the UK have now adopted GenAI in some capacity. That figure is predicted to grow for many years to come.

            With this in mind, and as we hit the crest of the GenAI hype cycle, it’s critically important that leaders focus closely on the potential risks of AI deployment. They need to proactively prepare to mitigate them, rather than picking up the pieces after an incident.

            Navigating the risky transition to AI

            The benefits of AI are well-proven. For finance teams, AI is a powerup that unlocks major performance and efficiency boosts. It significantly enhances their ability to generate actionable insights swiftly and accurately, facilitating faster decision-making. AI isn’t here to take over but to augment the employees’ capabilities. Ultimately improving leaders’ trust in the reliability of financial reporting.

            One of the most exciting aspects of AI is its potential to enable organisations to do more with less. Which, in the context of an ongoing talent shortage in accounting, is what all finance leaders are seeking to do right now. By automating routine tasks, AI empowers accountants to focus on higher-level analysis and strategic initiative, whilst drawing on fewer resources. GenAI models can help to perform routine, but important tasks. These include producing reports for key stakeholders and ensuring critical information is effectively and quickly communicated. It enables timely and precise access to business information, helping leaders to make better decisions.

            However, GenAI also represents a new source of risk that is not always well understood. We know that threat actors are using GenAI to produce exploits and malware. Simultaneously levelling up their capabilities and lowering the barrier of entry for lower-skilled hackers. The GenAI models that power chatbots are vulnerable to a growing range of threats. These include prompt injection attacks, which trick AI into handing over sensitive data or generating malicious outputs.

            Unfortunately, it’s not just the bad guys who can do damage to (and with) AI models. With great productivity comes great responsibility. Even an ambitious, forward-thinking, and well-meaning finance team could innocently deploy the technology. They could inadvertently make mistakes that cause major damage to their organisation. Poorly managed AI tools can expose sensitive company and customer financial data, increasing the risk of data breaches.

            De-risking AI implementation

            There is no technical solution you can buy to eliminate doubt and achieve 100% trust in sources of data with one press of a button. Neither is there a prompt you can enter into a large language model (LLM).

            The integrity, accuracy, and availability of financial data are of paramount importance during the close and other core accountancy processes. Hallucinations (another word for “mistakes”) cannot be tolerated. Tech can solve some of the challenges around data needed to eliminate hallucinations – but we’ll always need humans in the loop.

            True human oversight is required to make sure AI systems are making the right decisions. We must balance effectiveness with an ethical approach. As a result, the judgment of skilled employees is irreplaceable and is likely to remain so for the foreseeable future. Unless there is a sudden, unpredicted quantum leap in the power of AI models. It’s crucial that AI complements our work, enhancing rather than compromising the trust in financial reporting.

            A new era of collaboration

            As finance teams enhance their operations with AI, they will need to reach across their organisations to forge new connections and collaborate closely with security teams. Traditionally viewed as number-crunchers, accountants are now poised to drive strategic value by integrating advanced technologies securely. The accelerating adoption of GenAI is an opportunity to forge links between departments which may not always have worked closely together in the past.

            By fostering a collaborative environment between finance and security teams, businesses can develop robust AI solutions. They can boost efficiency and deliver strategic benefits while safeguarding against potential threats. This partnership is essential for creating a secure foundation for growth.

            AI in accountancy: The road forward

            The accounting profession stands on the threshold of an era of AI-driven growth. Professionals who embrace and understand this technology will find themselves indispensable.

            However, as we incorporate AI into our workflows, it is crucial to ensure GenAI is implemented safely and does not introduce security risks. By establishing robust safeguards and adhering to best practices in AI deployment, we can protect sensitive financial information and uphold the integrity of our profession. Embracing AI responsibly ensures we harness its full potential while guarding against vulnerabilities, leading our organisations confidently into the future.

            Founded in 2013, FloQast is the leading cloud-based accounting transformation platform created by accountants, for accountants. FloQast brings AI and automation innovation into everyday accounting workflows, empowering accountants to work better together and perform their tasks with greater efficiency and accuracy. Now controllers and accountants can spend more time delivering greater strategic value while enjoying a better work-life balance.

            • Artificial Intelligence in FinTech
            • Cybersecurity in FinTech

            Author

            Russ Rawlings

            Published

            25 September 2024

            Estimated Read time

            4Mins

            Russ Rawlings, RVP, Enterprise, UK&I at Databricks, on the future of AI in FinTech

            Strict regulation, along with time and cost restraints, means financial services must take a measured approach to technological advancements. However, with the emergence of GenAI, particularly large language models (LLMs), organisations have an opportunity to maximise the value of their data to streamline internal operations and enhance efficiencies. 

            Embracing GenAI has never been more important for organisations looking to stay ahead of the curve. 40-60% of the global workforce will be impacted by the growth of AI. Moreover, global adoption of GenAI could add the equivalent of $2.6tn to $4.4tn in value annually to global industries. The banking sector stands to gain between $200-340 billion.

            But whilst the financial services industry can gain incredible benefits from GenAI, adoption is not without its challenges. Financial organisations must prioritise responsible data management. They must also navigate strict privacy regulations and carefully curate the information they use to train their models. But, for companies that persevere through these obstacles, the benefits will be substantial. 

            Building customised LLMs for financial services 

            Consumer chatbots have brought GenAI to the mainstream. Meanwhile, the true potential of this transformative technology lies in its ability to be tailored to the unique needs of any organisation, in any industry. Including the financial sector. 

            Risk assessment, fraud prevention, and delivering personalised customer experiences are some of the use cases of custom open source models. Created using a company’s proprietary data, these models ensure relevant and accurate results. And are more cost-effective due to their smaller datasets. For instance, banks can use a customised model to seamlessly analyse customer behaviour and flag up any suspicious or fraudulent activities. Or, a model can leverage sophisticated algorithms to assess an individual’s eligibility for a loan.

            Another huge benefit of these tailored systems is trust and security. Deploying a custom open-source model eliminates the need to share sensitive information with third parties. This is crucial for organisations operating within such a highly regulated industry. This approach also democratises the training of custom models. Furthermore, it allows organisations to harness the power of GenAI whilst retaining control and compliance.

            Using data intelligence to boost AI’s impact

            To truly harness the power of GenAI, organisations must cultivate a deep understanding of data across the entire workforce. Every employee, regardless of how technical they are, must grasp the importance of proper data storage. Also how data can be used to improve decision-making.

            Organisations can use a data intelligence platform to help implement this. Built on a lakehouse architecture, a data intelligence platform provides an open, unified foundation for all data and governance. It operates as a secure end-to-end solution tailored to the specific needs of the financial services industry. By adopting such a platform, businesses can eliminate their reliance on third party solutions for data analysis. They can create a streamlined approach to data governance and accelerate data-driven outcomes. Users across all levels of the business can navigate their organisation’s data, using GenAI to uncover important insights.

            The future of AI in the financial sector

            The path to success lies in embracing GenAI as a canvas for crafting bespoke solutions. Whilst no two financial institutions are exactly the same, the industry’s tools must strike a delicate balance between supporting specific use cases and addressing broader requirements, Customised, open source LLMs and data intelligence platforms hold the key, sparking transformative change across the sector. These tailored solutions will empower financial businesses to integrate cutting-edge innovations and ensure  security, governance and customer satisfaction. Organisations that embrace this change will not only gain a competitive edge, but also pave the way for larger transformations, re-shaping the financial landscape and setting new standards for the industry.

            Databricks is the data and AI company with origins in academia and the open source community. Databricks was founded in 2013 by the original creators of Apache Spark™, Delta Lake and MLflow. As the world’s first and only lakehouse platform in the cloud, Databricks combines the best of data warehouses and data lakes to offer an open and unified platform for data and AI.

            • Artificial Intelligence in FinTech

            Author

            Pat Bermingham

            Published

            16 September 2024

            Estimated Read time

            8Mins

            Pat Bermingham, CEO of B2B digital payment specialist Adflex, asks what impact will Artificial Intelligence really have on B2B payments?

            Visit any social media newsfeed and countless posts will tell you AI means “nothing will ever be the same again”. Or even that “you’re doing AI wrong”. The volume of hyperbolic opinions being pushed makes it almost impossible for businesses to decipher between hype and reality.

            This is an issue the European Union’s ‘AI Act’ (the Act), which came into force on 1 August 2024, aims to address. The Act is the world’s first regulation on artificial intelligence. It sets out how to govern the deployment and use of AI systems. The Act recognises the transformative potential AI can have for financial services, while also acknowledging its limitations and risks.

            Within the debate about AI in financial services, B2B payments are an area where AI has huge potential to accelerate digital innovation. Let’s go beyond the hype to provide a true perspective on what AI really means for B2B payments specifically.

            Understanding what AI is, and what it isn’t

            AI is a system or systems that can perform tasks that normally require human intelligence. It incorporates machine learning (ML). ML has been used by developers for years to give computers the ability to learn without being explicitly programmed. In other words, the system can look at data and analyse it to refine functions and outcomes.

            A newer part of this is ‘deep learning’, which leverages multi-layered neural networks. This simulates the complex decision-making power of our brains. The deep learning benefits outlined later in this article are based on Large Language Models (LLMs). LLMs are pre-trained on representative data (such as payment/transaction/tender data). Deep learning AI does not just look at and learn patterns of behaviour from the data. It is becoming capable of making informed decisions based on this data.

            Before we explore what this means for B2B payments, let’s make one caveat clear: human supervision is still needed to ensure the smooth running of operations. AI is a supporting tool, not a single answer to every question. The technology is still maturing. You cannot hand over the keys to your B2B payments process quite yet. Manual processes will retain their place in B2B payments. AI tools will help you learn, adapt and improve more quickly and at scale.

            The AI Act – what you need to know

            The Act attempts to categorise different AI systems based on potential impact and risk. The two key risk categories include:

            1. Unacceptable risk – AI systems deemed a threat to people, which will be banned. This includes systems involved in cognitive behavioural manipulation, social scoring, and real-time biometric identification.
            2. High risk – AI systems that negatively affect safety or fundamental rights. High-risk AI systems will undergo rigorous assessment and must adhere to stringent regulatory standards before being put on the market. These high risk systems will be divided into two further categories:
            3. AI systems that are used in products falling under the EU’s product safety legislation, including toys, aviation, cars, medical devices and lifts.
            4. AI systems falling into specific areas that will have to be registered in an EU database.

            The most widely used form of AI currently, ‘generative AI’ (think ChatGPT, Copilot and Gemini), won’t be classified as high-risk. However, it will have to comply with transparency requirements and EU copyright law.

            High-impact general-purpose AI models that might pose systemic risk, such as GPT-4o, will have to undergo thorough evaluations. Any serious incidents would have to be reported to the European Commission.

            The Act aims to become fully applicable by May 2026. Following consultations, amendments and the creation of ‘oversight agencies’ in each EU member state. Though, as early as November 2024, the EU will start banning ‘unacceptable risk’ AI systems. And by February 2025 the ‘codes of practice’ will be applied. 

            So, with the Act in mind, how can AI be used in a risk-free manner to optimise B2B payments?

            AI will transform payment data analysis

            Today’s B2B payment platforms are not one-size-fits-all solutions; instead, they provide a toolkit for businesses to customise their payment interactions.

            AI-based LLMs and ML can be used by payment providers to rapidly understand and interpret the extensive data they have access to (such as invoices or receipts). By doing this, we gain insights into trends, buyer behaviour, risk analysis and anomaly detection. Without AI, this is a manual, time consuming task.

            One tangible benefit of this data analysis for businesses comes from combining payment data with knowledge of a wide range of vendors’ skills, products and/or services. AI could then, for example, identify when an existing supplier is able to supply something currently being sourced elsewhere. By using one supplier for both products/services, the business saves through economies of scale.

            Another benefit of data analysis comes from payment technology experts. Ours have been training one service to extract data from a purchase order or invoice, to flow level 3 data, which is tax evident in some territories. This automatically provides the buyer with more details of the transaction, including relevant tax information, invoice number, cost centre, and a breakdown of the products or service supplied. This makes it easy and straightforward to manage tax reporting and remittance, purchase control and reconciliation.

            AI-driven data analysis isn’t just a time and money-saver, however. It also adds new value by enabling providers to use the data to create hyper-personalised payment experiences for each buyer or supplier. For example, AI and ML tools could look out for buying and selling opportunities, and perform a ‘matchmaking supplier enablement service’ that recommends the best payment methods – and the best rates – for different accounts or transactions. The more personalised a payment experience is, the happier the buyer and more likely they are to (re)purchase.

            Efficient data flows mean stronger cash flows

            Another practical application of AI is to help optimise cash management for buyers. This is done by using the data to determine who is strategically important and when to pay them. It could even recommend grouping certain invoices together for the same supplier, consolidating them into one payment per supplier, reducing interchange fees and driving down the cost of card acceptance.

            AI can also perform predictive analysis for cash flow management, rapidly analysing historical payment data to predict cash flow trends, allowing businesses to anticipate and address potential challenges proactively. This is particularly valuable in the current economic climate where cashflow is utterly vital.

            By extracting value-added, tax evident data from a purchase order or invoice, AI can rapidly analyse invoices and receipts to enable efficient, accurate automation of the VAT reclaims process. Imagine: the time comes for your finance team to reclaim VAT on recent invoices and receipts, but they don’t have to manually go through every receipt or invoices and categorise them into a reclaim pile or not reclaimable. It sounds like a dream but it will be the reality for business everywhere: AI does the heavy lifting and humans verify it, saving significant time and resources.

            Quicker, more accurate invoice reconciliation

            The third significant benefit of AI is automated invoice reconciliation. By identifying key information from an invoice and recognising regular payees, AI can streamline and automate the review process. This has the potential to significantly speed up transactions and enable more efficient payment orchestration.

            Binding together all supporting paperwork, such as shipping, customs, routes, and JIT (just-in-time) requirements can also be done by AI, and it’s likely to be less prone to human error.

            This provides an amazing opportunity to make B2B payments faster, reduce costs and increase efficiency.  Businesses know this: 44% of mid-sized firms anticipate cost savings and enhanced cash flow as a direct result of implementing further automation within the next three years. According to American Express, 48% of mid-sized firms expect to see payment processes accelerate, with more reliable payments and a broader range of payment options emerging.

            When. Not if.

            There are significant opportunities to leverage AI in B2B payment processes, making it do the heavy lifting. It is, however, essential to view these opportunities with a balanced understanding of the limitations of AI.

            While all the opportunities for AI in B2B payments outlined here are based on relatively low-risk AI systems, human oversight of these systems is still essential. However, with all the freed-up time and resource achieved through the implementation of AI, this issue can be avoided.

            AI in B2B payments is not an if, but a when. The question is, when will you make the jump, hand in hand with technology, rather than fearing it or passing full control over to it.

            In order to grow, it is essential for users to see the tangible benefits. For example, by enhancing efficiencies in account payable (AP), businesses can reallocate time and resource previously spent in AP to other areas. Early adopters are starting to test the water but only time will tell how much of an impact AI will make.

            Most businesses will likely wait for the early adopters to fail, learn and progress. If something goes wrong in B2B payments, it can have a huge impact on individuals, businesses and economises. Only when the risk is clearly defined and manageable will AI truly become the gamechanger in B2B payments that all the hype claims.

            “Adflex has been at the heart of the B2B fintech revolution from the beginning. We are known for fostering innovation and helping companies harness the power of digital payments. Our technology and expertise bring together buyers and suppliers to make transactions fast, cost-effective and straightforward to manage. We take the pain out of the supply chain by delivering seamless and secure payment integration that adds value to both buyers and merchants.”

            • Artificial Intelligence in FinTech
            • Digital Payments

            Author

            Michael Donnelly

            Published

            12 September 2024

            Estimated Read time

            3Mins

            Michael Donnelly, Head of Client Success at BlueFlame AI, on how to prepare your firm to attract and retain the next generation of AI talent

            In the fast-paced world of financial services, a new generation is stepping in with high expectations for generative artificial intelligence (AI) in the workplace. Recently, BlueFlame AI conducted a specialised training session for one of our private equity clients, aimed at their newly hired summer intern class. The experience was eye-opening for us. Furthermore, it also provided a great lesson in the growing importance of AI in the industry and the expectations today’s young professionals have as they enter the workforce

            AI & LLMs

            The comprehensive training session covered vital areas such as AI and Large Language Models (LLMs), a review of the most popular use cases the industry has adopted, and hands-on practical training in prompt engineering. Moreover, our goal was to show this next generation the skills they’ll need to leverage these tools effectively. New roles could revolutionise alternative investment management processes like due diligence, market analysis, and portfolio management.

            We also used this as an opportunity to survey the group about their experience of and expectations for AI use in the workplace – and it yielded some striking insights. A significant 50% of the interns reported using ChatGPT daily, with 83% utilising it at least weekly. Furthermore, these numbers suggest young professionals expect these tools to be available to them in their professional lives. In the same way they are available in their personal lives and set to become as commonplace as traditional software in the workplace. The interns’ expectations regarding AI’s impact on their work efficiency are even more telling. An overwhelming 94% believe these tools will enhance their productivity, indicating strong faith in the technology’s potential to streamline tasks and boost performance.

            These high expectations have key implications for employers. A significant 89% of interns expect their employers to provide enterprise-grade AI/LLM access. This statistic is a wake-up call for companies that have yet to invest in AI technologies, highlighting the need to stay competitive not just in terms of products and services but also in workplace technology provision.

            Talent Acquisition & Retention

            Perhaps most important is AI’s potential impact on talent acquisition and retention. One-third (33%) of interns surveyed indicated they would reconsider their choice of employer if they didn’t offer access to enterprise-grade AI/LLM tools. A response that could throw a serious wrench into any Financial Services firm’s hiring plans.

            The message is clear for businesses looking to stay ahead of the curve when it comes to supporting their employees. Investing in AI technologies and training is no longer optional. Firms must be ready to meet the expectations of the incoming workforce. They need to provide them with the best technology to maintain a competitive edge in an increasingly AI-driven business landscape. Companies that embrace AI and provide their employees with the tools and training to harness its power will likely see significant productivity, innovation, and talent retention advantages.

            AI Revolution

            Private and public investment firms stand to benefit greatly from this AI revolution. As this new generation brings its enthusiasm and expectations for technology tools into the workplace, firms that are prepared to meet these expectations will be better positioned to tap into fresh perspectives, drive innovation and reap significant efficiency and productivity gains. And if firms can take a proactive approach to training and commit to developing a forward-thinking, AI-enabled workforce, they will be able to enhance their teams’ capabilities and shape the future of work in the financial sector.

            Generative AI and the workplace expectations it has created mark a new paradigm in the market. The next generation of professionals is not just ready for AI – they’re demanding it. Firms that recognize and act on this trend will be well-positioned to lead the pack when it comes to innovation, efficiency and talent acquisition.

            Founded in 2023 BlueFlame AI is the only AI-native, purpose built, LLM-agnostic solution for Alternative Investment Managers.

            • Artificial Intelligence in FinTech

            Author

            Dan Brightmore

            Published

            4 September 2024

            Estimated Read time

            4Mins

            Financial institutions are increasingly turning to artificial intelligence (AI) to gain a competitive edge. AI tools streamline operations, improve customer…

            Financial institutions are increasingly turning to artificial intelligence (AI) to gain a competitive edge. AI tools streamline operations, improve customer support, and automate processes, making banks more efficient and customer-focused.

            Research by McKinsey shows that over 20 percent of an organisation’s digital budget goes towards AI. The study links significant investments in AI to a 10-20 percent increase in sales. AI will play a central role in boosting efficiency, customer service, and overall banking productivity.

            Introduction to AI in Personalised Banking

            Delivering personalised experiences is crucial for customer satisfaction and retention. AI helps banks achieve this by collecting and analysing customer data. This data is then used to create recommendations, product offerings, and even financial advice tailored to each customer’s needs.

            AI tools can optimise workflows through a technique called prescriptive personalisation, using past data to predict future behaviour. Real-time personalisation takes this further, incorporating current information alongside historical data. 

            This allows banks to deliver highly customised virtual assistants and real-time recommendations powered by natural language processing (NLP) models. These AI-powered assistants not only build trust and user engagement but also simplify interactions with the bank.

            Tool 1: Predictive Analytics

            Predictive analytics, powered by AI tools, unlock a new level of customer personalisation in banking. These tools analyse data to uncover hidden patterns and trends that traditional methods might miss. This knowledge reveals sales opportunities, possibilities for cross-selling, and ways to improve efficiency.

            Predictive analytics use past data to forecast customer behaviour and market trends. This foresight allows banks to tailor marketing strategies and sales approaches to meet changing customer needs and capitalise on emerging opportunities.

            Tool 2: Chatbots and Virtual Assistants

            One key advantage of chatbots is their constant availability. This is especially helpful for customers who need assistance outside of regular operating hours.

            AI chatbots learn from every interaction, improving their ability to understand and meet individual customer needs. By integrating chatbots into banking apps, banks can provide personalised banking experiences and recommend financial products and services that fit a customer’s specific situation.

            Erica, a virtual assistant developed by Bank of America, handles tasks like managing credit card debt and updating security information. With over 50 million requests handled in 2019 alone, Erica demonstrates the potential of chatbots as efficient assistants for customers.

            Tool 3: Recommendation Engines

            Banks use AI tools to analyse vast amounts of customer data, including purchases, browsing habits, and background information. This deep understanding helps banks recommend products that truly fit each customer’s needs.

            These personalised recommendations extend beyond credit card suggestions. AI can identify potential investments or loans that align with a customer’s financial goals. By providing customers with relevant information, banks allow them to make informed financial decisions. 

            Tool 4: Sentiment Analysis with AI

            AI sentiment analysis translates written text into valuable insights. AI uses NLP to understand emotions and opinions in written communication. By examining things like customer feedback, emails, and social media conversations, banks gain a much clearer picture of customer sentiment.

            Tool 5: Voice Recognition

            AI-powered voice assistants offer a convenient way to handle everyday banking tasks. From checking balances to paying bills, all a customer needs are simple voice commands.

            These assistants use NLP to understand customer requests and respond accurately. Voice authentication adds another layer of security by verifying customer identity during transactions.

            Tool 6: Process Automation

            Robotic Process Automation (RPA) automates repetitive tasks, boosting operational efficiency. It tackles up to 80 percent of routine work and frees up workers for more valuable tasks requiring human judgement.

            RPA bots can handle tasks like issuing and scheduling invoices, reviewing payments, securing billing, and streamlining collections – all at once. NLP empowers these bots to extract information from documents, simplifying application processing and decision-making. 

            Tool 7: Facial Recognition with AI

            Facial recognition helps banks verify customer identities during tasks like opening accounts, accessing information, and making transactions. Compared to traditional passwords, facial recognition offers stronger security and greater convenience. It eliminates the need for remembering complex passwords or worrying about stolen credentials, making banking interactions smoother and less error-prone. This technology also helps prevent fraud by identifying attempts to impersonate real customers.

            Capital One AI Case Study

            Capital One demonstrates how AI can personalise banking. Their AI assistant uses NLP to understand customer questions and provide immediate answers. Capital One also incorporates AI into fraud detection. Machine learning and predictive analytics help pinpoint suspicious credit card activity to strengthen security measures.

            Conclusion

            AI tools offer a significant opportunity for banks to improve customer experiences and achieve long-term success. By personalising banking services with AI, banks can better meet individual customer needs. This leads to higher satisfaction and loyalty, which enhances the bank/customer relationship.

            AI has the potential for an even greater impact. As banks integrate more advanced AI capabilities, they can create even more engaging and personalised interactions. This focus on ‘hyper-personalisation’ could be the next big step for financial institutions to set them apart in a competitive market.

            • Artificial Intelligence in FinTech

            Author

            Dan Brightmore

            Published

            27 August 2024

            Estimated Read time

            4Mins

            Banks are adopting artificial intelligence (AI) technology to provide more personalised experiences. A study by the AI Development Company projects…

            Banks are adopting artificial intelligence (AI) technology to provide more personalised experiences. A study by the AI Development Company projects that 75 percent of financial institutions will invest $31 billion in integrating AI into their existing systems by 2025. The trend is driven by customer demand for faster and more convenient banking options.

            AI excels at analysing enormous amounts of data. This lets banks find patterns and trends to personalise customer service and boost efficiency. For example, AI-powered chatbots offer 24/7 help with basic questions, freeing up customer service staff for trickier issues. AI can also analyse customer behaviour to predict their needs and suggest relevant services or support, from personalised investment options to flagging suspicious account activity.

            Benefit 1: Increased Efficiency

            Long wait times and impersonal interactions often leave customers frustrated with traditional bank customer service. Fortunately, AI streamlines the experience by providing quick and accurate answers. It eliminates the need to navigate complex phone menus.

            AI personalises interactions and saves customers from endless button-pressing and long hold times. AI in customer service can also analyse vast amounts of customer data. The data helps banks anticipate customer needs and recommend tailored solutions, preventing problems before they arise. This results in higher customer satisfaction and a smoother banking experience.

            Benefit 2: Personalisation

            AI can analyse vast amounts of customer data, including purchases and browsing habits, to create detailed customer profiles. These profiles help banks recommend relevant products and services that fit individual needs.

            For instance, a customer who often pays bills online might be recommended a new budgeting tool. Similarly, someone who regularly saves for travel could receive information about travel insurance or currency exchange. These personalised suggestions can come through various channels, like the bank’s website, email alerts, or chatbots.

            Benefit 3: Cost Savings

            Cost savings are a major advantage of AI-powered customer service in banking. One key way AI achieves this is through automation. Chatbots powered by AI can handle many routine customer inquiries, freeing up human agents for complex issues. This reduces labour costs while also improving response times.

            AI also helps with better staffing management. It can analyse past data to predict how many calls are coming in. Banks can then ensure they have the right number of agents available, avoiding overstaffing or understaffing that can significantly impact costs.

            Benefit 4: 24/7 Support

            Traditionally, reaching a support agent often meant waiting on hold during peak hours. However, AI in customer service is transforming the industry by offering immediate assistance through chatbots. These virtual assistants provide instant support the moment a customer reaches out.

            Unlike human agents with limited working hours, chatbots are available 24/7. This ensures customers get help whenever they need it, regardless of location or time zone. This is especially valuable in the globalised world, where customers might need support outside of regular business hours.

            A great example of this success is Photobucket, a media hosting service. After implementing a chatbot, they offered 24/7 support to international customers. This results in a three percent increase in customer satisfaction scores along with a 17 percent improvement in resolving issues on the first try.

            Benefit 5: Multilingual Support

            AI-powered chatbots offer multilingual support, breaking down language barriers and creating a positive banking experience. These chatbots can figure out a customer’s preferred language at the start of a conversation. This ensures clear communication, no matter what language the customer speaks.

            Conclusion

            A study by Global Market Insights predicts the conversational AI market will reach $57.2 billion by 2032. This technology is making big strides in banking, particularly by automating routine tasks and inquiries. By taking care of these repetitive tasks, AI frees up human agents to focus on more complex customer issues. This improves efficiency and helps banks manage their operating costs. A streamlined customer service experience builds trust and loyalty, which can lead to business growth for financial institutions.

            • Artificial Intelligence in FinTech

            Author

            Dan Brightmore

            Published

            11 August 2024

            Estimated Read time

            4Mins

            The growing complexity of financial markets presents new challenges for asset and wealth managers. Therefore, to navigate this evolving environment,…

            The growing complexity of financial markets presents new challenges for asset and wealth managers. Therefore, to navigate this evolving environment, many are embracing artificial intelligence (AI) for assistance with investment decisions. AI acts as a powerful tool, improving efficiency and effectiveness across various aspects of asset management.

            From analysing market trends to building diversified portfolios, AI’s strength lies in processing massive amounts of data. Furthermore, it uncovers hidden patterns empowering managers to make data-driven investment choices across financial services.

            Introduction to AI in Asset Management

            Asset management involves managing investment portfolios for individuals, institutions, and businesses. This includes stocks, bonds, real estate, and other financial assets. The main goal is to grow value over time while minimising risk and meeting client goals.

            AI is transforming asset management with its data processing and analytics capabilities. Additionally, AI algorithms can quickly analyse massive amounts of financial data, market trends, and economic indicators. This helps uncover hidden patterns and connections that human analysts might miss. A data-driven approach empowers asset managers to make better investment decisions and develop more accurate market forecasts.

            Portfolio Management

            AI is transforming asset management by offering powerful tools for better decision-making. Moreover, machine learning (ML), AI analyses vast amounts of historical market data to identify patterns and predict future trends, providing valuable insights for building portfolios.

            Natural language processing (NLP) lets computers understand human language. NLP can unlock information from unstructured sources like news articles, social media, and analyst reports. The algorithms then analyse sentiment and extract key information that feeds into portfolio decisions.

            AI optimisation algorithms help construct optimal portfolios. These algorithms consider risk tolerance, return goals, and investment limitations. By using these tools, portfolio managers can create portfolios designed to maximise returns while minimising risk.

            Risk Management

            AI is changing how investment decisions are made. The AI algorithms can analyse massive amounts of historical market data and complex risk models.

            The analysis provides a deeper understanding of individual asset risk and the overall portfolio’s exposure. With this knowledge, investment managers can proactively identify potential risks and develop strategies to lessen them.

            AI offers real-time risk monitoring. An AI-powered system continuously tracks portfolio performance, alerting managers to any significant changes in risk. This allows for swift adjustments as market conditions evolve.

            Automated Trading

            Traditional automated trading tools execute trades based on pre-programmed instructions from human traders. These tools function within the parameters set by the user and can’t analyse markets on their own.

            AI offers truly independent systems with tools that can analyse markets using technical and fundamental analysis with minimal human input.

            AI uses sentiment analysis, ML, and complex algorithms to process vast amounts of information and identify trends. This data-driven approach removes the emotional bias that can affect human traders.

            Case Studies

            The asset management industry is seeing a rise in firms using AI to improve performance. A recent example is Deutsche Bank’s collaboration with NVIDIA. This multi-year project aims to integrate AI across their financial services. This includes virtual assistants for easier communication and AI-powered fraud detection. The bank expects faster risk assessments and improved portfolio optimisation.

            Morgan Stanley is also making strides in AI adoption. Partnering with OpenAI, their financial advisors now have access to a massive research library at high speed. Advisors can explore client portfolio strategies and find relevant information in seconds, leading to better-informed advice.

            Future Prospects

            A PwC report predicts artificial intelligence will significantly boost global GDP, contributing up to $15.7 trillion in 2030. This advancement could reshape asset management in the coming years, leading to entirely new business models and investment strategies.

            One future possibility involves fully automated investment platforms powered by AI. These platforms would manage investment portfolios with minimal human involvement and use real-time data analysis to create personalised investment plans.

            Moreover, AI could pave the way for more dynamic investment strategies that respond to market changes. By constantly analysing market conditions, AI can automatically adjust investment portfolios to optimise returns and minimise risks. This could lead to more resilient and adaptable investment systems that are better equipped to navigate various market environments.

            • Artificial Intelligence in FinTech

            Author

            Published

            5 August 2024

            Estimated Read time

            3Mins

            Data analysis is critical for predicting risks and returns. The ever-growing size of data has overwhelmed human capacity. This is where artificial intelligence (AI) comes in.

            AI is transforming the financial sector by automating routine tasks and efficiently analysing large and complex data sets. It can analyse vast amounts of data with unprecedented speed. The instant but comprehensive insights that this capability provides lead to significantly improved accuracy.

            Introduction to AI in Financial Forecasting

            Financial forecasting can be challenging for smaller businesses. They often rely on assumptions and human judgement. This can result in inaccuracy, especially when unexpected events occur.

            AI can analyse massive amounts of data to find hidden patterns that drive revenue. It automates routine tasks and enables a more detailed analysis than humans can achieve on their own.

            Predictive Analytics

            By automating data processing and interpretation, AI empowers financial teams to make informed decisions based on a strong analytical foundation. It goes beyond basic analysis by employing advanced algorithms and machine learning (ML) to extract valuable insights from data.

            This not only improves the accuracy of forecasts but also unlocks a deeper understanding of market complexities that were previously out of reach.

            Risk Assessment

            AI algorithms use advanced data processing to spot patterns, unusual activities, and connections that traditional methods might miss. 

            By training ML models on past data, AI can learn to identify patterns associated with fraud. These models then analyse new transactions, compare features, and flag potential problems in real-time.

            Real-time Data Analysis

            Slow reporting and analysis have hindered companies’ ability to adapt. AI-powered systems overcome these issues by enabling real-time analysis and decision-making.

            AI’s ability to process massive amounts of real-time market data helps financial experts identify opportunities and adapt to market shifts quickly. This translates to increased resilience and competitiveness for businesses.

            Case Studies

            Financial institutions are increasingly using AI to improve their forecasting and data analysis for managing operational risk. This trend is likely to continue as IndustryARC expects the AI market to reach US$400.9 billion by 2027, growing at a compound annual growth rate (CAGR) of 37.2% during the forecast period of 2022–2027.

            Deutsche Bank‘s collaboration with NVIDIA on “Financial Transformers” shows the potential of AI for early risk detection. These models can identify warning signs in financial transactions and speed up data retrieval, helping banks address potential problems quickly and ensure data quality.

            AI also plays a key role in anti-money laundering (AML) efforts. By analysing transaction patterns, customer behaviour, and risk indicators, AI can identify suspicious activities for investigation. This not only improves detection rates but also streamlines the process. Google Cloud’s AML AI is a prime example; it helped HSBC find many more real risks while significantly reducing false positives, saving them time and resources.

            Future Prospects

            AI in finance is expected to significantly reshape financial forecasting. Analysts and executives will see widespread AI adoption for tasks like data analysis, pattern recognition, and automation. This trend is driven by the projected growth of global AI in the finance market. A report by Research and Markets predicts it will reach $26.67 billion by 2026, growing at a rate of 23.1% each year. 

            For investment firms, AI can make highly accurate forecasts and execute complex trading strategies, optimising investment decisions and returns. Banks will also benefit from AI’s capabilities. AI-powered data analysis can give banks a deeper understanding of their customers, enabling personalised financial services. Chatbots and robo-advisors used for customer service and financial planning will continue to evolve, becoming more advanced and even more human-like in their interactions.

            • Artificial Intelligence in FinTech

            Author

            Dan Brightmore

            Published

            26 July 2024

            Estimated Read time

            3Mins

            Customer service significantly influences the overall customer experience and brand reputation. Artificial intelligence (AI) has taken customer service to new…

            Customer service significantly influences the overall customer experience and brand reputation. Artificial intelligence (AI) has taken customer service to new heights, including in the insurance industry.

            Financial technology development has offered a better customer experience with enhanced accessibility and convenience. Mobile banks and digital wallets make it possible to contact the customer service team through online platforms. With the help of AI, FinTech companies escalate their services by offering more personalised, prompt, and efficient service.

            AI Chatbots and Virtual Assistants

            Conversational AI, which focuses on creating human-like interactions like chatbots and virtual assistants, improves customer service efficiency.

            Chatbots are automated programmes that promptly address customer service queries. They can assist customers with inquiries and provide support for product information, account balances, or transaction details. AI-powered chatbots can give an immediate response and handle multiple customers at the same time.

            Meanwhile, virtual assistants are voice-activated apps that can comprehend and carry out tasks based on users’ commands. These assistants offer personalised support by understanding the customers’ needs. For instance, they can deliver investment guidance tailored to customers’ risk tolerance and financial objectives.

            These AI solutions can also assist human assistants by handling routine tasks, allowing them to focus on more complex work. Thus, the employment of AI assistants can reduce operational costs and effectively allocate resources to more important tasks.

            Personalised interactions with AI

            This approach can provide more personalised interactions by using algorithms and predictive tools to understand and respond to each customer’s preferences. AI algorithms can analyse large datasets of customers’ past interactions, browsing behaviour, and demographic information.

            Meanwhile, predictive analytics tools can be used to anticipate customer needs and offer relevant financial products or services. These recommendations are constantly updated based on real-time client interactions and feedback.

            24/7 Support

            AI-powered customer service has the benefit of around-the-clock availability. It can operate continuously without being bound by office working hours like human-based customer service. Faster response times and enhanced availability help FinTech companies improve overall customer satisfaction.

            Case Studies

            Paypal, a digital wallet company, is one of the FinTech companies that has successfully used AI to improve its customer service. After implementing chatbots, PayPal experienced a 20 percent decrease in customer support costs and a 25 percent increase in user engagement. These chatbots can handle routine inquiries, resolve issues, and make personalised product recommendations.

            Another example is Citi, a US retail bank that developed an AI-powered Customer Analytic Record (CAR). This programme can consolidate customer data, including financial records, used products, and interactions across online banking. The data is linked to automated decision-making AI software for analysis. The system can then recommend personalised offers to customers via text and mobile banking.

            Future prospects

            According to David Griffiths, Citigroup’s chief technology officer, AI has the potential to revolutionise the banking industry and improve profitability. With the continuous development of AI technology, the fintech industry can further improve its customer service.

            Ronit Ghose, another executive at Citigroup, predicts that in the future, every client will have an AI-powered device in their pocket. This innovation will improve their financial lives with enhanced AI in customer service.

            However, there are still concerns about AI’s scalability limitations in handling vast amounts of tasks. In addition, AI’s access to customers’ data makes security an important area to ensure its credibility. FinTech companies should ensure digital compliance to earn customers’ trust.

            • Artificial Intelligence in FinTech

            Author

            Dan Brightmore

            Published

            22 July 2024

            Estimated Read time

            3Mins

            The banking industry is slowly adopting artificial intelligence (AI) technology. It offers many benefits for financial institutions, from upgrading customer…

            The banking industry is slowly adopting artificial intelligence (AI) technology. It offers many benefits for financial institutions, from upgrading customer experience to automating menial tasks. However, many are still cautious about using AI in certain areas, such as regulatory compliance management.

            Given the continuously evolving legal requirements, good regulatory compliance management is crucial for banks. AI solutions can help effectively manage compliance by automating repetitive tasks, detecting suspicious activity, and providing real-time insights.

            Automated compliance monitoring with AI

            Artificial intelligence allows banks to perform continuous tasks around the clock with automated compliance monitoring. The previously labour-intensive work can be done more efficiently to ensure the bank follows all regulatory obligations.

            The bank’s compliance teams usually handle monitoring processes, but AI automation can reduce costs. The compliance team can also focus on more important tasks rather than repetitive work.

            The increased efficiency also means reduced compliance risk and non-compliance damage like fines.

            Risk management

            Financial institutions face regulatory compliance risks in various areas, which can lead to legal sanctions, financial loss, or a bad reputation. Advanced AI solutions can aid in risk management by identifying and mitigating risks more effectively.

            AI-powered solutions can develop more accurate risk models and provide real-time responses. Many banks use this technology to help streamline compliance while improving the security of sensitive financial data. Furthermore, AI can detect compliance gaps and ensure adherence to laws and regulations.

            Data analysis

            AI can quickly analyse large volumes of data, a novel capability in the industry. A data analysis system can be designed to keep track of the latest regulatory changes and ensure the bank remains compliant.

            Machine learning models can identify suspicious patterns and detect anomalies to report any breach of regulation. They can also analyse historical data and predict compliance risks. These allow banks to mitigate risks and address compliance issues before they escalate.

            Case studies

            Several banks have successfully used AI for regulatory compliance solutions. HSBC, for instance, uses AI-powered Know Your Customer (KYC) verification. This system can analyse customer data quickly, identify potential risks, and alert compliance officers for investigation. This bank also used Google Cloud’s Anti Money Laundering (AML) AI to combat and detect fraudulent activities in real-time. With these, HSBC has reduced the verification time by 80 percent and experienced a significant reduction in false positives.

            Meanwhile, Danske Bank has also earned benefits from using fraud detection AI. The bank witnessed a 60 percent reduction in false positives and a notable decrease in fraudulent activities.

            Future outlook for AI in regulatory in compliance

            AI solutions are predicted to fundamentally change financial institution compliance management in the next five years, according to McKinsey. In the future, implementation for regulatory compliance in banks will be more widespread. Over 80 percent of C-level executives who participated in an Accenture survey planned to commit 10 percent of their AI budget by 2024 to address regulatory compliance.

            AI offers many benefits, and as accessibility to this financial technology increases, more financial institutions will be inclined to adopt it, according to the Financial Stability Review.

            Technology will evolve, giving better automation capabilities, more extensive data analysis, and enhanced interpretation. This could further reduce the manual effort required in the banking industry.

            As adoption increases, ensuring the AI systems used are ethical and unbiased is necessary. Thus, banks need to provide transparency for AI in banking and adherence to guidelines.

            • Artificial Intelligence in FinTech

            Author

            Sean Galea-Pace

            Published

            8 May 2024

            Estimated Read time

            7Mins

            Satya Mishra, Director, Product Management at Amazon Business, discusses how CPOs have become an important voice at the table to drive digital transformation and efficient collaboration.

            Harnessing efficiency is at the heart of any digital transformation journey.

            Digitalisation should revolve around driving efficiency and achieving cost savings. Otherwise, why do it?

            Amazon is no stranger to simplifying shopping for its customers. It is why Amazon has become a global leader in e-commerce. But, business-to-business customers can have different needs than traditional consumers, which is what led to the birth of Amazon Business in 2015. Amazon Business simplifies procurement processes, and one of the key ways it does this is by integrating with third-party systems to drive efficiencies and quickly discover insights. 

            Satya Mishra, Director, Product Management at Amazon Business, tells us all about how the organisation is helping procurement leaders to integrate their systems to lead to time and money savings.

            Satya Mishra: “More than six million customers around the world tap Amazon Business to access business-only pricing and selection, purchasing system integrations, a curated site experience, Business Prime, single or multi-user business accounts, and dedicated customer support, among other benefits.

            “I lead Amazon Business’ integrations tech team, which builds integrations with third-party e-procurement, expense management, e-sourcing and idP systems. We also build APIs for our customers that either they or the third-party system integrators can use to create solutions that meet customers’ procurement needs. Integrations can allow business buyers to create connected buying journeys, which we call smart business buying journeys. 

            “If a customer does not have existing procurement systems they’d like to integrate, they can take advantage of other native tools, like a Business Analytics dashboard, in the Amazon Business store, so they can monitor their business spend. They can also discover and use some third-party integrated apps in the new Amazon Business App Center.”

            Why would a customer choose to integrate their systems? Are CPOs leading the way?

            Satya Mishra: “By integrating systems, customers can save time and money, drive compliance, spend visibility, and gain clearer insights. I talk to CPOs frequently to learn about their pain points. I often hear from these leaders that it can be tough for procurement teams to manage or create purchasing policies. This is especially if they have a high volume of purchases coming in from employees across their whole organisation, with a small group of employees, or even one employee, manually reviewing and reconciling. Integrations can automate these processes and help create a more intuitive buying experience across systems.

            “Procurement is a strategic business function. It’s data-driven and measurable. CPOs manage the business buying, and the business buying can directly impact an organisation’s bottom line. If procurement tools don’t automatically connect to a source of supply, business buying decisions can become more complex. Properly integrated technology systems can help solve these issues for procurement leaders.”

            Satya Mishra, Director, Product Management at Amazon Business

            Beyond process complexity, what other challenges are procurement leaders facing?

            Satya Mishra: “In the Amazon Business 2024 State of Procurement Report, other top challenges respondents reported were having access to a wide range of sellers and products that meet their needs, and ensuring compliance with spend policies. 

            “The report also found that 52% of procurement decision-makers are responsible for making purchases for multiple locations. Of that group, 57% make purchases for multiple countries.

            “During my conversations with CPOs, I hear them say that having access to millions of products across many categories through Amazon Business has allowed them to streamline their supplier quantity and reduced time spent going to physical stores or trying to find products they’re looking for from a range of online websites. They’ve also shared that the ability to ship purchases from Amazon Business to multiple addresses has been very helpful in reducing complexity for both spot-buy and planned or recurring purchases. Organisations may need to buy specific products, like copy paper or snacks, in a recurring way. They may need to buy something else, like desks, only once, and in bulk, at that. Amazon Business’ ordering capabilities are agile and can lessen the purchasing complexity.”

            How should procurement leaders choose which integrations will help them the most? 

            Satya Mishra: “At Amazon Business, we work backwards from customer problems to find solutions. I recommend CPOs think about what existing systems their employees may already use, the organisation’s buying needs, and their buyers’ typical purchasing behaviors. The buying experience should be intuitive and delightful. 

            “Amazon Business integrates with more than 300 systems, like Coupa, SAP Ariba, Okta, Fairmarkit, and Intuit Quickbooks, to name just a handful. With e-procurement integrations like Punchout and Integrated Search, customers start their buying journey in their e-procurement system. With Punch-in, they start on the Amazon Business website, then punch into their e-procurement system. With SSO, customers can use their existing employee credentials. Our collection of APIs can help customers customise their procure-to-pay and source-to-settle operations. This includes automating receipts in expense management systems and track progress toward spending goals. 

            “My team recently launched an App Center where customers can discover third-party apps spanning Accounting Management, Rewards & Recognition, Expense Management, Integrated Shopping and Inventory Management categories. We’ll continue to add more apps over time to help simplify the integrated app discovery process for customers.

            “Some customers choose to stack their integrations, while others stick with one integration that serves their needs. There are many possibilities, and you don’t just have to choose one integration. You can start with Punchout and e-invoicing, for example, and then also integrate with Integrated Search, so your buyers can search the Amazon Business catalog within the e-procurement system your organisation uses.”

            Are integrations tech projects?

            Satya Mishra: “No, integrations should not be viewed as tech projects to be decided by only an IT team. Integrations open doors to greater data connectivity and business efficiencies across organisations. Instead of having disjointed data streams, you can connect those systems and centralise data, increasing spend visibility. You may be able to spot patterns and identify cost savings that may have gotten lost otherwise. 

            “It’s not uncommon for me to hear that CPOs, CFOs and CIOs are collaborating on business decisions that will save them all time and meet shared goals, and integrations are in their mix of recommendations. 

            “One of my team’s key goals has been to simplify integrations and bring in more self-service solutions. In terms of set-up, some integrations like SSO can be self-serviced by the customer. Amazon Business can help customers with the set-up process for integrations as well.”

            How has procurement transformed in recent years?

            Satya Mishra: “Procurement is no longer viewed as a back-office function. CPOs more commonly have a seat at the table for strategic cross-functional decisions with CFOs and CIOs.

            “95% of Amazon Business 2024 State of Procurement Report respondents say the purchases they make mostly fall into managed spend. Managed spending is often planned for months or years ahead of time. This can create a great opportunity to recruit other stakeholders across departments versus outsourcing purchasing responsibilities. Equipping domain experts to support routine purchasing activities allows procurement to uplevel its focus and take on higher priorities across the organisation, while still maintaining oversight of overarching buying patterns. It’s also worth noting that by connecting to e-procurement and expense management systems, integrations provide easy and secure access to products on Amazon Business and help facilitate managed spend.”

            What does the future of procurement look like?

            Satya Mishra: “Bright! By embracing digital transformation and artificial intelligence to form more agile and strategic operations, CPOs can influence the ways their organisations innovate and adapt to change.”

            Read the latest CPOstrategy here!

            Author

            Dan Brightmore

            Published

            12 October 2023

            Estimated Read time

            2Mins

            Read in brochure format

            Nigel Greatorex, Global Industry Manager at ABB, on how digital technologies can support decarbonisation and net zero goals

            Nigel Greatorex is the Global Industry Manager for Carbon Capture and Storage (CCS) at ABB Energy Industries. He explains how digital technologies can play a critical role in the transition to a low carbon world by enabling global emissions reductions. Furthermore, he highlights the role of CCS and how challenges can be overcome through digitalisation.

            Meeting our global decarbonisation goals is arguably the most pressing challenge facing humanity. Moreover, solving this requires concerted global action. However, there is no silver bullet to the global warming crisis. The solution requires a mix of investment, legislation and, importantly, innovative digital technologies.

            Decarbonisation digital technologies

            It’s widely recognised decarbonisation is essential to achieving net zero emissions by 2050. Decarbonisation technology is becoming an increasingly important, rapidly growing market. It is especially relevant for heavy industries – such as chemicals, cement and steel. These account for 70 percent of industrial CO2 emissions; equal to approximately six billion tons annually.

            CCS digital technologies are increasingly seen as key to helping industries decarbonise their operations. Reaching our net zero targets requires industry uptake of CCS to grow 120-fold by 2050, according to analysis from McKinsey & Company. Indeed, if successful, it could be responsible for reducing CO2 emissions from the industrial sector by 45 percent.

            A Digital Twin solution

            ABB and Pace CCS joined forces to deliver a digital twin solution. It reduces the cost of integrating CCS into new and existing industrial operations. Simulating the design stage and test scenarios to deliver proof of concept gives customers peace of mind. Indeed, system designs need to be fit for purpose. Also, it demonstrates the smooth transition into CCS operations. Additionally, the digital twin models the full value chain of a CCS system.

            Read the full story here

            • Sustainability Technology

            Author

            Dale Benton

            Published

            16 October 2019

            Estimated Read time

            10Mins

            Read in brochure format

            In early 2019, the Voluntary Health Insurance Scheme (VHIS) was introduced in Hong Kong by the Food and Health Bureau…

            In early 2019, the Voluntary Health Insurance Scheme (VHIS) was introduced in Hong Kong by the Food and Health Bureau to regulate indemnity hospital insurance plans offered to individuals, with voluntary participation by insurance companies and consumers. The VHIS was designed as a means of encouraging and supporting customers to purchase private healthcare services and for Koh Yi Mien, Managing Director Health and Employee Benefits at AXA Hong Kong, this scheme represents a broader transformation of healthcare and insurance services. “Currently, the demand on healthcare in Hong Kong in the public sector is incredibly high with very long waiting times and waiting lists,” she explains. “As a result, people just aren’t getting timely access to treatment. The private sector in Hong Kong, which is world-class, has capacity. So, if we can rebalance and shift some of the elective work from public to private, it will free up more people to use the public service in a timely fashion.”

            Yi Mien also points to a global drive for greater transparency, accountability, use of data and technology as well as promoting customer choice as key drivers of change in the insurance space. “It’s no longer a case of simply providing reimbursement to people when they need treatment,” she says. “It’s about being the patient’s partner throughout their whole life so that when they need healthcare, whenever and wherever they are, we are there to help and support them in their times of need.” 

            The modern-day insurance customer is very different from the customer of the past. We live in times of greater access to information through the advent of social media and the increasing influence of the Internet and this has resulted in insurance customers being more knowledgeable about their conditions and asking more questions of their doctors than ever before. As a result, the balance between the customer and the healthcare provider is becoming more equitable. “Customers and patients, as a result, are becoming more demanding,” says Yi Mien. “Gone are the traditional ideas that doctor knows best. It’s not uncommon for patients to see their doctor with a list of demands, while expecting to be serviced.”

            Running parallel to becoming more knowledgeable and demanding is the use of smartphones and how it has created a culture of service in an instant. When customers purchase etiquettes or use banking services, they expect the ability to be able to access and complete these transactions and services via their smartphone devices. Fewer and fewer people are accessing physical bank branches and the healthcare insurance sector, despite being still very traditional, is feeling the effects of this instant demand. “Healthcare is a very traditional sector sure, but asking patients or customers to book weeks in advance and telling them they don’t really have any choice is becoming increasingly unacceptable and so healthcare becomes a commodity,” says Mie Koh. “They, like any other customer, vote with their feet and want 24/7 access to quality healthcare without waiting directly from us as the insurer.”

            The informed customer and patient have also transformed the relationship between customer and doctor. It is no longer a bilateral relationship and the entire healthcare ecosystem works to provide services from prevention right through to treatment. The result? Insurers like AXA work with customers before they are sick and encourage them to maintain their health, but they also work with clients during their illness and even afterwards AXA will continue to treat them in their rehabilitation. “During their healthcare journey, customers want some handholding in order to navigate the very complex healthcare system, to make sure they get the right healthcare provider, doctor and hospitals that are best for them in their time of need,” says Yi Mien. “This can only happen if we are using digital so that it becomes more real time.”

            AXA has been embracing technology for a number of years to be able to serve and effectively work with its customers. It achieves this by starting with the definition of a product, because the product sets the rules. Yi Mien highlights that the rules would often be how AXA would spell out the terms and conditions, the provisions, but these rules also set the customer expectations. Throughout late 2018 and 2019, AXA has invested in digital to enable its customers to buy online, service online, claim online and check-up online. The company also launched a servicing app called Emma, a ‘digital companion’ that enables even faster service. Yi Mien describes this app as a true “health companion”. She is also keen to highlight that the technology is only part of the story. AXA has built a vast medical network with some of the leading hospitals and doctors and customers simply having to log into their companion app to be able to access this network at the touch of a button. “All they need to show is their digital card, their e-card, and with the QR code, the provider just scans it. All of the data is downloaded and all they need to do is sign, get their treatment, and then when they discharge, just sign that they have received the treatment and off they go,” she says. “The hospital will bill AXA directly so there’s no out of pocket. The data is also transmitted to AXA which means that we have more comprehensive and more reliable data.”

            Comprehensive and reliable data is crucial to the technology journey of AXA, but it is also integral to the customer journey. With a customer’s entire electronic medical records stored effectively and securely, as Yi Mien notes, why would they go anywhere else? The data that an insurer handles is often complex in nature, but this data is processed through artificial intelligence, with AI being used to process claims more effectively and interpret the information to allow AXA to create rules and algorithms to better serve its customers. AXA also utilises AI through its companion app Emma. “Emma is our chatbot,” explains Yi Mien. “Emma has been built up based on a multitude of Q&As that our customer services team have recorded and collected over many months and years. As we continue to build, and more people use Emma, then the quality of the responses she has in her arsenal will improve.” In the first two months of operations, Emma recorded an accuracy level of 50%. Yi Mien firmly believes that as more people engage with Emma and as a result, the chatbot will evolve and become more of a real-time navigator that can direct customers across the whole ecosystem.

            In the global discussion around AI, the topic of transparency is often a key point of debate. With governments around the world shining a spotlight on exactly what data is collected and how it is used, AXA ensures that it maintains an open and transparent dialogue with its customers. As customers engage with Emma and the companion app, they can at any time request their transcripts. Should they choose to speak with a human adviser, all calls are recorded and again they can access those recordings should they wish. Not only is this an example of AXA complying with global governing laws, it also highlights that the customer is at the very heart of every decision it makes and it maintains this as it continues to implement new technologies. “If you look at banking as an example, we all are so used to accessing our bank accounts at any time, be it through our phones or online,” says Yi Mien. “If we want to speak to someone, we can. If we want to go into a branch, we can. I believe this is the way to go with insurance as well. We make it easy for our customers to contact us. We are doing everything we can to allow that.”

            “Healthcare is quite personal, so we are doing what we can to allow customers to speak to people, should they not wish to use our chatbot. These are very personal journeys and digital is still in its early days, so we really have to provide different avenues and channels for our customers to contact us.”

            As Yi Mien notes, AXA designs its customer journey by starting at the product and going through all the way to treatment. The company makes every decision with the customer’s perspective in mind. As a doctor by trade, Yi Mien sees that all new products are designed by doctors because they understand how the patients move throughout the whole healthcare ecosystem. When AXA designs new products, it does not operate within a vacuum. It has a customer insight group, where around 1,000 customers operate as a real-time focus group in which AXA can test its products with. “When I think about future products, we will test with this group of people and get feedback to see whether we are aligned with the current customer need. So, it’s not just technology per se, but actually meets a customer’s needs,” she says. “One other area to make sure that we are doing the right thing, because technology also costs money, is to make sure that we are very robust in what we do. AXA is unique in that we sell life insurance, health insurance, employee benefits, and we also have P&C. So, being a multi-line insurer, we have the opportunity of having one approach and cross-selling across the business lines, which is a fantastic opportunity. We can only do that through technology.”

            Over the course of her career, Yi Mien has been a champion of the transformative effect of technology in becoming a greater enabler for healthcare and healthcare insurance providers around the world. One area in particular that is close to her heart is the mental health space. In Hong Kong, the waiting time to see a psychologist is close to two years and if patients were to seek private care, it is an expensive solution. “Look at a country like Hong Kong, or Australia, they are so vast that there just aren’t enough practitioners to cover the breadth of the geography. Digital is the solution,” she says. “Digital enables people to seek, support and care at the time that is most convenient for them.”

            “In the past two to three years, there has been a proliferation of digital tools. Recent studies have shown that digital tools are as good as, if not better, than in-person therapy because customers prefer to talk to a robot rather than face-to-face because they feel that the robot is not judging them.”

            Another example that Yi Mien highlights is in the UK, where a VR program has been developed by programmers that is therapy through gameification. The treatment is consistent every time and because of its mobile platform, it is accessible. “We can provide it where you work,” she says. “That’s just one example as to how we can destigmatise mental health through technology.”

            AXA operates within a broad healthcare ecosystem, an ecosystem made up of partners, providers and doctors and Yi Mien stresses that in the future of insurance, it will be impossible for insurers to control the ecosystem. “I don’t foresee a future where that happens,” she says. “Partnerships are incredibly important. Things are moving so fast there’s no way we can catch up alone. We need to have partners, collaborators, who are working together to ensure we are at the top of our game and at the forefront of innovation.”

            “Over the course of our lives, so many different things can happen and so people will need better care and support. By having a collection of data that represents our customer’s needs we are able to push or suggest services that better meet those needs. In order for us to do that, we need to have players collaborate in the ecosystem. It’s imperative.”

            As AXA continues this digital growth journey, the next few years will be defined by improving the agility of the digital companion in order to improve the interaction with customers. AXA will also be looking at developing a digital marketplace in which customers can go shopping within an AXA owned digital platform. For Yi Mien, though, the future is clear for AXA and in order to be successful, she feels it’s down to one thing. “AXA has a clear digital strategy for sure, where it will transform its digital system and build new IT infrastructure to transform the customer experience,” she says. “But the technology is only one part of the story.”

            “Unless we can transform the customer experience to deliver a service they truly value, then technology doesn’t do anything. It’s important to recognise that technology is enabling us to transform healthcare, to make it easier, faster, and cheaper for people to receive care. That means in the long-term, sustainable healthcare and health services, which fits into sustainable insurance.”