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
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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.
Ben Goldin, Founder and CEO of Plumery, explores the key banking trends for 2026 – from fraud and digital assets to stablecoins and AI applications
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As we head into the second half of the decade, several emerging trends will come to the fore in 2026. The interconnectedness among these trends is also noteworthy. Artificial intelligence (AI) and progressive modernisation act as common threads.
A strong current throughout 2026 is the shift from customer-first banking to human-first banking. This relates to the concept of ethical banking. It focuses on creating financial services that have a positive social and environmental impact.
Human-first banking aims to get even closer to the customer by understanding their actual human needs, rather than just consumer needs. For example, a bank should be acting as a coach to improve a customer’s financial health, not solely as an advisor on which products they should buy. Banks can build trust in a digital world through tailored and empathetic interactions, effectively simulating the experience customers formerly had with their personal banker.
To attain that level of hyper-personalisation, banks will need to be capable of processing vast amounts of transactional data, which can only be accomplished by deploying AI and big data tools. This requirement, in turn, will turbocharge progressive modernisation, another trend that has been bubbling under the surface for the past few years.
Traditional banks are using progressive modernisation to deal with legacy infrastructure that is not fit for purpose in a digital-first, AI-driven world. Instead of a big bang replacement of core banking systems, which is risky and can take years, banks are creating change from within existing architecture. Banking is leveraging technologies that support a multi-core strategy. With this approach, banks can add new cores for specific products that require greater agility and innovation. Modern cores are necessary for deploying the latest AI and big data tools because they provide a unified, real-time data foundation to deliver hyper-personalisation.
Fraud Threats
Fraud will remain a top concern throughout 2026. Adversaries use AI to expand the range of techniques, such as impersonation scams and identity theft, as well as accelerate and scale fraudulent activity.
According to the UK Finance Half Year Fraud Report 2025, £629.3 million was stolen by criminals in the first six months of this year, and there were 2.09 million confirmed cases across both authorised and unauthorised fraud. Card not present cases rose 22% to 1.65 million and accounted for 58% of all unauthorised fraud losses.
However, the good news is that there was a 21% increase in prevented card fraud in the first half of 2025. The £682 million which was stopped from being stolen is the highest-ever figure reported.
To combat fraud, new and improved tools to help banks identify, verify and onboard customers will come to market in 2026. The move away from paper-based identity (ID) and widespread adoption of digital ID will play a key role in the fight against fraud. Hence the UK government’s recently announced plans to roll out a new digital ID scheme.
In addition, I expect to see a fundamental shift in fraud detection using real-time behavioural analytics, data analytics for proactive risk identification, and other applications of AI and machine learning in this space.
Digital Assets and Stablecoins
Digital ID verification is also essential for fighting fraud in the digital assets and stablecoins space. Another hot topic at several banking and payments industry conferences last year.
In 2026, digital assets and stablecoins will become much more mainstream. Banks have left the sidelines and are now actively engaged with running pilots. For example, in September a consortium of nine European banks, including CaixaBank, ING and UniCredit, announced an initiative to launch a euro-denominated stablecoin.
Central banks and regulators are developing a comprehensive agenda for digital assets. Banks will need to blend traditional fiat currencies and assets with their digital counterparts. This trend is also driving a progressive modernisation approach, as legacy core banking systems weren’t designed to manage digital assets, nor do they support moving money via blockchain-based rails. I expect to see more banks looking to deploy a multi-core strategy where digital assets are managed and stored elsewhere, but they can still provide a seamless and unified experience to customers.
AI
Last year, I predicted that the industry would adopt a ‘meet-in-the-middle’ approach to AI, with banks beginning to uncover the real value that the technology can deliver. I also predicted consolidation, recalibration and stabilisation in the market.
GenAI Banking Applications
My predictions held true, by and large. In 2025, institutions explored what is possible, relevant and achievable within the banking context, then specifically for each individual institution within its legacy architectures and technological environments.
This trend will evolve into more practical actions and initiatives over the next 12 months to provide greater clarity around where GenAI shines versus where it’s not applicable.
To gain clarity, it’s important to understand the difference between AI and GenAI. The latter is built on stochastic principles, which uses probability to model systems that appear to vary in a random manner. This means that the same input could potentially generate different outputs – this isn’t acceptable for automated financial operations, which requires much more determinism. Hence, I believe that GenAI will be used chiefly in scenarios where there’s human intervention.
One area where GenAI is applicable is in conversational applications. For example, banks will begin launching more interactive user interfaces. Customers will be able to interact with the bank as they would a human. Moving beyond simple, frequently asked questions to actual actions.
GenAI in the Back Office
Similarly in the back office, banks can leverage GenAI to provide guidance to their employees and accelerate certain tasks. Using the technology to improve efficiency and help staff do more will have a positive impact on customer experience. Processes will take much less time.
It will also help to bring unbanked segments or non-standard customers, which are difficult and costly to onboard because they require a bespoke assessment, into regulated financial services. Applying GenAI can make the bespoke process much more efficient by providing data-driven insights to support faster and smarter decision-making. This will make it much cheaper to serve these segments. Including smaller and medium-sized enterprises, which will drive financial inclusion and improve customers’ financial health.
Plumery’s AI fabric is future-proofed and designed for use cases beyond today’s horizon
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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.
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
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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.
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
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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.
Neven Matas, Cybersecurity Team Director EU from Infinum, explores how FinTech companies can turn resilience into a source of innovation and business growth
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FinTech companies are under constant pressure to innovate rapidly while maintaining deep and ongoing trust in their platforms. And as AI becomes embedded into everything from credit decisions to customer support, these pressures are intensifying. The future of digital finance will not just be defined by who deploys the most advanced technology first but by who implements systems that can withstand attack, scale efficiently, and evolve without compromising compliance or customer confidence.
Resilience cannot be a technical afterthought; it is a strategic requirement for FinTech. Modular platform architectures, responsible AI operations, and proactive security testing are becoming the foundations of sustainable FinTech growth. Together, they define an operating model where compliance supports innovation instead of obstructing it and where trust becomes a true competitive differentiator.
FinTech Resilience Begins with Architecture
Many FinTech platforms have evolved as tightly integrated but ultimately separate systems. While these can move quickly at first, they will often struggle under regulatory change, evolving security threats or simply the pressure of scale.
Modular, API-driven architectures will enable organisations to compartmentalise risk. They also make it easier to upgrade specific services without disrupting the others and adapt to new regulatory obligations without impacting the whole business. Shared platform capabilities, such as identity management, encryption, logging and access control, will give every new product or feature an inherited baseline of good security practice and governance.
This approach is especially important as operational resilience regulations tighten across global financial services. Requirements around third-party management, continuity planning, and incident reporting demand systems that are secure, observable, and controllable. When resilience is engineered into the platform rather than bolted on, organisations can adapt far more confidently.
Crucially, modularity accelerates innovation rather than slowing it down. Teams can experiment at the edge without placing core systems at risk. New fraud detection models, customer features or AI-driven services can be deployed, tested and refined in isolation. Resilience, therefore, is not simply about withstanding disruption, it is what allows organisations to safely embrace continuous change.
Scaling Digital Products Without Tripping Over Compliance
Digital FinTech products are no longer judged just on usability. They are also evaluated on how transparently they handle data, how well they communicate risk, and whether they meet regulatory expectations across markets. Compliance, which was once seen as a barrier to innovation, is increasingly becoming a fundamental product design input.
The most resilient organisations will embed regulatory thinking directly into product development from the outset. Rather than treating compliance as a late-stage sign-off, they feed regulatory principles into experience design and system behaviours. Consent flows, audit trails, authentication rules, and data retention logic become part of the product’s core architecture rather than something that has been retrofitted.
This approach significantly reduces the operational burden of growth. As FinTech companies enter new regions or launch new services, they avoid the potential of costly remediation triggered by regulatory scrutiny. Instead, they operate from consolidated, well-governed platforms that limit the attack surface and simplify oversight, while also limiting duplication. The outcome is a stronger security posture and faster expansion into new markets with clearer trust signals for customers and partners.
AI as a Trusted Partner Not a Black Box
AI has rapidly become central to the FinTech value proposition. Real-time fraud detection and automated operational processes, for example, depend on increasingly sophisticated models. However, AI also introduces new risks, including opaque decision-making, potential bias, and heightened regulatory exposure when automated systems influence financial outcomes.
The strategic shift now is from experimental AI adoption to accountable AI operations. This begins with defining precisely where AI adds value and where human oversight remains essential. High-impact use cases, such as lending decisions, transaction monitoring and identity verification, all need explainability as well as accuracy. Organisations must be able to demonstrate how decisions were reached, what data was used and how bias is monitored over time.
Clear ownership, review processes, escalation paths, model validation and human-in-the-loop controls will help make large-scale AI deployment viable in a regulated environment.
AI also has a strong defensive capability. Behavioural anomaly detection, predictive threat monitoring and intelligent authentication systems allow fintech platforms to detect and respond to risk faster than traditional rule-based approaches.
When used responsibly, AI can strengthen both customer experience and operational resilience.
Proactive Security Testing as a Continuous Discipline
Modern FinTech infrastructure assumes exposure. APIs are public, ecosystems are interconnected and supply chains are large and complex. Under these conditions, security based solely on perimeter defences or annual audits is not enough. This means continuous, adversarial testing has become essential for resilient fintech organisations.
Mature players are moving beyond compliance-driven testing into ongoing penetration assessments, red-team exercises and social-engineering simulations. These practices uncover technical vulnerabilities, as well as weaknesses in response coordination, escalation decision-making and recovery planning. They test the organisation as a living system rather than a collection of isolated applications.
Integrating security into everyday development is equally critical. Secure coding standards, continuous testing pipelines and regular threat modelling will enable earlier detection of vulnerabilities, when issues are cheaper and easier to resolve. The goal is not to eliminate risk entirely, which is impossible, it is to reduce the time between exposure, detection and response.
Security as a Growth Enabler
The reframing of security from cost centre to growth driver is the most significant strategic transformation in FinTech. Having a strong security posture is not just about ticking compliance checkboxes, it is increasingly a prerequisite for partnerships, institutional trust and international expansion.
Organisations that demonstrate operational resilience, responsible AI governance and proactive security assurance move through due diligence faster. They onboard enterprise clients more easily, integrate with partners with fewer barriers and launch advanced digital services with greater confidence.
In crowded markets, trust is a commercial advantage.
From the customer perspective, security and transparency are inseparable from experience. Clear communication around data usage, visible protections and consistent reliability directly impact adoption, retention and loyalty. Resilience becomes part of brand equity.
Looking ahead, FinTech leaders will not be defined by who adopts new technology first but by who builds systems capable of absorbing disruption, scaling responsibly and evolving continuously. Modular platforms, trustworthy AI and continuous security assurance form the backbone of this.
Joe Jordan, co-founder at Adclear, on why FinTechs and other financial organisations need to find equilibrium between content and compliance
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FinProm. It might sound innocent enough. But in reality, these two small syllables represent a mountain of risk for FinTechs, banks, trading platforms and other financial institutions. FinProm, short for financial promotions, is the catch-all term for how finance brands market their products to customers. That means everything from YouTube ads and TfL posters, to in-app nudges and influencer collaborations. Like most things in finance, it’s an area that’s heavily regulated. And, in today’s fast-moving marketing world, it’s something that’s starting to trip companies up.
Navigating FinProm
Just this year, we’ve seen Robinhood fined $26M for regulatory breaches which included failure to properly oversee the influencers plugging their platform. And three UK “finfluencers” recently landed in court for falling foul of FCA FinProm rules. As the fly-wheel of content creation speeds up, fuelled by AI tooling, FinTech brands are facing a high-stakes conundrum: how can they keep pace with modern marketing strategies without running the risk of breaching the litany of rules set by bodies stretching from the FCA to the ASA?
Currently, fintechs and banks try to stay on the right side of the regulations by running all of their marketing content and promotions through their compliance teams. These experts review each image, video and piece of copy and suggest revisions. In the quest for compliance, this back and forth causes all sorts of friction. It slows down pace, waters down creativity, and burdens both teams with an admin-burden they’d rather do without.
The results? A slow marketing process which can’t capitalise on trends, nor tap into the rapid content personalisation and iteration made possible by the AI era. This means less growth and customer acquisition in a highly competitive market. The alternative? Playing fast and loose with compliance procedures in order to maximise marketing output. This might drive sales, but it could also drive firms right into the arms of some unhappy regulators.
Decision Time for FinTechs
This clash of priorities is creating the ultimate stress test for FinTechs and other financial organisations as they seek to find equilibrium between content and compliance in a world which demands more marketing output, delivered faster than ever before.
And it’s a stress test they cannot afford to fail. Regulators like the FCA are cracking down and the consequences of enforcement action can be devastating. And, as brands expand to new markets, the risk will only grow as they find themselves having to contend with an expanded set of regulators and rulebooks across the globe.
FinTechs can’t bury their heads in the sand on this issue. They must heed the cautionary tales we’ve seen in recent months and reset their FinProm blueprint. The AI-powered age of marketing can’t be capitalised on if it’s supported by old-school compliance processes. Nor can it afford to ignore the very real threat of a regulatory mis-step. To create a truly modern brand that is free to embrace the latest marketing strategies, compliance strategies need to be stepped up and modernised in tandem. Innovation on one side of the FinProm coin must be counter-weighted by innovation on the other.
FinTechs and finance platforms are used to pushing boundaries and disrupting the status quo. But to enable this to continue safely, effectively and on the right side of the law, the same energy and innovative zeal should now be applied to compliance. Without it, brands will be exposing themselves to risks and costs they likely cannot afford.
FinTech Strategy hears from the experts at DeepL, PagerDuty, Bitpace and Pleo who assess the impact of AI, crypto, stablecoins, tokenised payments and more on financial services in 2026
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Looking back at 2025, it was a pivotal year for financial services. The past 12 months have been marked by growing regulatory pressure, publicised outages, and a renewed focus on decentralised finance. In January, the Digital Operational Resilience Act (DORA) officially came into force across the EU, imposing new obligations on banks, insurers, investment firms and their technology providers to better manage ICT risks, report incidents and ensure continuity of operations.
That regulatory shift has come at a time when real-world failures are under intense scrutiny. A report from the Treasury Committee, prompted by a wave of IT glitches, revealed that nine of the UK’s largest banks and building societies suffered at least 803 hours of unplanned outages between January 2023 and February 2025, equivalent to more than 33 days of downtime. Alongside revision of traditional finance strategy, pro-crypto policy emerging from the US with the new administration has also buoyed investor confidence in newer assets like stablecoins, with the global market slated to hit $500 to $750 billion in coming years.
These events have reinforced a hard truth across the sector: digital infrastructure is no longer just a supporting pillar, it is mission-critical. Against this backdrop, many firms are now rethinking how they build, monitor and respond to technology risk. In this transformational moment, the voices below outline why 2026 may well become the year financial services firms turn lessons into lasting change, providing predictions about FS in 2026.
Eduardo Crespo, VP EMEA, PagerDuty:
“By 2026, financial services firms have turned hard-won lessons from the Treasury’s 2025 outage reports into action. Years of costly downtime and lost trust pushed the industry to rebuild around resilience. Always-on access is non-negotiable. Customers leave if they can’t transact in real time, and regulators are watching. In response, banks are overhauling legacy stacks and embedding AI at the core of incident management.
“AI isn’t a pilot project anymore, it’s become part of frontline defence. Systems now detect and diagnose disruption before it happens, enabling predictive maintenance and softening the blow of unplanned events. In 2026, resilience is a competitive edge.”
Anil Oncu, CEO, Bitpace:
“By 2026, digital assets will no longer be considered emerging. They will be fully embedded in mainstream finance. The shift is accelerating, driven by clearer regulation and stronger institutional participation across the US, UK and Europe. Pro-crypto policy is now the backbone of a global effort to build stablecoin-powered commerce at scale.
“In the UK, the Bank of England’s decision to allow stablecoin reserves to be held in short-term government debt is a significant signal of confidence. In the US, the GENIUS Act provides long-overdue oversight for dollar-backed tokens and replaces years of ambiguity with a clear path to legitimacy and widespread adoption.
“As global stablecoin supply moves beyond $300 billion, these digital dollars will support a rapidly increasing share of cross-border transactions. They reduce fees, eliminate settlement friction, and outperform traditional rails in both speed and transparency. At the same time, regulators are finally moving in the right direction. Stablecoins are moving from a speculative tool into a trusted infrastructure layer for modern payments.
“By 2026, digital assets will no longer sit alongside traditional finance. They will power its next phase of development. Stablecoins, crypto ETFs, and tokenised payments will be used directly within the financial stack and will be part of everyday business and consumer activity worldwide. This is not hype. It is execution, and the market is already moving.”
Ed Crook, VP Strategy & Operations, DeepL:
“2026 will be make-or-break for many financial services providers. In a competitive market, the edge goes to providers who adopt useful AI to cut through inefficient workflows. In this sector, where every interaction is highly regulated and reputational risk is acute, businesses need the right tools for the job. This includes data protection, account security, compliance, IT ops and customer service – keeping fundamental lines of communication open and effective. These are all areas where AI is already solving critical problems.
“AI is fast becoming the connective tissue of international finance, and this trend will continue in 2026, particularly in customer engagement and operational support. Our FS research found that over a third (37%) of client interactions in UK finance already involve AI. Over half (52%) use AI for multilingual translation, the top use case, directly addressing linguistic fragmentation. Moving into the new year, Language AI will be a key practical tool for financial services firms. But these companies first need to iron out their strategy around AI integration. Staff will inevitably look for workarounds if the tools provided don’t meet their needs. This is why companies need to get ahead by providing secure, fit-for-purpose solutions. By building a collaborative approach between IT and frontline teams, and avoiding pitfalls around shadow AI, financial service firms can maintain a unified, strategy approach to AI deployment, protecting against cybersecurity threats, while still realising the full benefits of trusted AI.”
Jeppe Rindom, CEO and Co-Founder, Pleo:
“Automation and “agentification” will redefine the fintech landscape. Most of what’s considered operational today will be handled by intelligent systems, from finance ops to customer support. That playing field will level and expectations will rise.
“To stand out, companies will need to inject identity – the one thing only humans can create. That could be through exceptional product design and user experience, considered use of human touchpoints where emotion and trust matter most, or the depth in which problems are solved for customers, not just how fast they can be solved.
“As the average becomes automated, greatness will come from creativity, clarity and crafting products and experiences that still feel unmistakably human.”
The Next 12 Months
The start of 2026 marks a massive turning point for financial services. After a year defined by renewed pressure on service uptime and improvement, around outages, regulatory pressure and rapid technological acceleration, the industry is now moving from reaction to reinvention.
In the coming year, we’ll see that firms embedding resilience, embracing intelligent automation and identifying new trends in service provision will lead the pack. The future of finance will hinge on trust, modernisation and operational strength, backed by technology.
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
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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.
Jamil Jiva, Head of Asset Management at Linedata, on unlocking the benefits of AI for Private Equity
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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.
FinTech Connect was a crossroads for strategy and execution. Global banks, FinTech challengers, regulators and investors gathered to define 2026 priorities, debate operational challenges and benchmark technology roadmaps.
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A Decade of Fintech Innovation
FinTech Connect marked its 10th anniversary at ExCeL London. Drawing 5,000+ industry professionals, 140+ speakers and 100+ exhibitors to explore banking, payments, compliance, digital transformation and blockchain innovation. The co-location with Tokenize: LDN brought deeper coverage of tokenisation and digital-asset infrastructure alongside core FinTech topics.
AI in Fintech: From Vision to Practice
A theme threaded through almost every theatre was AI adoption in financial services. But unlike earlier years’ speculative hype, this edition focused on practical deployment and risk management.
One standout panel, “GenAI That Customers Can Trust: The One Zero Digital Banker Story,” shared how One Zero built responsible generative AI features tailored for banking workflows, emphasising transparency and user trust. Industry leaders underscored that explainability, governance and compliance are no longer optional in enterprise AI.
A direct follow-on session, “How Do We Make AI Responsible in Practice?”, featured Rajeev Chakraborty from the Home Office discussing model governance and ethical safeguards for operational AI—an area rapidly becoming central to CIO and risk officer agendas.
Across both days, panels also explored how AI can reduce backlog in financial institutions, with Santander UK’s Head of AI demonstrating measurable impact on operational efficiency, and tackling tech debt at scale—a perennial challenge heightened by the influx of automation projects.
Key takeaway: AI’s role has shifted from emerging trend to core enterprise infrastructure, but success now hinges on responsible implementation, observable outcomes, and regulatory alignment.
Digital Transformation & Core Banking Strategies
Transforming legacy systems was another anchor topic. The Digital Transformation stage hosted robust discussions around neobanks and challenger strategies, with executives from TSB Bank and HSBC highlighting how incumbents are adopting agile ways of working while balancing risk and customer expectations.
The session “All In on Legacy? Driving Time to Market Without Big-Bang Migrations” resonated with many practitioners: incremental modernisation beats wholesale lift-outs when prioritising stability and customer continuity.
Another practical highlight, “Engineering Productivity Measurement: Traditional Bank to UK’s Largest Fintech,” narrated the journey of building measurable engineering benchmarks to align business goals and product delivery.
Key takeaway: Attendees left with a reinforced understanding that successful transformation blends cultural shift, incremental modernization, and strategic tech investment—not hurried replacement of core systems.
RegTech & Ethical Compliance: Balancing Innovation with Governance
RegTech, Compliance & Security sessions tackled the tension between rapid innovation and tightening regulatory guardrails—a debate central to fintech scaling.
A standout session titled “Ethical AI in Regulatory Technology: Balancing Innovation & Compliance” featured voices from governance, compliance and data-ethics functions. Panelists discussed strategies for embedding fairness, bias mitigation and traceability into machine-assisted workflows—a crucial step for institutions deploying automated decisioning.
Another forward-looking talk, “How Quantum Innovation Will Redefine Regulatory Operations,” examined how future computing paradigms could reshape compliance tooling and data verification—but also stressed the need to prepare today’s infrastructure for tomorrow’s disruptions.
Key takeaway: Compliance isn’t just a cost centre; speakers argued that robust RegTech can be a competitive advantage, reducing risk while enabling faster scaling.
PayTech & eCommerce: Securing the Digital Commerce Era
The PayTech & eCommerce stage delivered insights on securing payment flows and shaping the next wave of commerce innovation.
In “Emerging Global Tech Trends in Payments & Cash Management,” HSBC’s payment leaders unpacked how real-time rails and open APIs are influencing cross-border flows. Fintech Connect 2025
The panel “Transforming Payment Security with AI” brought together payment experts and academics to examine fraud detection innovations—AI-enabled risk scoring, adaptive authentication and cooperative intelligence sharing—as a defence against evolving threats. Fintech Connect 2025
A later session on “Tackling Cyber Threats in a New Era of Digital Payments,” addressed real-time threat detection, third-party risk and securing complex ecosystems, underscoring cybersecurity’s front-and-centre role for digital commerce. Fintech Connect 2025
Key takeaway: Payments remain fertile ground for innovation, but trust and security are foundational determinants of user adoption and ecosystem resilience.
The Tokenize: LDN co-located stage brought in robust debate around real-world asset (RWA) tokenization and Web3 infrastructure—not as fringe buzzwords, but as emerging institutional tools.
Panels like “Bridging the RWA Infrastructure Gap” unpacked regulatory friction points and scaling challenges, highlighting custody risk, compliance complexity and standardisation needs—critical prerequisites to institutional adoption.
Another session on “Expanding Investment Opportunities With Fractional Ownership” featured cross-sector thought leaders, including Dr Lisa Cameron (MP & Crypto APPG Chair), exploring how tokenised assets can democratise access to traditionally illiquid markets.
Web3 panels examined trust, privacy and compliance in blockchain ecosystems and navigated the practicalities of smart contracts and decentralised identities—topics that are rapidly gaining traction with enterprise adopters.
A key session, titled Blockchain and CBDCs: At the Heart of Public Transformation? featured NatWest’s Head of Group Payment Strategy Lee McNabb, EY’s Emerging Tech & Innovation Leader Igor Mikhalev and Joy Adams, COO for Digital Assets at Deutsche Bank. A lively debate chaired by CommerzBank’s Poonam Ahuja examined the pros and cons of digital currencies and the rise of stablecoins.
Key takeaway: Tokenisation is still nascent, but panels stressed it’s transitioning into a practical institutional infrastructure conversation, with regulatory clarity and integration tooling cited as catalysts for broader uptake.
Startup Innovation & Demo Highlights
The Innovation & Start Up stage and Start-Up LaunchPad provided rapid-fire exposure to emerging companies pushing the frontier.
These sessions were among the most interactive parts of the show, with founders directly answering questions on integration, compliance and product-market fit.
Key takeaway: Startups revealed solutions that dovetail with enterprise needs—especially around AML automation, customer engagement and data orchestration—making them compelling partners for larger financial services buyers.
Networking, Community & Celebration
FinTech Connect didn’t just deliver talks; it facilitated dense networking across peer groups, investors, regulators and tech leads. The AI-powered networking app helped attendees pre-book conversations and tailor agendas, turning serendipity into structured discovery.
The 10th anniversary celebration—complete with drinks, a Christmas Market theme and live entertainment—reinforced the community aspect and capped the event on a high note.
Conclusion: A Hard-Working Fintech Forum
FinTech Connect 2025 proved to be more than a conference—it was a strategic inflection point. While technology and vendor showcases were abundant, it was the panel debates and operational talks that delivered the most actionable insight. Attendees departed with:
A clearer view of AI adoption roadmaps;
Practical frameworks for RegTech and compliance transformation;
Nuanced understanding of payments security and real-time rails;
Emerging tokenisation playbooks suitable for institutional pilots.
As FinTech leaders prepare 2026 budgets and technology plans, FinTech Connect has reaffirmed itself as a must-attend forum where strategy, innovation and regulation intersect—and where the next decade of financial services will continue to take shape.
Driving Business Transformation Through Cloud & AI
Microsoft’s Shruti Harish, Head of Solution Engineering for Cloud and AI Platforms across the tech giant’s Manufacturing and Mobility vertical, talks to Interface about how to achieve successful AI implementations augmented by Cloud. Our future focused fireside chat covered everything from driving value through cloud modernisation to responsible AI.
“Leaders should align AI initiatives with clear business outcomes and foster a culture that embraces change. The focus is shifting toward AI-operated, human-led models where intelligent agents handle tasks and humans guide strategy.”
Virgin Media O2: Democratising Data as a Cultural Movement
Mauro Flores, EVP for Data Democratisation at Virgin Media O2, talks to Interface about the leading telco’s data journey and how it is supporting colleagues to innovate faster, make smarter decisions and deliver brilliant customer experiences.
“Data-driven insights are essential. They’re helping power our decisions like optimising our network performance, anticipating outages before they happen, identifying and preventing fraud, personalising offers and pricing to build customer loyalty, and forecasting demand so we invest in the right things.”
CIBC Caribbean: Shaping the future of Banking in the Caribbean
Deputy CIO Trevor Wood explains how CIBC Caribbean is blending technology, culture, and customer-centricity to deliver seamless digital experiences across the region with a ‘Future Faster’ strategy.
“We want to lead in every market we operate, build maturity across our practices and be architects of a smarter financial future for all.”
And read on for deep AI insights from ANS’s CIO on why AI isn’t just for big business, Emergn’s CTO on how your business can get AI-ready and Kore.ai’s Chief Strategy Officer on taming AI-sprawl with governance-first platforms.
We also hear from Celonis, Snowflake, ServiceNow, Make and Zoom with their tech predictions for 2026 and chart the key dates for your diary with global networking opportunities at the latest tech events and conferences across the globe.
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
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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.
Abdenour Bezzouh, Chief Technology Officer at myPOS on how AI is revolutionising FinTech from reactive to proactive solutions
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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.
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
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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?
Chief Operating Officer Bhavna Saraf gives us the lowdown on the genesis of Quidkey and how it is leveraging APIs & AI to transform open banking networks into merchant-ready solutions driving higher conversion and borderless coverage with no-cost simple integration
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Founded in early 2023, Quidkey has quickly established itself as a trusted provider of next-generation Account-to-account (A2A) payments. Also known as ‘Pay by bank’. Leveraging AI-powered bank prediction, instant settlement, and a streamlined user experience, Quidkey has created a bank-branded checkout system powered by Open Banking. It combines refunds, rewards, and real-time settlement bringing together cash flow, trust, and convenience for merchants. Its growth in the UK and EU is now being expanded to service Australia and the US corridors.
Chief Operating Officer Bhavna Saraf met CEO Rob Zeko and CTO Rabea Bader, Quidkey’s co-founders, at the end of her time with Santander. They were pitching Quidkey’s offering to top bank executives. Their vision was ambitious:
Democratising access to bank products amongst its customers through a single channel
Leveraging and monetising its API stack for payments
Providing value add services making open banking usable for businesses
“I remember thinking it wasn’t a standard FinTech pitch,” recalls Bhavna. “It was a real infrastructure story that was additive and complimentary to all ecommerce ecosystem players, merchants, banks, PSPs and consumers. When I began figuring the next steps in my career, Rob reached out. The discussion evolved into a collaboration – the timing was serendipitous.
Rob believes A2A payments are the future of commerce, and merchants deserve simpler, faster and fairer ways to get paid. “We’ve built a model designed to scale responsibly,” he notes. “Bhavna brings the structure and operational depth to help us do just that.”
Rabea is responsible for technology and product at Quidkey. With a seasoned background in technology, he has developed the core engine driving Quidkey’s diverse solutions. These include bank-prediction algorithm, refund automation, and multi-currency settlement, through simple API integrations.
“Our aim is to make the technology invisible,” Rabea explains. “If it feels effortless for merchants, it means we’ve done the hard work well.”
Together, Rob and Rabea laid the foundation. Bhavna’s arrival added the operational layer needed to take Quidkey global.
FinTech Strategy spoke with Bhavna to learn more about her journey. And how her experience is driving Quidkey’s progression across the payments landscape…
Tell us about your approach to leadership at Quidkey… How do you reflect on what has been achieved during your time with the organisation?
Learning has always meant leaning into the unknown. It’s not just about a strategy, but a mindset. Taking on new business lines, exploring unfamiliar customer segments, getting closer to technology, or stepping into entirely new organisations. It’s important to look outside your comfort zone, because that’s where you find growth. Each pivot builds experience equity. The instinct to link problems with solutions, to adapt with nuance, and to lead effectively no matter the context.
It’s the same mindset that underpins my approach to leadership. That it’s not just about hierarchy but influence. Creating an environment where people feel trusted, empowered, and part of something larger than themselves. It’s important to build a feel-good factor where collaboration replaces control and purpose drives performance. Such a philosophy can shape teams and inspire peers. It has helped me forge strong connections across clients, colleagues and ecosystems alike.
What drives and inspires you?
At the core of my journey is a relentless drive to deliver progress. Time is money. And… Impossible is nothing. Those words capture my pragmatism and optimism. Qualities that have guided me from scaling trade finance at Citi, to launching digital propositions at Lloyds, to leading payments innovation and strategy at Santander UK. Each chapter has broadened my perspective and sharpened my instinct for where financial infrastructure is headed next. At Quidkey, I get to bring all I’ve learned from building at Citi Ventures to leading across banks and apply it where innovation and impact truly meet on a day-to-day basis.
Could you share how your extensive experience with the dynamics of payments across your career (Citi, Lloyds, SWIFT, Santander etc) have honed your skills in the space? How is it enabling you to drive positive change in the market through your role at Quidkey?
Across leadership roles at Citi, Lloyds, Santander and HSBC, I built and scaled businesses that fuse technology, finance, and innovation. Taking ideas from zero to one or propelling growth to the next level. The focus has consistently been on unlocking near-term value while shaping future-ready roadmaps aligned with market trends, regulatory change, and evolving customer needs.
Alongside my day job, at Citi, I first experienced entrepreneurship, as the founder of an intra-bank start-up within Citi Ventures’ D10X program. We raised funding, assembled a team and developed algorithms to match clients across the bank’s global network. The project advanced to Seed 2 funding, earning recognition from Citi’s Global TTS CEO and the Head of Citi Ventures.
I caught the founder’s bug. That experience showed me the power of turning an idea into reality. It taught me to balance innovation, risk, and speed. And gave me a deep respect for what it takes to build something new.
Tell us about the genesis of Quidkey and its mission…
Quidkey was born from a simple idea, that merchants should be able to grow with confidence, scale sustainably, and offer customers a seamless payment experience, at home or abroad.
For too long, fragmented rails and card scheme costs have added friction to the payment ecosystem, especially hurting SMBs. Quidkey changes that. Our payment solution requires no change to the checkout experience yet simplifies payment routing, reconciliation, and settlement optimisation behind the scenes.
By cutting out unnecessary intermediaries and using Open Banking rails, Quidkey delivers faster, more transparent and cost-efficient payments, empowering merchants to grow and helping banks realise greater value from existing infrastructure.
This novel approach sets the foundation for what could evolve into a global clearing layer for digital commerce, removing friction, reducing cost, and reshaping the future of payments.
What industry challenges can Quidkey solve?
Payments today are still more complicated than they need to be. Merchants face high fees, chargebacks, and slow settlements, while banks and PSPs struggle to turn their Open Banking investments into meaningful value. The result is a fragmented system that creates friction for everyone.
Quidkey bridges that gap. By simplifying how money moves between banks, fintechs, and merchants, we make payments faster, cheaper and transparent. The outcome is better liquidity and smoother experiences for merchants, stronger customer relationships, and a real return on infrastructure for the banks that power it all.
What benefits are your clients experiencing from Quidkey’s approach to open banking?
Open banking adoption is accelerating fast. There are already more than 15 million UK consumers and small businesses taking advantage of open banking-powered services, generating two billion transactions per month and growing. We expect Open Banking payments to generate about 5x more in global revenue by 2030.
Quidkey is at the centre of this evolution, turning Open Banking into measurable value through intelligent settlements, stronger customer loyalty, and real returns on investment. We optimise payment rails for merchants, enhance efficiency for banks, and keep payments frictionless for consumers.
Why should UK businesses and consumers embrace open banking with Quidkey? How does Quidkey make the cross-border rails more usable so everyone can benefit?
With the rapid global expansion in consumer adoption of A2A payments, global A2A transaction volume is expected to increase by 209% in the next 5 years. From 60 billion in 2024 to over 185 billion by 2029. This growth is driven by cost efficiency, speed, convenience and enhanced security compared to traditional card payments. It is especially prevalent across key markets like Europe, where A2A is a leading online payment method in several countries.
Quidkey offers merchants the ability to seamlessly integrate this new technology and deploy it both domestically and for cross-border purposes, while simultaneously reducing transaction costs by up to 60-70% as compared to legacy payment models:
Consumers enjoy frictionless, bank-authenticated payments with protections
Merchants save on processing costs, increase conversions, and reduce fraud/chargebacks
Banks strengthen customer primacy and democratise access to their products at checkout.
API – Application Programming Interface. Software development tool. Business, modern technology, internet and networking concept.
How easy is it for merchants to deploy Quidkey?
Quidkey offers easy integrations via Shopify plug-in, WooCommerce, or iFrame with set up in minutes… No code and zero impact to existing payment options – just faster payments that generate capital to invest in growth.
With fair fees and no lock-ins, Quidkey’s daily settlement can cut costs and optimise cash flow with product bundles designed for growth. Additionally, Quidkey delivers an Apple Pay–style one-tap experience but over bank rails that reduce fraud and charge back risks.
Talk us through some of the big success stories for Quidkey that will provide a platform for future growth?
Our early priorities focused on go-to-market execution – getting the Quidkey solution in the hands of consumers to iterate and prove product-market fit. Quidkey is among the few companies approved to service Shopify checkout globally.
Additionally, we’ve announced a strategic partnership with Tryp.com to power next-generation ‘Pay by Bank’ travel payments. The collaboration is delivering instant settlement, loyalty rewards, and a frictionless A2A experience – achieving a 12% checkout take-up rate versus <1% for traditional Open Banking solutions. The early data shows strong consumer resonance, with room to grow through education and incentivisation. Quidkey’s tech is industry-agnostic – already extending to sectors like fashion, cosmetics, jewellery, and home goods. And we plan to expand next into globalised B2B payments.
What’s next? What forthcoming initiatives are you particularly excited about for 2025 and beyond…
“The transition from multinational banking to fintech is less of a leap and more of a return. In a bank, you have all the resources but with layers of bureaucracy; in a start-up, full permission but no resources. The goal is to combine both, the creativity of a start-up with the rigour of an institution.
Looking ahead, Quidkey’s focus is clear: scale globally, expand merchant adoption, deepen ecosystem partnerships, and build a sustainable, purpose-driven organisation.
Cross-border commerce remains one of the toughest challenges – yet also the biggest opportunity. Global payment flows reached $45 trillion in 2023 across B2B, e-commerce, and remittances, and are expected to hit $76 trillion by 2030. Still, businesses face high fees, slow settlements, and fragmented rails.
Quidkey is tackling this head-on by building a merchant-facing clearing layer that harmonises domestic and cross-border payments, making it as easy to sell abroad as it is at home.”
Tell us about some of the partnerships Quidkey has forged?
Quidkey recognised the geographical limitations in the A2A payments market presented a significant adoption barrier. It’s an increasingly globalised economy, with existing open-banking providers unable to provide full-service cross-border functionality. So, we’ve been hard at work developing a new payments paradigm with mutually beneficial partnerships to help us deliver on the full potential of globalised A2A payments. Now, with our initial solutions fully tested and our user experience optimised to provide seamless integration across channels, we are focusing on cross-border flows to build out the foundations that will underpin Quidkey as the next generation A2A global clearing house.
For example, our partnership with Transfermate enables cross-border A2A ecommerce, harnessing open banking technology to replace costly card rails with a faster, more efficient model of payments. TransferMate’s global network of payments, receivables, and local accounts will power Quidkey’s merchant offering, enabling instant or near-instant settlement in domestic markets and accelerated cross-border payments worldwide, with a waiting list of 100+ merchants in Australia selling into EU, UK and US.
“We believe execution doesn’t slow down innovation – it amplifies it. I want to make sure Quidkey scales with purpose – fast, but in control, ambitious, yet trusted.”
About Quidkey
Quidkey is a cross-border payments technology company enabling merchants to accept instant account-to-account payments across the UK, EU, and US. By operating alongside existing PSPs rather than replacing them, Quidkey gives merchants a seamless path to lower costs, faster settlement, and higher checkout conversion. Quidkey is simplifying today’s fragmented payment mix (cards/wallets), enabling tomorrow’s open banking corridors, and preparing for the future of tokenised money – capturing the $2.6tn and growing global e-commerce payments opportunity.
Emma Steeley, CEO of Infinian, the global real time credit intelligence bureau providing data to banks, lenders and other data businesses, explains the consequences of credit data being stuck in the past, and how banks and fintechs can overcome the mounting consequences
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Despite a cost-of-living crisis and unpredictable economic outlook, too many lenders are forced to make credit decisions using information that belongs to another era. This outdated data is based on small samples, derived from national averages and historical surveys that fail to capture the volatility and diversity of financial realities defining life in the UK today.
That disconnect between data and reality harms consumers, distorts pricing, and drags on the wider economy. In short, affordability decisions are outdated before they are made. Borrowers are judged on figures that don’t reflect their actual costs, creditworthy customers are turned away, while others are approved for loans they can’t afford. Real-time, accurate, large-sample data is essential for fair and functional credit markets, and as an industry we must work to ensure decision-making is dragged into the modern day, to support the integrity of financial services and the aims of Consumer Duty for the good of financial services and consumer duty.
Legacy Models Versus Modern Risks
For years, affordability models have relied on spending benchmarks from the Office for National Statistics (ONS) and other national-level datasets. ONS data, often sourced from the Living Costs and Food Survey, can lag real-world conditions by more than a year. It captures what households spent yesterday, not what they face today.
When models depend on national averages and retrospective surveys, they miss the nuances of how people earn and spend. Workers on variable incomes, renters, and those without long credit histories are most likely to be penalised. They may be financially stable, but legacy data can’t see that, leading to unnecessary declines and reinforcing the gap between those who can access affordable credit and those who can’t. Moreover, outdated data also increases the risk of false positives, meaning lenders may approve those who are likely to default.
False positives and negatives aren’t the only concerns, but also compliance – the Financial Conduct Authority’s Consumer Duty makes clear that firms must deliver “good outcomes” for retail customers, including through fair pricing and practical support. If lending decisions are based on incomplete or obsolete data, it becomes difficult to evidence that duty. The FCA’s own CONC 5.2A rules require a “reasonable assessment” of a customer’s ability to repay; data that misrepresents current affordability can’t reasonably support that test.
Legacy benchmarks, once a useful proxy, now risk embedding unfairness. They distort pricing, entrench exclusion, and hold back lending when the economy most needs momentum.
Gaining a True Perspective on Affordability
Fresher, more granular data is changing what responsible lending can look like. Real-time or high-frequency data streams from verified income flows, transaction activity, and recurring payment histories provide lenders with a comprehensive picture of affordability.
Unlike static surveys, these sources track actual behaviour. They show how a household’s disposable income shifts month to month, how energy or rent payments fluctuate, and how consistently people meet obligations. When used responsibly, this information enables lenders to make faster, more informed decisions that align with each borrower’s actual circumstances.
The payoff is fairer, more inclusive, and more responsible: three goals that don’t have to be in tension. Real-time credit intelligence can also help reduce unnecessary declines, extend access to consumers previously considered “thin-file,” and still maintain prudent risk controls. In other words, responsible lending doesn’t have to mean lending less; it means lending smarter.
It also helps lenders identify early signs of financial stress. If outgoings begin to rise faster than income, that signal appears immediately rather than months later, allowing firms to step in with tailored support before problems escalate. By closing the gap between reality and response, real-time data enables lenders to be both fairer to customers and more agile in managing their portfolios.
The Commercial Case for Better Data
Aside from the moral argument, and the benefits it will bring to compliance and consumer protection, there’s also commercial incentives to modernise credit data.
With access to better data, lenders can approve more of the right customers without increasing risk. Decision engines will become sharper, with improved acceptance rates and portfolio performance simultaneously.
Speed is another advantage. Consumers nowadays expect instant answers and laggy underwriting processes can make customers shift to faster competitors. Access to real-time credit data enables lenders to expedite these processes, thereby improving satisfaction and conversion rates. In a crowded market, those gains translate directly into loyalty and market share.
Basing decisions on current financial behaviours also reduces the need for unnecessary full-bureau checks and manual interventions, lowering the cost per decision and freeing up resources for higher-value activity.
Ultimately, modernisation is about competitiveness. Financial institutions, whether banks or fintechs, that invest in real-time credit intelligence today will be well-placed to earn trust, loyalty, and market advantage.
The Future of Fair Finance
Credit markets rely on accuracy, and accuracy in turn depends on timeliness. When the information behind lending decisions lags behind real life, fairness falters, capital is mispriced, and opportunities are lost.
Real-time, representative data allows lenders to extend credit responsibly, price risk precisely, and support customers before problems arise. It strengthens inclusion while improving overall performance.
In a world where household finances can change in weeks, lending models must keep pace with reality. Institutions that invest in live, comprehensive data today will set the benchmark for fair and effective finance in the years ahead.
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
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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.
With the rise of AI-enabled fraud in mind, Dave Rossi, Managing Director at National Hunter, argues the need for a radical rethink
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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.
At AWS, we’re obsessed with helping our customers harness the benefits of cloud and AI. While maintaining robust security, resilience…
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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
Financial Services Director Arunkumar Gopalakrishnan on how Publicis Sapient is developing the playbook for delivering successful AI-led digital transformations across the financial services landscape
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Publicis Sapient doesn’t sell tools; it delivers human-led, AI-enhanced solutions that blend proprietary platforms with deep industry expertise across global banking. The organisation is shaping the future of financial services by delivering complex digital transformations across continents – from the UK and Southeast Asia to the Middle East and the United States – anchored by a belief that true innovation lies at the intersection of business insight and technological depth.
Publicis Sapient utilises a SPEED philosophy – Strategy, Product, Engineering, Experience and Data. “For us, SPEED isn’t just a framework. It’s the way we align our capabilities to accelerate transformation,” explains Financial Services Director Arunkumar Gopalakrishnan. “My focus is the ‘P’ in that process – Agile Program Management and Product Management; helping clients move from vision to value at pace.”
Transformation at SPEED
During his time with Publicis Sapient, Arunkumar has seen the power of transformation at SPEED on a variety of high-stakes projects: helping a major UK bank launch a digital-only entity on the cloud; partnering with a leading Thai bank to revitalise its mobile-banking experience for a fast-growing, tech-savvy customer base; supporting a sovereign-funded startup bank exploring blockchain for trade finance in collaboration with Microsoft; building a holistic wealth-management platform for a large US custodian bank; and helping another lead the way in AI adoption. These are the type of innovation journeys where Publicis Sapient excels at moving from groundwork to exponential scale.
At the Intersection of Business and Technology
Publicis Sapient excels by fusing two disciplines often treated as separate. “We are at the intersection of business and technology,” explains Arunkumar. “You need deep business acumen to understand client challenges. However, you must have enough technical depth to engage meaningfully with engineering teams. That balance is what enables real problem-solving.”
From fraud prevention to blockchain and digital banking the industry is changing fast,” he notes. “Working with Generative AI today feels like standing on a new frontier. It keeps us on our toes, but it’s also what drives us – to stay relevant, deliver outcomes and connect both worlds of business and technology.”
Meeting the Challenge: Balancing Innovation and Risk
The biggest challenges facing financial services clients are not purely technological. They are structural and cultural. “Banks operate in complex regulatory environments,” notes Arunkumar. “There’s always a tension between innovation and risk management. On one hand, you want the next shiny thing; no one wants to be left behind in the technology race. On the other, you can’t bring something to life without going through the proper regulatory and risk-management controls.”
That balance defines the work Publicis Sapient does. “We are a people + product business,” he says. “Our strength lies in talented people, strong domain understanding, platforms, tools and a culture that says to clients: We have your back; we get it done.”
Many of the firm’s engagements, he explains, involve deep collaboration, experimentation and iteration. “Some of the use cases we take on aren’t easy. We work with partners, we research, we prototype, we unlearn and relearn. Progress in this space is continuous, not linear.”
A Digital Transformation Success Story
Publicis Sapient partnered with a large US Bank to lead digital transformation efforts focused on GenAI implementation and scaling. It worked in collaboration with Google and the Bank to design, build, and adopt GenAI to spur innovation, enhance risk management and improve productivity.
The high-level solution is composed of modular components including a secure GenAI Gateway for LLM access control, RAG framework for contextual retrieval, and Vertex AI integration leveraging Gemini models for high-quality natural language responses.
Delivering the Solution
The solution delivered integrated, repeatable accelerators designed to solve the central business challenges of speed, risk, and control from the ground up.
Publicis Sapient’s Financial Services Director Arunkumar Gopalakrishnan explains how the platform was built upon some core pillars:
Unified LLM Access & Model Context Protocol: “We streamlined the model consumption layer with a foundational gateway, built on a resilient Model Context Protocol (MCP). The MCP acts as an essential abstraction layer, ensuring all data streams, model inputs, and application requests are managed consistently, securely, and in compliance with governance rules.”
Integrated Governance & Security: “We implemented a ‘shift-left’ security approach, embedding continuous guardrails directly into the GenAI pipelines. This pre-processing step, coupled with adversarial testing, proactively minimizes human error, reduces operational risk, and ensures a continuous, audit trail.”
Proprietary Knowledge Grounding (RAG): “The platform enables the Retrieval-Augmented Generation (RAG) pattern. This involves securely indexing the bank’s vast internal repository of operational knowledge and compliance guides – by using this verified knowledge to ‘ground’ LLM responses, the platform ensures every AI output is based on the bank’s accurate, proprietary data. This successfully mitigates factual errors, minimizes hallucination risk, and protects brand integrity.”
Agent Orchestration (Agentic AI): “Moving beyond simple chat, the platform includes capabilities for managing Agentic AI workflows which greatly improves efficiency. These agents are goal-directed systems that execute multi-step business processes (e.g., investigating a service ticket, performing patching operations etc. with human-in-loop for oversight). This is the crucial layer for end-to-end automation of complex, cross-functional tasks.”
Unified Observability: “The final pillar establishes a system for tracking crucial metricstied to defined business outcomes. This enterprise-level observability framework captures data like response latency, quality, consumption rates etc. This data allows leadership to continuously monitor output quality and reviewing against the standards of accuracy and trustworthiness.”
Realising the Benefits
The positive impact of the work Publicis Sapient is doing includes:
Scalable Framework: Designed as a Platform-as-a-Service (PaaS) model to support future GenAI use cases across the enterprise; agent-driven extensibility enables enhancements with rapid time-to-market deployments.
Accelerated Onboarding: Automates the provisioning process by surfacing relevant documentation, policies, and procedures instantly.
Knowledge Reuse: Leverages existing enterprise knowledge bases to reduce redundancy and improve consistency.
Building with Purpose: From Vision to Scale
At the heart of Publicis Sapient’s transformation philosophy is its Digital Business Transformation Framework. Teams use a playbook to take clients from problem definition to scaled delivery. “It starts with Ignite – understanding the problem and bringing in strategic expertise,” explains Arunkumar. “Then comes Hunt & Shape – identifying and defining value, mapping MVPs and roadmaps. And finally Build & Scale – turning ideas into outcomes by building the right solutions.”
Scaling, he insists, is not only about size but certainty. “You don’t scale right away. You start small – proofs of concept, limited users, experiments and learn fast. Once you know what works, you can accelerate.”
He points to a current AI engagement as an example. “We started with one application hosted on the platform last year. Now we have twenty-plus, and many more coming. Building the foundation took months, but once we understood the landscape, everything else became a fast follower. You develop a playbook, you know the risks, and then it’s about momentum.”
Generative AI: A Catalyst for Reinvention
Few technologies have captured the imagination of financial services like Generative AI. Arunkumar sees its impact as both profound and pragmatic. “While business leaders talk about productivity gains, CIOs are using GenAI to drive measurable productivity and cost efficiency by modernising high-friction IT Service Management processes,” he notes.
Publicis Sapient identifies three areas where the shift is most visible:
Enhanced self-service: Intelligent Virtual Assistants act as the first line of defence. They automate a big chunk of initial inquiries, freeing human agents and improving response times.
IT-agent augmentation: GenAI synthesises ticket histories, diagnoses root causes and drafts expert-level resolutions. It drastically shortens the mean time to Resolution for critical incidents.
Developer velocity: Secure, context-aware coding assistants are improving efficiency, allowing engineers to focus on high-value work.
Publicis Sapient’s next frontier is Agentic AI, where intelligent agents move beyond analysis to orchestration and action. Teams have been testing these systems within IT service environments for major banks. “We started with a simple knowledge-search application,” he recalls. “It consolidated information across multiple systems to provide accurate, high-performance answers.”
From there, Publicis Sapient expanded into process automation. “Imagine an IT engineer under pressure to fix issues fast,” he says. “The knowledge-search tool becomes a force multiplier, identifying root causes instantly. Next, you automate the actions – patching servers, routing tickets, escalating tasks – with a human in the loop for control.” The goal is productivity gains with safety.
Challenges remain – particularly model drift and AI hallucination – but these can be mitigated with rigorous evaluation frameworks. “AI is probabilistic, not deterministic,” says Arunkumar. “You can’t expect one-plus-one to always equal two. That’s why continuous grounding, validation and human oversight are key.”
Innovation in Action: Real-World Use Cases
For Arunkumar, the most exciting part of AI transformation lies in the unexpected. “Some of the best use cases aren’t flashy but support everyday processes that, when optimised, deliver outsized value.”
He describes one example from a banking client: improving the reliability of customer statements. “A bank may send hundreds of thousands of daily communications – statements, notifications, alerts. Sometimes statements fail to send, and by the time customers notice, the issue snowballs into reputational risk.”
AI, he notes, can detect these failures proactively. “If a statement isn’t generated by 7 a.m., the system flags it very soon and resolves it before customers even notice. Predictive AI identifies the anomaly; GenAI drafts the corrective communication for review. It’s small, but it saves time, cost and reputation.”
Such “mundane” use cases, he argues, are where the real transformation happens. “Everyone talks about the big, shiny things. But in complex, regulated environments, it’s the subtle automations that drive consistent outcomes.”
Platforms for the Future
Publicis Sapient’s investment in AI platforms underscores its commitment to innovation. Arunkumar highlights three in particular:
Bodhi, the foundational system for building intelligent agents
Slingshot, designed to accelerate software-development lifecycles
Sustain AI, focused on IT service management and operational resilience
“These are our three-pronged approach to transformation,” he explains. “Each builds on the other – Bodhi as the foundation, Slingshot for velocity, and Sustain AI for long-term stability. And there’s more in the pipeline…”
Culture, Collaboration and the Power of Small Wins
For all the technology involved, Arunkumar insists transformation ultimately depends on people. “In any transformation, you work with stakeholders who have competing priorities,” he says. “The key is to focus on agreements first – find small wins and move forward. Progress builds trust.”
Publicis Sapient is a people + product business where Arunkumar encourages his teams to balance ambition with empathy. “If a meeting is contentious, end with one thing agreed. Take the rest next time. Transformation isn’t about forcing alignment; it’s about building it. We tell our clients, and our teams, ‘We have your back’. That trust is what makes complex programs succeed.”
Looking Ahead: Building Expertise and Depth
The focus for 2026, and beyond, is on cultivating deep, dual-disciplinary expertise. “Our teams sit between business and technology,” he explains. “You must be good at both. No one can master all financial services, it’s too vast, but you can specialise. Pick a niche within key areas – asset management, wealth, retail banking, corporate banking, payments, financial crime – and become excellent at it.”
At the same time, he urges his teams to stay curious about technology. “Even if you’re not implementing solutions yourself, you need to understand them and speak the same language as engineers and architects. That’s how collaboration works.”
Continuous learning, he believes, is non-negotiable. “There’s so much information out there – training, communities, conversations. We just need to channel it, understand the basics and keep moving forward.”
Transformation: A Continuous Journey
At Publicis Sapient transformation is never static. “We don’t fix something once and move on,” he says. “We think, test, learn, and build again. You must define the real problem before you solve it, validate your progress and inspire others to see the vision.”
Purpose and persistence turn complexity into clarity for Publicis Sapient’s clients. “The journey is continuous,” says Arunkumar with characteristic calm. “But that’s what makes it exciting. Every challenge is an opportunity to learn, collaborate and move forward – one small win at a time.”
AI commerce is set to transform how people shop and buy. Find out how Visa Intelligent Commerce empowers AI agents to deliver reliable and secure experiences at every step…
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Artificial Intelligence is transforming the way we shop and pay. Visa is leveraging the power of its network and its decades of experience to bring trust and security to AI-powered commerce, bringing to life Visa Intelligent Commerce, which enables AI to find and buy.
This is an innovative initiative that opens Visa’s payment network to the developers and engineers who are building the foundational AI agents that are transforming commerce.
“Soon, people will have AI agents that will browse, select, purchase, and manage on their behalf,” according to Jack Forestell, Chief Product and Strategy Officer at Visa. “These agents will need to be trusted with payments, not just by users, but also by banks and merchants.”
Similar to the transition from physical to online shopping, and from online to mobile shopping, Visa is setting a new standard for a new era of commerce. Now, with Visa Intelligent Commerce, AI agents can find, purchase, and pay on behalf of consumers according to their pre-selected preferences. Each consumer sets the limits, and Visa helps manage the rest.
Creating a Trusted Future for AI Commerce
Millions of people will soon rely on AI to find the perfect sweater, search for a new vacation destination, or complete a shopping list. Visa will eliminate the friction from payment, making it possible to transact securely and reliably in an AI-powered world.
Visa Intelligent Commerce is built on 30 years of experience working with AI and machine learning to manage risk and fraud and to deliver secure payment experiences. Alongside industry leaders such as Anthropic, IBM, Microsoft, Mistral AI, OpenAI, Perplexity, Samsung, Stripe, and others, Visa will facilitate personalised and secure AI commerce on a global scale.
“We are working with companies at the forefront of AI innovation to drive engagement on AI platforms and support new ways to pay, with security and trust as our number one priority,” Forestell added. “Together with our partners, we are fully harnessing the potential of AI to transform every aspect of commerce, payments, and business.”
Empowering Consumers, Merchants, and Developers
The transformation of AI commerce – today a futuristic and relatively unknown concept – into a frictionless, secure, and personalised experience for both merchants and consumers is underway at Visa.
Visa Intelligent Commerce incorporates a set of integrated APIs and a merchant partner program into AI platforms, allowing developers to implement Visa’s AI commerce capabilities securely and at scale.
Visa Intelligent Commerce offers:
AI-Enabled Cards. Replaces card data with tokenised digital credentials, which enhances security for consumers and simplifies payment processes for developers. In turn, it confirms that the agent chosen by the consumer is authorised to act on their behalf. This incorporates identity verification into AI commerce. Only the consumer can instruct the agent on what to do and when to activate a payment credential.
AI-Powered Personalisation. The consumer is in control. It shares basic spending and shopping information from Visa with the consumer’s consent to improve the agent’s performance and personalise purchasing recommendations.
Simple and Secure AI Payments. Allows consumers to easily set spending limits and conditions, providing clear guidelines for the agent’s transactions. Additionally, it shares real-time commerce signals with Visa, enabling Visa to monitor transactions and help manage disputes.
Visa’s payment technologies, including tokenisation and authentication APIs, will help enable transactions that are secure and frictionless, providing confidence to consumers who use AI to make purchases. Visa has decades of experience in fraud management, along with a robust data platform, and uses this expertise to power the Visa Intelligent Commerce program.
Our cover star Scott Gunther, General Partner at IAG Firemark Ventures, reveals how the company is bringing powerful investments to…
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Our cover star Scott Gunther, General Partner at IAG Firemark Ventures, reveals how the company is bringing powerful investments to life to transform the ways insurance is delivered.
Scott Gunther, General Partner at IAG Firemark Ventures, tells FinTech Strategy how the company is championing key InsurTech investments to transform how insurance is delivered.
“We realised that if we were going to bring the best of the outside world in, we needed to be a truly global CVC.”
Publicis Sapient
Financial Services Director Arunkumar Gopalakrishnan tells us how Publicis Sapient is developing the playbook for delivering successful AI-led digital transformations across the financial services landscape.
“Working with Generative AI today feels like standing on a new frontier. It keeps us on our toes, but it’s also what drives us – to stay relevant, deliver outcomes and connect both worlds of business and technology.”
Techcombank
Chief Strategy & Transformation Officer, PC Chakravarti reveals the operating model, Data & AI foundations, culture and talent playbook, and the partnerships turning ambition into market leading outcomes at Techcombank in Asia.
“Tech is not the limiting factor – it’s about supporting people and talent to leverage capabilities to enhance business models.”
CIBC Caribbean
Deputy CIO Trevor Wood explains how CIBC Caribbean is blending technology, culture, and customer-centricity to deliver seamless digital experiences across the region with a ‘Future Faster’ strategy.
“We want to lead in every market we operate, build maturity across our practices and be architects of a smarter financial future for all.”
Nationwide
Dan Wilson, Head of Customer Journey at the trusted mutual, reveals the strategic ambition driving payments innovation to modernise Nationwide’s platform delivering a resilient and secure financial future for customers across the UK.
“We’re seeking to modernise the Society’s core infrastructure but also build the tools and features our customers need to help them manage their money and payments.”
Chief Information Officer, Jan Bouwer, explores the work Alexforbes has undertaken to modernise and expand its financial services for its 1.2 million members and retail customers alike. “Alexforbes can now engage its 1.2 million members more directly, offering a wider range of services.”
Paul Sweeney, Product Integration Officer at Aryza, explores how AI is reshaping customer engagement in credit and collections — not by replacing people, but by making every interaction more human
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Today, we’re constantly bombarded with requests for our personal data, from market researchers and government census takers to supermarket loyalty schemes that demand we flash a QR code at every checkout. It’s no wonder consumers are tuning out. So, when credit and collections organisations come calling for more information, customers are already halfway to disengaging before the conversation even begins.
From Forms to Conversations
Today, many forward-thinking organisations are turning to conversational AI to make these interactions feel more natural and less like a chore. Instead of filling out endless forms or providing data step-by-step, AI now enables something far more natural, a dialog. The system intelligently recognising what’s already been shared and gently prompting for what is still needed. It flows better and feels less transactional and more human.
Great customer service teams remember what you’ve told them before, pull up your files and data seamlessly, and avoid that infuriating pretence that they don’t know who you are, because let’s face it, nothing frustrates us more than companies we pay money to acting like we’re strangers.
The Rise of Everyday AI
Customers have long relied on tools like Google to hunt down information, adjusting phrases to get the right results. Now, generative AI has taken that habit to the next level. With platforms such as ChatGPT becoming a top five consumer application, it has started acting as a personal assistant for everything from daily decisions about what to cook for dinner to how to deal with financial dilemmas.
For credit and collections, it’s easy to imagine the potential. Simply upload your bills, take a photo of your accounts, and ask it to prioritise payments or even draft a response to the bills you can’t cover yet. It predicts your follow-up questions, suggests next steps, and can whip up a formal letter to your utility provider explaining the delay and what you’d like to happen next. If you haven’t already tried this, do so. It’s an eye opener and a glimpse of what’s coming. In fact, the use of AI is becoming increasingly common for financial advice, as it ranks as the second most common use case (41%)
Trusting the Machine: What the Data Shows
A recent report by Intuit Credit Karma revealed that 66% of people surveyed have already used generative AI to seek financial guidance, with the highest adoption rates among Gen Z and millennials. It’s a clear sign of the growing level of trust in AI-driven insights, in fact, 80% of respondents said they acted on the advice received and felt it improved their financial situation. However, the findings also underscore a deeper issue, as many people are turning to AI for financial questions, they feel too embarrassed or uncertain to ask elsewhere, highlighting the ongoing need for greater financial confidence and education.
Looking ahead, this kind of interaction will become the norm rather than the exception. Each customer will have their own form of AI assistant, one that knows their context, frames the right questions, and guides them smoothly towards their goals.
Empowering the People Behind the Screens
On the other hand of the equation, customer service staff are getting a major boost from AI too. Good systems now automatically tag and direct incoming messages, prioritising urgent ones from vulnerable customers over the routine inquiries. Conversations are summarised in real time, providing agents with a clear overview of what’s been discussed, how the issue is progressing, and the odds of a positive outcome. These AI tools handle the heavy lifting on volume, spotlighting complexities or trade-offs, ushering in an era where every worker has an AI co-worker.
What kind of AI assistant would a contact centre supervisor need? How about a C-suite executive, what features would they require? And if you’re an enterprise architect, would you want part-time reps generating policy docs or asking high-level questions? Probably not. You’d insist on guardrails, strict policies, and complete auditability at every step of AI-driven interactions. Generic AI will deliver generic experiences. For supervisors and decision-makers, AI assistants will also become indispensable coordination and decision-support tools, monitoring performance across teams, flagging bottlenecks, and recommending the best subsequent actions to maintain service quality and compliance. Those that deeply understand the challenges you face across all the lending cycle are best placed to power the AI assistants that you will depend on in the future.
The AI revolution is already here, but now’s the time for everyone to zero in on the data, its journey, and the models powering this future. Deep data architecture will be critical: each role, customer, agent, and supervisor requires access to tailored data and AI capabilities that fit their needs. That’s how we move from one-size-fits-all automation to truly personalised, intelligent experiences that improve outcomes for everyone.
Stock Investing has become increasingly popular over the last few years. The self-directed investing trend is in full swing and…
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Stock Investing has become increasingly popular over the last few years. The self-directed investing trend is in full swing and retail investors are looking for smarter and better ways of looking at the markets to identify winning stocks. A plethora of web services and chats now exist solely to service this market. Many of these services process the same limited types of data, such as market prices and tape, fundamental data, filings or even news sentiments.
Navigating Investment Services
How can investors navigate this crowded landscape of services? It all depends on what the investor is looking for. Broadly, there are three levels of investing behaviour and tools:
Level 1: The Stock Tip.
This investor just wants a stock tip – simply what to buy and when to sell without trying to understand the why. He may “ask the audience” and use a Telegram chat or Discord chat service for that, “phone a friend” who just takes a “50/50” guess. The platforms providing these services are usually unsophisticated operations often with one or two individuals animating a series of chats. Speculation, misinformation and meme stock “pump and dump” schemes are frequent.
Outcome: This looks great on the surface as the user gets an immediate stock tip, but what happens later is worrying. The investor will have no idea about when to sell since they did not work to understand the real reasons of why the trade has been initiated in the first place.
Level 2: Raw and Calculated Data
The investor relies on data platforms for research and to decide how to identify promising candidates. From Yahoo Finance to Investing.com, many platforms offer raw and calculated data in tables and charts. These include financial data from the company (either as reported or harmonised), analysts’ recommendation price targets or estimates, company filings (13F, Form 4, 8k …) even public databases of senatorial and congressional registered trades.
This overabundance of data can create information overload, sometimes leaving users more confused than when they started. With hundreds of fields and ratios, it takes significant financial literacy and experience to know where to look, which metric to focus on and the ones to leave out. Coupled with the information already available via a brokerage platform, often the investor is now facing a “wall” of data. Recently, new conversational AI tools that use natural language have been touted as game changers that can make sense of it all. Unfortunately these tools come with their own limitations and biases that are not always visible..
Outcome: The investor is more confused than at the beginning of the process, unless he is trained in using the right metrics for his analysis, this is a losing game. These ChatGPT-like platforms bring a false sense of intelligence as they combine news and data from various sources in a nice summarized paragraph, which is neither reliable, accurate or fool-proof.
Level 3: Derived Proprietary Data
At this level, the investor would turn to a team of financial market professionals who would generate proprietary rating or scoring for each stock helping an investor focus on the right opportunities.
These methodologies are either “proven” or “tested” representing many years of financial market expertise. This layer of human experience makes all the difference in generating valuable insights. Investor’s Business Daily has one of the best known services, providing ratings alongside a respected news bureau that has helped investors for decades.
This approach is probably one of the best for a serious investor – one that would consume this proprietary derived data and combine it with news and other market events for a comprehensive investing picture.
Level 4: LLMs
This level of investing is where not only human experience and skills are in the mix but also Large Language Models processing vast amounts of unstructured data. It is processed from news or filings for a comprehensive view of market conditions and sentiment from text based data. It also brings the most important “human insight” contained in the ranks and scoring in the service.
Stock Investing Solutions
Beyond this vertical hierarchy, there is also a horizontal challenge; that is that the breadth of data is also an issue. Many platforms provide their own niche services, such as focusing on 13F filings, a specific technical analysis, earnings estimates or option flow. As a result, investors often end up subscribing to several services to gain a comprehensive view of the market.
The solution: a flexible, comprehensive platform that delivers everything an investor may need including scoring, rankings and proprietary indicators but while integrating AI models to enhance and supercharge research efforts.
Making data meaningful is the future of investing. Human expertise can be blended with intelligent technology, while modern platforms close the intimidation gap between professional insight and everyday understanding. The world is overflowing with information and trustworthy innovation lies in simplification.
Alex Carteau is the CEO and Founder of EPSMomentum, with more than 25 years of expertise in financial market software across Asia, Europe, and the United States. He spent more than a decade at Bloomberg, advising investment managers through advanced data and market insights. Following his work on Bloomberg’s specialised equity derivatives team, he expanded his career with leadership roles at RaisePartner and TradingScreen.
At EPSMomentum, Alex applies his deep knowledge of hedge fund technology, stock-picking analytics and trading systems to create tools that simplify investing for everyday investors. Drawing on his background in financial technology, his work emphasises clarity and actionable insights. With a drive to challenge outdated approaches, he is committed to providing investors with professional-level resources and advancing the evolution of smarter investing drawing on insight gained over decades of experience.
ABBYY survey finds financial services industry leading on innovation, but challenges exist with deployment
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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
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
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
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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.
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…
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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:
BANK
2025 INDEX
2024 INDEX
2024-25Change
JPMorganChase
1
1
–
Capital One
2
2
–
Royal Bank of Canada
3
3
–
CommBank
4
5
+1
Morgan Stanley
5
10
+5
Wells Fargo
6
4
-2
UBS
7
6
-1
HSBC
8
7
-1
Goldman Sachs
9
11
+2
Bank of America
10
15
+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.
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:
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.
Wells Fargo and Google Cloud have expanded their strategic relationship to deploy Agentic AI tools across the bank. As an early…
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Wells Fargo and Google Cloud have expanded their strategic relationship to deploy Agentic AI tools across the bank. As an early adopter of Google Agentspace, Wells Fargo is equipping teams with AI agents that will help improve the customer experience, automate routine tasks, and unlock new levels of innovation.
With a strong commitment to responsible AI, Wells Fargo and Google Cloud are focused on modernising financial services and empowering employees with Generative AI solutions to deliver more personalised support and services. This strategic relationship reflects Wells Fargo’s dedication to innovation and transforming how the bank serves its customers.
About Wells Fargo
Wells Fargo is a leading financial services company that has approximately $2.0 trillion in assets. We provide a diversified set of banking, investment and mortgage products and services, as well as consumer and commercial finance, through our four reportable operating segments: Consumer Banking and Lending, Commercial Banking, Corporate and Investment Banking, and Wealth & Investment Management. Wells Fargo ranked No. 33 on Fortune’s 2025 rankings of America’s largest corporations. News, insights, and perspectives from Wells Fargo are also available at Wells Fargo Stories.
CIBC launches GenAI platform, CAI, for data analysis, accelerated research, light coding and more…
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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.
Embat and MicroFin strategic alliance delivers AI-powered cash management, reconciliation and real-time visibility for finance teams managing complex, multi-entity operations
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Embat, the leading European financial management and treasury platform, has formed a strategic partnership with MacroFin, part of Cooper Parry Digital and the UK’s leading NetSuite Alliance Partner. The collaboration combines MacroFin’s market-leading NetSuite implementation expertise with Embat’s next-generation treasury technology. The alliance will help finance teams tackle the growing complexity of international operations.
MacroFin has been recognised as NetSuite Alliance Partner of the Year since 2021, reflecting its reputation and expertise for delivering the UK’s most complex ERP implementations. Following its acquisition by Cooper Parry, MacroFin has further solidified its position as one of the UK’s premier NetSuite partners.
Facing the Challenge to Transform
As companies scale – particularly in sectors such as SaaS, e-commerce, retail, and hospitality – their finance teams face challenges to transform that outgrow traditional tools such as Microsoft Excel. Multi-currency operations, multiple legal entities, high transaction volumes, and increased regulatory demands. This partnership ensures NetSuite clients have access to Embat’s treasury platform bidirectionally connected to NetSuite, offering:
Real-time cash visibility across accounts and currencies
Advanced forecasting to support strategic planning
Automated treasury operations to streamline day-to-day processes
Seamless NetSuite integration for consistent, efficient workflows
TellMe, Embat’s AI-powered treasury analyst, which enables finance teams to save up to 75% of their time on manual tasks. Freeing them to focus on strategic decision making
Treasury Management
“Treasury management has evolved from a back-office task to a strategic driver of business growth and efficiency. By working with MacroFin, we’re making advanced treasury technology accessible to NetSuite clients who need real-time visibility and automation to manage complexity with confidence.”
Theo Wasserberg, Head of UK&I at Embat
“When clients face complex international and multi-entity challenges, we look for solutions that go beyond NetSuite’s native functionality. Embat’s direct integration and AI-driven automation deliver the clarity and efficiency CFOs need in today’s environment.”
Ross Latta, Co-Founder of MacroFin
This partnership underscores Embat and MacroFin’s shared commitment to innovation in financial technology and toempowering CFOs and finance teams with tools that enhance both operational efficiency and strategic insight.
About Embat
Embat is a leading European financial management and treasury platform that enables finance teams in medium and large companies to centralise all operations from banking relationships to their financial management processes. It allows finance teams to save up to 75% of their time on manual tasks by using TellMe, our AI-powered treasury analyst, so they can focus on strategic decision-making. The main functions of Embat are treasury automation, automated accounting, and payments. Clients experience cost savings (by optimising their working capital management), time savings, reduced errors and an increased quality of life.
About MacroFin with 3RP and the CP Digital Family
MacroFin is a UK-based consultancy specialising in finance-led ERP (Enterprise Resource Planning) transformations centred around the NetSuite platform. Founded in 2018 by chartered accountants, their approach emphasises embedding finance expertise at every stage of implementation. They offer services including NetSuite implementation, optimisation, training, support, and custom development.
In 2024, MacroFin joined Cooper Parry to form CP Digital alongside 3RP and Cloud Orca, creating a digital transformation hub with wider expertise and tech partnerships.
MacroFin has implemented NetSuite for leading brands like Babylon, Depop, PensionBee, and Zego, achieving average go-live in four months.
New data from Evident shows banks are increasingly turning AI research into real-world tools
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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
CI&T and Reuters Events report – poor data quality is the biggest barrier to AI transformation, says almost ¾ of UK underwriters
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CI&T, a global AI and tech acceleration partner, has released new research highlighting the AI opportunity in the UK. The report, created alongside Reuters Events, reveals poor data quality, rather than technology limitations, is the number one obstacle preventing UK underwriters from accelerating AI adoption.
Strategising for the AI Insurance Revolution
The report, Strategising for the AI Insurance Revolution, draws on original UK survey data and real-world case studies. It aims to uncover how insurers are tackling the AI opportunity. And what’s holding them back. Much of the market discussion focuses on technology capabilities. The findings show that data fragmentation, unstructured formats and siloed systems are the real roadblocks. The goal is to deliver faster, more accurate underwriting and pricing.
Key Findings from the Study
Efficiency over personalisation: Just 15% of claims leaders believe greater personalisation will significantly improve customer satisfaction. Compared with 41% prioritising streamlined internal processes and 39% favouring a blend of digital and human touchpoints.
AI as a cost shield. 60% of claims leaders believe AI-led efficiency will be crucial to offset rising claim costs and premiums.
Sandbox before scale. Insurers are adopting Generative AI cautiously, testing in sandbox environments. This mitigates risks such as hallucinations, bias, and data privacy breaches.
Proven ROI in action:
Working with CI&T, Mitsui Sumitomo (part of Asia’s largest insurance group) saved £800,000 annually. And cut quotation times by 54% through strategic modernisation.
A leading Brazilian insurer cut SME onboarding time by 46%. And achieved a 26% fraud denial rate, automating 2.2 million claims.
Mike Young, VP Insurance Industry Growth at CI&T
“AI’s success in insurance won’t be determined by how advanced the algorithms are, but by the quality and accessibility of the data that feeds them. This research shows UK insurers are ready to innovate—but they need to get their data house in order first.”
With deep experience in the insurance sector, CI&T has helped insurers modernise legacy systems, improve customer journeys, and achieve measurable operational gains. Central to this is CI&T FLOW, CI&T’s enterprise-grade GenAI platform. It is designed with rigorous governance and privacy safeguards so insurers can innovate without compromising sensitive data.
About CI&T
CI&T is an AI and tech acceleration partner. We help businesses navigate the complex, changing European technological landscape to unlock real, measurable impact with digital-first solutions. CI&T brings a 30-year track record of helping clients deliver accelerated impact through tech-integrated business solutions, with deep expertise across AI, strategy, customer experience, software development, cloud services, data and more. As one of the world’s first digital native companies, innovation is in our DNA, helping us empower clients to win by embedding digital maturity into the heart of their operations. With over 7,400 employees across 10 countries, we combine the expertise of a global business with an entrepreneurial mindset to drive transformation at scale and turn strategy into action.
About Reuters Events
Reuters Events is one of the largest and fastest growing events companies anywhere in the world. Reuters Events serves a diverse range of industries and places a focus on the challenges and opportunities resulting from technological and strategic innovation. Our purpose is to provide senior level executives with the trusted insight and meaningful connections they need to confidently navigate change, unlock opportunity and inform their strategy. We curate world-class events and content that are high value to our customers. For more information, visit reutersevents.com. .
Trilliam Jeong, CEO at Wealthblock, analyses the key investment industry trends in 2025 so far…
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Every year the once-staid investment management industry experiences trends in technology, markets, and services that are viewed by many as sure to change the next year’s ways of doing business. Here are three key trends shaping up in 2025… And three from the recent past that turned out to be not-so-trendy.
Trend 1: AI Will Continue to Transform All Areas of Investment Management
Artificial Intelligence (AI), like cloud computing a decade ago, is reshaping the way investment firms acquire, onboard, and manage clients. In 2025, we expect to see deeper integration of AI, providing real-time portfolio insights and automating client communications. Firms will increasingly rely on AI to enhance efficiency and reduce operational costs.
Throughout the past year, leading investment firms have been upgrading their platforms to automate tasks like investor onboarding, marketing, and reporting. This has reduced manual work and human errors. Today, with rapidly advancing technologies like AI and cloud-based solutions, firms are creating customised workflows. These solutions not only benefit clients but allow firms to quickly adjust to changing compliance needs.
Trend 2: Secondary Market Growth
The market for private stakes is likely to expand, offering clients liquidity options beyond traditional public market exits like IPOs. Investors may look to secondary markets for more flexible and immediate exposure to private equity investments. With IPOs remaining limited, secondary market transactions (where private equity stakes are bought and sold) are expected to grow. Both Limited and General Partnership secondaries provide liquidity without requiring a full exit, making them appealing in a market with constrained traditional exit options.
Trend 3: Hyper-Personalisation Through AI
The move toward hyper-personalisation will intensify, with AI tailoring investment firm client interactions to individual preferences. This is crucial for retaining clients in a competitive market. To ensure continues success in 2025, organisations should focus on adopting AI, strengthening their capabilities in secondary markets, and enhancing cybersecurity to protect client data.
Investors now expect quicker, more transparent communication. The state-of-the-art engagement and analytics tools available today have helped reduce delays, but demand for even faster response remains. We foresee further advances in 2025 and beyond.
Beyond these positive trends it is interesting to take note of some oft-hyped predictions in investment technology over the recent past that have not exactly worked out as predicted:
ESG Investing
ESG investing, which gained significant traction between 2019 and 2022, is now witnessing a notable decline. The percentage of new funds labeled as ESG has sharply decreased, and online searches for ESG investing have reverted to 2019 levels.
Tokenisation of Investments
Blockchain and tokenization initially promised a revolution in private investments. But adoption has been slow, primarily due to complex regulations. Firms are now being more selective about blockchain’s real value.
Neobanks and Digital Wallets
Neobanks for private investors have struggled to compete with traditional banks’ digital offerings, leading to a shift in focus. Digital wallets also face security and compliance hurdles in private investment.
AI and Cloud Takes Centre Stage
Generative AI is clearly transforming private equity, with firms exploring AI tools for due diligence, portfolio optimisation, and cost reduction in portfolio companies. While this area is rapidly growing, the high cost and expertise required could limit smaller firms from fully implementing AI solutions across the board.
In 2025, you can expect to see AI more deeply integrated, from real-time portfolio insights to automating investor communications. Firms will likely lean on AI to cut costs and improve response times, making operations smoother overall.
We see the continued investment in AI and Cloud as the overriding trend in 2025. As AI is deployed to help streamline everything from data analysis to investor communication, firms that focus on automating routine tasks will find their team can spend more time on high-level strategy.
The Financial Transformation Summit (FTS), presented by MoneyNext, took place June 18-19 2025 at London’s ExCeL Centre, Royal Victoria Dock. With over 2,000 attendees, 300+ speakers, and 400 roundtables, it stood out as one of the most immersive and interactive events in the financial services calendar.
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FinTech Strategy hit the conference floor at the heart of the action delivering insights from experts across Banking, Insurance, Wealth, and Lending at Financial Transformation Summit (FTS).
Financial Transformation Summit attendees from banking, insurance, wealth, lending, fintech, consultancy, and regulatory sectors convened for two days packed with keynotes, panel talks, immersive demos, and networking among 60+ exhibitors and startups.
Co-located streams – Banking, Insurance, Wealth, and Lending part of themed zones– meant that ticket-holders could explore adjacent sectors fluidly across a guiding theme: culture, collaboration, and customer centricity driving tech adoption and transformation.
Programme Highlights
Keynotes & Panels
1. Data Silos & Cross‑Institutional Collaboration
A panel featuring senior leaders from EVLO, Aon, Schroders, and Brit Insurance tackled how institutions – despite collectively spending over $33 billion annually on data – still struggle to collaborate due to privacy concerns and regulation. Innovative solutions included federated learning, anonymised client IDs and consent-backed APIs.
2. Digital Insurance via Wallets
Anna Bojic (Miss Moneypenny Technologies) unveiled a fresh take on insurance – embedding policy and claim data into Apple/Google Wallets. The idea: dynamic customer interaction directly from smartphone wallets, enhancing real‑time engagement and retention.
3. ESG Economics & Market Reality
Marc Kahn (Investec) challenged ESG orthodoxy, urging firms to emphasise human and planetary wellbeing – beyond purely financial returns – to capture stakeholder trust and sustainable growth.
4. People & Psychological Safety
Kirsty Watson (Aberdeen Group) and Vikki Allgood (Fidelity International) underlined that technological investments are futile without organisational design and psychological safety. Allgood cited a McKinsey study revealing only 26% of leaders build teams with a sense of safety – a critical step toward innovation.
5. Human‑Centred AI
Monica Kalia (Planda AI) championed AI that models individual financial contexts – recognising diversity within demographic cohorts and personalizing services accordingly.
Roundtable Experiences at FTS
At the event’s heart were the TableTalk roundtables – 400+ small-group sessions, each led by a subject-matter expert. These were limited to six participants each, enabling deep, peer-led discussions on themes like:
AI in risk and compliance
Open banking integration
ESG data standards
Cyber resilience
Change management and culture adaptation
Attendees consistently praised their interactive nature – far removed from the stage‑focused “listening” format often critiqued at other conferences.
Demonstrations & Exhibitor Showcase
Over 60 exhibitors presented tech-driven innovations: Generative AI, open‑banking APIs, ESG reporting tools, embedded finance solutions, and more. A few standouts were:
CRIF highlighted AI-powered credit scoring with ESG overlays – promising dynamic risk assessments backed by sustainability data
Emerging FinTechs demoing AI compliance engines, digital wallet insurance packaging, and data-sharing platforms
Hylanddemonstrated the intuitive end-user experience of its Hyland Content Innovation Cloud™ and showed how easy it is to configure, tailor and deploy solutions that can empower key stakeholders across any business
The demo zone allowed engaging, hands-on exploration and real-time Q&As; it complemented the content with practical insights.
Standout Themes & Strategic Insights
1. Tech is Not Enough Without Culture
Recurrent messaging emphasised that culture, trust, governance, and psychological safety are foundational – not secondary – to digital initiatives. Technology alone won’t deliver transformation without a people-first mindset.
2. Cross‑Sector Data Collaboration
Despite heavy investment, institutions still operate in silos. Shared, secure infrastructure and regulatory-aligned frameworks are being prototyped, but broad adoption remains a work in progress.
3. AI-as-a-Personalisation Backbone
AI is shifting from automation to empathy. Organisations showcased tools to hyper-personalise offers yet maintain privacy and inclusion – moving beyond outdated demographic frameworks into genuine behavioural understanding.
4. Embedded Finance & Digital Wallets
Insurance via wallet applications and embedded finance models point to seamless customer journeys – less app hopping, more value delivered at the point of need.
5. Rebalancing ESG & Profit Metrics
Speakers emphasised integrating ESG factors into performance metrics – not just for compliance, but as an operative advantage anchored in long-term stability and stakeholder trust.
Who Should Attend FTS Next Year?
Ideal for:
Transformation and change leaders
CTOs, CIOs, and Heads of Innovation
Data and AI strategists
Operational and HR leaders focused on culture
FinTech innovators and solution providers
If you’re crafting digital transformation strategies, an attuned leader in financial services, or a consultant embedding tech in legacy environments, this summit provides rich, actionable content.
Expect next year’s event to build on this foundation:
More AI-specific tracks, possibly Generative AI streams
ESG deep-dives with case studies on implementation
Expanded regulator involvement around data governance and cross-border compliance
FTS: Final Verdict
Overall, the FTS 2025 delivered on its brand promise:
Interactive and inclusive: 400 roundtables empowered voices across levels.
Cross‑sector learning: Banking, Insurance, Wealth, and Lending streams offered both breadth and depth.
Insightful keynotes: Big ideas on AI, ESG, data-sharing, and culture were well-explored.
Real-world relevance: Exhibitor demos connected theory with practice.
Networking with purpose: Opportunities to engage, learn, and collaborate were abundant.
The Financial Transformation Summit struck a compelling balance between big-picture vision and granular, execution-level insight. It emphasised that while technology enables; culture, customer centricity and collaboration drive real progress. The format – with its roundtables, demos, and keynotes – offered a dynamic platform for knowledge exchange.
If you attended, chances are you left with practical next steps. If you didn’t, you missed one of the most interactive, future-focused events shaping financial services transformation today.
Alexandra Mousavizadeh, CEO and Co-Founder of Evident, with her top five AI innovations advancing financial services in 2025
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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.
Rob Israch, President at finance automation specialists Tipalti, reflects on the post-hype AI landscape for innovation in financial services
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The initial excitement around AI In finance is shifting toward a more practical focus on real business value. Many companies were swept up in the early enthusiasm. However, companies are now leaning toward integrating artificial intelligence more meaningfully into core workflows to deliver lasting value.
While 92% of companies plan to increase their AI investments over the next three years, just 1% of leaders say their organisations are truly AI mature. True maturity means AI drives measurable outcomes and is central and streamlined into daily operations.
So for finance teams, this shift is critical. In an economy shaped by changes in inflation, tariffs and taxes, every investment must deliver clear ROI and help the business by streamlining operations, enhancing forecasts and adopting predictive analytics.
As companies push for sustainable growth and a thawing IPO market signals possible opportunities, scalable and integrated AI solutions will be key to business success.
Building for Real Problems, Not Hypothetical Gaps
Most companies agree that innovation in the finance department is key to unlocking the next level of growth. However, despite growing ambition to adopt AI and automation, 84% of finance teams still rely heavily on manual processes. Leaving little leftover time for strategic thinking.
To truly drive value, AI must be applied not just tactically, but strategically for each business. Research shows that while 74% of companies have adopted AI, only 4% have advanced capabilities that drive clear business value. Real impact is delivered when the technology goes beyond simple workflow automation and becomes a source of real-time, predictive insight across the finance function.
Take treasury operations, for example. Traditionally, treasury teams have faced mounting challenges in managing cash flow, forecasting liquidity, and overseeing global bank relationships. With AI-powered tools, finance teams can now gain real-time, intelligent cash visibility across thousands of banks, ERP systems, and data sources. This transformation not only empowers leaders to make faster and smarter decisions but also underscores the importance of streamlined systems within the finance function.
From a Surplus of Tools to One Unified Platform
What businesses don’t want is extra layers of complexity; they need a straightforward, unified platform that solves real problems.
Large enterprises may seek ‘AI-first’ products and invest in cross-functional AI platforms. But they typically have the resources to fund extensive IT teams or consultants to customise these systems. However, for most businesses, this level of support isn’t a reality. So, businesses without reams of IT people, benefit more from a consolidated system that delivers efficiency and scalability. This allows them to stay focused on growth and innovation.
If AI is seamlessly embedded within these solutions, it can enhance performance without increasing complexity. Whether improving automation, workflow management or operational efficiency, AI should be an integral part of the product.
Staging the Runway for the Next Stage of Growth
Companies that fully integrate AI will be more ready for sustainable growth. However, integration is just the start… Once AI is embedded, organisations must focus on how it can deliver real, strategic value. This means designing solutions not only to automate processes but to provide actionable insights. Currently, only 26% have developed the skills to move beyond AI conceptually and deliver real value. In the finance function, using AI strategically can lower processing costs by 81% and speed up processing times by 73%.
As more advanced models are integrated into workplaces systems, they can predict payment patterns, cash flow trends, and vendor behaviour. In today’s dynamic environment, companies that have sustainable, AI-powered solutions centred on usability and scalability are best positioned for the next stage of growth.
The Continued Road to AI Maturity
As finance teams navigate a more mature AI landscape and prepare for future growth, the focus is shifting from individual features to foundational value. With investors sharpening their focus, they seek durable business models. The companies that succeed will be those that have applied AI to maximise their investment.
These companies haven’t just chased metrics; they’ve spent the past few years strengthening their foundations and embedding AI deeply into their architecture.
We Fix Boring founder Andrej Persolja on why investors are making bigger bets on fewer teams via the impact of AI, enhanced profiling and better targeting
How founders can improve their chances of raising investment – team alignment, production and business differentiation, and customer-centered strategy. Creating a story that investors can easily understand and buy into.
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If you’re planning a FinTech investment pitch, the chances are that your first thoughts will relate to the numbers. You’ll open your spreadsheets and dig out your margins, forecasts, CAC-to-LTV ratios, and KPIs. You’ll do everything you can to make your brand look impressive on paper. It’s what you’ve been taught to do because metrics matter. Of course they do. However, what many founders don’t realise is that although metrics clearly carry value, they should only ever be the starting point of any investment pitch. Because, at the end of the day, investors are people first, and their decisions are based on emotion as much as they are on money.
The Human Factor in FinTech Funding
Investors are not machines. It might sound like stating the obvious, but when so much hinges on investor approval, it can be hard to remember that you’re dealing with human beings. So, you focus on upselling your financial model, growth projections, and market opportunity, entirely overlooking the value of an emotional response. One influenced by your narrative, your team, your product vision, and your belief in your startup’s ability to reshape an industry. And that’s where so many fintechs go wrong.
In sectors like FinTech, where technical innovation is everywhere, what often sets a pitch apart is its ability to tell a compelling story. One that communicates not just what the product does, but why it matters. That emotional connection can often provide the edge that secures the deal.
Positioning and Emotional Resonance are Often the Missing Links
Innovation often outpaces regulation in fintech, and profitability can be years away. So, what convinces an investor to take a bet on an early-stage startup? The potential return on investment matters and will always be a factor. But it’s rarely the only factor. Because there are countless high-growth opportunities out there. So why choose yours?
The answer is belief. Belief in your vision. Belief in your ability to execute. And the belief that your product solves a real, meaningful problem in a way that others haven’t. That’s why positioning, and the emotional resonance behind it, plays such a critical role in raising capital.
When fintech investors evaluate opportunities, they aren’t just looking at your tech stack or your runway. They’re asking themselves: What does this company stand for? What kind of disruption do I want to back? What values do I want my capital to reflect? If your pitch doesn’t communicate that clearly and emotionally, it becomes just another deck in a crowded inbox.
Strong positioning grounds your FinTech in something bigger than features or metrics. It communicates purpose. And when you pair that with an emotionally resonant brand narrative, you give investors a reason to care. Not just about your product, but about why it exists and where it’s going. Because trust, change, and vision are core themes that can move an investor from ‘interested’ to ‘committed.’
Crafting a FinTech Brand Narrative to Drive Investment
Building a compelling brand narrative in FinTech is no longer optional. It’s a critical part of your investment strategy. And it all starts with one fundamental question: What is your why? Beyond monetisation and market sizing, what real-world problem are you solving? Why does it matter now? Whether you’re streamlining payments, reimagining lending, or building infrastructure for digital finance, your deeper purpose is what sets your FinTech apart. And it’s what investors are really looking for. That, and a strong user experience (UX) that shows commitment to your customers and the potential to build loyalty.
The Role of UX in Investment Pitching
Traditionally, FinTech companies have been held back by one major challenge: compliance. But in today’s digital-first environment, where every player in banking, insurance, and payments is competing for speed, convenience, and trust, the challenge has become twofold: compliance and user experience.
In digital finance, the core area of competition is how quickly you can get the user to value. That means having crystal-clear user journeys and a focus on where and how users perceive value. Using one of my clients – a SaaS solution for institutional investors – as an example, by simplifying the user experience across our landing pages and onboarding, we increased conversion from 0% to 37%. That didn’t just improve user experience. It provided quantifiable traction that could be shown to investors. And if you need to prove traction to investors, every click matters.
With FinTech investment rebounding – up 5.3% in H1 2025 compared to 2024 – now is the time to act. But standing out means more than just showing attractive metrics. Investors want a clear narrative that combines numbers with a strong strategic story. They’re looking for confidence in the team, clarity in the vision, and proof that your product is ready to scale. Both operationally and emotionally.
So, to reiterate. Yes, if you’re preparing an investment pitch for your FinTech, the financial model matters. But seasoned investors know markets shift, projections change, and competition intensifies. A fintech company that can articulate a powerful vision, show traction through product-led growth, and tell a story that resonates on a human level will always have an edge.
So, take your ideas and take your numbers, and make them look as pretty and appealing as possible. But don’t forget to wrap them in a story if you want to spark your investor’s imagination.
Rob Vann, Chief Solutions Officer at Cyberfort, on the importance of the human factor for successful AI integration in financial services
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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.
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.
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.
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.
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.
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.
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.
David Sewell, Chief Technology Officer at Synechron on why robust digital infrastructure is the missing link in the UK’s AI ambitions
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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, connectivityis 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.
Join industry leaders and innovators in London at the 5th Annual Digital Banking Summit – October 21-22, a premiere event designed to explore the most transformative trends shaping the banking sector in the digital era.
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The Digital Banking Summit two-day conference covers a range of critical topics. From AI-driven banking and open finance to financial inclusion and the future of digital identity. Discover how cutting-edge technologies like edge computing, hyper-personalisation and APIs are redefining corporate and retail banking. Engage in discussions around legacy system modernisation, sustainability through ESG initiatives and the regulatory landscape, including DORA and GDPR.
With sessions led by top executives from global financial institutions (including Santander, Revolut, Citi and Lloyds), attendees will gain actionable insights on leveraging innovation to streamline operations, enhance customer experience, and build resilient financial ecosystems. Take advantage of networking opportunities and 1:1 meetings to connect with senior leaders and experts. Don’t miss this opportunity to be part of the conversation shaping the future of digital banking.
Digital Identity: Onboarding, Compliance and Embedded Finance
Cross-Industry Collaboration in Banking
Banking for a Digital Workforce
Hyper-Personalisation in Wealth Management
Edge Computing
The Role of APIs in Transforming Corporate Banking
Digital Resilience
Legacy Systems vs Modernisation
AI in Banking
Digital Banking Summit Day 2
Automation and Cloud Banking
Data Monetisation: Ethics and Opportunities
Digital Marketing in Banking
CBDCs
Sustainable Banking Future with ESG
Navigating DORA, GDPR and Beyond
Digital Wallets
Mobile Banking
Crypto, Instant Transfers and Banking
AI-Driven Fraud
Customer-Centric Innovation
Cybersecurity: Deepfakes, AI Attacks and Quantum Risks
What Attendees Really Think About the Digital Banking Summit
“Very well organised conference with a lot of possibilities to meet people and very interesting topics in the banking world”
Director, ERI Bancaire S.A.
“The energy at the event was truly invigorating, as industry leaders shared innovative ideas that are reshaping the future of banking”
Digital Product Lead, Unicredit
“Great experience! In order to meet with professionals from the industry, a lot of networking opportunities. Great topics!”
Strategy Manager, Akbank
“Valuable learning and interesting conversations”
Director, Wise
“The audience is on a very senior level, a lot of participants. Speakers are also on a very high level, everybody learned a lot. We are very, very happy!”
Head of Regional Marketing CEE & CIS, Finastra
“A great opportunity to meet the industry experts and get inspirational thoughts!”
FinTech Strategy meets Eastern Horizon Founder & CEO Christine Le to discuss client expectations and the changing landscape of wealth management
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Financial Transformation Summit 2025 EXCLUSIVE
At Financial Transformation Summit, Christine Le, a Chartered Financial Planner and Founder & CEO of Eastern Horizon Wealth Management, spoke on an investment panel – “Generational Wealth Transfer: Meeting the Expectation of Younger Clients”. Appearing with industry colleagued representing Citi Global Wealth, HFMC Wealth and Lightbox Wealth, Le considered: What trends and technologies are shaping NextGen investment decisions, and how can WMs stay ahead? Can digital wealth platforms meet the demand for hyper-personalised, user-friendly experiences? How does social responsibility & ESG investing influence younger investors, and how can advisors align with these priorities? How can wealth managers build and maintain trust with NextGen investors?
Following the panel, we spoke with Christine to find out more…
Hi Christine, tell us about your role at Eastern Horizon?
“I’m a Chartered Financial Planner and the Founder & CEO of Eastern Horizon Wealth Management. We are a financial advisory firm and also a partner practice of St. James’s Place. They are among the biggest wealth management firms in the UK based on assets under management. We get a lot of support from St. James’s Place in terms of technology compliance and investment solutions. At my practice, we focus on a diverse range of clients including ethnic minorities, especially British Asians in the UK. I’m also the president of the Vietnam Investment and Finance Association in the United Kingdom (VIFA). We aim to provide useful financial information for Vietnamese people in the UK and become a bridge between Vietnam and the UK.”
You were part of a panel at this Summit focused on Generational Wealth Transfer. Can you give us an overview of your thoughts?
‘’Having worked in the financial services industry for over 15 years, I’ve observed a persistent gap in how the industry serves diverse client segments – particularly ethnic minority communities in the UK. This gap is especially pronounced when it comes to financial education and long-term planning, including wealth transfer across generations. When I speak to members of my own Vietnamese community, I often find that there’s a limited understanding of how to navigate financial systems effectively – from managing investments and pensions to planning for intergenerational wealth. It’s not due to a lack of interest or ambition, but rather a lack of access to culturally relevant and accessible financial advice.
“This is where I believe I can make a meaningful difference. I not only bring professional expertise and technical knowledge to the table, but also a deep understanding of the cultural values, family dynamics, and communication styles that shape financial decision-making in the community. That cultural insight is key to building trust, something that is essential when discussing personal finances and planning for the future. My goal is to help bridge that gap – to empower families with the knowledge and tools they need to make informed financial decisions, preserve their wealth, and pass it on confidently to the next generation.’’
Why is this an exciting time for the business?
“At the moment the world is so integrated, and many people can benefit. A lot of people want to go to the UK, invest into the UK. I think with that in mind this is an exciting time to run my business and to be able to bridge that gap, providing sufficient knowledge for people as a trusted source when they come to the UK and need to understand the financial regulations. We can give people solid support to understand the financial processes of settling and building wealth in the UK.”
What other trends are you seeing across the Financial Services landscape? What will be important for you and your customers?
“Right now, everyone is talking about AI, and for good reason. In my business, we rely heavily on digital tools to streamline administrative tasks. It’s truly a game-changer. Compared to starting a business 15 years ago, when I would have needed a full-time assistant just to take meeting notes and summarise action points, many of those processes can now be automated, saving both time and cost. Another advantage is in how we communicate. Many of my clients are British Vietnamese. While they understand and speak English, they often feel more comfortable communicating in Vietnamese. We use AI-powered translation tools to make this process faster and more seamless. These technologies are allowing us to broaden the range of services we offer and tailor our support to each client’s needs.”
What pain points are your clients experiencing that you need to address? How are you meeting the challenge?
“It’s about meeting the client’s highest priority. When people come to me, they maybe want to support their children to get onto the property ladder or plan for their retirement. They might be looking to buy a new car or move home. So, as a regulated financial advisor, I can sit with a client and talk them through key priorities and tailor the solutions best for them and help them overcome the pain points of decision-making.
“Additionally, the UK’s financial regulations are complex and changing all the time. It’s very difficult for people to follow. It’s my job as a financial advisor to follow up those changes and stay up to date with the regulations to assess how it can impact our clients and then give them the best recommendations. Allied to this, many of our clients will need support with cross-border services as they move freely between different countries they need somebody they can trust, an expert that knows what they’re doing and who can provide the right financial services for them.”
Tell us about a recent success story…
“Success for Eastern Horizon is to know that our clients feel they have somebody to rely on. For example, I have an old friend who came to me as a client. She was based in Vietnam but wanted to relocate to the UK. She had assets across Europe and in Vietnam and needed to understand the big picture of financial planning in the UK. We examined her assets across different countries to bring them into the UK and find the best solution for her to utilise tax efficient savings, pensions and investments to support her family and her business in the long term.”
What’s next for Eastern Horizon when it comes to wealth management? What future launches and initiatives are you particularly excited about?
“Over the next few months, we are keen to collaborate with different associations and communities across the UK – whether that’s related to Vietnam or British Asian communities and offer useful information and workshops and webinars tailored to different audiences. Also, with my work for the Vietnam Investment and Finance Association I want to organise workshops for those keen to invest in the UK but don’t know where to start. They often don’t have anyone to support them so I would like to focus on building a network to offer that bridge to investment in the UK.”
Why do you think the evolution of collaboration between traditional institutions and FinTechs is set to continue? What are you excited about?
“I spent five years working at the intersection of FinTech and WealthTech – where wealth management meets technology. During that time, I witnessed firsthand how the financial services landscape is evolving. Large incumbent banks bring undeniable strengths: scale, regulatory rigour, and long-standing client trust. However, they often struggle with agility. Their legacy infrastructures, many of which still aren’t cloud-based, make digital transformation slow and complex. On the other hand, FinTechs are born digital. They’re nimble, innovative, and quick to adapt to changing customer needs. But without the reputation and stability that traditional institutions have built over decades, they can face challenges in gaining consumer trust or navigating regulatory environments alone. What became clear to me is that banks and FinTechs cannot operate in silos.
“Collaboration is not just beneficial, it’s essential. When they work together, they combine the best of both worlds: the reliability and compliance of traditional finance with the innovation and customer-centric design of new technology. With my own practice, we apply this mindset. We actively look for ways to streamline administrative processes using digital tools – reducing costs, improving efficiency, and freeing up more time to focus on what matters most: building strong, human relationships with our clients. The goal is to use technology not to replace that human connection, but to enhance it. By doing so, we can deliver modern, efficient, and deeply personalised financial services that clients trust.”
Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for Eastern Horizon?
“I’ve attended several events this year, and this has truly been one of the most enjoyable and well-organised in the UK. What stood out was the impressive mix of voices – from established financial institutions to bold, forward-thinking startups. Engaging with such a diverse group of speakers has been both insightful and thought-provoking. I’ve come away with fresh perspectives, challenged some of my own assumptions, and found new ideas to explore as we continue building meaningful partnerships for Eastern Horizon Wealth Management.”
About Christine Le and Eastern Horizon Wealth Management
As an Appointed Representative of St. James’s Place, Practice Lead, and business owner, Christine leverages over 15 years of experience in financial services and wealth tech to serve our clients, acquired through extensive work in multinational financial services firms in the UK. This rich background has equipped Christine with the skills and knowledge necessary to effectively oversee the business, ensuring that every facet is managed with the highest level of professionalism.
Christine founded and built this Practice to help clients prosper, build financial security, and attain peace of mind while overcoming financial obstacles.
Her primary focus is on nurturing enduring relationships with her clients, offering them trusted guidance as their financial requirements evolve over time. Throughout her advisory process, clarity remains paramount. By closely collaborating with her clients, Christine strives to identify the most efficient and tax-effective strategies to help them achieve their objectives. Specialising in tailored solutions, Christine is dedicated to understanding her clients’ financial goals and crafting strategies that align with their vision for the future.
FinTech Strategy meets with Citigroup’s Head of ESG Credit Management, Mauricio Masondo, to discover the future for ESG and sustainable finance
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Financial Transformation Summit 2025 EXCLUSIVE
At Financial Transformation Summit, Mauricio Masondo, Head of ESG Credit Management at Citigroup, featured on a sustainability panel – ‘The Future of ESG and Sustainable Finance: Balancing Profit and Purpose’. Alongside peers fromGenerali AM, Gallagher Re and Arma Karma, Masondo considered: What key metrics should FIs use to track ESG progress, and how can they ensure authenticity in their sustainability efforts? Developing a holistic ESG strategy amid evolving regulations – key challenges and solutions. How can FIs leverage technology to meet sustainability goals and drive long-term profitability? How can FIs move beyond offering ESG products to embedding sustainability into their core business models?
Following the panel, we spoke with Mauricio to find out more…
Hi Mauricio, tell us about your role at Citigroup?
“In my 32 years with Citi my career has primarily focused on wholesale credit, and in recent years I built out our portfolio management function. For the past year specifically, I’ve been leading the integration of ESG and climate considerations into our credit processes.As Head of ESG Credit Management, my role is to embed ESG requirements into our credit processes in a way that’s consistently and efficiently applied through technology, policies, training, and governance frameworks. Our strategic approach was not to create an ESG silo that replicates existing processes, but rather to integrate ESG considerations seamlessly into our current workflows. This means any credit analyst can now underwrite ESG credits, sustainable loans, or green loans, rather than requiring dedicated specialists. We’ve equipped our entire team with the knowledge and tools they need to handle these transactions effectively.”
You were part of a panel at this Summit focused on the future for ESG and sustainable finance. Can you give us an overview of your thoughts?
“Data standardisation is absolutely critical, especially as we advance into the AI era. I often reference Moody’s as an excellent example of strategic foresight. Moody’s operates two key businesses – credit ratings and data analytics – and early in their AI journey, they made the strategic decision to structure and normalise all their credit research data. This proved to be transformational because it enabled them to deploy AI solutions much more rapidly with clean, structured datasets. We’re working to apply this same principle at Citi. We’re developing processes to structure climate-related data in a way that will be usable across multiple applications. For example, we’re working on integrating emissions data and climate risk assessments into our credit risk rating models. We’re also exploring how this structured approach could support underwriting processes and securitisations, where comprehensive data packages could facilitate risk transfer transactions with institutional investors. The goal is to build normalised, structured data as the foundation for various applications, from portfolio management to AI-driven solutions. While we’re still in the early stages of many of these initiatives, the potential is significant.”
Why is this an exciting time for the business?
“We’re witnessing the convergence of several transformative trends. However, one of our biggest challenges is policy divergence across jurisdictions. Countries are taking vastly different approaches to ESG requirements, and for a global bank like Citi, this creates significant complexity in standardising processes across multiple regulatory environments. While challenging, this divergence also creates opportunities to develop scalable, cost-effective solutions that can adapt to various regulatory frameworks.Second, AI is revolutionising how we approach ESG challenges. It’s helping us structure data more effectively, enhance reporting capabilities, contextualise information, and identify trends that would have been impossible to detect manually.
“Previously, comprehensive ESG analysis required significant time, resources, and personnel. AI has made these processes more accessible and cost-effective.Most importantly, there’s been a fundamental shift in how the industry, and governments, view ESG. It’s evolved beyond compliance and emissions reporting to become a significant business opportunity. We need to capitalise on this transition – moving from reactive reporting to proactive opportunity capture. The capital is there, and if traditional banks don’t seize these opportunities, asset managers, private credit firms, and private equity will. We’re partnering strategically with reinsurance companies and asset managers to develop innovative solutions that unlock transition capital and help companies fund decarbonisation projects.”
What other trends are you seeing across the Financial Services landscape? What will be important for you and your customers?
“Trade flows are experiencing significant disruption due to current tariff policies. This creates both challenges and opportunities for our clients. Companies are reassessing their supply chain vulnerabilities and seeking greater resilience in their operations.I anticipate we’ll see a regionalisation of trade flows rather than a complete deglobalisation. European companies will likely increase intra-regional trade while reducing intercontinental transactions. We’re seeing similar patterns emerging in Asia and the Middle East. This shift requires banks to be more agile in how we structure trade finance and working capital solutions to meet these evolving needs.”
What pain points are you experiencing that you need to address? How are you meeting the challenge?
“Working capital finance requires increasingly creative solutions that leverage advanced technology. Banks are recognising that FinTechs often have greater agility in developing and implementing these technologies. There’s significant efficiency in having one FinTech serve multiple banks rather than each institution developing independent solutions. This collaborative approach allows us to move faster while reducing development costs and time-to-market.”
Tell us about a recent success story…
“I designed and led the implementation of an early warning monitoring system for Citi’s credit portfolio. The project began with a fundamental concept: create a data lake, develop meaningful metrics, and engage data scientists to interpret the insights. We collaborated with trade officers and partnered with external specialists to enhance our capabilities.Initially, there was scepticism about the system’s value, particularly because we built it as an independent function within our portfolio management organisation, separate from traditional banking and risk management structures. However, this positioning allowed us to collect unique client data and develop insights that weren’t available elsewhere in the organisation.A critical component of our success was establishing a dedicated credit expert team that oversees the entire process.
“This team leads the engagement and communication of alerts, ensuring that insights are properly interpreted and actionable recommendations reach the right stakeholders.The evolution was remarkable. We progressed from generating a few alerts daily to dozens per day, and eventually to hundreds of alerts weekly. More importantly, we developed sophisticated processes for interpreting and acting on these alerts, with our expert team serving as the bridge between data insights and business action. Bankers and risk managers began to recognise the value, and today, three years later, the system is integral to how we conduct annual reviews and client presentations. It’s incredibly rewarding to provide our bankers with comprehensive data and insights that strengthen their client relationships.”
What’s next for Citigroup when it comes to ESG? What future launches and initiatives are you particularly excited about?
“While it may sound clichéd, AI truly is transformative for our industry. The breadth of use cases and the rapid pace of learning make it essential to our strategic direction. We’ve established a strategic partnership with Google and are investing significantly in AI use case development and implementation across our operations. From an operational perspective, AI will undoubtedly increase our efficiency as an industry. More importantly, it’s enabling us to evolve our business models and create client solutions that weren’t previously feasible. This opens entirely new avenues for innovative product development. Additionally, since CEO Jane Fraser joined, we’ve embarked on a comprehensive transformation program that’s delivering strong results in terms of financial performance and returns. We’ve restructured and simplified our operations, which positions us more competitively as we refresh our leadership teams and attract new talent. The trajectory is very promising.”
Why do you think the evolution of collaboration between banks and FinTechs is set to continue? What are you excited about?
“The current tariff environment is creating opportunities for FinTechs that facilitate connections between banks, investors, and corporations. It’s also presenting consolidation opportunities for private equity firms within the rapidly expanding FinTech ecosystem.”
Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for Citigroup?
“The panel brought together diverse perspectives from FinTech, asset management, insurance, and banking – all addressing common challenges that span our sectors. This cross-industry dialogue creates tremendous opportunities for collaboration and mutual understanding.The key now is translating these conversations into action. We need to maintain these connections, expand the dialogue, and avoid making decisions in isolation. FinTechs possess the agility to implement changes in their operating models far more quickly than large incumbents like us. However, our procurement systems and processes aren’t always conducive to collaborating with smaller, innovative companies.Events like this highlight the need to streamline how institutions like Citi can collaborate with and learn from FinTechs. We must accelerate our ability to adapt to a rapidly changing world.”
We’re helping build more sustainable, economically vibrant communities around the world.
At Citi, helping our clients navigate the challenges and embrace the opportunities of our rapidly changing world is fundamental to our mission of enabling growth and economic progress.
FinTech Strategy meets Vikki Allgood, Director of Technology Strategy at Fidelity, to discuss the fundamental importance of culture in driving a successful business transformation
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Financial Transformation Summit 2025 EXCLUSIVE
At Financial Transformation Summit, Vikki Allgood, Director of Technology Strategy at Fidelity International, gave a keynote speech entitled ‘Psychological Safety – The Hidden Key to Transforming Your Business’. Following her appearance, we spoke to Vikki to learn more…
Hi Vikki, tell us about your role at Fidelity?
“I am Director of Technology Strategy for Fidelity. We’re looking at how we can ensure we can adapt our response to our business’ needs through our technology to meet whatever demand is coming over the horizons tomorrow. And in the years to come.”
You spoke at this Summit about psychological safety driving business transformation. Tell us more…
“At Fidelity, our strategy for our technology has culture as our foundational pillar. Talking with our leaders over the last 18 months, we looked to understand how we can create a brilliant culture, recognising that psychological safety is a fundamental element in that.
“Transformations often stumble because the business plan forgets its most volatile, and most valuable component, the people asked to deliver it. Without psychological safety, even well‑funded and organised programmes stall. Teams focus more on protecting themselves instead of challenging ideas. That’s when the risks remain hidden until it’s costly, and the collective new ideas to solve the biggest challenges are never formed. That’s why we ask leaders to invest time and energy in building a culture where it’s safe to question, experiment, challenge the status quo and admit what’s not working. In that environment the behaviours every transformation depends on (curiosity, creativity, problem‑solving, healthy challenge) all naturally emerge.
Psychological safety isn’t some new trendy HR slogan, it’s a timeless basic human need wired into our biology through millennia of evolution. When people sense social threat, the amygdala floods the body with cortisol and the prefrontal cortex (the part of our brain we rely on for reasoning, innovation, etc.) literally dims. Remove the threat, and the brain’s chemistry flips, dopamine and oxytocin rise, and teams move from cautious compliance to bold collaboration. Leaders must ask themselves if their teams can lean in and challenge effectively or if they are staying quiet to protect themselves. The hidden key is simple, but non‑negotiable, leaders must consciously, relentlessly and courageously build psychological safety through everything they do and say. If they do that, then your technology and transformation plans will have the human engine they need to succeed.”
Why is this an exciting time for Fidelity?
“I think that within the industry, all the opportunities that are coming along, and our ability to adapt to our customers’ needs, is what makes it exciting. We are all on an exponential curve of change. Technical possibilities, customer expectations, regulatory demand, industry landscapes, are all going to keep moving, with new challenges and opportunities presenting themselves. We are ensuring that we can meet those needs of our customers both today and tomorrow. Finding new ways to do that is pretty exciting.”
What trends are you seeing across the Financial Services landscape? What will be important for you and your customers?
“So, from a technology perspective, I would say that we are making sure that all our foundational elements are there so that we can respond and adapt. One of Fidelity’s differentiators is that we have historic long running relationships with our customers. We are reintegrating our data strategy to allow us to better leverage this, in addition to market data, allowing us to provide personalised solutions to our customers.
“AI is absolutely generating a buzz for us right now as well, and not just Generative AI. We’re seeing a push towards Agentic AI and how we can look to provide faster, quicker, more cost-effective services for our business partners who can then provide better outcomes for our customers. This in combination with our long-standing history gives us a unique opportunity.”
What pain points are your customers experiencing that you need to address? What are they asking you for help with? How are you meeting the challenge?
“We need to understand the new generations entering the wealth space and what their expectations are and how they engage with us. We’re looking to ensure we can keep pace with their demands. For example, we’ve just launched Pay by Bank allowing our customers to pay money into their accounts in a faster more secure way. This feature leverages the Open Banking Technology that is now available to financial institutions.”
Tell us about a recent success story for Fidelity…
“Across the technology landscape, we have been amplifying our existing cloud strategy by removing complexity in our hybrid setup, reducing the number of dependencies back to on-premises. This is a well-known challenge for financial institutions who have regulatory reasons to have highly confidential systems in house. This will allow us to respond at pace to what customers need. Looking a couple of years down the line nobody can be sure what the next big opportunities are going to be, so ensuring we’re building that foundation to respond to what comes over the horizon is fundamental.”
What’s next for Fidelity? What future launches and initiatives are you particularly excited about?
“Security is incredibly important to us. With that in mind, we are exploring Quantum to understand both the opportunities and risks that it could present in the future and how we can stay at the forefront of it. Ensuring a secure and reliable service for our customers is an absolute non-negotiable part of our strategy.”
Why do you think the evolution of collaboration between banks and FinTechs is set to continue? What are you excited about?
“I think the reality is that we need the collective mindsets to come together to create the best outcomes. We’re never going to have all the answers all by ourselves. So, starting to engage and work with people and collaborate means that we get to have a better, wider perspective. Coming to events like this, we get to learn, understand what other industries are doing, what other areas are looking at, and it helps to widen our perspectives and have more opportunities to find those out of the box ideas that are going to then help our customers.”
Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for Fidelity?
“I was particularly keen to attend this conference because I think transformation and how we can do this successfully is so important at the moment. The reality is, sadly, and I covered this in my talk, a staggeringly large number of transformations miss the mark or fall short. And so, learning and embracing how you can ensure that you go after it and you get the value that you’re aiming for, that is for me what’s important. As I said, getting that learning, talking to each other, understanding what’s worked, what hasn’t worked and sharing tips and techniques is actually incredibly powerful and something you can then take back and use at your organisation.”
It has been more than 50 years since we were founded. We’ve seen many market cycles – bull and bear, boom and bust. We have stayed the course through different investment environments regardless of market performance.
The needs of our customers have always steered our decisions, which is why we’ve stuck to our core activity of investing. We believe this is what allows us to excel – and, even more importantly, to repay the trust placed in us by our customers.
Whether you’re investing for the first time, or have a wealth of experience, it’s essential to be informed and to be comfortable with your decisions. Through Trustpilot, you can read up-to-the-minute, real-world reviews and see for yourself how Fidelity aims to put the customer first and make investing a bit easier.
Our do-it-yourself online services give you 24/7 access to our investment guidance, handy tools, and range of accounts from your computer, tablet or phone. Transfer your existing investments to us, or open a new account online and begin investing in just a few steps.
FinTech Strategy speaks with Matt Bazley, Account Executive at Hyland, to explore how the content intelligence and process automation specialists are helping to drive operational efficiencies for their financial services clients
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Financial Transformation Summit 2025 EXCLUSIVE
Hyland empowers organisations with unified content, process and applications intelligence solutions, unlocking the profound insights that fuel innovation. The Hyland team was at Financial Transformation Summit to reveal the ways organisations can transform their processes with the Hyland Content Innovation Cloud™. By combining AI-powered automation with built-in integrations to productivity tools and business applications, Hyland streamlines workflows across multiple channels, accelerating response times, boosting productivity and improving customer satisfaction.
At the event, Neil Rayment, Sales Solution Engineer, demonstrated the intuitive end-user experience and showed how easy it is to configure, tailor and deploy solutions that can empower key stakeholders across any business. We spoke to Hyland’s Matt Bazley, Account Executive for Financial Services, to find out more…
Hi Matt, tell us about your role at Hyland?
“I’m the Account Executive responsible for banking across the UK and Ireland. I’ve been with the company for just over 18 months. Across my career, I’ve been helping financial services institutions for over 15 years with digital transformations and various programmes.”
What are the key digital transformation solutions Hyland offers Financial Services organisations? How are they making a difference? What are some of the use cases you’re exploring?
“Hyland is at the cutting edge of the content space. We have what we call our Content Innovation Cloud, which is delivering content intelligence, process intelligence and application intelligence. What that means in reality is that we’re helping organisations get access to their content that they don’t currently have access to because it’s spread over many siloed systems and sat in an unstructured format. So, with our content and intelligence, we’re able to get access to that unstructured data, which is around about 80% of an organisation’s data in the financial services sector. And we’re able to then provide knowledge and insight on that content, which helps organisations to make better strategic decisions. Allied to that, with this process intelligence, we’re able to help automate processes across the business. Whether it be orchestrating use cases and workflows or integrating with other systems to deliver application intelligence, we’re able to manage that whole end-to-end life cycle of information across an organisation.”
Why is this an exciting time for the business?
“We’re excited because our strategy is really leading the way. We’re leveraging large language models (LLMs) and AI to be able to deliver these real-life use cases that solve actual challenges. A lot of the time AI projects fail because businesses are trying to implement AI that isn’t actually a solution solving a problem. Whereas the AI we’re using is to actually solve a real-life challenge that businesses face because they want to be hyper-personalised for customers and more customer-centric. And you can’t really do that if you’re only leveraging 20% of the data you hold about your customers. And that’s why getting access and insight around this unstructured data is really vital for financial services organisations right now. We are able to help them leverage that unstructured data and meet them where their data is at. So, it’s not a case of having to migrate all of that data into different platforms or into our platform. We confederate across your information wherever it’s held as a financial services organisation; and that’s really a game-changing position for us and for the industry.”
What trends are you seeing across the Financial Services landscape? What will be important for you and your customers?
“AI is the big one. Although it is a bit of a buzzword that everyone’s mentioning nowadays, we’re actually delivering AI solutions to solve problems that businesses face. And that’s one of the real trends in the industries. Most AI projects fail, and companies want AI projects that succeed and deliver real value. The other thing we’re seeing is the rise of hyper-personalisation as part of being really customer-focused and customer-centric. Again, by helping businesses leverage that 80% of information around their customers that they don’t currently have access to, and provide insights on that information, we’re helping those organisations to become really specific and personalised in their dealings with their customers.
“The final piece is around data and governance. So, security around our data as customers, because we’re all consumers at heart and want to know that our information is secure. Using best-in-class processes around security and governance is what we’re really focused on. And that’s a real trend in the market as well. We’re making sure that while we’re leveraging that information about customers, we’re keeping it safe and only using it for what it’s intended for and making sure the processes and governance around that information are really robust.”
What other pain points are clients in the FS space experiencing that you need to address? What are they asking you for help with? How are you meeting the challenge?
“The one big one is the siloed information across multiple systems as part of digital transformation strategies. Over the years, I’ve seen many businesses implement point solutions. They might be best-in-class point solutions… But that means you end up with information and data and processes across 10, 15 or 20 systems. How do you then unify that data and leverage it to make the user journeys more effective? And also the customer journeys better, whatever channel those customers are using?
“What we see is that while trying to be omnichannel for their customers, organisations end up with multiple solutions. One for their mobile app, a solution for their website, a solution for in-branch banking… So, you end up with omnichannel processes that are actually siloed processes. What we are trying to help businesses do is to unify those processes. We can break down those silos and make it a really seamless, integrated journey internally and externally for colleagues and customers.”
Tell us about a recent success story …
“A great example is our work with ABN AMRO – a bank that is one of our longstanding and valued customers. They were looking for a solution because of this very challenge. The bank had multiple siloed systems holding a lot of information and a very complex architecture. They went to market and Hyland was able to prove our solution was able to manage the sheer volume and complexity of the information and content that they had. And most importantly we were able to help them integrate with their line-of-business systems very easily to create that seamless internal/external journey for both users and customers.”
What’s next for Hyland? What future launches and initiatives are you particularly excited about?
“It’s all about continuing to grow for us. With the Content Innovation Cloud, the reception we’ve received from the market, from our customers, has been absolutely tremendous. Businesses are so excited to see the ability and capability of what we’re able to do. And what we’re able to deliver for them in terms of real value through the Content Innovation Cloud. We’ve got customers onboarded already. It’s now about expanding that list of customers who are going to see real value from leveraging the cloud, our AI solutions and driving efficiencies with our content process and application intelligence across their businesses.”
Why do you think the evolution of collaboration between banks and FinTechs is set to continue? What are you excited about?
“Across the market over the last 15-20 years the banks are starting to see FinTechs more as allies than competitors. And they’re leveraging these technologies rather than trying to challenge them. I think that’s going to continue because FinTechs are far more agile. And as customer expectations continue to evolve and become more demanding, banks need to evolve and deal with these demands more effectively and more fluidly. And that’s why leveraging FinTechs is going to be a key differentiator over the next 10 years. That trend is going to continue where banks and FinTechs work together and collaborate rather than challenge each other.”
Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for Hyland?
“It’s my fourth year coming here with a couple of different companies and I always find this event really valuable. Not only to obviously promote our products and our brand… But to speak to key decision-makers and peers across financial services. We aim to learn from them about whether the challenges we perceive as a vendor are seen by them as a customer. We will continue to learn and evolve our business around key market challenges. Hyland can then focus our solutions around the real-world problems our peers are seeing across financial services. Coming to this event is a great way to meet as many people as possible. And just really enjoy having those meaningful conversations with leaders in the financial services sector.”
Hyland puts your content to work, making it smarter and more accessible in the moment of need.
Hyland’s content, process and application intelligence solutions empower customers to deliver exceptional experiences to those they serve. The solutions capture, process and manage high volumes of diverse content, helping you improve, accelerate and automate operational decisions and workflows.
Our cover star Rebecca Fitzgerald, Director of Data & AI at Yorkshire Building Society, reveals a digital transformation journey meeting…
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Our cover star Rebecca Fitzgerald, Director of Data & AI at Yorkshire Building Society, reveals a digital transformation journey meeting customers, wherever they are.
Yorkshire Building Society: Data, AI & Inclusive Leadership
Our cover story focuses on the data revolution taking place at Yorkshire Building Society (YBS)… Navigating this journey of change is Director of Data and AI, Rebecca Fitzgerald. Her ambitious vision is to transform the 160-year-old mutual through ethical, human-centred data strategies and AI innovation. In a rapidly evolving digital landscape, she aims to ensure YBS does not just keep up but leads from the front.
“I’m accountable for developing and implementing strategies to enhance data-centricity and drive value from data and AI for our customers and colleagues,” Rebecca states. This directive is grounded in strong governance, positive data culture, and the empowerment of people through data literacy and technological upskilling.”
Tyme Group: Scalable Global Digital Banking
Dietmar Bohmer, Chief Analytics Officer at Tyme Group, on operationalising innovation, cultivating a culture of empowerment and driving transformation from the inside out…
“It’s been wild ride from a technology point of view,” admits Dietmar… Today, that foresight is paying off. The cloud-native architecture has provided Tyme with the elasticity, resilience, and speed it needs to support its rapid growth across emerging markets. “With each new deployment, the organisation has evolved and refined its technological foundation,” notes Dietmar. “When the time came to launch GoTyme Bank in the Philippines, lessons learned from the rollout of TymeBank in South Africa enabled the team to rethink and redesign their stack, optimising for scale, performance, and localised feature delivery.”
ČSOB: A Digital Transformation Journey
ČSOB Slovakia is undergoing a major transformation aimed at future-proofing its technology, enhancing customer experience, and reinforcing its leadership in digital banking. Under the stewardship of its CIO Ludek Slegr, the bank’s IT team is navigating a major upgrade of its responsibility, overhauling core IT systems and implementing agile methodologies to meet its strategic goals. At the heart of this transformation is a focus on delivering value through technology, supporting people development, and fostering sustainable innovation.
“The next step for digital-first is continuous improvement of straight-through processing ratio, i.e. reducing involvement of manual work in our processes.”
Money20/20 Europe
FinTech Strategy also reports from the conference floor at Money20/20 Europe in Amsterdam. Bringing together the world’s leading innovators, institutions, investors, and influencers from across the FinTech and financial services spectrum, more than 8,000 delegates from over 2,300 companies were in attendance… We sat down with Standard Chartered’s Head of Digital Assets – Financing & Securities Services, Waqar Chaudry, to discuss how the bank is connecting traditional with digital, collaborating with FinTechs and taking a measured approach to entering the crypto market. And we spoke with Veritran’s CMO, Jorge Sanchez Barcelo, to find out more about the tech firm’s partnership with Manchester City which is reimagining CX to create a frictionless digital experience for fans.
Financial Transformation Summit
The Financial Transformation Summit at London’s ExCel is one of the most immersive and interactive events in the financial services calendar. As a media partner, FinTech Strategy took the temperature of industry innovation at our stand with on camera hot takes from the tech leaders pushing the boundaries at Hyland, Fidelity, HSBC, Citigroup and more…
Also in this issue, we keep you up to date with the key FinTech events across the globe; and read on for more insights from InsurTech disruptors Qover, lending innovators iwoca and investment experts Eastern Horizon…
Our cover story explores the digital transformation journey of RAKBANK in the UAE. Head of Digital Transformation, Antony Burrows, reveals the agile practices, enterprise-wide enablement and people-first culture delivering digital banking with a human touch.
“Culture is the cornerstone,” Antony stresses. RAKBANK codifies this into its Four Cs Framework – Connect, Communicate, Collaborate and Celebrate. “Here in the UAE, banks are pivoting from a model of ‘we know everything’ to recognising that one of the best ways to deliver continuous change and value to customers is through partnerships with startups and FinTechs. It’s no longer banks versus startups – it’s banks and startups, working together for the customer. This shift is especially meaningful as banks expand beyond traditional services to focus on customers’ broader financial lives.”
MTN MoMo: Empowering Africa Through FinTech
Hermann Tischendorf, Chief Information & Technology Officer at MTN MoMo (the telco’s mobile money division) reveals a bold roadmap for leveraging FinTech to drive financial inclusion across the African continent.
“MoMo is comparable in monthly active users to some of the top ten FinTechs globally. We’re playing in the same league as Revolut or Nubank – but in much more complex markets,” notes Hermann. “Access to financial services is fundamental. Without it, people are excluded from the global economy. Our services are the equaliser allowing individuals in frontier markets to participate in trade, store value, and ultimately improve their quality of life.”
Republic Bank: Building a Digital Bank
Republic Bank has been serving customers via its branches for over 185 yearsand now serves 16 different countries across the Caribbean and beyond. It’s “a regional bank with a growing global reach,” explains Group Chief Information & Digital Transformation Officer, Houston Ross.
His team is building a digital bank during a Year of Delivery and Accountability (YODA). “When we talk about digitalisation it’s a journey that never ends. And product is the vehicle to make sure we’re continuously improving.This is our digital pathway and we have to change minds in terms of going beyond the challenges to achieve what’s possible with the right frameworks, tools and processes for our people to serve our customers.”
Silverfin’s CEO, Lisa Miles Heal, on how the accountancy industry must innovate with technology to evolve
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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.
Morne Rossouw, Chief AI Officer at Kyriba, on leveraging AI skills to enhance decision-making and compliance in financial services
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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.
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
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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.
As of 2025, artificial intelligence (AI) tools are revolutionising the financial industry by enhancing efficiency, accuracy, and decision-making across various…
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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.
Anshul Srivastav, Senior Vice President and Head – Europe for Zensar Technologies on securing AI with blockchain
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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.
Alexandra Mousavizadeh, Co-Founder & CEO at Evident, on the rise of Agentic AI in financial services
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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.
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
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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.
Russell Gammon, Chief Solutions Officer at Tax Systems, on the benefits of AI in automating routine processes to make time for higher level strategic tasks
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In the past two and a half years since the launch of ChatGPT – and the likes of Copilot – the world has been gripped with generative AI fever. However, after the initial rush of enthusiasm, many businesses today are taking a more cautious approach. Trying to identify tangible benefits and use cases that can prove its worth before making costly investments.
One industry where the use cases are becoming more evident day by day is Financial Services. Repetitive and time-consuming tasks, traditionally completed manually with all the risk of human error that entails, can now be automated. Capabilities such as machine learning, generative AI, and advanced data analytics algorithms are being used to help ensure organisations remain compliant through delivering accurate, timely calculations, tax filings and reports. And creating clearer visibility.
AI Revolution
By automating routine processes, such as data analysis and reconciliation, finance executives can spend more time on higher level strategic tasks. AI can also provide insights beyond the capacity of humans thanks to its ability to crunch vast volumes of data, It can uncover trends that might otherwise go unnoticed. This enables real-time reporting and analysis with AI insight forming the basis of smarter decision-making.
For finance, this is just the beginning of the AI revolution. Look deeper into any finance sector and a huge variety of more specialised applications are revealed. Take the tax industry, for example, where a sizeable cohort of professionals still spend a considerable amount of time checking long lists of numbers on invoices or using spreadsheets to track spending. Not only is this work frustratingly boring, it is also prone to human error. AI has the potential, at a single stroke, to handle such tasks.
Navigating Choppy Regulatory Waters
Staying in the tax-related field, AI can also play a pivotal role in handling incoming regulations, such as Pillar Two. Multinational corporations are grappling with the complexities of this legislation. AI is emerging as a game changing tool in compliance management, transforming tax reporting, risk mitigation, and regulatory adaptation.
AI is being used to automate compliance and reporting processes. It can streamline data aggregation, ensure accurate reporting, and adapt to evolving regulations. AI-powered compliance tools optimise the evaluation, monitoring, and reporting of Pillar Two obligations. This can reduce complexity and improve precision. They can also integrate and standardise financial data across jurisdictions, improving consistency in tax computations.
These solutions seamlessly connect disparate systems, extracting and harmonising data from multiple sources regardless of format. By normalising and processing this information in line with BEPS regulations, AI can swiftly identify potential compliance risks. Advanced algorithms can flag irregular transactions between related entities and pinpoint inconsistencies in transfer pricing. This helps to detect possible profit-shifting activities before they become regulatory concerns. AI thus has the potential to change compliance management from a costly obligation to a strategic advantage.
Be Wary of AI’s Limitations
So, there is clearly a lot of potential for AI to transform financial services in terms of daily operations and compliance. However, it is important to remain wary of its limitations. Chief amongst them, is AI’s propensity to ‘hallucinate’ or make information up if it can’t find the right answer. That casts a shadow over the accuracy of all of its output. And underlines the importance of professional gatekeepers who can verify AI content and ensure it is correct.
AI also currently lacks the ability to interpret subtle context, which humans can more easily respond to. This can feed into spurious responses and misinterpreted data. However, with the right training, monitoring and oversight, AI tools can overcome such weaknesses.
Supporting, Not Replacing, the Human Touch
Understandably, given AI’s potential, many are concerned about the impact on jobs. If AI can digest thousands of lines of data and spit out a report in seconds, what do we need interns for? But it’s important to see AI as an augmentation of existing human talent, not a replacement for it.
As noted above, the possibility of hallucination means that qualified professionals will always have a role to play in quality checking output. So, what we are seeing is the development of a symbiotic relationship wherein professionals are freed from the drudgery of repetitive grunt work. They can focus on more strategic objectives, while AI handles it under their careful eye.
For the tech-savvy Gen-Z entering the workplace today, this is a hugely positive change. The finance and tax industries have become a less attractive career option for this generation, due to the traditional processes and lack of technological innovation. What graduate wants to spend their days entering data after years of studying their chosen subject? With AI ready as a helping hand, they can enter the workplace and use their skills and knowledge to assess the technology’s output, rather than spending hours manually doing it themselves. The finance industry is now in a position to embrace this opportunity that AI has presented. And encourage new talent into the industry.
Given the financial services sector is plagued with skills shortages, and ever-growing workloads, employers can now offer more attractive career opportunities. Furthermore, striking the right balance to drive improved efficiency, productivity and performance and reap the rewards of an AI-enabled future.
Digital DNA – Exploring core infrastructure, platform strategies, and foundational technologies.
Embedded Intelligence – AI, machine learning, data strategies, and real-time analytics.
Beyond Fintech – Partnerships between fintechs and other sectors like retail, health, and climate.
Governance 2.0 – Regulation, digital identity, privacy, and ESG compliance.
Day three featured more impactful sessions across all four pillars, offering attendees more valuable insights and strategies for innovation.
Highlights from Key Sessions at Money20/20 Europe:
How to Create and Leverage FinBank Partnerships
The discussion focused on the evolution and success of FinTech partnerships with banks. Key points included the shift from transactional partnerships to more collaborative, value-driven relationships, emphasizing joint KPIs and product creation.
Alex Johnson, Chief Payments Officer, Nium
“You really have to differentiate. You really have to stand out for a bank to say, ‘Yeah, I like what you offer enough to go through, six months of onboarding.’ Dare I say, maybe more.”
John Power, SVP, Head of JVs & AQaaS, Fiserv
“The legacy system, it’s a fact of life. They’re there. They’re pervasive. They’re going to be here for a long time, and banks historically have made huge investments in those platforms and systems. So I think both the challenge for the for the bank and the opportunity for the FinTech is, how do you at the front end of those legacy systems develop new products that can scale and that you can bring cross border easily and readily.”
“It really is cutting the line to be able to deliver opportunity for customers and to be able to expand propositions for new customers.”
“The economic development supply chains shifting to low to middle income countries are incredibly important right now, and cross border payment rails have not been good in low middle income countries.”
Where Fintech goes Next: Tapping into Platforms and Verticals
The discussion centred on the democratisation of financial services through embedded finance. The panel emphasised the importance of data quality, personalisation, and strategic partnerships in delivering seamless financial experiences – ultimately enhancing customer satisfaction and improving business efficiency.
“Embedded finance is going to be defined by region and use cases.”
Amy Loh, Chief Marketing Officer – Pipe
“Small businesses don’t want to manage their business through a bunch of different tools that are stitched together. They’re looking to platforms to do everything for them and keep high end services.”
“Most platforms or merchants out there trying to diversify revenue, and they will get auxiliary revenue, or maybe get primary revenue through FinTech activity.”
The Neobanks Strike Back
In a dynamic exploration of neobanking’s evolution, Ali Niknam revealed bunq’s remarkable journey from a tech-driven startup to a sustainably profitable digital bank. By leveraging AI across every aspect of their operations, bunq has transformed traditional banking, reducing support times to mere seconds and creating a hyper-personalised user experience. Niknam emphasised the power of user-centricity, showing how innovative features like simple stock trading and multi-language support can democratise financial services.
The bank’s strategic approach – focusing on user needs rather than investor expectations – has enabled them to expand thoughtfully, with plans to enter the UK and US markets. By embracing technological change and maintaining a relentless commitment to solving real customer problems, bunq exemplifies the next generation of banking.
Ali Niknam, Founder & CEO, bunq
“Somewhere in the 70s, we let go of the gold standard, and now currencies are basically floating. The only reason why a dollar or a euro is worth what it’s worth is because of trust and perception. Philosophically, it’s very logical that we have found another abstraction layer by introducing stablecoin, which is not much else than a byte number that has a denomination currency as a backing asset that itself doesn’t have anything as a backing asset. A lot of people might ask, ‘Why would you need a stablecoin? We have euros. I go get a coffee, pay with Apple Pay or cash.’ But there are many countries on this planet where the local currency is not stable. If your country has an inflation rate of 30,000% like Zimbabwe, you would really love to use a different currency. The US dollar has been the currency of choice, but as a normal person, you cannot access the US dollar. A US dollar stablecoin that you can access by simply having a mobile phone – that’s going to be transformational for large groups of people.”
Innovating When Regulation Can’t Keep Up: Lessons from NASA
Lisa Valencia covered an array of topics, from her 35 year career at NASA and Guinness World Record to the rise of private entities like SpaceX, which has launched 180 missions this year, and the increasing role of public-private partnerships in space exploration. The speaker also touched on international collaborations, particularly with the European Space Agency and the Italian Space Agency, and the potential for space tourism and colonization of the moon.
Lisa Valencia, Programme Manager/Electrical Engineer – Pioneering Space, LC (ex NASA)
“Back in the day, NASA got 4% of the national budget. Now it’s down to just 0.1%, so we’ve had to get creative with private partnerships. SpaceX is the perfect success story. They came to us in 2007 needing money after some rocket mishaps, and look at them now! From my balcony, I see their launches every other day. They’re planning 180 launches this year alone.Talk about a return on investment!”
“We’re planning to colonise the South Pole on the moon. The idea is to extract water and hydrogen from the regolith—both for living there and for fuel.”
Scaling Internationally in 2025: Funding, Innovating, and Breaking into New Markets
The conversation focused on the growth and strategy of fintech companies, particularly those with a strong presence in Europe and the US. The panel featured Ingo Uytdehaage, CEO and co-founder of Adyen, and Alexandre Prot, CEO of Qonto. Both leaders expressed a preference for organic growth over acquisitions, emphasizing the importance of scaling efficiently before pursuing an IPO.
Ingo Uytdehaage, CEO and co-founder of Adyen
“I think an important part of scaling a company is not just thinking about your product, but also considering the markets you want to address, and how you ensure you become local in each country.”
“We realised over time that if we really want to bring the customers, we need to have the best licenses to operate. A banking license gives you a lot of flexibility.”
“Being independent from other companies, other financial institutions, that gives you flexibility to build what your customers really want.”
“I think it’s very important, also in Europe, that we continue to be competitive. If you think about regulations and AI, we shouldn’t try to do things completely differently compared to the US.”
Alexandre Prot, CEO of Qonto
“We need to be very strict about tech integration and avoiding legacy which slows us down.”
“We still need to scale a lot before we have a successful IPO. A few team members are working on it and getting the company ready for it. But, the most important thing is just scaling efficiently in the business, and maybe an IPO would be welcome in a couple of years.”
Putting The F in Fintech
The panel discussion focused on the role of women in FinTech based on personal experiences.
Iana Dimitrova, CEO, OpenPayd
“At times, being underestimated is helpful, because if you’re seen as the competition, driving an agenda is becoming more difficult. So what I found, actually, over a period, is that bringing your emotional intelligence, leaving the ego outside of the outside of the room, and just focusing on execution is is incredibly helpful.”
Megan Cooper, CEO & Founder, Caywood
“The moment we start defining ourselves as like a female leader or a female entrepreneur, you almost kind of put yourself in a bit of a box. And so I think just seeing yourself on an equal playing field and then operating it on an equal playing field and interacting in that way is quite advantageous.”
“We can’t just want diversity and hope it happens. We actually have to be intentional about creating it.”
Valerie Kontor, Founder, Black in Fintech
“Black women make up 1.6% over the FinTech workforce, but when we look at the financial reality of black women by the age of 60, only 53% of black women have enough money in their bank account to retire. We need to start marrying people in FinTech and the people that we need to serve.”
Money20/20 Europe 2025 closed its doors but the next edition of the conference will return to Amsterdam from June 2–4, 2026, promising to continue the tradition of shaping the future of financial services…
Day two of Money20/20 Europe 2025 at RAI Amsterdam continued the momentum with a focus on digital assets, stablecoins, and…
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Day two of Money20/20 Europe 2025 at RAI Amsterdam continued the momentum with a focus on digital assets, stablecoins, and the evolving regulatory landscape. The event attracts over 8,000 attendees, including FinTech leaders, investors, and policymakers, all eager to explore the future of finance.
Money20/20 Conference Themes & Tracks
Money20/20 Europe 2025 is structured around four thematic content tracks:
Digital DNA – Exploring core infrastructure, platform strategies, and foundational technologies.
Embedded Intelligence – AI, machine learning, data strategies, and real-time analytics.
Beyond Fintech – Partnerships between fintechs and other sectors like retail, health, and climate.
Governance 2.0 – Regulation, digital identity, privacy, and ESG compliance.
Day two featured more impactful sessions across all four pillars, offering attendees further valuable insights and strategies for innovation.
Highlights from Key Sessions at Money20/20 Europe:
Digital Wallets and Co-opetition
A standout session featured industry leaders from Fluency, Curve, PayPal, and BLIK discussing the competitive yet collaborative nature of Europe’s digital wallet ecosystem. The panel delved into how traditional financial institutions and FinTech startups are navigating partnerships and competition to enhance user experiences and expand market reach.
Africa’s Fintech Innovation
Another significant discussion spotlighted Africa’s role in global fintech innovation. Representatives from 500 Global, Tech Safari, and Moniepoint highlighted how African startups are leveraging technology to drive financial inclusion and create scalable solutions that could influence global markets.
Digital Assets
A standout session featured Waqar Chaudry, Head of Digital Assets for Financing & Securities Services at Standard Chartered. In a fireside chat titled “The Digital Assets Opportunity: How Banks Can Win at Web3,” Chaudry, alongside Sygnum Bank’s Aliya Das Gupta, delved into the evolving landscape of digital assets.
Chaudry highlighted Standard Chartered’s initiatives in digital asset custody, tokenisation, and the launch of tokenised money market funds. Furthermore, he discussed the development of stablecoin solutions aimed at improving liquidity and settlement times. Chaudry underscored the importance of banks adopting robust digital asset strategies to meet growing client demands and navigate the complex regulatory environment. Drawing from his regulatory background at the Abu Dhabi Global Market, Chaudry provided a unique perspective on balancing innovation with compliance.
WealthTech Evolution
Leaders from Raisin, Upvest, and PensionBee explored the transformation of wealth management through AI and APIs. The panel emphasised the importance of personalised financial services and the integration of technology to meet the evolving needs of consumers.
Central Bank Digital Currencies (CBDCs)
A fireside chat with officials from the European Central Bank and the Bank of England provided insights into the development of the digital euro and pound. The discussion covered technical challenges, regulatory considerations, and the potential impact of CBDCs on the financial ecosystem.
Navigating the Evolving Cyber Threat Landscape
The financial services sector faces an unprecedented convergence of threats with sophisticated cyber attacks and the rise of new technologies… Recorded FutureCEO Christopher Ahlberg assessed the evolving threat landscape and strategies for building secure digital ecosytems. He was joined by In Security CEO Jane Frankland and Mastercard EVP Johan Gerber
Networking, Partnerships, and Brand Activations at Money20/20
Notable Announcements:
Money20/20 and FXC Intelligence Report: A collaborative report titled “How Will Europe’s Money Move in the Future?” was released, offering insights into the future of European cross-border payments and the impact of emerging technologies.
Policy Exchange Roundtables: Money20/20 introduced focused roundtable discussions involving central banks, regulators, and industry leaders to address critical regulatory challenges in the digital financial landscape
Day two of Money20/20 Europe 2025 underscored the dynamic interplay between traditional financial institutions and emerging FinTech innovations. Discussions on digital assets, stablecoins, and regulatory frameworks highlighted the industry’s commitment to embracing change while ensuring stability and compliance. The second day underscored the event’s role as a catalyst for innovation, collaboration, and growth within the fintech industry. As the conference progresses, stakeholders remain focused on shaping a resilient and inclusive financial future.
Money20/20 Europe 2025 opened its doors to a full-capacity audience at the RAI Convention Centre in Amsterdam. Bringing together the…
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Money20/20 Europe 2025 opened its doors to a full-capacity audience at the RAI Convention Centre in Amsterdam. Bringing together the world’s leading innovators, institutions, investors, and influencers from across the fintech and financial services spectrum. With more than 8,000 delegates from over 2,300 companies in attendance, the opening day set a high-energy, insight-rich tone for the rest of the week.
“Money Morning Live”
The day kicked off with “Money Morning Live”. A signature fast-paced keynote session hosted by Tracey Davies (President of Money20/20), Scarlett Sieber, and Zachary Anderson Pettet. The morning show served as a pulse check for the industry. Combining thought leadership with entertainment to engage both newcomers and veterans.
Rahul Patil, CTO of Stripe, delivered a keynote on AI’s role in payments infrastructure. Highlighting how machine learning is now essential for fraud detection, customer service, and onboarding. He emphasised AI should not merely be viewed as an efficiency tool, but as a strategic pillar to create personalised user experiences. And deliver scalable innovation across markets.
David Sandstrom, CMO at Klarna, reflected on the Swedish FinTech giant’s evolution, particularly its use of generative AI for customer engagement and internal operations. Sandstrom noted Klarna’s AI assistant, which now handles two-thirds of its customer queries globally, has dramatically improved both customer satisfaction and cost efficiency.
Money20/20 Conference Themes & Tracks
Money20/20 Europe 2025 is structured around four thematic content tracks:
Digital DNA – Exploring core infrastructure, platform strategies, and foundational technologies.
Embedded Intelligence – AI, machine learning, data strategies, and real-time analytics.
Beyond Fintech – Partnerships between fintechs and other sectors like retail, health, and climate.
Governance 2.0 – Regulation, digital identity, privacy, and ESG compliance.
Day one featured impactful sessions across all four pillars, offering attendees valuable insights and strategic foresight.
Highlights from Key Sessions at Money20/20 Europe:
Open Banking & Payment Rails
“Putting the Bank Back in Open Banking Payments”, saw speakers from Token.io, Santander, and BNP Paribas examine how banks are reclaiming relevance in the open banking conversation. While FinTechs initially led the charge, the panel noted banks now play a crucial role in building trusted, interoperability, and high-volume “pay by bank” solutions. The debate touched on customer adoption hurdles, PSD3’s role in shaping future APIs, and the monetisation challenges still plaguing the open banking model.
Card Issuance at Scale
In a fireside chat led by Thredd’s President Jim McCarthy, representatives from Railsr, Worldpay, Flagship Advisory, and Caxton discussed the complexities of issuing card programs globally. The group addressed fragmentation across regulatory environments. Especially in regions like LATAM and Asia-Pacific. They urged the need for programmatic flexibility, local compliance, and better BIN management. The panel agreed that the future of card issuing lies in seamless orchestration between platforms, banks, and third-party fintechs.
Agentic AI: Ready for Prime Time?
A standout session focused on the concept of Agentic AI — autonomous agents capable of completing financial tasks without manual prompts. Industry leaders from NVIDIA, bunq, and Visa debated how ready the financial services sector truly is for deploying such systems. While the technology is progressing rapidly, concerns around regulatory clarity, model interpretability, and risk frameworks remain.
NVIDIA’s Head of Financia Technology, Jochen Papenbrock, stressed the need to democratise access to compute infrastructure. And bunq’s AI evangelist, Ali El Hassouni, showcased how the challenger bank is testing semi-autonomous agents in customer support workflows. Meanwhile, Visa SVP for Products & Solutions, Mathieu Altwegg,emphasised the importance of embedding guardrails in agentic systems to ensure ethical AI practices. Especially in credit scoring and wealth advisory roles.
Scaling AI Across the Enterprise
A collaborative session featuring leaders from Stripe, Starling Bank, AWS, and Swift delved into the challenges of scaling AI initiatives beyond prototypes. The discussion spotlighted the importance of clean, real-time data pipelines, strong governance structures, and cross-functional collaboration between engineering, data science, and compliance teams.
Networking, Partnerships, and Brand Activations at Money20/20
Notable announcements:
Beyond the conference rooms, the exhibition floors buzzed with product demos, startup pitches, and impromptu huddles among VC firms, banks, and emerging FinTechs. Exhibitors such as Plaid, Adyen, Marqeta, and Fireblocks showcased new tools for embedded finance, real-time treasury management, and blockchain settlement.
Wise teased a new enterprise FX tool tailored for SMEs.
Checkout.com introduced an AI-enhanced fraud prevention dashboard.
Avalanche Foundation launched an initiative to bring blockchain-based micro-insurance products to underserved markets in Eastern Europe.
A particularly significant development emerged around stablecoins, with clear signals that regulated, bank-issued digital currencies are entering a new phase of maturity:
U.S. Megabanks Signal Joint Stablecoin Initiative Executives from JPMorgan Chase, Wells Fargo, Bank of America, and Citigroup confirmed that initial groundwork has begun on a joint U.S. dollar-denominated stablecoin, subject to the passage of the pending GENIUS Act (Guiding and Establishing National Innovation for U.S. Stablecoins). The stablecoin aims to offer faster, cheaper cross-border settlement and programmable liquidity for enterprise clients. Bank leaders emphasized that this would complement, not replace, traditional banking rails.
Ripple Expands in the UAE In a regional announcement, Zand Bank and fintech firm Mamo revealed a partnership with Ripple, using its blockchain infrastructure to enable real-time, low-cost cross-border remittances. This move, anchored in the UAE’s pro-digital asset stance, aligns with broader ambitions to make the country a hub for regulated digital currencies.
Institutional Stablecoin Custody Panels featuring speakers from Fireblocks, Anchorage Digital, and Circle addressed the evolving role of stablecoins in treasury operations and FX management. There was widespread agreement that tokenised cash equivalents, including USDC and EURC, are increasingly being used for short-term settlement and yield farming, particularly in Asia and Europe.
These discussions signalled a broader institutional acceptance of stablecoins, with an emphasis on compliance, transparency, and integration into traditional finance rather than bypassing it.
Day one of Money20/20 Europe 2025 delivered on its promise of convening the brightest minds to create the future of finance. From headline-grabbing keynotes and deep-dive panels to global product launches and off-stage networking, the conference created a rich mix of thought leadership, practical innovation, and human connection.
Whether it was the evolution of AI in banking, the future of programmable money, or the balance between innovation and regulation, the discussions revealed a clear consensus: collaboration will define the next chapter of FinTech. Day two at Money20/20 promises even more, with upcoming sessions on decentralised finance, digital identity, and CBDCs.
Dave Murphy, Head of Financial Services EMEA & APAC at Publicis Sapient, on unlocking data to unleash the intelligence with AI
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In today’s financial services landscape, the promise of artificial intelligence is everywhere… Hyper personalisation, intelligent automation, real-time insights, and AI-assisted customer experiences. But here’s the truth: AI doesn’t run on ambition. It runs on data.
If your customer and transactional data remains locked inside monolithic core systems, even the most sophisticated AI will underdeliver. The most effective path to AI-powered transformation isn’t a complete rebuild of your core – it’s strategic decomposition. By making high-quality data available in near real-time to your channels and platforms, banks can unlock AI’s full potential without overhauling their entire architecture.
At Publicis Sapient, we believe unlocking your data is the critical enabler for harnessing the full value of AI across the financial enterprise. It is no longer necessary to completely rebuild your core infrastructure. Instead, what’s required is strategic decomposition of monolithic systems to ensure near real-time data availability to your channels and AI applications.
The Data Access Conundrum
Banks are acutely aware that their legacy systems create data silos. Research reveals that 70% of banks’ IT budgets are still spent on maintaining legacy systems. Moreover, more than half cite the limitations of their core as the primary barrier to transformation.
Despite a shared recognition of the need to change, many institutions remain hesitant, concerned by the perceived complexity, cost and risk of restructuring their data architecture and overhauling foundational platforms. But this hesitation comes at a cost. As customers demand more personalised and seamless experiences, and digital challengers launch AI-enabled services at speed, traditional institutions risk falling behind.
Why Data Accessibility Unlocks AI’s Potential
The simple truth is: AI cannot thrive in isolation. It needs high-quality, accessible, and timely data. It needs customer and transactional information that’s available near real-time. And it needs a composable, event-driven architecture where data can flow freely across customer journeys and operational workflows.
Decomposing monolithic core banking systems enables all of this. By creating strategic APIs and data layers, banks can liberate critical information from legacy platforms and make it available to AI-powered services without the need for complete core replacement. In our work with leading banks globally, we’ve seen accessible data unlock:
1:1 personalisation at scale
Real-time fraud detection and risk modelling
AI-assisted customer onboarding and service
Automation across lending, compliance and operations
This is not theoretical. It’s already happening. In one engagement, we helped a regional bank transform its operating model via a phased core modernisation programme – delivering a one-to-one return on investment over five years by shifting from reactive IT spend to proactive value creation through accessible data.
Progressive, Not Paralysing
One of the biggest myths around core modernisation is that it requires a disruptive, ‘big bang’ transformation. That’s no longer the case. Advances in architecture, engineering tools, and AI-powered development platforms – such as our own Sapient Slingshot – now make it possible to modernise progressively and liberate critical data, rather than rebuilding everything from scratch.
Techniques like multi-core routing, event-driven orchestration and domain-driven design allow banks to gradually make customer and transactional data available near real-time to channels and AI applications – all without jeopardising day-to-day operations or requiring full core replacement.
Reorienting Around Data and People
Technology alone is not enough. Successful transformation requires a cultural shift – one that reorients the organisation around data, agility, and human outcomes. The future-ready bank is not only AI-enabled but data-led and human-centric.
By unlocking and democratising data through modern architecture, banks can power everything from predictive decision-making to better colleague collaboration. We are already seeing leading firms embed AI into their customer and employee journeys. Not as add-ons, but as integral parts of reimagined experiences built on liberated data.
The Future Belongs to the AI-Enabled
As AI capabilities continue to evolve, the divide between data-rich and data-poor, and AI-enabled and AI-limited institutions will widen. The leaders will be those that treat transformation not just as a technical challenge, but as a strategic imperative – reshaping how they operate, compete and serve.
Now is the time to act. Unlocking your data through strategic core modernisation is no longer a question of ‘if’, but ‘how’. Because in the age of AI, the intelligence of your bank will only ever be as strong as the data it can access and learn from, and ultimately the systems that underpin it.
Find out more from Publicis Sapient about core modernisation here
Welcome to the Middle East’s biggest fintech event for 25 years. Seamless Fintech brings together big tech, government, banks, financial institutions, fintechs, investors, and media. Perfect for anyone passionate about the Middle East’s fintech and payments landscape. This event allows you to explore the fast-evolving ecosystem and engage with top industry players and innovators. And visit the Identity Showcase to discover cutting-edge solutions.
“If I wanted to take a pulse of the vibrancy of the region, then look around at Seamless. The amount of interest and intent people are showing in us and FinTech in the region is very visible at Seamless Middle East.”
Managing Director, Amazon Payments Service
Furthermore, whether you’re presenting your latest payment innovations or showcasing impactful demos, this is your opportunity to foster connections and accelerate business growth. Join 25,000 attendees and 800 exhibitors gaining insights from a stellar line up of 750+ expert speakers from the likes of Revolut, J.P. Morgan, Monzo, Citi and more.
Seamless Digital Commerce
Seamless Fintech will be co-located with Seamless Digital Commerce. This event caters to payments companies seeking to connect with merchants, SMEs, retailers, and e-commerce platforms. The event offers valuable insights into revolutionising in-store experiences, optimising e-commerce strategies, and mastering digital marketing techniques. It provides unmatched opportunities for growth and collaboration in the digital commerce space.
This event is perfect for those looking to forge new partnerships, gain valuable insights from industry trailblazers and drive innovation to stay ahead in the ever-evolving digital landscape. Moreover, whether you’re a startup, an established player, or an SME, Seamless Digital Commerce is designed to push the industry forward.
Join FinTech’s greatest event when Money20/20 Europe returns to Amsterdam’s RAI Arena June 3-5
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FinTech Strategy is proud to be a media partner for Money20/20 Europe 2025.
Launched by industry insiders in 2011, Money20/20 is the heartbeat of the global FinTech ecosystem. Some of the most innovative, fast-moving ideas and companies have found their feet (and funding) on its show floor. From J.P. Morgan, Stripe, and Airwallex to HSBC, Deutsche Bank, and Checkout.com.
Furthermore, this is where you’ll find new connections, business-critical insights from inspirational speakers, innovation, and partnerships you need to ensure your business succeeds for whatever comes next in money.
The Agenda for 2025
Come and create the future for financial services at Money20/20 Europe… This year’s agenda tracks cover Beyond FinTech, Digital DNA, Embedded Intelligence and Governance 2.0. Expert speakers include leaders from Mastercard, Monzo, Bank of England, Visa, IBM, Starling Bank, Revolut and more offering key insights on everything from agentic AI and cross-border payments to open banking and embedded finance.
Why Money20/20?
FinTech Strategy spoke with a host of leaders from across the FinTech spectrum. They all agreed on one thing, Money20/20 Europe is ‘the’ place to make connections and build your business.
“It’s the first time I’ve attended Money 20/20 and, we’ve had some fascinating impromptu conversations that will lead to great opportunities. All the big names are here and it’s clearly a popular event from a thematic perspective – payments is a big theme this year. I have a very high regard for the quality of what’s on offer and the way the event has been organised – it’s a great customer experience, the way it’s all been structured, at scale, is actually one of the best I’ve ever seen. The response has been fantastic…”
Stephen Everett, MD Payables & Receivables, Lloyds Banking Group
“The majority of people at Money20/20 genuinely get up in the morning with a growth and innovation mindset. Therefore, you have to balance and recognise that when you walk into this big venue that there will be some wacky ideas. From my experience, I have seen many infant ideas turn into successful ventures, whereas I have also seen some ventures becoming unsuccessful despite having great innovation ideas. Fintechs will fail. Innovation will fail. Experiments will fail. And that’s fine. That’s what Money20/20 is all about.”
Michelle Prance, CEO, Mettle (NatWest Group)
“It’s good for Mettle to come here because we are a fintech that was incubated inside a large bank (NatWest) for fintechs. Quite often their route to market, route to capitalisation, is by going into a main bank being acquired. So, it’s that marriage between a big organisation and the small nimble fintech. People are really interested in what we’re doing because big incumbents want to be fast and nimble. They don’t always have the capital to invest in something like we’ve been able to do with Mettle. So, they’re interested to know the right route to go down. Do they incubate in house? Or do they buy it in? And what’s the right way to do that without killing the culture? These are the types of interesting conversations we’ve been having here.”
“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, Stripe… And then you have the Heads of Payments from companies like Booking.com, Minted and Summit who are coming here with their team 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 with that would normally take months to arrange. So, the ROI from this week is really powerful just from being able to have these conversations.”
“Paymentology is homegrown out of the UK so it’s important for us to make sure we’re representing the business across Europe. This is the centre of the world for banking innovation. We have customers here from Singapore, Dubai, Saudi Arabia, Ghana and beyond. People look to this event to really learn about what’s happening in the industry globally and discover what trends are going to come up. What should we be doing? How can we innovate together and learn from each other? That’s one of the things I really love about Money20/20; the talks in all of the panels are so interesting and I always leave knowing more. Being in the payments industry, and especially being an issue processor, it’s important for us to learn from the industry and understand where we need to move so that we can stay at the forefront of developments.”
“This is my sixth straight Money20/20 and it gets busier every year! It’s great to learn more about the ecosystem at large. You can see developing trends each year, and it’s always a little bit different. You build relationships at Money20/20 that stay with you for the rest of your life. And it’s a perfect opportunity to meet people in the flesh that you might normally only see on screen. You can get a pretty direct read on what they’re working on and it’s exciting to be here making new connections.”
Dave Murphy, Head of Financial Services EMEA & APAC at Publicis Sapient, on why retail banking is at an important crossroads and must react
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Retail banking stands at a pivotal juncture. As digital-first generations reshape customer expectations and competitive pressure from FinTechs and neobanks intensifies, traditional banks face a critical choice: modernise now or risk obsolescence. Publicis Sapient’s latest Global Banking Benchmark Retail Banking Report underscores that “digital by default” is no longer an aspiration. It’s an immediate necessity.
Drawing on insights from 600 retail banking executives across 13 countries, the report highlights a convergence of transformative forces… The accelerated adoption of Gen AI, the decline of legacy IT infrastructure, and an urgent need to reimagine customer engagement for a younger, mobile-first demographic.
Digital or Die: A Defining Moment
Retail banking has been evolving for over two decades, but the stakes have never been higher. In Q1 2025, JPMorgan Chase reported a net income of $14.6 billion, up 9% year-over-year. This was driven by robust trading revenues and investment banking fees. Meanwhile, UK neobanks are making significant strides. Revolut achieved a net profit of $1.0 billion in 2024, marking its first billion-dollar annual profit, with revenues soaring 72% to $4.0 billion. Monzo also reported its first full year of profitability, posting a pre-tax profit of £15.4 million and doubling its revenue to £880 million.
Despite these advancements, 62% of retail banking executives admit their pace of transformation lags behind competitors. This isn’t a minor delay – it’s a strategic disadvantage in a market where 44% of new currents accounts are already being opened with digital banks and FinTechs.
Gen AI: Catalyst and Compulsion
Among all the changes underway, generative AI has emerged as the most powerful and potentially disruptive force. According to the benchmark study, data and AI are the top investment areas for digital transformation over the next three years. Executives are betting big on AI not only to improve customer engagement but also to modernise operations and accelerate core transformation. The impact of Gen AI in banking is tangible. It can:
Personalise customer journeys at scale
Accelerate software development lifecycles
Write code and automate data management
Deliver hyper-relevant product recommendations
Power AI agents with human-like customer service abilities
In short, Gen AI makes what was once prohibitively expensive and time-consuming not only possible but scalable.
The banking customer has changed
The report makes it clear: retail banks must stop building for yesterday’s customer. Gen Z, who will make up one-third of the workforce by 2030, already prefer mobile-first, always-on banking. They value immediacy, customisation, and authenticity. A staggering 83% of Gen Z consumers say they are frustrated with current bank processes.
Compounding this generational shift is the growing irrelevance of traditional customer segmentation. Today’s consumers defy linear categorisation. The same individual can be a small business owner, a parent, and a new homeowner. Yet banks often treat them as three separate customers because of product-centric data silos.
The core problem with legacy thinking
Legacy systems continue to be the biggest barrier to meaningful transformation. 70% of banking executives say their legacy infrastructure is hindering their ability to deliver the digital experiences customers expect. Many core systems are COBOL-based and nearing end-of-life. Yet banks are reluctant to modernise due to perceived risk and complexity.
The irony is clear: the risk of maintaining outdated systems now outweighs the risk of change. With Gen AI, banks finally have the tools to confront the 800-pound gorilla in the room – core modernisation.
Why Core Modernisation is the linchpin
Modernising the core is about more than infrastructure. It’s the key to unlocking the full value of AI, data, and digital transformation. A modern, cloud-native core enables:
Real-time access to first-party and third-party data
Agile delivery through microservices
Better governance and regulatory transparency
Faster go-to-market with new apps and services
Retail banks that modernise their core can stop building costly middleware just to access data. Instead, they gain a unified view of the customer and the agility to respond to banking market shifts in real time.
The virtuous cycle of AI and Core
What’s truly powerful is the feedback loop between Gen AI and a modernised core. Gen AI helps accelerate the core transformation by generating code, automating testing, and streamlining documentation. Once modernised, that core then enhances Gen AI’s capabilities with clean, structured data. This virtuous cycle creates exponential value, making digital transformation faster, cheaper, and more sustainable.
Retail banks are already allocating 35% of their customer experience digital transformation budgets to Gen AI. Furthermore, many are embedding AI across the entire software development lifecycle using tools like Sapient Slingshot to reduce human error, increase test coverage, and ship better code faster.
From Product-Centric to People-Centric banking
Ultimately, the report urges retail banks to shift from a product-centric to a people-centric mindset. That means designing experiences around life moments, not product categories. It means knowing that the mortgage customer is also a small business owner and a parent, and offering solutions that reflect that reality.
With modern core systems and Gen AI, banks can personalise outreach, tailor financial advice, and meet customers where they are. This holistic view is essential not only for growth but also for loyalty.
The era of deferral is over. Banks can no longer afford to delay core transformation. Gen AI has lowered the cost, reduced the complexity, and increased the speed of change. The only question left is whether banks are ready to lead or risk falling behind.
Publicis Sapient is working at the intersection of Gen AI and core modernisation every day… Helping banks link strategy to execution and deliver on the full promise of digital transformation. The future of retail banking isn’t coming – it’s already here. The time to act is now.
Vikas Krishan, Chief Digital Business Officer & Head of EMEA at Altimetrik, on the disruptive power of AI in FinTech
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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.
Arsalan Minhas, AVP Sales Engineering, EMEA & APAC, at Hyland, on how AI revolutionising financial services
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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.
Scott Zoldi, Chief Analytics Officer at FICO, explains why there should be no AI alone in decision making processes
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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.
InsurTech Insights Europe 2025: A Transformational Gathering for the Future of Insurance
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InsurTech Insights Europe 2025, held on March 19-20 at the InterContinental London – the O2, reaffirmed its status as the premier conference for insurance technology professionals across the continent. Drawing more than 6,000 attendees from over 80 countries, the event brought together C-level executives, startup founders, investors, and tech leaders. They explored the evolving future of insurance powered by innovation and digital transformation.
Key Themes
With seven stages and over 400 speakers, the conference agenda was packed with compelling keynotes, forward-looking panel discussions, fireside chats, and practical workshops.
The overarching theme of the 2025 edition was crystal clear: artificial intelligence (AI) is no longer a futuristic concept, it’s the driving force behind today’s insurance innovation. Topics like automation, generative AI, claims transformation, underwriting analytics, embedded insurance, cyber security, and ESG all reflected a dynamic industry poised for rapid acceleration.
A Focus on Leadership & Diversity
One of the standout sessions was the panel discussion titled “The ROI of Gender Diversity: Breaking the Glass Ceiling for Women in Leadership”, held on the Purple Stage. Featuring high-level voices from Solera, unlock VC, and AXA XL, the panel addressed the often-overlooked yet crucial importance of gender diversity in executive roles. The discussion didn’t stop at raising awareness; it presented measurable business outcomes tied to diverse leadership and called for action to foster inclusivity across all levels of the industry.
Complementing this session was “The Women in Insurance Power Group Meet-up”, a networking event held at the Sky Bar on the 18th floor. Attendees not only connected over lunch but were also invited into an exclusive WhatsApp group, encouraging long-term collaboration and support among female leaders and allies in the space.
The Innovators Hub and the ITI Marquee: Where the Future Was Born
A major addition to this year’s conference was the debut of the ITI Marquee. A vibrant, purpose-built zone dedicated to showcasing bold ideas and startup brilliance. This space housed the Innovators Hub, which included its own dedicated Innovator’s Stage. Here, early-stage ventures and InsurTech pioneers pitched their solutions to panels of VCs, corporate innovation leads, and fellow founders.
This setting offered more than exposure, It cultivated real-time connections between startups and investors, giving many smaller players their first shot at meaningful partnerships or funding opportunities. The diversity of ideas, from AI-powered claims processors to data-driven risk models for climate insurance, reflected the industry’s hunger for next-gen solutions.
Keynote InsurTech Highlights
One of the most talked-about moments of the event came from Daniel Schreiber, CEO and Co-Founder of Lemonade, whose opening keynote explored how AI can dramatically enhance customer experience in insurance. He challenged the audience to rethink not just how insurance is sold or serviced, but why it’s offered. And how technology can transform its social impact.
Another crowd favourite was the session on “The Path to Embedded Insurance”, which unpacked how insurance products are increasingly being bundled into digital ecosystems like ecommerce platforms, mobility apps, and smart home technologies. This wasn’t just a hype piece. Real-world case studies from European neobanks and auto insurers illustrated how embedded models are already driving customer growth and retention.
Among the compelling keynotes on the Main Stage, Sofia Kyriakopoulou, a Fintech Strategy AI Champion and Group Chief Data & Analytics Officer at SCOR, revealed how GenAI innovation at one of the world’s largest reinsurers is transcending the realm of proof of concepts to become fully productive.
InsurTech Deep Dives: AI, Data & Digital Claims
Sessions throughout the week made it clear that AI is at the forefront of virtually every area of insurance operations. Whether it was applied in predictive underwriting, fraud detection, or personalised customer engagement, companies are looking to AI not just for marginal gains but foundational transformation.
A standout workshop on AI in Claims Automation included live demos from startups using computer vision and NLP to automate damage assessment. Meanwhile, a session on Data-Driven Underwriting shared how insurers are replacing traditional risk proxies with real-time data streams, from wearables to smart meters.
Cybersecurity was another hot topic, with insurers discussing how to build resilient cyber products in the face of increasing digital threats and regulatory complexity.
Global Meets Local: The Power of Diversity
Although a European event at heart, the conference had a distinctly global flair. Speakers came from the U.S., Singapore, Brazil, South Africa, and the Middle East. They brought diverse perspectives on shared challenges such as climate change, digital regulation, and consumer trust.
Simultaneously, European startups shone on stage. Companies from the UK, Nordics, DACH, and Benelux presented innovative, often niche solutions for localised market challenges—from parametric crop insurance to real-time mobility coverage.
Trade Exhibition & Brand Visibility
The exhibition floor was a hive of activity, featuring booths from established players like Munich Re, Swiss Re, Guidewire, Duck Creek, and Cognizant, alongside vibrant startup showcases. Product demos, swag giveaways, and live challenges kept engagement high and made it easy for brands to stand out.
The conference proved to be a golden opportunity for brand elevation, allowing companies to position themselves as thought leaders or rising disruptors in front of an incredibly curated audience.
InsurTech Insights Europe: The Verdict
The closing remarks from Kristoffer Lundberg, CEO of InsurTech Insights, captured the spirit of the event:
“It’s a privilege for us to gather together the sharpest minds in the industry to discuss the role of AI in insurance. The direction and impact of these technologies will shape the space for decades to come.”
Indeed, InsurTech Insights Europe 2025 wasn’t just a conference, it was a strategic gathering. A melting pot of ideas and a launchpad for the next generation of insurance products and platforms. Attendees walked away not just with new business cards, but with fresh ideas, collaborative leads, and the motivation to drive innovation within their own organisations.
As the insurance industry continues to evolve amid mounting global challenges and rapidly advancing tech, this event served as a timely and energising reminder… The future is not something to wait for—it’s something to build, together.
MoneyLIVE Summit 2025: A stellar combination of thought leadership, cutting-edge technology showcases and unparalleled networking opportunities
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The MoneyLIVE Summit 2025, held on March 10th-11th at London’s Business Design Centre, once again positioned itself as one of the most significant events in the banking and financial services industry. With over 1,500 attendees, 200+ speakers, and an agenda packed with insights on digital transformation, AI-driven innovation, and payment advancements, the event delivered a comprehensive overview of the future of financial services.
As one of Europe’s most influential FinTech and banking conferences, MoneyLIVE Summit attracted executives from leading institutions, including HSBC, Revolut, Standard Chartered, Barclays, Google, and Mastercard, providing attendees with unparalleled networking opportunities and deep dives into the latest industry developments.
The 2025 edition of MoneyLIVE Summit focused on several key themes within the financial sector, including:
AI and Automation in Banking
The Future of Payments and Open Banking
Sustainability and ESG in Finance
The Evolution of Embedded Finance
Cybersecurity and Fraud Prevention
Modernising Legacy Systems
AI and Automation: The Next Frontier
One of the most anticipated discussions centredd on Artificial Intelligence (AI) and Automation in Financial Services. Keynote speakers such as Taylan Turan (CEO, Retail Banking, HSBC) and Francesca Carlesi (CEO, Revolut UK) highlighted how AI is revolutionising customer interactions, risk assessments, and fraud detection.
A standout panel featured representatives from Google Cloud, Lloyds Banking Group, and Monzo, discussing the ethical implications of AI-driven banking and how institutions can balance efficiency with regulatory compliance. The consensus? AI is no longer a futuristic concept but an operational necessity.
On the opening day we spoke with Tim Mason, Managing Director for Artificial Intelligence at Deutsche Bank, and Publicis Sapient VP Jan-Willem Weggemans, about the rise of Agentic AI. Look out for this feature in the May edition of FinTech Strategy Magazine. Publicis Sapient also hosted an AI Champions Meet Up.
The Future of Payments and Open Banking
With open banking continuing to disrupt traditional financial models, this year’s summit included multiple sessions on its evolution. Speakers from Visa, Mastercard and Stripe explored how real-time payments and digital wallets are reshaping the customer experience.
One of the most engaging sessions was on CBDCs (Central Bank Digital Currencies) and the impact of digital currencies on global trade. Representatives from the Bank of England and the European Central Bank provided valuable insights into regulatory developments and the long-term feasibility of CBDCs in mainstream banking.
Sustainability and ESG in Finance
The financial industry’s role in Environmental, Social, and Governance (ESG) initiatives was another critical theme. With growing investor interest in sustainable finance, executives from Barclays, NatWest, and BlackRock discussed how banks can integrate ESG principles into lending and investment strategies.
A major highlight was a fireside chat with Ana Botín, Executive Chairman of Santander Group, who emphasised the need for banks to take the lead in financing climate action while maintaining profitability. She stressed that FinTech innovation must align with sustainability goals to drive real change.
Notable Speakers & Thought Leadership
MoneyLIVE Summit 2025 featured an impressive lineup of speakers, including CEOs, policymakers, and FinTech pioneers. Notable names included:
Francesca Carlesi (CEO, Revolut UK) – Discussed the role of challenger banks in redefining customer expectations.
Taylan Turan (CEO, Retail Banking, HSBC) – Spoke about how traditional banks must adapt to stay competitive in an increasingly digital world.
Saif Malik (CEO, UK, Standard Chartered Bank) – Shared insights on the rise of embedded finance and its impact on global banking.
Anne Boden (Founder, Starling Bank) – Highlighted the impact of neobanks on legacy banking institutions.
Google Cloud & AWS Representatives – Covered AI’s growing role in fraud prevention and customer engagement.
Lee McNabb (Head of Payment Strategy, NatWest) – Shared views on modernising core payment architecture for the long term.
The diversity of perspectives provided attendees with a well-rounded understanding of the industry’s challenges and opportunities in the coming years.
MoneyLIVE Networking & Attendee Experience
Networking has always been a key highlight of MoneyLIVE Summit, and the 2025 edition did not disappoint. The event provided ample opportunities for professionals to connect, with dedicated networking zones, private meeting areas, and an exclusive VIP lounge for C-level executives.
The FinTech Startup Village was a must-visit area, showcasing some of the most innovative fintech startups in Europe. Several emerging companies, specializing in AI-driven financial advisory, blockchain-based payments, and RegTech solutions, presented their groundbreaking products.
A standout initiative was the Women in Finance Roundtable, which focused on fostering greater gender diversity in leadership roles within the financial industry. Featuring influential female leaders from Citi, JPMorgan, and Monzo, the discussion encouraged actionable steps towards inclusivity and representation. Publicis Sapient also hosted a networking session on Celebrating Women in Finance.
Exhibition & Innovation Showcase
The exhibition hall was bustling with activity, featuring booths from major players like IBM, Microsoft, Accenture, and Salesforce, as well as FinTech disruptors showcasing cutting-edge solutions. Attendees had the opportunity to experience hands-on product demos, including AI-powered chatbots, biometric authentication for secure banking, and blockchain-based smart contract platforms.
One of the most talked-about innovations was Quantum Computing in Financial Services, presented by IBM. Experts explored how quantum computing could enhance complex financial modelling, risk analysis, and fraud detection, potentially transforming the industry in the next decade.
Key Takeaways & Industry Impact
MoneyLIVE Summit reaffirmed its reputation as a forward-thinking, insightful event that brings together the brightest minds in finance and technology. Some of the key takeaways included:
AI is mainstream – Banks and fintech firms must embrace AI-driven solutions to enhance customer experience and operational efficiency.
Payments are evolving rapidly – With open banking, digital wallets, and real-time payments on the rise, banks need to innovate or risk being left behind.
Cybersecurity remains a top priority – With increased digital transactions, fraud prevention and regulatory compliance are more critical than ever.
Sustainability cannot be ignored – ESG-focused financial strategies are no longer optional but a necessity for long-term growth and investor confidence.
Embedded Finance is the future – Traditional banks and fintechs must collaborate to integrate financial services seamlessly into everyday life.
MoneyLIVE: The Verdict
MoneyLIVE Summit 2025 lived up to expectations, delivering a stellar combination of thought leadership, cutting-edge technology showcases and unparalleled networking opportunities. For professionals in banking, payments, fintech, or regulatory compliance, this event provided invaluable insights into the industry’s trajectory.
The only potential downside? With so many high-quality sessions running simultaneously, attendees had to make tough choices about which discussions to prioritise. However, the availability of on-demand session recordings meant that all the key insights attendees need were available.
With an impressive lineup of speakers, a strong focus on industry trends, and excellent networking opportunities, MoneyLIVE Summit remains a must-attend event for financial professionals looking to stay ahead in an ever-evolving landscape.
Fouzi Husaini, Chief Technology & AI Officer at Marqeta, answers our questions about Agentic AI and its applications for businesses
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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.”
Stuart Cheetham, CEO at MPowered Mortgages, on how AI-powered technology allows mortgage lenders to fully underwrite loan applications in minutes
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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.
Fernando Henrique Silva, SVP Digital Solutions, EMEA at CI&T, on how finance firms can best leverage AI to unlock bespoke services and rapid issue resolution
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When OpenAI released ChatGPT in November 2022, businesses in banking and finance quickly recognised the commercial potential of Generative AI (GenAI). However, due to the AI technology’s nascent qualities, archaic legacy systems and a lack of established strategies for integration, leaders have struggled to translate GenAI into greater revenues.
Two years on, the landscape is finally taking shape. According to PwC, 70% of global CEOs now expect GenAI to significantly reshape how their operations create value. Furthermore, more than two-thirds are already working with AI, having reworked their tech strategies to align with AI-driven opportunities.
Of course, the banking and finance sector is no stranger to technological change. The first plastic credit card was introduced in 1959, by American Express. The ATM was launched in London by Barclays Bank. And today, mobile banking, investing and high-level financial management can be done by any smart device nestled in a person’s pocket.
However, as with any new frontier tech, GenAI has its risks: implementation challenges, upskilling, regulatory and ethical considerations—these risks are heightened in finance and banking. And there’s the classic possibility of simply getting it wrong. Plus, what’s hot in GenAI today may be old news tomorrow.
To help organisations drive change within, let’s explore the good, the bad, and the ugly of GenAI adoption through the lens of recent insights from CI&T research and case studies.
The Good side of GenAI
The analogy between the Old West and GenAI holds up: both involve exploring new territories, uncovering valuable resources, and building infrastructure. Today, these frontier outposts are becoming cities, and full-scale reinvention is on the horizon for financial institutions.
So, what’s the new gold rush? According to CI&T’s new research, The Future of Finance: How AI is powering the intelligence era, the answer is ‘hyper-personalisation.’ This field is ripe, with fintech firms using it to deliver two key benefits: bespoke services and rapid issue resolution.
Using Customer Data Profile software—tools that gather and standardise data from online and offline sources to create detailed customer profiles—GenAI is helping these firms take personalisation to new depths. This can enable bespoke services in real-time. Indeed, McKinsey reports that personalisation drives profit: companies that prioritise it achieve growth rates 40% higher than their peers. For example, it enables institutions to offer solutions that foster smarter money habits among customers. This can be done by aligning services with consumption patterns and inflationary trends. This strengthens customer loyalty while driving cross-selling opportunities. Similarly, by facilitating enhanced financial decision-making, financial institutions can provide tailored advice and tools that differentiate their services in a competitive market, boosting retention rates.
On the investment side, hyper-personalisation creates avenues for smart investment moves by delivering customised strategies aligned with individual risk profiles. This not only attracts more customers but also improves portfolio performance, translating into increased asset management fees and long-term profitability.
GenAI is also giving businesses the gift of time. By 2030, up to 30% of current hours worked could be automated. For example, in the financial sector, portfolio managers are using GenAI to automate routine performance and risk reports. Hyper-personalisation could lead to strategies tailored to individual risk appetites, the latter being a revenue opportunity.
The Bad with GenAI
GenAI is like the newest member of the crew, full of promise but with some questionable traits. Without oversight, it can enable manipulation, misinformation, and privacy breaches. The tech, unmanaged, can also be prone to biases and inaccuracies. Often presenting errors as facts, adding pressure on teams to manage them. Moreover, it poses a security risk, requiring businesses to safeguard their data, or risk being ‘robbed in the night.’
To manage these risks, GenAI is increasingly subject to complex regulations. Gartner predicts that by 2026, 50% of governments will introduce regulations and policies to enforce the responsible use of AI. These challenges will be even more significant in banking and finance.
Balancing the pros and cons of GenAI is the key to extracting value. GenAI itself can often help. For example, CI&T assisted fintech firm Bulla, which specialises in flexible credit and benefits, with managing common complaints. Using our enterprise-ready GenAI platform, CI&T FLOW, Bulla analysed customer service data to gain a detailed view of pain points and rethink support systems. They also used it to give employees access to essential information and to train staff in GenAI.
The Ugly side of Artificial Intelligence
When the going gets tough, our relationship with GenAI can take an ugly turn if outdated legacy systems stand in the way. The challenge of digging through impenetrable layers, reworking outdated processes, extracting valuable data, and training staff accustomed to old ways of working is no easy feat. Moreover, the costs can quickly add up.
Historically, banking has been one of the sectors worst affected by legacy hardware. Nearly six in ten bankers see their legacy systems as a major business challenge, describing them as a ‘spaghetti junction’ of interconnected but antiquated technologies. So, much like digging through rock in search of gold, the rigid hardware architectures designed for specific banking functions—based on old programming languages and databases—are holding back innovation. In fact, 60% of executives cite skills gaps as an obstacle to overcome in their digital transformations.
The banking sector may be on the brink of a breakthrough. We’re starting to see more AI-driven chatbots, fraud prevention, and the speeding up of time-consuming tasks such as developing code and summarising reports. However, it’s updating the legacy hardware where the real commercial value lies.
Ironically, GenAI holds the key. For one of CI&T’s leading clients, a large global bank based in South America, CI&T FLOW was able to modernise its systems by supporting the transition from COBOL to Python using a code refiner. This resulted in accelerated developer delivery, a 54% lead time reduction, and a 33% improvement in user story quality. Highlighting the power of strategically harnessing the technology. The challenge is also the solution.
As the world of GenAI transitions from Wild West to civilised modernity, businesses are going to have to get smart about how they look for commercial value. Often, the solution lies in GenAI itself. So, get started, and get started now. And in the immortal words of Clint Eastwood’s Blondie: “Two hundred thousand dollars is a lot of money. We’re gonna have to earn it.”
To learn more about how CI&T can help your business commercialise GenAI, download The Future of Finance: How AI is powering the intelligence era here.
Glenn Fratangelo, Head of Fraud Product Marketing & Strategy at NICE Actimize, on financial services fraud prevention in 2025.
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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.
FICO’s use of Blockchain for AI model governance wins Tech of the Future: Blockchain and Tokenisation award
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Global analytics software leader FICO has won the Tech of the Future – Blockchain and Tokenisation award. The Banking Tech Awards in London recognised FICO for its innovative work using Blockchain technology for AI model governance. FICO’s use of blockchain to advance responsible AI is the first time blockchain has been used to track end-to-end provenance of a machine learning model. This approach can help meet responsible AI and regulatory requirements.
FICO’s AI Innovation and Development team has developed and patented an immutable blockchain ledger. It tracks end-to-end provenance of the development, operationalisation and monitoring of machine learning models. The technology enforces the use of a corporate-wide responsible AI model development standard by organisations. It demonstrates adherence to the standard with specific requirements, people, results, testing, approvals and revisions. In addition to the Banking Tech award, Global Finance recognised FICO’s blockchain for AI technology with The Innovators award last year.
Responsible AI
“The rapid growth of AI use has made Responsible AI an imperative,” commented Dr. Scott Zoldi, chief analytics officer at FICO. “FICO is focused on technologies that ensure AI is used in an ethical way, and governance is absolutely critical. We are proud to receive another award for our groundbreaking work in this area.”
FICO is well-known as a leader in AI for financial services. Its FICO® Falcon® Fraud Manager solution, launched in 1992, was the first fraud solution to use neural networks. Today it manages some four billion payment cards worldwide. FICO has built advanced analytics capabilities into FICO® Platform, an applied intelligence platform for building decision management solutions.
Adam Zoucha, MD EMEA at FloQast, on how businesses will modernise financial processes in 2025
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With 45% of accountancy firms and in-house finance teams facing talent shortages, 2025 is going to be a critical year for many. Financial transformation is going to be the watchword. The conditions companies are facing will push them to speed up the transformation of their operations, modernising their financial processes while strengthening their company culture and vision.
The year ahead will likely see a continuation of the current period of instability, posing serious challenges for accounting teams looking to grow their business. The impact of global geopolitics is hard to predict which, twinned with the UK economy’s persistently slow growth rate, means companies will need to innovate to succeed – embracing automation, AI, and cutting-edge compliance processes.
It’s not all about the macro trends, though. On an individual level, our research this year has shown that employees are feeling the strain, and business leaders will need to take that seriously in 2025. The talent shortage is a vicious cycle – the harder it is for companies to find and retain talent, the more pressure remaining team members end up having to shoulder. The right technology can play a crucial role in reducing that stress and breaking the cycle.
Alongside those real challenges, there are real opportunities. The accounting business is changing fast, and it’s a great time to be in the industry. As we draw 2024 to a close, here are five key things accounting firms can expect to see in the new year.
Financial Transformation moving up the agenda
We’ve already looked at some of the reasons why financial transformation is going to be critical in 2025, but that doesn’t mean every CFO and accountant in the business is rushing to deliver. Based on our research 60% of accountants and CFOs still do not consider it a top priority – mainly because most don’t truly know what it means for their business, so education is key.
In essence, companies should aim to align their finance functions more closely with their organisational goals, enabling accountants to bring their expertise and insight to the decision-making process. As the finance function’s strategic role grows, there will be an urgent need for agile, digital tools that enhance collaboration and efficiency. For CFOs, embracing this transformation is essential to navigate new complexities with precision and effectiveness.
Accountancy teams will embrace new tools for the future
The talent gap present in the industry is unlikely to change any time soon. It takes time to train people, and accounting has a bit of a PR problem – its status as a secure, skilled job is battling with perceptions of stress and burnout.
As a result, in 2025, leaders will increasingly look to keep accountants motivated, engaged, and fulfilled as the declining population of new candidates continues to heap pressure on accounting teams—a trend that’s unlikely to reverse anytime soon.
It’s essential that business leaders retain their finance professionals by fostering a fulfilling work environment. They can help by upskilling accountants and adopting technologies to reduce mundane and repetitive tasks. CFOs can play a key role by equipping their teams with future-focused skills, blending technology with strategic insight to drive real value within their organisations.
AI will power Tansformation in 2025
Transformation in 2025 won’t be limited to removing internal silos and improving staff retention, crucial though those things are. We’re also going to see AI helping accountants become key players in driving business success. The real value of AI will become apparent this year. For finance teams, it will act as a copilot, automating routine tasks and giving time back to accountants to become strategic assets for their organisations.
This shift will help the industry tackle talent shortages with agility, turning challenges into opportunities for growth. Embracing AI isn’t just about keeping pace; it’s about unlocking accountants’ full potential as key players in driving business success.
Compliance will become a value-generating asset rather than a tick-box exercise
Compliance and risk, when managed properly, can drive real value for organisations. In 2025, the nuanced relationship between compliance, reputation, and risk means it’s likely to move up the corporate agenda.
Technology can be a real driver here, and compliance strategies are fundamental to the larger accounting transformation journey. By taking a more holistic approach to compliance, rather than treating it as a mere check-box exercise, compliance can become a valuable asset. Currently, only 16% of organisations take this strategic view, revealing a significant opportunity for those willing to innovate and elevate their compliance efforts.
Overall, accounting businesses may be facing rough seas, but with the right tools and investments in place, they can unlock new value in 2025: transforming financial processes, improving employee satisfaction, and stepping further into their growing role as strategic advisors.
Paul O’Sullivan, Global Head of Banking and Lending at Aryza, on the rise of AI in banking
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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.
Join FinTech’s greatest event when Money20/20 Europe returns to Amsterdam’s RAI Arena June 3-5 2025
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FinTech Strategy is proud to be a media partner for Money20/20 Europe 2025.
Launched by industry insiders in 2011, Money20/20 is the heartbeat of the global fintech ecosystem. Some of the most innovative, fast-moving ideas and companies have found their feet (and funding) on its show floor. From J.P. Morgan, Stripe, and Airwallex to HSBC, Deutsche Bank, and Checkout.com.
Furthermore, this is where you’ll find new connections, business-critical insights from inspirational speakers, innovation, and partnerships you need to ensure your business succeeds for whatever comes next in money.
Why Money20/20?
FinTech Strategy spoke with a host of leaders from across the FinTech spectrum. They all agreed on one thing, Money20/20 Europe is ‘the’ place to make connections and build your business.
“It’s the first time I’ve attended Money 20/20 and, we’ve had some fascinating impromptu conversations that will lead to great opportunities. All the big names are here and it’s clearly a popular event from a thematic perspective – payments is a big theme this year. I have a very high regard for the quality of what’s on offer and the way the event has been organised – it’s a great customer experience, the way it’s all been structured, at scale, is actually one of the best I’ve ever seen. The response has been fantastic…”
Stephen Everett, MD Payables & Receivables, Lloyds Banking Group
“The majority of people at Money20/20 genuinely get up in the morning with a growth and innovation mindset. Therefore, you have to balance and recognise that when you walk into this big venue that there will be some wacky ideas. From my experience, I have seen many infant ideas turn into successful ventures, whereas I have also seen some ventures becoming unsuccessful despite having great innovation ideas. Fintechs will fail. Innovation will fail. Experiments will fail. And that’s fine. That’s what Money20/20 is all about.”
Michelle Prance, CEO, Mettle (NatWest Group)
“It’s good for Mettle to come here because we are a fintech that was incubated inside a large bank (NatWest) for fintechs. Quite often their route to market, route to capitalisation, is by going into a main bank being acquired. So, it’s that marriage between a big organisation and the small nimble fintech. People are really interested in what we’re doing because big incumbents want to be fast and nimble. They don’t always have the capital to invest in something like we’ve been able to do with Mettle. So, they’re interested to know the right route to go down. Do they incubate in house? Or do they buy it in? And what’s the right way to do that without killing the culture? These are the types of interesting conversations we’ve been having here.”
“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, Stripe… And then you have the Heads of Payments from companies like Booking.com, Minted and Summit who are coming here with their team 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 with that would normally take months to arrange. So, the ROI from this week is really powerful just from being able to have these conversations.”
“Paymentology is homegrown out of the UK so it’s important for us to make sure we’re representing the business across Europe. This is the centre of the world for banking innovation. We have customers here from Singapore, Dubai, Saudi Arabia, Ghana and beyond. People look to this event to really learn about what’s happening in the industry globally and discover what trends are going to come up. What should we be doing? How can we innovate together and learn from each other? That’s one of the things I really love about Money20/20; the talks in all of the panels are so interesting and I always leave knowing more. Being in the payments industry, and especially being an issue processor, it’s important for us to learn from the industry and understand where we need to move so that we can stay at the forefront of developments.”
“This is my sixth straight Money20/20 and it gets busier every year! It’s great to learn more about the ecosystem at large. You can see developing trends each year, and it’s always a little bit different. You build relationships at Money20/20 that stay with you for the rest of your life. And it’s a perfect opportunity to meet people in the flesh that you might normally only see on screen. You can get a pretty direct read on what they’re working on and it’s exciting to be here making new connections.”
FinTech Connect shapes the future of financial services with the UK’s only full FinTech ecosystem event at London’s Excel December 4-5
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Join us as FinTech Connect welcomes world leading Fintechs, Financial Institutions, Challenger Banks, Merchants, Scale-Ups and StartUps, Investors, Accelerators and Media to The ExceL, London.
FinTech Connect
Each year we welcome visionaries from the UK, Europe and beyond all looking to innovate within the market, expand their footprint and drive businesses forward. The event brings all this under one roof, over two insight-packed days, sparking ideas, forging partnerships and accelerating change.
Tackling the hottest topics and biggest challenges in the fintech market. Including: embedded finance, Web3, cross-border payments, investment, scaling, Gen AI, crypto, regulation, digital innovation and customer experience (CX).
Our mission is to connect the global thought leaders across the FinTech ecosystem in an event like no other. Set yourself up for a strong 2025 by signing up for the UK’s only full FinTech ecosystem event and join 2,000+ fintech leaders in London.
Insights from FinTech’s biggest names
We’ll be asking the big questions… What AI elements do financial institutions need to follow? Build, buy or partner? What opportunity works best in the modern ecosystem? How are banks advancing their digital transformations in 2024? Who owns the CX?
Gain insights on these topics and more from some of the biggest names in financial services. Speakers include Victoria Cleland, Executive Director – Payments, Bank of England; Rory Tanner, Head of UK Government Affairs at Revolut and Nick Kerrigan, Managing Director, Swift. Thought leaders will also be taking to the stage from HSBC, DZ Bank, Lloyds Banking Group, BT and a host of other leading institutions.
Keep up to date with the latest speakers, discussions and more. Download the full agenda here.
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
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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.
Fred Fuller, Global Head of Banking at Endava, on how banks can effectively communicate AI advancements and demonstrate ROI to investors
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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.
Gabe Hopkins, Chief Product Officer at Ripjar, on how GenAI can transform compliance
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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.
Sejal Mehta and Andrew Rodgers from Odgers Berndtson’s Global FinTech Centre of Excellence and Randy Bean, a Senior Advisor to Odgers Berndtson and industry author, explore the dynamics shaping leadership in the UK fintech sector
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The UK FinTech sector is undergoing a significant transformation, marked by maturation, consolidation, and a more selective investment landscape. Funding is increasingly funnelled towards profit-generating scale-ups, and away from newer entrants.
At the same time, the sector is shaped by a multi-generational workforce with varied perspectives. Meanwhile rapid advancements in AI foster apprehension and excitement. These converging factors make FinTech one of the most dynamic and competitive spaces to work in today. This presents both challenges and opportunities for its leaders.
From our perspective as global FinTech executive search and leadership advisors at Odgers Berndtson these shifts are reshaping the demands placed on leadership. They are also influencing what it takes to lead effectively in this fast-changing sector. Here, we explore the leadership trends that are emerging as a result.
Ethical FinTech leadership
Venture capital funding is now more selective and private equity investors are increasingly targeting fintechs with solid exposure. This is creating a difficult environment for new start-ups. Those attracting funding are typically cash-positive scale-ups.
Amidst these challenges, more FinTech firms are opting to list on the NASDAQ rather than the London Stock Exchange, as the UK navigates more stringent regulation. The need for payments licences, extensive reporting, and compliance demands weigh heavily on FinTech leaders.
In this landscape, we’re seeing leaders with experience in regulated financial services bring a valuable skillset. The ability to operate within defined regulatory frameworks while generating growth. FinTech boards are looking for leaders with high authenticity and who can make ethical decisions. And while balancing ambition and growth with the realities of working in a highly regulated space.
Founder replacements
We are in the midst of the FinTech sector’s maturation. Start-ups are transitioning into scale-ups, requiring different leadership competencies. For many, this requires the founder to step down or step into a board role and appoint a CEO who can take the business through its next stage of growth.
This requires leaders who are commercially driven, capable of shaping market strategies, and adept at understanding customer needs and product-market fit. Navigating risk and regulation becomes crucial, while the founder’s creative, opportunity-led approach typically no longer dominates the new operational and strategic demands.
Boards and investors are looking for CEOs with a broader skillset and deep regulatory expertise. These leaders must also be able to attract and retain the type of talent that can sustain growth and innovation, while maintaining the ‘DNA’ that made the business so attractive in the first place.
A multi-generational workforce
Intergenerational divides are becoming more pronounced for all businesses and noticeably in sectors like FinTech. Here, younger generations with fresh perspectives are working alongside older, more experienced professionals – often from traditional financial services backgrounds.
This diversity in age, experience, and approach can be a powerful asset, but only if integrated effectively. Typically, Gen Z and Millennials prioritise flexibility, technological integration and experimentation. Meanwhile, Boomers bring valuable expertise in regulatory environments and operational effectiveness, but may be more accustomed to traditional structures and leadership styles.
Increasingly, we see FinTech leaders attempt to bridge these divides by emphasising open communication, promoting mentorship opportunities, and encouraging cross-generational collaboration. With less funding and more regulation, FinTech leaders recognise the need to identify and capitalise on the strengths of a multigenerational workforce if they are to succeed.
Leadership team dynamics
As FinTech companies scale, leadership is no longer just about the capabilities of individual leaders but about the dynamics of the entire executive team. Successful scale-ups understand the importance of assembling a leadership team that brings a diverse mix of skills, and generational perspectives to the table.
We are starting to see FinTech companies think about leadership team dynamics as they scale up. Boards are looking for a blend of strategic, operational and ethical considerations. As well as how well team members work together. Do they solve problems cohesively? Are there any unresolved tensions or conflict? Are they aligned and equipped to collectively deliver on the leadership mandate?
Many leadership teams are not optimising their potential due to misalignment of strengths. For example, we recently worked with a FinTech creating an executive team profile to identify the leadership competencies needed to deliver their mandate. This exercise enabled the team to reallocate executive responsibilities for strategic initiatives based on the required strengths, regardless of traditional job roles.
Polarising views on Gen AI
Leading organisations are experiencing a transformational moment due to accelerated interest in AI and Generative AI. 89.6% are increasing their investments in AI, while 64.2% of companies have indicated that AI will be the most transformational technology in a generation. In response, organisations are hiring for the data and AI leadership roles required to prepare their companies for an AI future.
However, this integration of Gen AI has sparked both excitement and nervousness, particularly around issues of data protection and privacy. Generational differences are especially noticeable. Younger professionals are often less concerned about data privacy, while older generations remain cautious about the security implications.
This divergence in attitudes can create tension within the organisation, as leaders grapple with how best to leverage Gen AI while ensuring compliance with stringent data protection regulations. For some FinTechs, AI is seen as a specialised area requiring dedicated focus. Meanwhile, others believe AI represents a fundamental shift in how business can be conducted and AI strategy should be woven into the fabric of every leader’s responsibilities.
This divide in attitudes reflects the broader challenges we see FinTech companies face in incorporating AI. Leaders must now navigate the balance between embracing innovation and safeguarding sensitive information. They must also ensure AI is not seen as a siloed function. It must be an integral part of their commercial and strategic vision. Given the fundamental changes in the sector, the emphasis on leadership capabilities is changing for both the individual and executive team.
Hugo Farinha, Co-founder and CTO at Virtuoso QA on why AI is driving organisational change across financial services
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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.”
Cullen Zandstra, CTO at FloQast on mitigating the risks of AI to deliver benefits to financial services
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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.
Russ Rawlings, RVP, Enterprise, UK&I at Databricks, on the future of AI in FinTech
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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.
Pat Bermingham, CEO of B2B digital payment specialist Adflex, asks what impact will Artificial Intelligence really have on B2B payments?
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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:
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.
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:
AI systems that are used in products falling under the EU’s product safety legislation, including toys, aviation, cars, medical devices and lifts.
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?
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.
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.
“Adflexhas 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.”
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
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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.
Financial institutions are increasingly turning to artificial intelligence (AI) to gain a competitive edge. AI tools streamline operations, improve customer…
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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.
Banks are adopting artificial intelligence (AI) technology to provide more personalised experiences. A study by the AI Development Company projects…
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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.
McKinsey & Co. is seeing an increase in the number of clients seeking artificial intelligence-linked projects, reports Bloomberg. Faster adoption…
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McKinsey & Co. is seeing an increase in the number of clients seeking artificial intelligence-linked projects, reports Bloomberg. Faster adoption of the technology is helping the consulting titan and its peers boost revenue, across industries like Insurtech, following a period of tumult.
About 40 per cent of the New York-based firm’s client projects involve the technology. The number of AI-related customers in the past 12 months is approaching 500, Rodney Zemmel, senior partner and head of the firm’s digital business, said in an interview.
“We believe the long- or the medium-term economic implications are very real,” Zemmel said. He was a final candidate in the recent global managing partner leadership elections at the firm. According to people familiar with the matter, who asked not to be identified discussing confidential information.
Though there’s some degree of hype around AI, “we’re seeing the organisations that are doing that are getting value from it,” Zemmel said. “It’ll be a little longer, and maybe, a little harder than people think, but we’ve got no doubt that the value is there,” he added.
AI adoption across Insurtech
Among those deploying automation rapidly are the traditional and regulated industries such as banking and insurance, Zemmel said. In a June report, Citigroup Inc. said AI is poised to upend consumer finance and make workers more productive. Additionally, with a high potential for 54 per cent of jobs across banking to be automated. Citi also said that the technology could add $170 billion to the industry’s coffers by 2028.
JPMorgan Chase & Co. Chief Executive Officer Jamie Dimon has called AI “critical” to his company’s future success. He also noted the technology can be used to help the firm develop new products, drive customer engagement, improve productivity and enhance risk management.
The surge in automation has come as a relief for the broader consulting industry. It has been battling a slowdown in demand for its traditional services. McKinsey, Ernst & Young and PricewaterhouseCoopers have been cutting jobs to weather the slump. Furthermore, Accenture Plc shares tumbled in March after the company warned it’s seen financial-services customers, including Insurtech, rein in spending on its software.
AI’s rise is also diverting some budgets toward specialist consultancies. Although AI-focused units like McKinsey’s QuantumBlack are growing rapidly, according to Zemmel.
McKinsey – QuantumBlack
McKinsey, which has advised everyone from the U.S.’ Pentagon to China’s Ping An Insurance Group Co., currently has about 2,000 people working across QuantumBlack. It has 7,000 staff in total in tech-related fields, according to Zemmel’s estimates. McKinsey’s headcount stood at about 45,000 globally as of 2023 and revenues were at a record $16 billion.
Zemmel said that the firm is still evaluating how the use of AI will impact its own headcount over the longer run. McKinsey had earlier warned about 3,000 of its consultants that their performance was unsatisfactory and will need to improve.
“We’re certainly planning on being agile about it,” Zemmel said. “One thing that’s clear is everybody in our organization’s going to need to know how to use AI and incorporate in their day-to-day work if they’re going to remain relevant to their clients.”
AI-powered threat detection, automation, and data analysis are empowering fintech cybersecurity teams to more effectively meet the challenges of an evolving world.
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Artificial intelligence (AI) is driving a new generation of modern cybersecurity solutions. The technology is transforming how organisations protect against evolving digital threats, as predictive and big data analytics bring new benefits to the sector.
How is AI transforming cybersecurity for fintech teams?
AI’s importance in cybersecurity lies in its ability to provide advanced threat detection, automate responses, and adapt to evolving threats. It can also handle large amounts of data, making monitoring networks and detecting issues easier without increasing risks.
AI learns from past experiences, recognising patterns and improving over time. This makes it good at spotting weak passwords and alerting the right people. AI can also block harmful bots that try to overload websites. AI automates large amounts of tasks, allowing for 24/7 monitoring and quicker responses to security threats.
Its machine learning algorithms analyse vast datasets in real-time, identifying patterns and anomalies to detect emerging threats. As AI excels in behavioural analytics, it establishes a baseline of normal behaviour to spot deviations that indicate security threats.
Unlike traditional methods that rely on predefined signatures, AI can identify zero-day threats—new and previously unknown vulnerabilities—promptly. This proactive approach allows organisations to respond swiftly, preventing potential breaches before they occur.
AI also enhances threat intelligence by automating the analysis of code and network traffic, freeing up human analysts for more complex tasks. It, in turn, facilitates automated incident responses, rapidly mitigating attacks and minimising damage.
Predictive AI in Fraud Detection
AI is revolutionising fraud prevention by using predictive and behavioural analysis to detect and prevent fraudulent activities. By analysing historical data, AI identifies patterns that often precede fraud. This approach not only enhances detection accuracy but also reduces false alarms, distinguishing between normal and suspicious behaviours with greater precision.
In real-time, AI monitors multiple transactions simultaneously, flagging suspicious activities as they happen to mitigate risks promptly. It learns individual customer behaviours to detect anomalies, such as large transactions or unusual patterns. These triggers prompt alerts for investigation or automated protective measures, such as account freezing.
Despite challenges such as data privacy and the need for extensive datasets, AI’s advancements in machine learning promise increasingly effective solutions for protecting financial systems.
Industry case studies: Vectra and Kasisto
Fintech companies like Vectra use AI-powered technologies such as Cognito to automate threat detection and response. These systems analyse vast datasets to detect and pursue cyber threats swiftly, ensuring comprehensive security measures against malicious activities.
Tools like Kasisto’s KAI enhance customer experiences by providing personalised financial advice through AI-driven chatbots. This demonstrates AI’s versatile applications in improving both security and service delivery within the fintech sector.
AI’s use cases in cybersecurity are expected to increase. AI will revolutionise how users are authenticated. It will use advanced biometric analysis and behaviour tracking to make it harder for unauthorised users to gain access while ensuring a smooth experience for legitimate users.
This approach strengthens security by verifying identities with methods like fingerprints or facial recognition and detects unusual behaviours for added protection. AI’s ability to learn continuously from new data means cybersecurity systems will become smarter and more effective over time, adapting quickly to new threats.
The growing complexity of financial markets presents new challenges for asset and wealth managers. Therefore, to navigate this evolving environment,…
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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 isDeutsche 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
APwC 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.
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.
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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.
Customer service significantly influences the overall customer experience and brand reputation. Artificial intelligence (AI) has taken customer service to new…
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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.
The banking industry is slowly adopting artificial intelligence (AI) technology. It offers many benefits for financial institutions, from upgrading customer…
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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 theFinancial 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.
FinTech Strategy and Interface joined Publicis Sapient at Money20/20 in Amsterdam for the launch of its third annual Global Banking Benchmark Survey and spoke with Head of Financial Services Dave Murphy about its findings
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The third annual Global Banking Benchmark Study from Publicis Sapient draws on insights from 1000+ senior executives in financial services across global markets. The study focuses on the goals, obstacles, and drivers of digital transformation in banking.
Global Banking Benchmark Study
The study was launched during Money20/20 Europe in Amsterdam last month. Eoghan Sheehy, Associate MD, and Grace Ge, Senior Principal, highlighted the banking industry is focused on improving existing processes rather than introducing new ones. Data Analytics and AI are identified as key priorities for digital transformation. Additionally, there is a focus on internal use cases and efficiency.
Eoghan and Grace also discussed the challenges faced by the banking industry. These include regulation, competition from companies like Amazon, and the need to attract talent. They emphasised the importance for financial institutions of modernising core infrastructure. Also, building cloud infrastructure to support ongoing digital transformation. Moreover, the study notes the prevalence of the development of custom-made tools and internal use cases for AI implementation. Furthermore, Eoghan and Grace provided examples of repeatable use cases and discussed the success factors for Data Analytics and AI.
Four key takeaways from Publicis Sapient
Four key tracks came out of the study…
Modernising the core will always be important. But modernising the core for its own sake and also building the cloud infrastructure that supports it or allows for it to be modern. A decent chunk of the survey responders are still very focused on this. Executives are stating they want to make sure their people can make the best use of the beautiful core they’ve now built.
GenAI is an area of thoughtful experimentation for the Neobanks. We’re talking about scaled microservices here. Instances where, across Neobanks, you’ll have the same machine learning model and the same GenAI text generator facilitating retail and SMEs. That’s pretty sophisticated and something everyone has to contend with.
Data Analytics transformation is a key priority using GenAI to do so along with bringing new talent into the game.
Payments has been a big theme at Money20/20… We’re seeing lots of activity around ancillary individual product areas.
“The study focuses on how to think about solving problems end-to-end. Banks are dealing with legacy issues and taking a customer first view into solving the challenges. The practical application of AI across the banks is a significant theme as they look to automate decision-making and deliver better credit risk models. AI is finally delivering a set of use cases that truly can impact the way banks operate and build their own technology.” Dave Murphy, Head of Financial Services, EMEA & APAC
Be among the first to receive the study by signing up here
The RAI Amsterdam Convention Centre was the location for the world’s leading fintech conference.
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Money20/20 Europe offered a unique blend of insightful keynotes, panel discussions, and networking opportunities. These underscored the transformative power of emerging technologies in financial services.
This year’s theme was ‘Human X Machine’. Money20/20 Europe explored the relationship between humans and intelligent machines, focusing on how the partnership between artificial and human intelligence will forge a new era in finance…
Innovations in AI and Open Banking
Artificial Intelligence was a major theme throughout Money20/20. A notable session featured Patrice Amann from Microsoft and Kevin Levitt from NVIDIA. They discussed the role of Generative AI in transforming customer experiences in banking. They highlighted the importance of integrating business-specific data to enhance the accuracy and effectiveness of AI solutions.
Open banking also garnered significant attention at Money20/20. Mastercard and bunq announced a partnership enabling users to consolidate multiple bank accounts through bunq’s AI-driven money assistant, Finn. This move is part of a broader trend towards greater financial integration and personalised banking experiences. Additionally, Token.io and Prommt unveiled a collaboration to improve open banking payments. This illustrated the increasing importance of seamless, user-friendly payment solutions in the fintech landscape.
Michelle Prance, CEO, Mettle (NatWest Group): “It’s good for Mettle to come here because we are a fintech that was incubated inside a large bank for fintechs. Quite often their route to market, and capitalisation, is by going into a main bank being acquired. It’s a marriage between a big organisation and the small nimble fintech. People are really interested in what we’re doing because big incumbents want to be fast and nimble. They don’t always have the capital to invest in something like we’ve been able to do with Mettle. So, they’re interested to know the right route. Do they incubate in house? Or do they buy it in? And what’s the right way to do that without killing the culture? These are the types of interesting conversations we’ve been having here.”
Episode Six
Craig Ramsay, MD Business Development, Episode Six: “Networking is really important for us as a small company. There are lots of people here who can actually solve problems and it’s the collaboration I get quite excited about. What I’ve seen change in recent years is that the big banks are looking to find small organisations like us to figure out how to solve their payments problems. And that’s different to when I was working for a bank only a few years ago. You just have to be here at Money20/20… What I’m seeing, since we returned after Covid, is how many people from different parts of the world are coming here to actually talk to each other in person. If you’re not here at Money20/20, then it’s actually hard to be relevant in this industry.”
