Filip Pesek, Founder & CEO of DonnaPro, a European executive assistant agency for CEOs and founders, on the AI paradox
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It’s funny how AI is everywhere in finance, but nobody has extra time. Whatever sector you’re in, finance teams are fully embracing all that AI has to offer. But the impact on time seems minimal. We’re not seeing reduced workloads or even smaller teams. And the reason is simple: AI hasn’t removed the finance workload. It’s moved it. Rather than producing information, finance teams are now checking it. And few businesses have prepared for that.
The work automation hides
Automation can now handle all sorts of tasks, from drafting board commentary to spotting exceptions in a reconciliation. But it can’t reliably perform the final checks. It can’t compare final figures with source data. It can’t review the wording or manage approvals. And it certainly can’t take responsibility when something goes wrong. And that’s the difficulty finance teams are facing. Particularly in FinTech, where AI adoption has moved quickly.
While a payments company might use AI to prepare parts of its board pack, someone in finance still needs to check every number against the underlying management accounts. And it’s a scenario repeated across lending, auditing, neobanks, investment firms… For every request that AI can help with, a human has to be there for the final review. Even an automatically generated investor update needs to be checked. And it all takes time and people. That’s why a finance team can look highly automated while still apparently taking forever to get things done.
The pressure is loaded. Management expectations are high. And timeframes are reduced, in the expectation that AI can do it all. But despite their growing digital tool kit, finance staff remain overloaded and overstretched. Not because they’re working inefficiently, but because they’re trying to do their job properly, in an environment where the real work is rarely understood.
The finance checking problem
Of course, this isn’t unique to fintech. Research from the Bank of England and FCA found that 75% of UK financial services firms were already using AI in 2024, with another 10% planning to adopt it within three years. Yet just 2% of AI use cases were fully autonomous. The rest still involved people somewhere in the process. And that matters, particularly in finance, because there’s so little tolerance for error. Accuracy is everything. The numbers have to add up. Which is why people still play such a vital role.
The same research found that 62% of AI use cases in financial services are considered low materiality, compared with 16% considered high materiality. Showing that AI is generally only being used where the consequences of an error are small. So, the more a business stretches AI use out to more sensitive and important areas, the greater the need for effective human oversight becomes. And the more output AI generates, the more there is to review.
What being AI-trained should really mean
And that’s one of the problems businesses are facing with AI training. Far too often, being “AI-trained” means little more than that a staff member knows how to write better prompts. When what it should mean is that they understand which finance tasks are appropriate for automation in the first place.
It’s a lesson that really could be helpfully applied to every sector. But in finance, where so much sensitive data is involved, and people’s life savings can be at stake, that distinction matters even more. Before handing anything to an AI system, all team members need to ask whether it should be handling that information at all. Because when the system gets something wrong, you then have the complicated issue of accountability.
Another thing highlighted by the BofE’s research is that although AI has its uses when working independently, it always renders better results when people validate and improve what it produces. Underscoring the fact that AI’s role isn’t to replace people, but to support them, and that people are still needed to provide the essential control points that make AI so valuable. So, perhaps instead of obsessing over the time that AI is saving finance teams, the better focus would be on how AI output is being checked, who’s validating, and who’s giving the final approval. And all too often, the answer eventually comes back to the CFO, founder, or finance lead… Which creates its own problems.
When every workflow ends with one person, you don’t just create bottlenecks; you make that person indispensable. That means that while some phases of the work happen more quickly, the overall process is slower. And when, for whatever reason, that person is unreachable, everything is at risk of coming to a standstill.
That’s why the checking layer matters
When you build in the right support – an executive assistant who understands the business and knows which tasks can safely be automated – you start removing that operational blockage. They’re not there to make executive decisions, but to check data and make sure that things that need senior approval, reach the right person for the job. And that’s what AI processes need if you want them to work effectively.
The thing about AI is that it can do a lot, but just like a newly employed junior assistant, it needs oversight and support. Particularly in finance. And that’s where businesses are coming unstuck.
Manas Saha, Head of Financial Services UK at Publicis Sapient, on why firms that build client context, adviser judgement, past decisions and compliance outcomes – held in a form that both people and AI agents can use – will compound their advantage. Firms that don’t will be renting the same intelligence as everyone else
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For decades, wealth management has operated under a simple equation in which growth required proportionally more advisers, more operational capacity and more cost, so as assets under management increased, firms scaled through people, processes and organisational complexity. Artificial intelligence is now breaking that equation, and the speed at which it is happening has caught much of the industry off guard.
Earlier this year, the launch of Hazel, an AI-driven tax-planning tool from Altruist, triggered billions in stock selloffs across listed wealth managers as markets began to question whether the traditional fee model can survive in a world of near-zero-cost analytics. Whatever one makes of the scale of that reaction, it revealed something important about how investors now view the sector, because they are already repricing wealth firms for AI risk before most of those firms have worked out what AI will change inside their own operations.
The model will not be the moat
The harder truth behind that repricing is that the models themselves are heading towards commodity status. Within a couple of years every wealth manager will have access to a powerful foundation model, bought from the same small group of providers on broadly similar terms, which means the lasting advantage will sit in the layer each firm builds on top of it, made up of client context, adviser judgement, past decisions and compliance outcomes held in a form that both people and AI agents can use. Firms that build that layer will compound their advantage with every client interaction, while those that neglect it will find themselves renting the same intelligence as everyone else.
Scarcity still shapes the business
Traditional wealth firms have long operated under scarcity, with scarce expertise, scarce adviser capacity and scarce client touchpoints shaping how the business runs. Clients now expect 24/7 service and hyper-personalisation, while advisers are already overloaded with back-office work that leaves them less time for the client conversations that create value. Most firms continue to rely on workflows built around manual reviews, disconnected systems and information that sits across CRM platforms, portfolio management tools, planning software, emails and adviser notes, so important client context is often fragmented across the organisation rather than available where decisions are made. Advisers spend valuable time gathering information instead of interpreting it, reviews happen according to calendars rather than real-world conditions, and work moves between planning, portfolio management, compliance and operations through error-prone manual handoffs.
Adviser time is the lever
With demand growing and adviser supply dwindling, productivity has become a strategic lever, and if AI can free up even 30 to 50 per cent of adviser time, a firm can channel that capacity into new clients, deeper relationships or significant cost savings. Capturing that value requires AI to become more than another productivity tool bolted onto existing processes, because the first generation of AI, which focused on summarising meetings, drafting communications and automating routine tasks, left the underlying operating model largely untouched.
Three stages of autonomy
The next stage embeds AI much more deeply into business processes through AI agents that coordinate data sources, prepare recommendations and manage multi-step workflows under human supervision and guidance. For a regulated firm the question that matters most is where the boundaries sit, and in practice it helps to think in three stages.
In assistive automation, the adviser initiates the task, checks the output and remains fully responsible for the decision, with AI supporting discrete work such as summarising, extracting data or drafting content under basic usage policies and human review. With guided orchestration, AI coordinates across systems, proposes actions and prepares fuller outputs such as follow-ups or scenario analysis, while a person supervises the workflow, approves recommendations and manages exceptions within defined permissions, approval points and audit trails. A fully AI-orchestrated workflows, agents execute multi-step processes within agreed rules, monitoring portfolios, triggering actions and routing exceptions, while people set the boundaries, monitor outcomes and intervene wherever judgement is required.
Governance from the first day
That third stage is where governance becomes most demanding, and UK firms already have a framework that should shape their design from the very first day. Under the Senior Managers and Certification Regime, accountability for any activity that AI supports, recommends or executes remains with the firm and its senior managers, so every agentic workflow needs a named owner, clear audit trails, escalation routes, outcome testing, explainability, bias monitoring and board-level oversight capable of showing that AI is supporting better decisions and better client outcomes rather than simply faster processes. Governance designed this way becomes part of the operating model itself, and the ability to preserve context, explain recommendations, trace decisions and coordinate work within clearly defined boundaries is precisely what allows AI to operate safely at scale across regulated workflows.
The Intelligent Wealth Firm
In our recent white paper with PIMFA, we describe the destination as the Intelligent Wealth Firm, a continuously learning organisation in which human expertise and AI capabilities operate as an integrated system. A single client interaction may be informed by hundreds of AI-driven activities spanning research, planning, compliance, servicing and portfolio management, and every recommendation, decision and outcome feeds back into the firm’s memory, making the organisation progressively smarter over time. That memory captures historical recommendations, adviser actions, client interactions, compliance outcomes and business results, and over the years this experience becomes institutional knowledge that improves future decisions and creates an advantage that competitors, technology vendors and foundation model providers cannot easily replicate.
Context informs technology, so the data comes first. Most of what a wealth firm knows about a client sits in unstructured form: adviser notes, emails, meeting transcripts, provider documents, suitability reports, call recordings. Conventional systems cannot read it. AI can. Agents ingest that material, connect it to the structured records in the CRM and portfolio systems, and hold the result where the next adviser or the next agent can use it.
The gains show up in metrics that operations teams already track. First time right, the share of cases that clear onboarding, suitability or review without rework, should improve significantly when AI tools, ours included, work from information that is complete from the start. Every reworked case that disappears returns capacity to the back office. Some of that capacity lowers the cost of running the operation. The rest goes back to advisers and service teams, who get more time with clients.
Screening shows the effect clearly. A politically exposed person check can return 5,000 potential hits. An agent working with the right context, including the client’s history, source of wealth and earlier decisions, can narrow that to the ten that need a person’s judgement and record its reasoning on the rest. The time saved is direct, and it lands in the back office first.
Growth, retention and the client
Efficiency is half the case. The other half is revenue. Advisers with more time and better information can serve more clients, respond faster and see needs before a client raises them. Clients who feel known stay. A firm built around the client, not around its own systems, keeps more of the relationships it wins.
The obstacle is practical. Client data lives in many places, so the questions are how to bring it to one place, how to mine it quickly and how to consolidate the platforms underneath. We have invested significantly in all of our products to answer them. Sapient Bodhi builds and orchestrates agentic workflows with industry context-built in. Sapient Slingshot reads legacy code, documents the current-state by extracting business rules and dependencies and then migrates to a more modern codebase in an accelerated time, while taking much of the risk out by folding several platforms into fewer.
The enterprise context graph
Bringing the data together is the start. An enterprise context graph maps how clients, accounts, advisers, products, policies, decisions and outcomes relate to one another, and keeps that map current. It is the shared context layer behind our platforms, and it grows more accurate the more it is used. An agent preparing a client review can then see the holdings, and also the meeting where the client’s appetite for risk changed, the recommendation that followed and the compliance sign-off attached to it. The graph belongs to the firm. Models can be swapped as better ones arrive while what the firm has learned stays where it is.
Governing the context layer
A layer that holds client history, advice and compliance outcomes needs governing as carefully as the agents that draw on it. Access should follow permissions, so an agent sees only what the person it serves is entitled to see. Every piece of context needs a traceable source, so any recommendation can be followed back to the information behind it. Outputs need testing against client outcomes, and each workflow needs a named senior owner under the same regime. UK GDPR applies to the graph as it does to any other client record.
The first is to define decision rights for humans and AI
Establishing clear responsibilities for where AI can recommend, where it can execute and where human judgement remains essential, with governance, accountability and transparency designed into every workflow from the start. A useful exercise for any leadership team is to ask which activities in the business genuinely require human judgement and which persist simply because that is how they have always been done.
The second priority is to build the context layer
Connecting client information, adviser expertise, investment policies and historical decisions into a shared context that both advisers and AI agents can draw on when a decision needs to be made. This is where the durable advantage lies, and it is worth every firm asking what proprietary context, experience and decision-making knowledge it holds that a competitor, technology vendor or foundation model could not easily reproduce, since that knowledge is the raw material from which institutional memory is built. When we work with wealth firms on this through Sapient Bodhi, our enterprise agentic platform, most of the early effort goes into connecting that knowledge and building the governance around it, because the model a firm uses will keep evolving while its institutional memory keeps growing in value with every client interaction.
The third is to measure client outcomes in place of AI adoption
This sounds obvious but runs against how most AI programmes currently report their progress. It is tempting to track the number of use cases launched or the share of staff using a new tool, yet those figures say very little about whether the firm is getting better at what it does, whereas client results, decision quality, operational efficiency and improved governance are the measures that will matter to boards, to regulators assessing Consumer Duty and ultimately to clients themselves. They are also the measures that show which workflows deserve to be scaled across the organisation, so that every deployment strengthens the firm’s institutional memory and makes the whole system more capable with each client interaction.
Any wealth leader can start that work tomorrow by asking which decisions in their firm are still waiting for a meeting, a spreadsheet or a manual review, because each one of those is a place where the firm’s own memory could already be doing the work.
Errol Rodericks, Product & Solutions Marketing Director at Denodo, on why infrastructure, security, governance, resilience and accountable business authority remain essential
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For years, people have discussed sovereignty in financial services primarily in terms of geography. Where is the data stored? Which jurisdiction applies? Who operates the infrastructure? Those questions still matter. But they are no longer enough.
Banks and insurers now operate through an increasingly complex web of cloud providers, SaaS platforms, data ecosystems, third parties, AI models and legacy systems. Modern financial services could not function without these dependencies. The more important question is whether the institution remains in control.
Can it determine where sensitive information is used? Will it understand which critical services depend on which providers? Can it change a platform without rebuilding every data integration? Can it preserve consistent policies and business definitions as information crosses systems? And if a provider, region or legal basis suddenly becomes unavailable, can it continue serving customers within acceptable tolerances?
That leads to a different definition of sovereignty. Sovereignty is not technological self-sufficiency. It is controlled choice.cThe objective is not to remove dependency, but to ensure that critical dependency remains deliberate, governed and, where necessary, reversible.
The sovereignty problem is becoming an architecture problem
One of the biggest threats to institutional control is not simply fragmented data. It is fragmented control.
A typical financial institution has authoritative information distributed across core systems, SaaS applications, multiple clouds, data warehouses, lakehouses, APIs and external providers. Over time, integrations multiply, data is copied, pipelines proliferate, policies are implemented differently across platforms, and business definitions diverge. Each individual decision may appear reasonable. Collectively, they can make the institution increasingly difficult to govern.
The same customer may be represented differently across systems. A masking policy may exist on one copy but not another. An application can become tightly coupled to a provider-specific schema. During an incident, identifying which services, data and customers are affected may require manual reconciliation. The institution may still own its data and hold contracts with its providers, yet its practical ability to exercise control has diminished.
Risk accumulates at every hand-off through aggregation risk, policy drift, conflicting meaning and exit friction. The sovereignty problem therefore becomes architectural. How do you maintain consistent control across a distributed environment without forcing every workload into the same place? Geography remains one control. Architecture determines whether that control survives across the wider estate.
From data sovereignty to decision sovereignty
AI makes this question more consequential. When technology primarily stored and reported information, sovereignty could be discussed largely in terms of data, infrastructure and access. AI consumes information to interpret, recommend and increasingly act.
That introduces another question: who controls the context through which the institution is understood? Consider a fraud system assessing an unusual international payment. The transaction may genuinely look anomalous, and the model may be functioning perfectly. But perhaps the payment is associated with a corporate Treasury event that has already been authorised.
The data may be correct. The model may be correct. Yet without the relevant business context, the resulting action may still be wrong. The same issue appears elsewhere in financial services.
A lending decision requires more than a credit score. It may also require current exposure, affordability, product policy and delegated authority. An insurance claim cannot always be understood from the claim record alone: policy conditions, catastrophe events, customer circumstances, fraud indicators, documents and third-party information can all change the appropriate response. An AI customer-service agent may know the customer and product but still need to understand consent, entitlement, jurisdiction and what actions it is authorised to perform.
Sovereignty therefore has to extend beyond controlling where information resides to controlling how it is understood and how that understanding influences decisions. That is the emerging challenge of decision sovereignty: can the institution reconstruct what information was used, what it meant at that moment, which policies applied, which technology participated, what authority was delegated and why the resulting action occurred?
Institutional understanding should not belong to the model
This creates an important architectural question for enterprise AI. Financial institutions are unlikely to depend on one AI model forever. They will use combinations of commercial models, specialist models, internally developed models, agents and orchestration technologies. Those technologies will change rapidly.
Institutional understanding should not have to change with them. If the meaning of “customer exposure”, “vulnerable customer”, “suspicious transaction” or “eligible claim” is embedded independently inside every application, vector store or AI model, changing technology becomes much harder. Part of the institution’s business understanding has effectively become coupled to the technology consuming it.
A more sovereign architecture separates the two. Authoritative enterprise sources remain authoritative. A governed information and semantic layer can provide approved views, business definitions, relationships, lineage, access policies and current operational context. Applications, analytics and AI can then consume that governed context rather than reconstructing institutional meaning independently.
This leads to an important principle for AI-era sovereignty: Models should be replaceable. Institutional understanding should not be. The architecture in the underlying briefing makes this separation precise: enterprise sources remain authoritative and policy-bound, governed semantic context sits between those sources and AI, and model selection remains a separate policy decision.
Sovereignty does not mean zero-copy
The same nuance applies to data movement. It is tempting to equate sovereignty with never moving data. But that is neither practical nor always desirable. Some workloads require persistent history. Others need caching for performance or continuity. Some analytical workloads suit physical data platforms better.
The better question is whether movement is a governed decision rather than an integration default. Can a query run against the authoritative source when appropriate? If information must be cached, can the institution control where that cache resides, how long it persists and how it is protected? If a physical copy is required, is its purpose explicit and are retention, deletion and access obligations preserved?
Live federated access, controlled caching and physical pipelines can all be legitimate patterns. The appropriate choice depends on sensitivity, lawful location, freshness and latency requirements, source capacity, continuity requirements and economics. The principle is not “copy nothing.” Do not make another copy the automatic price of using information.
Policy must survive consumption
Control can also disappear between policy definition and actual use. An institution may have strong privacy, security and conduct policies, but if those controls are implemented differently across copies, applications and channels, policy is no longer consistently authoritative. The risk grows when information is dynamically assembled for analytics or AI.
A stronger architectural approach brings together identity context, approved semantic views, fine-grained controls and source-native permissions at the point information is accessed. As the briefing argues, policy is strongest when business meaning, access context and source controls agree.
That does not mean technology determines policy. Legal, compliance, risk and business functions still decide what is permissible. Technology’s role is to make those decisions enforceable and evidenced when information is actually used.
Exit clauses are not the same as exit capability
Another uncomfortable aspect of sovereignty is something financial institutions increasingly need to confront. A contract may contain excellent portability and termination provisions. That does not mean the institution can actually leave.
Applications may depend on proprietary schemas. Pipelines may embed provider-specific logic. Business definitions may exist only inside a platform. Identities, caches, networks and operational processes may all be intertwined with the provider.
True reversibility therefore requires more than contractual rights. It requires stable interfaces, reusable semantics, visibility of dependencies, replacement patterns and, critically, rehearsal.
As the briefing puts it, exit is a capability only after rehearsal. Contracts are necessary, but measured execution determines whether portability is real. That gives boards a more useful question. Do not ask only, “Do we have an exit clause?” Ask, “When did we last prove that we could exercise it?”
Controlled interoperability is the alternative to isolation
None of this means financial institutions should retreat from cloud, SaaS, external data, ecosystem partnerships or AI. Quite the opposite. Greater control can enable greater openness.
A bank that knows where its information comes from, understands how it is being used, can enforce access policies consistently, preserves its own business semantics and can change consuming technology is in a stronger position to collaborate.
This is why the future of sovereignty is not technological isolation. It is controlled interoperability. Institutions should be able to connect what they need without surrendering control of what they own.
That requires infrastructure controls, identity and cryptographic controls, cybersecurity, model governance, contractual protections, operational resilience, human authority and accountable business ownership. No data platform replaces those responsibilities.
A logical data and context layer can, however, address an important part of the problem: keeping distributed information connected, governed and understandable without requiring every consumer to create another proprietary representation of the institution.
The board question is changing
Financial-services sovereignty should ultimately be judged not by where a server is located or by a provider’s nationality, but by the institution’s ability to exercise control when conditions change.
Can it identify critical dependencies? Can it govern sensitive information wherever it is legitimately used? Can it preserve its business definitions and institutional context? Can it change providers, platforms or AI models without reconstructing its information environment? Can it continue operating when a dependency fails? And when an automated decision has consequences for a customer, can it explain what happened and remain accountable for the outcome? Those questions turn sovereignty from a political or technological abstraction into an operational capability.
Financial institutions will remain interdependent. They will continue to rely on global cloud providers, SaaS platforms, data ecosystems, and rapidly changing AI technologies. The objective is not independence from them, but ensuring dependency never quietly becomes a loss of control. That requires an architecture in which authoritative data can remain governed, institutional meaning can remain consistent, and applications, platforms and AI models can change without forcing the organisation to reconstruct its trusted information environment each time.
In conclusion
This is where a governed logical data and context layer plays an increasingly important role. By connecting distributed information without making another central copy the default, preserving semantics and lineage, enforcing governance at the point of access, and making trusted business context reusable across applications and AI, financial institutions can preserve control while continuing to innovate.
They do not make an institution sovereign. Infrastructure, security, governance, resilience and accountable business authority remain essential. But by helping separate institutional data and understanding from the technologies that happen to consume them today, they can help preserve something increasingly valuable as those technologies change: control of the institution’s data and understanding remains with the institution.
Chris Wilmot, Chief Financial Officer at Medius, on how CFOs who lead automation with a people-first approach can simplify operations, reduce errors, and unlock strategic value to bring the full benefits of digital transformation to their finance teams and the broader organisation
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Digital transformation in finance is often framed as a technology challenge. But the real challenge lies in managing complexity.
The Association for Financial Professionals reports that more than half of FP&A professionals rely on eight or more planning tools and nearly ten reporting systems each quarter. This highlights just how fragmented the financial technology landscape has become. And in 2026, this complexity is now a top concern at the leadership level. According to Gartner, 42% of CFOs say unifying finance function, technology, and AI strategies is one of their biggest challenges. These inefficiencies slow performance and impact the day-to-day workflows of the teams managing these processes. This contributes to frustration, disengagement, and lost productivity.
As the leaders responsible for overseeing the financial performance and technology investments of their organisations, CFOs play a critical role in helping to reduce workflow complexity. Addressing this challenge requires more than just implementing new tools. It means eliminating fragmented systems, adopting automated workflows, and ensuring that teams are supported in every step of the way.
CFOs who take a people-first approach to automation not only streamline processes and reduce friction but also unlock strategic value and set their teams up for long-term success.
Automating the right processes in a Digital Transformation
Medius’ 2025 global survey found that finance professionals spend more than three and a half hours each day on repetitive tasks that could be automated. Over a year, that adds up to more than twenty-three working weeks lost to manual work. Which is time that could be spent on higher-value activities such as forecasting, analysis, and strategic planning. With fragmented systems consuming so much time, CFOs’ most immediate opportunity is to identify the processes that automation can help to simplify.
Much of this inefficiency comes from disconnected workflows. These require teams to manually transfer, reconcile, and validate data across multiple systems. This friction slows operations, increases the risk of errors, and contributes to frustration among finance teams.
CFOs should begin by identifying which processes create the most friction and where automation can have the greatest impact. Tasks that are repetitive and span multiple systems – like invoice processing, matching, and reporting – are often where automation can deliver the fastest, measurable results.Targeting these bottlenecks head-on can help eliminate unnecessary steps and make the finance function more agile.
Realising the Power of AI
Adopting an AI-powered accounts payable solution, for example, enables finance teams to centralise invoice and payment workflows that previously spanned multiple platforms into a unified software suite. This approach reduces manual data transfers, improves accuracy, accelerates approvals, and enhances cash flow visibility. It also allows finance teams to detect discrepancies earlier, before they escalate into larger issues.
Beyond improving efficiency, this shift also enables finance teams to operate more collaboratively across the business. With a unified software suite, finance teams can more easily collaborate with suppliers, procurement, and other internal stakeholders, improving visibility and coordination across workflows. This more connected approach helps streamline processes, reduce miscommunication, and strengthen relationships across the broader financial ecosystem.
Even as technology drives efficiency, human expertise must remain central. Automation should support judgment and strategic insights rather than replace them. By guiding teams through adoption, CFOs can simplify workflows, minimise errors, and ensure strategic impact.
Putting People First in Automation
Identifying the right processes to automate to reduce complexity is only half the challenge. What really sets CFOs apart is how they support their teams through adoption. Automation and AI are effective only if finance professionals at all levels of the organisation trust the technology, understand its purpose, and feel confident using it.
Introducing new technology can create uncertainty. Especially when past automation initiatives have been associated with layoffs or restructuring. Transparency is crucial in all communications surrounding AI. CFOs must communicate clearly why new systems are being implemented, how workflows will change, and what this means for individual roles.
Successful AI adoption also requires meaningful investment in training. Teams need hands-on support to see how new tools reduce friction in their daily work, not just how they support long-term transformation goals. Experiencing the benefits firsthand makes employees far more likely to embrace change, boosting engagement, productivity, and retention.
This approach also reinforces a key point: automation is meant to complement human expertise, not replace it. Finance professionals bring judgment, context, and strategic insight that technology cannot replicate. Keeping that expertise at the center ensures automation frees teams from repetitive, low-value tasks while empowering them to focus on critical decision-making.
By leading with this people-first mindset, CFOs can streamline operations to reduce errors and unlock greater value across the finance function.
Turning Automation into Strategic Impact
Automation and AI can transform finance operations, but only if CFOs lead with intention. By focusing on the right processes and keeping people at the centre, leaders can reduce complexity, free teams from repetitive work, and create real strategic value.
How CFOs can tackle complexity and drive value through automation:
Communicate purpose and impact: Explain how each initiative simplifies workflows, reduces friction, and benefits both the business and the team.
Invest in training and support: Give teams hands-on guidance so they can use new tools confidently and efficiently.
Reframe workflows around expertise: Let automation handle repetitive tasks while keeping human judgment at the center, reducing unnecessary complexity.
Monitor adoption and adjust: Track usage, gather feedback, and tweak processes to ensure workflows remain streamlined and effective.
It’s also essential to make sure that employees see the benefits of automation firsthand. When finance professionals see how technology simplifies workflows and creates space for strategic contributions, they’re far more likely to embrace change, and engagement, productivity, and retention improve along the way.
CFOs who lead automation with a people-first approach can simplify operations, reduce errors, and unlock strategic value to bring the full benefits of digital transformation to their finance teams and the broader organisation.
Serge Bejjani, Co-Founder & CEO of Shootday, on the importance for FinTechs of treating brand indentity as intellectual property
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It’s a pervasive idea that reproducing the visual language of credible, established institutions lets new FinTech entrants borrow trust by association. It’s also wrong, and the consequences are real.
Familiarity isn’t reassuring anyone
FinTech customers have, over the last decade, run into all manner of low-trust players dressed in the same polished aesthetic. The reassuring visual cues that once signalled FinTech’s credibility have been hijacked by unscrupulous outfits as a cheap disguise.
Where generic branding might once have read as safely conventional, it now reads as deliberately evasive. Sameness has become the hallmark of a company with nothing much to say for itself, a shrug that leaves the customer with nothing to go on when deciding whether it’s worthy of their trust.
Trust in financial services isn’t built through aesthetic conformity but through demonstrable reliability and a coherent identity. That takes time, and for a weak FinTech, time only increases the odds that its shortcomings get exposed.
At a moment when customers have every reason to approach fintech with caution, messaging that whispers “this could be any FinTech” is a red flag. Customers who can’t tell one FinTech from another aren’t reassured by the homogeneity, it just strips them of any basis to form an opinion, good or bad, which is a poor foundation for a relationship built on money.
Similar by accident
No firm decides, one day, to become generic as a strategy. Product, growth and marketing teams independently optimised toward frictionless onboarding, familiar messaging and low-risk visuals. Because those choices tested well, cleared compliance faster and reassured investors used to recognisable patterns. Individuality got lost across a decade of small, rational decisions. What comes next will happen a great deal faster.
Copy and paste
Generative tools now produce marketing copy and design at a pace no internal FinTech team can match, and they’re doing it by training on a body of content that’s already remarkably homogenous.
Ask any AI model to design a “trustworthy landing page” for the sector and, in an afternoon, it will hand back the same blues, the same reassuring sans-serif type and the same promises of ease and control that already define the sector’s digital presence, because that’s what the training data mostly contains.
Every firm using these tools without creative oversight is baking the same clichés into its marketing at scale. The firms moving fastest to fold AI into their marketing stack may, in practice, be moving fastest toward irrelevance. In 18 months, those who let AI construct their identity may wake up to find they don’t really have one.
Trust your people
This isn’t a problem a better font or a wider colour palette will fix. As AI increasingly shapes what FinTech content looks like, the advantage shifts to firms with a genuinely distinctive identity, one AI can’t replicate.
Executive headshots and stock imagery still have their place. They fill a LinkedIn profile or an investor deck. But a templated one, the same backdrop and the same pose every other firm is using, does nothing to separate one FinTech from the next, and an AI-generated stand-in does even less. What actually differentiates is real footage from real moments: a founder in an actual client meeting, a team at an actual product launch, a conference stand that looks like the people running it rather than a template.
That means treating brand identity as intellectual property worth protecting, and giving marketing and product teams room to choose to look different, rather than defaulting to the safest option every time.
Serge Bejjani is co-founder and CEO ofShootday, a global photo and video production partner providing corporate event photography, event videography, and headshot services for businesses in 150+ cities worldwide.
James Lawrence, Insurance Specialist at Netcall and Paul Bermingham, CEO at Ecliptic, on why in today’s market, the holy trinity of service, efficiency and compliance isn’t a luxury. It’s a competitive advantage
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For years, the insurance industry has operated under a persistent myth: that you can deliver excellent customer service, achieve operational efficiency, or maintain rock-solid compliance – but not all three at once. This idea of an inevitable trade-off between competing priorities has shaped countless transformation strategies, often limiting progress and frustrating innovation.
It’s a myth rooted in the industry’s history. Insurance has long been a world of complexity – fragmented systems, legacy platforms, and regulatory demands that make even minor changes feel high-risk. As a result, when businesses talk about improving the customer experience, it often comes at the cost of back-office control. When they talk about driving efficiency, compliance is treated as a barrier. And when compliance leads the agenda, service and speed are typically sacrificed.
But that mindset no longer holds. Technology has evolved. The tools now exist not just to manage this tension, but to eliminate it. And for insurers willing to rethink how they approach transformation, the so-called “holy trinity” of service, efficiency, and governance is no longer out of reach – it’s entirely achievable.
The low-code shift
What’s changed is the rise of low-code platforms. These technologies allow insurers to build and evolve business-critical applications using visual development tools rather than traditional coding. Crucially, low-code doesn’t require ripping out existing systems. Instead, it enables insurers to layer new capabilities on top of their core infrastructure – adding intelligence, automation, and user-friendly interfaces without destabilising the foundations.
In practice, this means change can happen faster, at lower cost, and with less risk. Processes like first notification of loss (FNOL), for example, can be rebuilt around modern expectations: triaged instantly, validated in real time, and – for low-value, low-risk claims – automated to settlement. At the same time, audit trails are maintained, compliance rules are enforced by design, and customers are kept informed with timely, personalised updates. Service, efficiency, and governance – working together, not competing for attention.
Automation and AI: Turning governance into a competitive edge
The evolution of intelligent automation and agentic AI has opened up new possibilities for insurers – not just in streamlining operations, but in embedding trust and control into every digital decision. These systems are no longer limited to simple rule-based tasks. They can interpret data, make context-aware decisions, learn from interactions, and adapt over time. In complex environments like claims processing, delegated authority, and regulatory reporting, this level of intelligence has real operational value.
But the success of automation doesn’t hinge on speed alone. It depends on trust. In regulated sectors like insurance, automation must be auditable, transparent, and capable of upholding internal policies and external obligations. This is where low-code plays a crucial role. Rather than retrofitting governance onto a system after it’s built, these tools allow insurers to embed governance by design – integrating rules, audit trails, escalation logic, and data safeguards directly into the workflow.
This approach changes the dynamic. Governance is no longer a limiting factor or a compliance hurdle. It becomes a source of confidence. Teams know that AI-driven decisions are accountable. Regulators can trace every step. And customers receive outcomes that are fast, fair, and well-documented.
Low-code also makes it easier to align AI with real business needs. Instead of chasing innovation for its own sake, insurers can focus on solving specific challenges – like improving FNOL response times or reducing the risk of customer complaints – and use automation to enhance outcomes without compromising oversight. Because platforms are configurable, not hard-coded, insurers maintain flexibility as policies, risks, and regulatory expectations evolve.
In this context, governance isn’t the price of innovation – it’s the foundation. It ensures that transformation is not only fast and scalable, but sustainable and defensible. The result is a smarter, safer path forward – one where automation improves service, boosts efficiency, and strengthens compliance in equal measure.
Meeting customers where they are
That experience is more important than ever. Today’s policyholders aren’t comparing insurers only to each other – they’re comparing them to tech giants like Amazon, which set new standards for simplicity, speed and personalisation. Insurance doesn’t get a pass. Yet too many customers still face long response times, unclear communication channels and paper-heavy processes that feel stuck in another decade.
The right approach changes that. European insurance company, Baloise, for example, used a low-code platform to quadruple straight-through processing (STP) from 15% to 65%. This meant faster responses, reduced error rates and a better overall experience for both customers and teams. By processing over 11,000 documents a day with greater accuracy and speed, Baloise could resolve enquiries and claims faster, without adding operational burden.
The impact went beyond efficiency. Customers benefited from a smoother, more consistent service that felt faster and more seamless at every touchpoint. Internally, employees were freed from repetitive, manual tasks and empowered to develop broader skill sets and handle more diverse work. That flexibility made it easier to respond to demand and reduced reliance on siloed roles.
These kinds of results don’t come from massive system overhauls. They come from smart, focused improvements – starting with the moments that matter most to customers. Low-code allows insurers to build around those touchpoints, while keeping core systems intact and compliance fully in check.
From compromise to convergence
The key is integration – not just of systems, but of skills. Transformation efforts succeed when business leaders, consultants, and technology teams work together from the outset. Today’s automated process mapping tools allow organisations to automatically capture, visualise, and analyse real-time workflows – highlighting friction points, inefficiencies, and opportunities for simplification before taking action. This upfront clarity ensures insurers aren’t simply digitising legacy inefficiencies but reimagining how work gets done.
It’s this combination of smart technology and domain expertise that moves the needle from compromise into convergence. When transformation is designed with strategy, service, and compliance in mind – from day one – insurers can build experiences that work for everyone: customers who want speed, regulators who want rigour, and teams who want tools that actually help them do their jobs.
The truth is, the trade-off is a choice – and it’s no longer a necessary one. With the right approach, insurers can achieve all three pillars of the holy trinity. Service doesn’t have to come at the cost of efficiency. Governance doesn’t have to slow things down. And efficiency doesn’t have to mean cutting corners.
Setting a new standard
Insurers that understand this – and act on it – will lead the next chapter of the market. Not because they’ve chosen a side, but because they’ve built systems where no one has to lose. In a market that’s only getting more competitive, that’s not just good strategy – it’s business-critical.
It’s time to retire the idea that you can’t have it all. You can. And you must. Because in today’s market, the holy trinity of service, efficiency and compliance isn’t a luxury. It’s a competitive advantage.
Cien Solon, CEO & Co-Founder of LaunchLemonade, on why the future of financial services will be shaped by those who can balance AI innovation with responsibility
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Every industry is racing to implement artificial intelligence. In financial services and other heavily regulated sectors, that race has been cautious. From automated underwriting and fraud detection to hyper-personalised customer experiences, AI offers FinTech companies unprecedented opportunities to innovate and scale. Yet alongside this promise comes the growing weight of regulation.
Different approaches to AI governance are already taking shape. In Europe, the EU is introducing a structured, risk-tiered framework that sets clear obligations depending on how AI is used. The UK is pursuing a more flexible, principles-based approach, allowing individual regulators to apply guidance tailored to their sectors. The US has a more decentralised model, blending federal direction with a growing patchwork of state-level rules. For FinTech founders operating across borders, this diversity can feel complex and, at times, overwhelming.
With these frameworks emerging, many fintech founders are beginning to hesitate. Questions around compliance costs, legal complexity and potential liability are causing some to reconsider or delay AI adoption altogether. For smaller fintech firms and scale-ups, the concern feels especially acute. Is the risk worth the reward?
It is. And founders who treat regulation as a strategic advantage, rather than a barrier, will be the ones who pull ahead.
Regulation as a Trust Multiplier
In financial services, trust is everything. Customers entrust FinTech platforms with their most sensitive data and critical financial decisions. Investors and partners demand reliability, transparency and accountability. Regulators expect firms to operate with integrity and control.
Robust AI governance can enhance trust across all of these stakeholders. FinTech companies that proactively adopt transparent and responsible AI practices signal maturity beyond their size. They demonstrate operational discipline alongside technical capability.
This matters especially now, when AI systems can appear opaque or unpredictable. Clearly communicating how AI is used, what decisions it informs, what data it relies on, and how risks are mitigated allows fintechs to differentiate themselves in a crowded market.
Compliance, when done well, builds credibility. And credibility, in financial services, is a powerful competitive advantage.
The Myth of ‘Compliance Paralysis‘
One of the most common misconceptions among FinTech SMEs is that AI regulation requires building entirely new compliance infrastructures from scratch. This belief often leads to what I call “compliance paralysis,” a reluctance to act because the problem feels too big to start.
In reality, much of AI governance maps directly onto frameworks that fintech companies already use. Risk management, model validation, data protection, auditability. These are familiar disciplines. AI extends them into new domains, but the foundations are already there.
Take model risk management. The steps a compliance team follows to validate a human-led underwriting process can be adapted to validate an AI-driven one. The logic is the same. The documentation requirements are the same. What changes is the technology performing the task, and the speed at which it operates.
I see this with our own clients. Regulated businesses assume they need to start from zero when it comes to AI governance. When we walk them through what they already have in place, the gap between current practices and regulatory expectations is almost always smaller than they feared.
The Agility Advantage of FinTech SMEs
Large banks may have dedicated compliance teams and significant legal budgets, but they also carry the weight of legacy systems. Retrofitting AI governance into complex, decades-old infrastructure is both costly and time-consuming.
Fintech SMEs have the advantage of agility. They can design and implement responsible AI practices from the ground up, embedding governance directly into their products and processes from day one.
This is exactly how we built LaunchLemonade. Every AI agent on our platform operates within a governance framework by default. Audit trails, data handling rules, transparency requirements. They are baked into the architecture, because bolting them on later is always harder and more expensive.
This compliant approach delivers real advantages. Long-term costs stay lower because governance is structural rather than retrofitted. Adapting to evolving regulatory requirements becomes faster because the foundations are already solid. And AI-enabled products reach the market ready to meet governance standards, rather than needing months of remediation after launch.
Regulation, in this sense, levels the playing field. Larger incumbents may have more resources, but smaller firms can move faster and design smarter. Those that embrace this will outpace competitors who delay.
Practical Steps for Leaders
For finance leaders looking to navigate AI regulation without overwhelming their teams, the key is to focus on pragmatic, high-impact actions.
Start by classifying AI risk early. A chatbot handling customer queries carries a very different risk profile from an algorithm making credit decisions. Understanding where your use cases sit on the risk spectrum allows you to prioritise governance efforts where they actually matter.
Be transparent by default. Inform users when AI is being used, explain its role in decision-making, and provide avenues for human review where appropriate. Clear communication builds trust and reduces regulatory friction.
Leverage what you already have. Map AI governance onto your existing risk and compliance processes rather than building a parallel system. This reduces duplication and accelerates implementation.
Build lightweight governance structures. You do not need a large compliance team to implement effective oversight. Documenting model assumptions, maintaining audit trails and assigning internal accountability can go a long way. At LaunchLemonade, we see regulated SMEs achieve meaningful governance with lean teams precisely because they focus on these fundamentals.
And know when to bring in expert input. Legal and regulatory expertise is valuable, but it does not need to be engaged at every step. Focus external support on high-risk use cases or areas of genuine uncertainty.
The Cost of Standing Still
If financial SMEs retreat from AI because of regulatory anxiety, innovation will not stop. It will simply concentrate in the hands of large incumbents and major technology companies with the resources to navigate complex compliance landscapes.
That concentration has broader implications. Competition reduces. Innovation cycles slow. Diversity in financial products and services narrows. Opportunities to improve financial inclusion and accessibility are missed.
Fintech and finserve has historically been a driver of disruption, challenging traditional models and expanding access to financial services. AI represents the next wave of that transformation. If smaller players step back now, they risk ceding that ground entirely.
Regulation as an Enabler
The conversation around AI regulation in financial services needs a shift in perspective. Regulation creates the guardrails that allow new technologies to scale safely and sustainably. It builds the trust required for widespread adoption. And it ensures that innovation benefits broadly, across industries and communities, rather than being concentrated among those who can afford to figure it out alone.
For fintech and finserve founders, the real question is how to innovate responsibly and strategically. Those who embrace that question will find that regulation is part of the answer.
AI is too important an opportunity for FinTech to ignore. Regulatory frameworks may seem daunting, but they are neither insurmountable nor inherently restrictive. When approached thoughtfully, they strengthen trust, streamline operations and unlock new avenues for growth.
Financial SMEs are uniquely positioned to lead in this space. Their agility, focus and ability to embed governance from the outset give them a distinct edge over larger, slower-moving competitors. The future of financial services will be shaped by those who can balance innovation with responsibility. The firms that recognise this will define the age of AI, rather than simply surviving it.
Cien Solon is the CEO and Founder of LaunchLemonade, a secure governed platform of AI agents for regulated industries. An experienced AI transformation leader, Cien has been building AI-powered solutions since 2018 and working with generative AI since 2022. Her mission is to ensure that smaller businesses in regulated sectors can adopt AI confidently, without the enterprise price tag. LaunchLemonade was included in Entrepreneur UK’s Top 100 Startups to Watch, and Cien was shortlisted in the Technology category of the 2025 Investec Early-Stage Entrepreneur of the Year Awards.
Following the launch of its Core Modernisation Playbook, Publicis Sapient’s Dave Murphy, Head of Financial Services – EMEA & APAC, explores why so many banks struggle to modernise despite investing heavily in AI. The challenge is no longer knowing what to transform, but how to execute transformation safely
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Most banks have spent the last decade applying AI where its impact is most visible, in chatbots, fraud scoring and personalised product recommendations. The results have been real, and in places significant, however, they have also been contained. AI has been working at the edges of the organisation, improving the surfaces that customers and compliance teams interact with, while the actual machinery of the bank carries on untouched. The core systems, the batch processing logic, the settlement rules written in COBOL three decades ago, have continued to run exactly as they always have, insulated from the transformation happening around them.
Meeting the AI Execution Challenge
That insulation is starting to break down. For CTOs weighing where AI investment should go next, the implications are considerable. The question is no longer whether banks need to modernise. Every major institution already knows it needs real-time payments, AI-ready operations, API-enabled architectures and cleaner data foundations. The challenge is execution. How do you modernise decades of critical systems without introducing unacceptable operational risk?
That is where AI is beginning to change the equation. Rather than focusing solely on customer experience or analytics, it is being applied to the engineering process itself. The discovery, extraction, transformation and validation work that has traditionally been the slowest, riskiest and most expensive phase of core modernisation is becoming increasingly automated. This is where the greatest opportunity now exists.
The Core Modernisation Playbook
Publicis Sapient explores this shift in its recent Core Modernisation Playbook. At the centre of that approach is Sapient Slingshot, a platform designed to apply AI across the engineering lifecycle rather than simply the coding stage. Slingshot applies AI across the entire modernisation lifecycle, from understanding legacy systems through to transformation and continuous validation within a governed workflow. The objective is not simply to write code faster. It is to make large-scale modernisation more predictable, auditable and easier to execute.
One global bank recently used this approach to analyse three million lines of COBOL and produce verified functional specifications in eight weeks, work that would previously have taken well over a year and required scarce legacy expertise that is becoming increasingly difficult to find. What changed was not the strategy or destination. It was the execution model.
Core banking code written over thirty or forty years rarely comes with a user manual. Business rules become embedded directly in software, operational workarounds accumulate over time, and critical knowledge often exists only in the minds of engineers who built the systems. As those engineers retire, banks are not simply losing people. They are losing the institutional knowledge needed to modernise safely.
This reflects a broader shift in how modernisation is delivered. Instead of treating discovery, code generation and testing as separate phases owned by different teams, they become part of a connected execution model. Understanding legacy systems, building modern services and validating every change happen as part of the same continuous process. That reduces risk while improving speed, which is exactly what heavily regulated organisations require.
AI-Powered Business
AI-powered business rule extraction is a direct response to that problem. Rather than depending on interviews with legacy experts and time-consuming manual code walkthroughs, automated analysis can surface the logic embedded in legacy programs, cross-reference it against actual processing behaviour, and generate specifications that engineers who’ve never touched the original COBOL can read and act on. In the case above, specification accuracy came in at 95%, and the time needed to analyse individual batch feeds dropped from 35 days to five. That compression changes the shape of what a modernisation programme looks like in its early phases. Work that used to occupy teams for the better part of two years can be substantially completed in months, and what comes out the other end isn’t an informal summary but a set of audit-ready specifications and, in this instance, more than 200 implementation-ready backlog items that become the foundation for everything that follows.
Establishing Strong Foundations For AI
Foundations matter because the validation phase of modernisation is where most programmes break down. There’s no shortage of ambition or strategic clarity in banking transformation. Most major institutions have roadmaps, target architectures and detailed business cases. What consistently falls apart is proving, with sufficient rigour, that a rewritten system behaves identically to the one it’s replacing.
Core banking processes handle exceptions, regulatory edge cases and accumulated business logic in ways that are difficult to fully enumerate up front, let alone test exhaustively by hand. Traditional testing models depend heavily on manually created test cases, subject-matter-expert review and long validation cycles. Coverage gaps are hard to detect, particularly when the legacy behaviour they’re meant to validate against is poorly documented in the first place. Banks end up completing the development work and then getting stuck proving the new system does what it’s supposed to.
AI-driven testing changes that equation by generating and executing test cases at a scale manual teams can’t match, covering standard transactions, edge cases and downstream dependencies systematically rather than sampling them. That doesn’t remove the need for human judgement on what ‘correct’ looks like, but, it does remove the bottleneck of generating enough coverage to have confidence in the answer.
Modernisation Strategy
None of this means banks can modernise the way digital natives do. Core banking platforms sit inside dense ecosystems of payments networks, risk engines, regulatory reporting systems and third-party providers. A change in one system can create consequences dozens of steps downstream, and banks must modernise around uptime, auditability and customer trust in a way a retailer redesigning a checkout flow simply doesn’t. That’s precisely why execution has become the defining challenge. Every bank already knows what it wants to build. The competitive advantage now lies in how effectively it modernises the foundations that make those ambitions possible.
Sapient Slingshot – Deploying An Execution Engine
What AI applied to the engineering process offers is a way to close that gap without pretending the underlying complexity has gone away. Understanding what a legacy system actually does, generating modern code and architecture that preserves the business behaviour underneath it, and validating the result continuously rather than at the end of a multi-year build: these are the three things that have historically made core modernisation slow, expensive and prone to stalling.
Applying AI across the engineering lifecycle does not remove the need for governance, auditability or human judgement. It strengthens them by making documentation, traceability and validation part of the execution process rather than activities completed afterwards. This is where platforms such as Sapient Slingshot are emerging as execution engines for modernisation, enabling banks to modernise with greater speed, control and confidence rather than simply generating code more quickly.
For CTOs, the practical takeaway is less about any single tool and more about where in the lifecycle AI is being pointed. The customer-facing layer of banking has benefited from a decade of AI investment. The engine room is only just beginning. The banks that move first will not simply modernise faster. They will build the AI-ready foundations that determine how quickly every future innovation can be delivered.
The Core Modernisation Playbook
Get your copy of the latest White Paper from Publicis Sapient here
Graeme Donnelly, Founder & CEO of 1st Formations, on how small businesses can master cash flow and target growth
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As British small businesses navigate a landscape of rising digital subscriptions and fluctuating utility costs, many are overlooking thousands of pounds in potential savings. While large corporations chase complex R&D credits, many limited companies are often leaving money on the table through sheer habit.
In partnership with leading company formation agent, 1st Formations, we have identified 10 of the most effective, yet frequently ignored, financial hacks specifically tailored for UK SMEs.
As Graeme Donnelly, founder and CEO of 1st Formations, puts it: “In 2026, operational efficiency is the new profitability. Many directors are so focused on top-line growth that they ignore the ‘silent drain’ of legacy bank fees, incorrect VAT categories, and dormant software subscriptions. Saving £200 a month through smarter digital choices is the equivalent of adding thousands to your annual turnover without the cost of acquisition. It’s about being as lean as you are ambitious.”
1. The power of the ‘sector group’
Many directors believe the Federation of Small Businesses (FSB) or local Chambers of Commerce are only for established firms. In reality, the membership fee is often offset by the discounts they provide on business insurance, legal HR support, and software packages like Xero or Microsoft.
2. The voluntary VAT advantage
If your turnover is under the £90,000 threshold, you aren’t required to register for VAT, but doing so voluntarily allows you to reclaim VAT on everything from stock and web hosting to digital ads. While it does introduce an extra layer of administration, the ability to reclaim input VAT often far outweighs the effort involved. It also levels the playing field when pitching to larger corporate clients.
3. Stress-test your small businesses VAT flat rate
The VAT Flat Rate Scheme can significantly reduce your administrative burden, but many businesses are stuck in the wrong sector category. Reviewing your percentage, for instance 14.5% for IT consultancy vs. 7.5% for certain retail, could instantly boost your margins.
4. Hunt for ‘hyper-local’ micro-grants
While national grants get the headlines, local Growth Hubs and Local Enterprise Partnerships (LEPs) often have pots of £500–£5,000 earmarked for digital upgrades or energy efficiency. These are often poorly advertised but easier to secure than larger funds.
5. Kill the manual spreadsheet
Directors often miss out on tax deductions by forgetting small receipts. Using real-time scanning apps like Dext (for receipts), Pleo (for spending rules), or Expensify (for reimbursements) ensures every deductible expense is captured. Connecting these directly to your bank feed via QuickBooks or Xero turns a year-end headache into a streamlined process and makes bookkeeping easier.
6. Look beyond the “big four” banks for funding
Traditional loans are increasingly difficult for micro-companies to secure. Modern alternatives like Tide’s Funding Options, Uncapped (for revenue-based growth), or invoice finance platforms like Kriya offer the flexibility SMEs actually need without the long-term fixed commitments many traditional loans carry.
7. Ditch the merchant account legacy
If you’re still paying monthly rental fees for a card machine, you’re overpaying. Switching to fintech providers, such as Square, Zettle, or SumUp, eliminates fixed monthly costs and integrates sales data directly with your bookkeeping.
8. Master the ‘work from home’ deduction
If you run your limited company from home, you can charge relevant costs back to the business. These are essential business costs, such as a portion of your broadband bills, mobile phone contracts, and mileage or petrol costs.
Whether using the flat-rate method (£6/week) or a formal rental agreement to claim a portion of mortgage interest and utilities, ensure you are documenting this correctly to stay HMRC-compliant.
9. The quarterly subscription audit
The ‘SaaS drain’ is real. Using tools like Cledara to flag duplicate project management tools or unused licences can shave hundreds off your monthly outgoings. To ensure this doesn’t slip through the cracks, set a recurring calendar reminder to audit your tools every quarter. Always negotiate at renewal; most providers have “retention rates” they don’t advertise.
10. Stop paying for basic banking
Many small firms are still paying monthly fees for basic banking, something digital-first providers have all but eliminated. Digital-first banks, such as Starling or Monzo Business, offer fee-free banking with superior app integration, saving both money and administrative hours.
Luke Trayfoot, Global Head of Strategic Partnerships at YouLend, on how embedded finance can help create a more resilient and inclusive lending ecosystem for SMEs
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Bank‑to‑business lending growth is expected to halve to just 3.5% this year, driven by global tensions, higher funding costs and ongoing economic uncertainty. For many small and medium‑sized enterprises (SMEs), this means tighter credit conditions and a growing risk of a funding gap. And at a time when fast, flexible capital is essential.
Traditional lenders will remain central to SME finance, but slower lending growth naturally limits how quickly banks can meet demand. SMEs often need capital at short notice to manage cash flow, invest in stock or respond to new opportunities. When bank lending slows, the pressure on these businesses increases. This is where alternative lenders and embedded finance providers are stepping in, offering models that work alongside the traditional system to ensure SMEs still have access to funding when they need it most.
A New Reality for SME Embedded Finance?
The slowdown in bank lending does not mean traditional finance is stepping back from the SME sector. UK Finance data shows that while gross SME lending rose for the second consecutive year in 2025, approval volumes have begun to flatten. Meanwhile, average loan sizes are trending downward, which signals that traditional lending capacity is tightening. What this really highlights is the need for a more diversified and resilient lending ecosystem. Alternative lenders, from online credit platforms to embedded finance providers, are becoming an important part of that shift by offering additional, data‑driven routes to capital.
These lenders are not here to replace banks. They are absorbing the demand that banks cannot meet quickly enough. Especially for SMEs that need fast decisions or more flexible repayment options. By using real‑time business performance data, alternative lenders can assess risk in a more dynamic way and offer funding that reflects how SMEs actually trade, rather than relying solely on historical, static credit files.
This becomes particularly valuable during periods of economic uncertainty. When traditional credit conditions tighten, SMEs with strong underlying performance can still access the capital they need, which helps prevent stalled growth or operational disruption.
Platforms as Distribution Hubs
E‑commerce marketplaces, payment providers and business software platforms are quickly becoming some of the most important channels for SME finance. A trend reflected in the scale of the market, with global embedded finance transactions projected to exceed $7 trillion by the end of 2026. These platforms sit at the centre of day‑to‑day business activity, which gives them a clear view of trading patterns, cash flow and overall performance. Because of that, they are in a strong position to offer funding at the exact moment a business needs it, whether that is during checkout, inside a dashboard or alongside a payment settlement.
For SMEs, this creates a much smoother path to capital. They can access funding without the paperwork or delays that often come with traditional loan applications. For platforms, it deepens customer relationships and opens the door to new value‑added services that support long‑term loyalty.
The Infrastructure Powering Embedded SME Finance
Behind the scenes, specialised FinTech providers supply the underwriting, risk modelling and compliance infrastructure that allows capital to move responsibly through non-financial channels. This infrastructure layer is what enables these business platforms to offer financing without becoming lenders themselves, while ensuring SMEs receive funding that is both fast and sustainable. It’s also creating a more standardised, interoperable foundation for SME credit delivery across the digital economy.
Government data shows that late payments continue to impact the UK economy to the tune of £11 billion annually, which creates a significant cash‑flow friction. By combining platform‑level insights with advanced risk analytics, embedded finance providers can tailor funding to real‑time business performance. This reduces default risk, improves capital efficiency and strengthens the resilience of the wider SME lending ecosystem, ultimately leading to better outcomes for both SMEs and the capital providers supporting them.
Smarter Underwriting is Becoming the Backbone of SME Finance
Real‑time performance data and AI‑enabled underwriting are reshaping SME finance by making funding decisions more accurate, more inclusive and more responsive to how businesses actually operate. Instead of relying solely on traditional credit scores, lenders can assess SMEs based on real trading activity such as sales patterns, seasonality and customer behaviour. This moves underwriting closer to the operational reality of a business, rather than a backwards-looking snapshot.
This approach opens the door for more businesses to access capital, including those that may be underserved by conventional credit models. It also helps ensure funding decisions remain stable even when market conditions are volatile, supporting business continuity and long‑term resilience. As these models continue to mature, they are becoming a core part of how lenders manage risk, price capital and build a more adaptive SME finance ecosystem.
Where SME Finance Goes from Here
As traditional lending growth slows, there is a real opportunity to build stronger partnerships between banks, FinTechs and digital platforms so SMEs can continue to access the capital they need. Embedded finance is becoming a mainstream, complementary channel that broadens access, speeds up decision‑making and supports day‑to‑day business growth.
The priority now is scaling these models responsibly. That means keeping transparency front and centre, maintaining strong risk frameworks and ensuring long‑term stability as more capital flows through embedded channels. If the industry can combine the strengths of traditional finance with the agility of modern FinTech infrastructure, we can create a more resilient and inclusive lending ecosystem for the SMEs that drive economic growth.
Simon Pamplin, CTO at Certes, on how Quantum computing will eventually force a reckoning with the cryptographic assumptions on which the financial system is built.
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Financial institutions hold some of the most sensitive and long-lived data of any sector. Customer identities, decades of transaction records, credit histories, and proprietary trading information must be kept confidential, not just today, but far into the future. The cryptographic systems guarding that data, however, were built for a different era; one in which quantum computing was purely theoretical. That era is ending.
Recent guidance from both the G7 Cyber Group and the UK’s National Cyber Security Centre has made the stakes explicit: financial organisations should complete their transition to post-quantum cryptography by 2035. For institutions managing sprawling, decades-old technology estates, that deadline is less reassuring than it might first appear. Ten years sounds like breathing room. In practice, for organisations of this complexity, it is barely enough.
Quantum Risk: The Threat Doesn’t Wait for the Technology
The most common mistake in discussions about quantum risk is treating it as a problem for tomorrow. The assumption is that until a quantum computer powerful enough to crack modern encryption actually exists, there is no immediate danger. That assumption is dangerously flawed.
Sophisticated adversaries are already operating on a longer time horizon. The strategy, widely referred to as “harvest now, decrypt later,” involves systematically collecting encrypted data today and storing it until quantum computing capabilities catch up. No quantum computer is needed to execute this phase of the attack, only patience and storage.
For the financial sector, the implications are particularly serious. The data being harvested now, customer records, transaction histories, and commercially sensitive communications, could have a useful life measured in decades. By the time it is decrypted, the individuals and organisations it concerns will still be very much affected by its exposure. This is not a theoretical future liability. It is a risk quietly accumulating in the background of every financial institution that has not yet begun to act on it.
Why Updating Financial Systems Is So Hard
The cryptographic methods underpinning most financial infrastructure today, primarily RSA and elliptic curve cryptography, derive their security from mathematical problems that are extraordinarily difficult for conventional computers to solve. Quantum computing threatens to render those problems tractable, potentially dismantling encryption that currently appears robust.
Responding to that threat, however, means working through some of the most technically complex environments in any industry. Core banking platforms were often built 20, 30, or 40 years ago. Cryptographic functions are frequently embedded deep within application logic, firmware, or hardware, in payment terminals, ATMs, and proprietary systems that were never designed with replaceability in mind.
Replacing or updating these components is not a software patch. It can mean recertifying devices, replacing physical infrastructure, or undertaking large-scale application rewrites, all while maintaining continuous service to customers and satisfying stringent regulatory requirements. Progress is possible, but it is measured in years, not quarters.
Rethinking Security from the Data Outward
The quantum threat is also accelerating a broader rethink of how financial institutions approach security architecture. Perimeter-based defences, firewalls, VPNs, and network segmentation have consistently shown their limitations when attackers can move laterally through systems using legitimate credentials or compromised access. Relying on the boundary to keep data safe has a poor track record.
A more resilient approach centres protection on the data itself. By applying strong controls directly to information as it moves across systems, organisations can maintain security even within legacy environments that cannot be immediately upgraded. Sensitive data remains protected regardless of where it travels or which network it traverses.
Alongside this, crypto agility, the ability to swap out cryptographic algorithms and rotate keys without overhauling entire systems, is becoming a fundamental requirement rather than a nice-to-have. The transition to post-quantum standards will not be a one-time event. Cryptographic best practice will continue to evolve, and institutions that have built flexibility into their architecture will be far better positioned to keep pace.
The Cost of Inaction
The financial sector has a well-established capacity to absorb and adapt to technological change. It has navigated the transition to digital banking, the rise of real-time payments, and successive waves of cybersecurity threats. The quantum challenge is different in one important respect: the timeline for harm is already running.
Regulatory expectations are being set now. Adversaries are collecting encrypted data now. And the internal processes required to modernise a major financial institution’s cryptographic infrastructure take years to complete. The gap between when action is needed and when it can realistically be delivered is not wide, and it is narrowing.
Preparation does not demand panic or the immediate replacement of every system. It demands a clear-eyed audit of where sensitive data lives, an honest assessment of which information carries long-term confidentiality requirements, and a commitment to building security architectures that can evolve as the threat landscape does.
Financial institutions that move early will not simply reduce their quantum exposure. They will emerge with stronger, more adaptable security postures and with the trust of customers, regulators, and partners intact.
Quantum computing will eventually force a reckoning with the cryptographic assumptions on which the financial system is built. The question is no longer whether that reckoning is coming. It is whether institutions will be ready when it arrives.
Rob Demain, CEO at e2e-assure, on why the clock is ticking when it comes to IT/OT convergence for Critical National Infrastructure (CNI) and associated organisations
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Many critical national infrastructure (CNI) operators lack the ability to protect their infrastructure despite the UK being subjected to daily sub-threshold cybersecurity attacks, according to the Strategic Defence Review 2025. It’s a situation that the Network and Information Systems (NIS) regulations, introduced back in 2018, sought to prevent. But since its inception, just over half of the operators of essential services have updated or strengthened their existing policies and processes, leaving many woefully unprotected.
In desperate need of reform, NIS is set to be superseded by the Cyber Security and Resilience Bill (CSRB), which is expected become law later this year, at which point a consultation on implementation proposals will commence followed by secondary legislation and an adjustment period for stakeholders. The bill will broaden the scope to include other organisations deemed critical to the national economy i.e. data centres, Managed Service Providers (MSPs) and critical suppliers. Plus, the government reserves the right to extend those categories still further as part of its ‘future proofing’, which will enable changes to be made to the act to accommodate emerging threats and potential targets.
New Demands
All of these new entities will need to comply with the Cyber Assessment Framework (CAF), which lays out expected cybersecurity and resilience outcomes. First published in 2018 to support NIS, it has undergone a number of revisions since, with v4.0 released in August 2025. This version places a far greater emphasis on proactive security and decision making based on real threat intelligence. As well as adding new contributing outcomes on understanding threats and secure software development and support, it also expands the sections on security monitoring and response and recovery, while an entirely new category has been added on threat hunting.
All of this points to a far greater emphasis on being able to demonstrate assurance and proactively monitor all aspects of CNI infrastructure and that means more scrutiny of both IT and Operational Technology (OT). Until recently, securing OT wasn’t seen as a priority. These systems were chiefly concerned with maintaining system availability and minimising downtime. But their increased integration with IT systems to connect with the industrial Internet of Things (IIoT) and deliver real-time monitoring, for example, are exposing these systems to attack, with threat actors able to move laterally from one environment to the other.
The threat posed by IT/OT convergence is well known, but it continues to be the Achilles heel of CNI, as revealed by the Volt Typhoon attack. This saw Chinese nation state actors maintain persistence across CNI in the USA since at least 2021 through the use of Living off the Land techniques, illustrating just how insidious and sustained these cybersecurity attacks can be.
Securing IT/OT Systems
It’s these types of threat the CAF addresses through its risk and asset management requirements. Organisations must risk assess systems with respect to their dependencies and interactions with other systems such as IT/OT, and document and understand those dependencies. But other complementary frameworks can also be used to map IT/OT system security, such as the ISMS within ISO27001 from an IT perspective and IEC 62443 from the OT side, in addition to ISO/IEC 27019 for process control systems.
Being able to follow these frameworks will require organisations to increase their security monitoring of both IT and OT and the transparency of their processes. They will need to transition from being reactive to proactive, and become resilient and risk informed, which will mean many will have to change their approach. These are really the only options available to them in this respect if they are to move the resilience needle.
The first is to decentralise and harden OT systems while keeping them segregated from IT. However, hardening alone can’t keep pace with digital transformation. Many OT assets cannot support multi-factor authentication (MFA) or accommodate rapid patching because they are downtime sensitive. So, surface hardening alone won’t confer the resilience needed long term.
The second option is to manage IT and OT together by giving everything an identity in a converged environment, but to do that you need to move the monitoring of OT into the Security Operations Centre (SOC). Centralised monitoring allows threats to be detected across both IT and OT networks, for teams to monitor east-west traffic, and to correlate alerts that might otherwise appear unrelated. And it’s this centralised management that will provide the visibility and control needed to improve IT/OT resilience.
Converged Cybersecurity
Such a converged SOC doesn’t just offer continuous visibility over IT, industrial control systems (ICS), OT and cloud environs, but also the real-time triage of critical alerts. These might include unauthorised PLC logic changes, unsafe set-point writes, abnormal OT protocol behaviour, lateral movement in ICS DMZs, OT malware,or unauthorised remote access into OT environments. These alerts are then grouped by operational impact, such as whether they present a safety critical risk or could lead to service degradation, so that they can be prioritised. Weekly threat hunts and detection surface validation over distributed environments provide the threat hunting capabilities needed to meet the CAF requirements and the SOC evidences and provides that all important audit-ready compliance mapping to meet the demands of other frameworks too such as IEC 62443, and ISO/IEC 27019.
Whether standing up a converged SOC internally or outsourcing, this capability is the most efficient way to adapt to the tightening regulations, particularly as we can expect ‘future proofing’ to lead to yet more demands. The emphasis is now firmly focused on the proactive monitoring of both IT and OT systems together, given their growing dependencies, so it makes sense for those organisations in scope – as well as those who could soon be – to begin to move their OT monitoring from the plant and into the all-seeing all-knowing enclave of the SOC.
Andrew Power, Head of UK&I at Tricentis, on why the right approach to AI can deliver the foundation for more resilient, predictable systems
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Artificial intelligence is reshaping software delivery in financial services. Code that once took teams weeks to develop can now be generated and deployed in a matter of hours. This isn’t just about faster delivery; it changes the fundamentals of how software is built and how it behaves in production.
Financial institutions have moved quickly to integrate AI across core systems, from customer operations to anti-money laundering (AML) and software development to capture efficiency and innovation gains. UK parliamentary evidence shows adoption is already widespread, with the majority of firms using AI, and more planning to follow.
But as adoption spreads and becomes more embedded within key systems, so does exposure. Risk is no longer confined to individual defects, but shaped by how quickly those defects can spread across interconnected environments.
AI has removed the limits on how quickly software can be created, but not on how confidently it can be trusted, and financial institutions can now generate and deploy code faster than they can safely validate it.
This creates a new paradox: AI is both accelerating the pace of software change and increasing the speed and scale at which failures can materialise.
Machine-Speed Failure
AI-driven development shortens the distance between change and consequence. Software updates can move through the pipeline from creation to production with significantly less friction. However, this also reduces the time available to identify, flag and contain any issues before they have an impact.
AI-driven software changes don’t just move fast, they scale fast. Unlike traditional failures, these are systemic risks. A single misstep in an AI-generated update can propagate unpredictably.
For financial services, this is especially significant when key systems are deeply interconnected, spanning complex layers of infrastructure, integrations, and third-party services. Even a minor defect can propagate quickly across systems, amplifying its impact.
What would once have been contained can now escalate, cascading across systems and causing wider disruption that affects customers, operations and, in some cases, market activity. In financial services, this is not just a technical issue but a business risk with direct implications for customer trust, regulatory compliance and financial stability. The challenge is no longer simply identifying defects but maintaining confidence in what is being deployed.
This risk is already being felt across the sector. Institutions are accelerating delivery to meet customer expectations and competitive pressures, but often without corresponding advances in validation. Tricentis’ research shows 68% of financial services organisations anticipate outages or serious incidents due to poor software quality.
Regulatory Pressure for AI is Increasing
The issue is also drawing attention from regulators. Earlier this year, the UK Treasury Committee warned that current approaches to AI in financial services are inadequate and could expose customers and the wider system to “serious harm”, highlighting the need for stronger guardrails, clearer accountability and more robust oversight to deploy it safely.
Traditional resilience frameworks were never designed for systems evolving in real time, and AI can no longer be treated as a marginal technology risk. It must become central to how organisations manage and assess resilience.
This marks a shift from software quality being an engineering concern to a board-level issue of operational resilience. If machine-speed change is the new operational hazard, then failure to address it becomes a strategic issue rather than a technical one. With that in mind, financial leaders must acknowledge AI’s dual role as both a driver of risk and a mechanism for preventing it.
AI as Both a Safeguard & Source of Risk
AI also offers the most effective and scalable way to manage the risks it introduces. Advanced AI-driven validation, continuous monitoring and risk-prioritised testing can identify issues earlier than any manual process, helping reduce the likelihood they reach production.
In effect, the same AI that accelerates software creation must now be applied to validation and governance – operating at the same speed and scale.
The same capabilities that facilitate rapid software production can be applied to validation and governance, continuously evaluating system behaviour, detecting anomalies and prioritising testing based on potential business impact, rather than volume. This allows organisations to move beyond rigid approaches and towards more adaptive, responsive quality models that more accurately reflect the way AI behaves.
Instead of relying on standard periodic testing cycles, systems can be validated on an ongoing basis. This enables earlier intervention before issues escalate.
AI can also help organisations better understand the complexity of their own systems. By analysing dependencies across applications and infrastructure, it becomes possible to identify which processes are most critical and where failures would have the greatest impact.
From Acceleration to Control
There is a clear mismatch in how financial organisations approach AI. While many are leveraging AI to accelerate development, far fewer are evolving their validation and governance to keep pace, and it’s in this gap that risk emerges.
This is the “confidence gap”, where organisations can create software faster than they can safely deploy it.
To address this imbalance, firms must treat software quality as a core component of their AI strategy. Development and validation must move forward together. Governance must adapt to continuous, AI-driven change. This requires a move from static testing and coverage metrics to continuous, risk-based validation, where software is assessed in real time based on potential business impact.
If AI is the engine driving software creation, validation must act as the braking system – built in, not bolted on at the end. At machine speed, gaps in control become points of failure. The aim is not to slow innovation, but to ensure it progresses in a way that is sustainable and safe. When validation keeps pace with development, firms can move quickly and competitively, whilst maintaining control over how risk is introduced and managed.
This is a change we are seeing across large enterprises adopting AI-driven quality approaches, where validation, monitoring and governance are increasingly orchestrated together rather than treated as separate processes.
Preventing the Next Outage
The financial sector has already seen how quickly failures can escalate in complex, interconnected environments. In March, an IT error at Lloyds Banking Group exposed the private financial information of nearly half a million customers, prompting the bank to issue £139,000 in compensation.
Such incidents aren’t isolated: over the last two years, more than 33 days of unplanned banking outages have been reported to Parliament, underlining the scale of the issue.
As AI increases the velocity of change, it also raises the stakes for getting it wrong. But the irony is that it also provides the tools needed to prevent these failures from happening in the first place. AI is both contributing to the risk of outages and becoming the most effective way to prevent them.
By applying AI to continuous validation, monitoring and risk detection, organisations can spot issues earlier, understand their potential impact and intervene before disruption occurs. This shifts the focus from reacting to outages to preventing them, and it’s where the paradox becomes constructive. AI doesn’t have to be a source of instability.
With the right approach, it can become the foundation for more resilient, predictable systems. Those that fail risk trading innovation for instability. In the AI era, speed without confidence is simply another form of risk.
Peer-reviewed Physical Review Research paper shows efficient preparation of financial distributions on quantum hardware
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Haiqu, a leading developer of quantum middleware, has announced the publication of joint research with HSBC in Physical Review Research demonstrating an efficient approach to encoding real-world probability distributions into quantum circuits.
Quantum State Preparation
Quantum state preparation, the process of encoding classical data into quantum states, is widely recognised as a major bottleneck when implementing many algorithms on hardware. This challenge is particularly relevant for applications such as financial risk modelling and simulation, where complex probability distributions must be loaded onto quantum devices.
The research uses matrix product state (MPS) methods to construct shallow circuits that encode smooth functions, including probability distributions, directly into quantum states. It also introduces a sampling-based workflow that avoids storing the full discretised dataset in classical memory, enabling larger encoding circuits to be generated.
The approach was validated on finance-relevant models including heavy-tailed Lévy distributions, commonly used to capture extreme market events.
On IBM quantum hardware, circuits up to 25 qubits produced samples that passed standard statistical tests, showing the method can accurately reproduce the probability distributions these models rely on in practice.
Sampling-Based Workflow
Using the sampling-based workflow, the researchers also executed circuits up to 64 qubits, reproducing qualitative features of the target distributions under realistic device noise and demonstrating feasibility at larger scales. Similar behavior was observed in simulations up to 156 qubits, indicating the approach can extend to substantially larger problem sizes.
“Preparing complex probability distributions efficiently is a key step in many quantum algorithms,” said Dr. Philip Intallura, Group Head of Quantum Technologies at HSBC. “This work shows how they can be implemented with much shallower circuits, bringing practical applications such as financial risk modelling closer.”
“One of the biggest practical barriers is getting realistic financial data onto today’s quantum hardware. This work shows a scalable path around that barrier and helps move quantum finance workflows from theory toward execution.” Mykola Maksymenko, Co-founder & CTO, Haiqu
Haiqu is an emerging leader in quantum software that supports the notion that near-term, commercially viable applications are achievable with the right software, even on current hardware. Haiqu’s hardware-agnostic software can run applications with up to 100x more operations on current devices compared to competitors. Headquartered in New York City in the United States, Haiqu’s expert team operates from US, Canada, Ukraine, UK, EU, and Singapore, contributing to the company’s mission to make quantum computing practical as soon as possible.
Mark Talbot, Director, CS AI Initiatives at Appian, reasons that as organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it
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Many organisations have long treated improvement as something that arrives as a top-down effort, not something built with the people doing the work. Specialists designed new processes, discussed them in formal forums, and introduced them through large change programmes that often felt detached from daily work. For most employees, ‘transformation’ meant being asked to follow new rules, rather than designing better ways of working.
AI is starting to reverse that pattern. Instead of concentrating control and decision rights in a small, central group, modern AI tools give more agency to the people closest to the work. They can see what is not working, imagine better approaches, and use AI to help redesign and improve the processes they rely on every day. This shift – which can be described as the democratisation of AI – changes who participates in improving the business. However, it is worth remembering that this shift only works at scale when AI is embedded within a platform that maintains governance, visibility and control.
Process Improvement in the Hands of Many
Until recently, fixing a broken process often meant filing tickets, waiting for a slot on an IT roadmap, or hoping that a specialist team would eventually address the issue. Creating applications, building automations or redesigning workflows were seen as highly technical tasks. For most employees, waste and inefficiency were things to work around, not things they had the tools or authority to change.
That obstacle is now deteriorating, as long as organizations don’t lose sight of the fact that governance remains essential, particularly in highly regulated environments
AI agents, generative AI and conversational interfaces allow people across the business to shape how work is structured. Within this model, someone in operations can describe an outcome in plain language and have an AI system propose and embed the steps within existing processes. Within a governed platform, non-technical users can adapt existing solutions and automate repetitive tasks without waiting months for central support. At the same time, process insights give developers visibility into what is being built, enabling them to refine, standardise and scale applications more quickly across the organisation.
Data is opening up as well. Data fabrics and related architectures connect scattered information sources into governed layers that a wider audience can access safely. Instead of waiting on static reports, people can access relevant, trusted data when they need it, and use AI to interpret and apply it to their decisions.
When process insight and data access reach this level, best practices move beyond documentation or occasional training. Tools and workflows embed them into daily work, improving performance across the organisation.
Scaling Improvement Across the Organisation
As more individuals understand how their work connects to broader outcomes, organisations unlock a powerful driver of change. Process improvement no longer depends only on a small group of specialists. Employees can recognise when processes are inefficient or risky and have the means to address them at scale, inside an AI platform.
By encoding domain knowledge into AI assistants and digital coworkers within an enterprise-grade AI platform, organisations can share expertise across roles and levels. These AI-powered helpers do not replace professional judgment. They strengthen it. They surface options, highlight inconsistencies and provide context, while humans make the final decision. Over time, each interaction becomes both a learning moment and a new piece of institutional knowledge that organisations can capture and reuse.
In this model, process improvement is no longer episodic or confined to formal transformation projects. It becomes part of everyday work, inside a platform with AI tools that provide real-time feedback and recommendations.
AI, Noise Reduction, and Better Oversight
This shift raises a key question: if AI platforms make analysis, decision support, and process design more accessible, what happens to deep expertise?
There is a concern that easy access to AI advice might weaken people’s understanding. If answers are always a prompt away, will teams still develop the knowledge that comes from working through complexity? If people follow AI suggestions without grasping the logic, how meaningful can human oversight really be?
Over-reliance on instant guidance can create only surface-level competence. People may treat AI outputs as instructions rather than as inputs to their own reasoning.
On the other hand, used well, AI can create more room for expertise, not less.
By handling repetitive tasks and routine decisions, AI reduces the volume of low-value work that consumes people’s time. Teams can then focus on exceptions and refine how they make decisions. Instead of dealing with every routine request themselves, they can focus on work where context and experience matter most.
When AI removes more of the routine burden, teams have more capacity to focus on judgement, process design and oversight. That helps build expertise while keeping improvement connected to the wider goals and governance of the business.
Shaping AI, Not Just Living With It
As organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it. AI is moving from something that happens to the workforce to AI being something that is built and refined with the workforce.
Organisations should treat people as partners in shaping AI, rather than as operators of automated systems. When AI platforms can be combined with process visibility and human judgement, employees can have an outsized effect on the systems around them. They can influence how work is structured and how decisions are made. In that sense, AI redistributes who participates in designing better ways of working, and creates an opportunity to anchor that shift in thoughtful design and human expertise.
Vincent Guillevic, Director of Fraud Labs at Entrust, argues companies that treat identity as a continuous thread rather than a single checkpoint will be better positioned to reduce losses and protect customers
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Identity verification and tackling fraud began as a face-to-face process, built on human trust. Opening a bank account involved meeting a banker in person and from there, trust was established because both parties could see and interact with each other directly in branch.
Fast forward to the digital age and a lot of services have moved online. Identity verification has therefore shifted from in-person checks to remote identity verification. Today, we’re in an era where identity is now central to every interaction we have online.
Fraud has followed the same trajectory. Much like a burglar would test every possible entry point rather than just the front door, fraudsters probe every stage of the customer journey. They look for weaknesses at onboarding, during login, and throughout ongoing transactions and data requests.
That challenge has intensified in recent years. AI has given fraudsters faster, sophisticated and scalable tools. Deepfakes can bypass checks, AI‑generated documents can appear real, and phishing and impersonation attacks can now be automated at scale.
Once a fraudster gains access to a legitimate account, the damage escalates quickly. Global losses from account takeover (ATO) fraud were projected to reach $17 billion in 2025, up from $13 billion in 2024. While the underlying intent of fraudsters seeking the weakest point of entry, the breadth, speed and sophistication of modern attacks have.
Identity Fraud Patterns Across the Customer Lifecycle
Fraud can occur at any stage of the customer journey. From verifying identity at onboarding to securing connections and fighting fraud in everyday transactions. Each stage introduces its own risks, and attackers adapt their tactics based on where value can be extracted most efficiently.
In 2025, patterns showed a clear distinction between industries targeted for new account fraud and those targeted for account takeover fraud. Businesses that offer immediate incentives such as promotional offers or sign-up bonuses are primarily targeted for new account fraud. In contrast, businesses where accounts accumulate long-term financial or data value face higher levels of ATO.
Industries built around sign-up incentives or instance access experience most fraud at onboarding. For instance, in crypto, 67% of fraud attempts occur during account creation, largely driven by sign-up incentives. Vehicle rental follows a similar pattern, with 67% of fraud taking place at onboarding as attackers use fake identities to gain short-term access to high-value assets. In these sectors, low-friction onboarding creates opportunities to harvest incentives or establish accounts that later become avenues for future money laundering.
Account takeover fraud reflects a different strategy. Rather than creating fake accounts, attackers focus on compromising established accounts using tactics such as stolen credentials, phishing, malware, or social engineering. Entrust data shows this is most common in industries where accounts hold enduring value. In payments, 82% of fraud attempts occur after onboarding, while in professional services the figure is 62%. High-value, long-standing accounts are attractive because they enable fund transfers, loans, and access to identity-rich data, making them more valuable than newly created accounts.
These patterns highlight two critical realities. First, organisations can no longer optimise for one type of risk at the expense of another. Defending a single point in the journey inevitably leaves gaps elsewhere. Second, fraud has become highly professionalised. Modern fraud operations are organised, strategic, and adaptive, moving toward the highest rewards and the weakest controls.
Prevention Must Span the Entire Journey
If fraud can occur at any stage, prevention must operate at every stage. Organisations that implement robust, lifecycle-wide identity strategies save an average of $8 million per year in fraud-related costs. These savings come from detecting threats earlier, more accurately, and beyond a single checkpoint.
There are three areas where that lifecycle approach needs to be strongest.
Get onboarding right
Onboarding is the first opportunity to establish genuine trust. Strong Know Your Customer (KYC) or Know Your Employee (KYE) processes combine document verification with biometric checks such as face recognition or fingerprint scanning to confirm that the person applying is who they claim to be. Liveness detection adds a further layer by distinguishing real users from synthetic identities and deepfakes, which are linked to approximately one in five biometric fraud attempts.
With strong identity verification at onboarding not only reduces immediate fraud, but also limits the downstream damage caused with fraudulent accounts.
Secure existing accounts with continuous authentication
Verifying identity once is no longer sufficient. Continuous authentication, combining multi-factor authentication with biometric re-verification like facial recognition, allows businesses to protect established accounts without creating unnecessary friction for legitimate users.
Crucially, it enables authentication requirements to adapt dynamically as risk levels change, rather than applying the same static check regardless of context. In payments businesses, where most fraud targets the authentication process itself, this adaptability is key to mitigating attacks before losses occur.
Monitor behaviour in real time, not just identity
Device intelligence and behavioural signals make it possible to assess risk based on how users interact with services, flagging unusual login patterns, device anomalies, or out-of-character transactions.
As AI-driven fraud becomes more sophisticated and convincing, behavioural indicators provide another layer of ongoing fraud detection. Focusing monitoring on high-risk actions, rather than only high-risk identities closes a critical gap in traditional defences.
The Window of Opportunity
Fraud has always followed the customer journey. What has changed is the availability of advanced technology capable of tracking, analysing, and responding to threats at every stage. The key question for organisations is whether these capabilities are deployed as a connected strategy or left as isolated controls with gaps in between.
Companies that treat identity as a continuous thread rather than a single checkpoint will be better positioned to reduce losses and protect customers, and preserve the trust that underpins long-term digital relationships.
Our cover star Shadman Zafar, Founder & CEO of Vibrant Capital, is building a CIO-led model for enterprise transformation. Vibrant Capital is an operator-led investment and company-building platform focused on scaling AI in the real economy. “We don’t spray investments across hundreds of AI startups. We curate a portfolio with purpose – selecting companies that solve the real mission-critical problems CIOs face in scaling AI adoption.”
FNB: Redefining Data Science in Commercial Banking
We also hear from Yudhvir Seetharam, Chief Analytics Officer at South Africa’s First National Bank (FNB) on a data science journey characterised by curiosity, culture and the drive for a competitive edge. “Ours is a holistic approach focusing on the customer,” he explains. “Understanding the context of each customer journey and then using that context so that when we interact with you, we’re able to drive the right conversation with the right customer, at the right time, through the right channel and for the right reason. These ‘five rights’ make our interactions with clients more impactful.”
Virginia Farm Bureau: An Enterprise CIO’s Journey
Shifting focus to the world of insurance at the Virginia Farm Bureau, we spoke withan Enterprise CIO at a complex mission-driven organisation. As he approaches retirement, Patrick (Pat) Caine reflects on his career as a CIO and the centennial of an organisation renowned for resiliency, collaboration, commitment to a greater cause, diversity and service to its members. “In my role as CIO, I’ve always been that person who connects the dots between business needs and technology execution. Virginia Farm Bureau is digitally relevant, collaborative, and well‑positioned for the future.”
Mastercard: Protecting Trust in the Digital Economy
Michele Centemero, EVP Services at Mastercard Europe explains why promoting awareness, stronger collaboration and data-sharing, and continued innovation of payments ecosystems, will be critical in reducing the impact of scams and protecting trust in the digital economy. “The combination of AI, robust identity controls and open banking can help protect consumers from scams, whether across card and account‑to‑account payments or in fraudulent account openings.”
Thales on AI Security: How FinServ’s Budget Priorities Signal a Boardroom Shift
Todd Moore, Global VP – Data Security Products at Thales, reveals why making AI security a boardroom priority today, will help firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation. “Balancing AI’s opportunity and risk means embedding security at every stage, from design to deployment and ongoing monitoring.”
Paymentology: The First Live AI-Agent Payment Is a Test for Credit Infrastructure
Thomas Benjaminsen Normann, Product Director at Paymentology, dissects the future for agentic payments and the progress still to be made. “Agentic payments demand something more granular: a clearer account of who or what acted, under what limits, and with what right to create a liability on the customer’s behalf.”
Also in this issue, we hear from Publicis Sapient, on why asset managers must redesign their enterprise for AI-driven decision intelligence; learn from Bitpace why the most resilient payments infrastructure will be the one with the most adaptability; rank the AI maturity of 12 of the largest payments networks in the latest Evident AI Index; and round up the key FinTech events and conferences across the globe.
Todd Moore, Global Vice President, Data Security Products at Thales, on why making AI security a boardroom priority today, will help firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation
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Financial Services organisations are responsible for some of the biggest growth in the global economy. Equally, they’re some of the most vulnerable. Like many other sectors, they’re racing to embrace AI, but with adoption comes new security risks.
According to Thales’ Data Threat Report: Financial Services Edition 81% of FinServ organisations are now investing in GenAI-specific security tools, with nearly a quarter using newly allocated budget. This surge in funding marks a turning point: AI security has moved from being an IT concern to a boardroom priority.
The fact that new budget lines are being carved out specifically for AI security signals a fundamental shift in corporate strategy. Boards increasingly recognise that protecting AI systems is as critical as safeguarding payment rails or core banking infrastructure. For an industry built on trust, resilience, and regulatory compliance, this investment wave shows how central AI has become to both risk management and competitive growth.
Balancing AI Innovation and Security
While FinServ organisations are aware of the security risks AI poses, they’re also seizing upon the opportunities it presents. The report has found that in 2024, FinServ businesses outpaced the broader market in AI deployment, leading in enabling employees to use AI and ahead in AI integration, which has continued into 2025. Additionally, 45% say they’re in the ‘integration’ or ‘transformation’ phases of their GenAI journey, compared to just 33% across wider industries.
AI’s ability to accelerate services, automate processes, and analyse data at scale makes it an exciting prospect, especially in the financial sector. This makes securing AI systems a priority for FinServ organisations, with increased GenAI integration reflecting developing organisational maturity and progress beyond experimentation.
The Risk
Yet the scale of opportunity is matched by the scale of challenge. AI systems require vast amounts of structured and unstructured data to conduct analysis and make recommendations.
For FinServ organisations, this often includes highly sensitive customer and transactional information, proprietary algorithms, and records bound by strict regulatory oversight. The risk is not only about whether AI systems themselves are secure, but whether the data they’re working from is accurate, as well as whether their adoption inadvertently creates new routes to data exposure and exfiltration.
Businesses need a clear strategy to fully understand how AI models are operating within their IT infrastructure, the applications they’re interacting with, and the data they’re accessing and pulling from.
The Response
Balancing AI’s opportunity and risk means embedding security at every stage, from design to deployment and ongoing monitoring. Newly allocated budgets for AI security, with nearly a quarter of FinServ firms making such investments, show how central AI has become to board-level strategy. These investments move firms beyond reactive fixes to proactive frameworks that evolve with the technology. AI security is no longer just an IT concern, it’s a strategic priority requiring collaboration between security, compliance, and business leaders. By factoring risk into early planning, organisations can align innovation with responsibility and build resilience for the long term.
Pioneering AI Security
Building on investment in AI-specific security is only the beginning. As scrutiny intensifies, the firms that will lead are those that treat AI security as integral to business strategy, not a bolt-on layer. Success will require visibility into how models behave, continuous validation against emerging risks, and adaptive controls that evolve with the threat landscape.
The financial services organisations that embed these safeguards into their core infrastructure will protect sensitive data as well as setting a benchmark for resilience and trust in an AI-driven economy. By making AI security a boardroom priority today, these firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation.
Thales: AI is the New Insider Threat
Thales 2026 Data Threat Report Finds 70% of Organisations Rank AI as Top Data Security Risk
Data security has taken centre stage as the success of enterprise AI initiatives increasingly hinges on consistent, controlled access to proprietary organisational data sources. The 2026 Thales Data Threat Reportexamines the complex calculus that organizations must undertake to enable innovation while securing their most valuable asset – their data.
This research was based on a global survey of 3,120 respondents fielded via web survey with targeted populations for each country, aimed at professionals in security and IT management.
Lee Fredricks, Director – Solutions Consulting, EMEA at PagerDuty, on why technology leaders should see 2026 as a time for operational resilience to shift from ambition to accountability
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Technology leaders should see 2026 as a time for operational resilience to shift from ambition to accountability. In 2025, too many cloud services outages and disruptions took place across the public and private sectors, and now regulatory, technological and cultural pressures are converging to say that enough is enough.
Outages often translate into broader repercussions for the organisation, including revenue impact, customer churn, share price pressure and potentially regulatory reporting obligations. Operational metrics must now be discussed alongside financial KPIs at the board level. C-suite leaders understand accountability, especially within the very regulated financial sector.
DORA’s First Birthday
It’s now been one year since the implementation of the Digital Operational Resilience Act, or DORA, introduced by the EU to strengthen the digital resilience of financial institutions. By now, organisations have had time to consider moving from mere compliance to creating a competitive edge from their investments.
Enterprise tech leaders are in the middle of a balancing act. They’re managing ongoing modernisation and transformation initiatives while navigating multi-jurisdictional regulatory scrutiny. At the same time, they face constant pressure from the board and must meet evolving customer needs—all competing for immediate attention. The stakes have never been higher. Operations teams are no longer viewed as a back-office IT function. Their success in keeping the organisation running and driving revenue is now a board-level concern.
For organisations today, IT is business delivery.
A year of DORA has seen organisations make the shift from focusing solely on mere compliance to setting meaningful demonstrable testing, third-party risk visibility and strictly mandated incident reporting timelines. Financial firms have lessened their exposure to risky situations. Payments providers aren’t only reliant on a single cloud region or SaaS supplier, or unable to provide evidence of real time incident response efforts and auditable logs after a disruption.
One benefit of these overall systemic improvements is enhanced supply chain accountability. Financial institutions and their technology partners are both liable for potential penalties and reputational risk, which makes it highly critical that they can prove their resilience capabilities.
Nevertheless, operational resilience is a continuous discipline. A fragmented incident response can expose firms to regulatory and reputational risk again and again if not addressed systemically. As such, many organisations are looking toward AI agents as part of a move towards ‘no-touch’ operations.
From Autonomy to Self-Healing
Under set policies, autonomous agents can handle incident response and operational tasks, such as detection, triage and remediation. AI agents deployed in operations may become the backbone of L1 (first contact) and L2 (more skilled) support. Contrast this with the traditional, reactive, ticket-driven model of IT. The industry can move much faster and with a higher successful close rate. Leveraging intelligent automation reduces mean time to detection/resolution and KPIs around lower incident volumes reaching L3. Additionally, it can lead to improved service availability percentages. Well integrated agents that actually support existing operations teams also help manage the issues around talent shortages faced by many organisations.
A typical incident lifecycle with agentic processes includes several stages depending on the model, but can be summarised as: Anomaly detected, correlated with recent deployment, a remediation script triggered and a human notified if set thresholds were breached. Such no-touch operations are golden in any sector, but particularly with industries such as digital banking and retail, where peak traffic periods demand near-instant response and poor customer experience is a powerful motivator for users to instantly change providers.
IT Standardisation
In addition, consider standardisation as part of strategic infrastructure best practices. There is a role for central operations clouds and operational ‘golden paths’ as solid foundations for reliable operational scale and dependability. Standardisation enables consistent, scalable operational excellence especially across large, distributed enterprises. ‘There is one way and it is the right way’ can be a great time and stress saver for operational teams – particularly if a regulatory notification and clear evidence is required.
For example, a global bank might define a single golden path for deploying customer-facing applications with pre-approved monitoring, incident response workflows, and regulatory reporting templates built in. In an outage, teams follow the same process and automatically capture the evidence required for regulators, avoiding confusion, delays, and compliance risk.
All of these possibilities take us to an exciting new place for an evolved set of developer and operational roles. When organisations enable AI to reshape daily engineering work away from manual firefighting and low-value work it frees headspace and time for developers and engineers to move into more architectural thinking and intelligent oversight of automated systems. These augmented teams will be empowered to manage simple situations instantly and devote more time and attention to the more difficult issues – the edge cases and the strategic necessities.
Enabling Agentic AI
Using another lens, businesses with agentic IT operations capabilities support their current talent, extending their reach and the speed of their response. The winning organisations will be those who deploy agents strategically, freeing up humans for that higher-value work – i.e. L3 expert support – and setting new standards for operational excellence that customers can rely on. Ideally this means making commensurate investment in existing people, training and organisational change management. A culture of continual upskilling and forecasting that points humans to where they make the best impact will be just as important as the autonomous tech tools working alongside them.
Autonomous agents allow many new services, and one of those can be described as self-healing operations. This evolution of the operations world is where predictive detection, automated remediation and embedded resilience all coalesce. With an autonomous process of testing, maintenance and remediation, organisations can focus on finely measuring improved customer trust. They can also enjoy the productivity and revenue benefits of high business continuity and availability.
AI is still a new technology, and many are legitimately concerned with the concept of autonomous agents. There is a need for clear guardrails, audit trails and explainability in automated remediation, and many technology partners have invested in their ability to support across these areas. Moreover, firms must maintain direction with policy-driven automation rather than uncontrolled autonomy, particularly in regulated industries.
Mandate Operational Excellence
This year is very likely to reward organisations that treat operational resilience as core to their business strategy. Those investing in automation, standardisation and governance will set the pace for their industries in an AI-enabled and increasingly autonomous world.
Regulators are already expanding their scrutiny and reliability expectations beyond financial services firms. Across the world, jurisdictions are increasingly looking to strengthen their economies and digital services in particular through resilience and cybersecurity measures. At the same time, agentic operations, and the organisational performance benefits they support, will rapidly become table stakes technology in all sectors. Inevitably, customers will judge brands on digital reliability as much as price or product features when evidence of outages are a click or a headline search away.
Start now. Audit internal incident response maturity, review the potentially complex web of third-party IT dependencies and identify where automation makes clear business sense. While resilience is an investment in compliance, it is also critical to ensure customer trust and future stability.
Jamil Jiva, Global Head of Asset Management at Linedata, on why the next chapter of AI-driven finance will be shaped not just by technology, but by creativity
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Beyond Data: Where AI Finds Unexpected Inspiration
The discussion about training AI largely focuses on concerns that accessible, human-generated data is limited and may soon run out completely. If this is the case, how can technology that depends on a seemingly endless stream of inputs to iterate, test, and adapt deliver the results we expect? AI relies on structured, high-quality data to thrive, but what happens when we run out of spreadsheets and financial models to train AI? We need new data sources to ensure it continues to learn, adapt, and deliver accurate insights. Video games stand out as offering some of the richest, most expansive, and complex environments for AI training.
At first glance, video games and financial operations seem to belong to entirely separate worlds. However, AI connects these domains, with models leveraging virtual-world training to tackle real-world financial tasks. Financial documents such as credit agreements and tax returns are often convoluted, unstructured, and labour-intensive to process. Therefore, AI designed to interpret such data must possess strategic reasoning, real-time adaptability, and advanced pattern recognition. So, could video games be the ideal training ground?
Contrary to popular belief, gameplay can significantly improve how people think, learn, and solve problems. The abilities required to excel at video games closely reflect the skills AI systems must acquire today.
Levelling Up: What Virtual Worlds Teach Machines
Practice leads to proficiency, a principle that applies to both humans and AI. Interestingly, many of the most significant advances in AI development have emerged not from conventional data training, but from taking creative approaches. Games push AI to emulate human thinking and sharpen its statistical intuition.
These game-trained models are neither expensive nor heavily reliant on resources, and they sidestep the issue of data scarcity. As a result, they are actively shaping the future of financial intelligence. The examples below offer a clear demonstration of the potential of gameplay.
Virtual Economies: Lessons from World of Warcraft
World of Warcraft, with millions of players interacting in an immersive and dynamic world, features an economy that closely mirrors real-world financial systems, complete with inflation, supply and demand cycles, and fraud risks. The game even inspired one of the most renowned epidemiological studies: when the in-game ‘Corrupted Blood’ plague spread unpredictably, scientists used it as a model for real-world pandemic simulations.
Financial models depend on vast, interconnected data networks, much like the economy in World of Warcraft. Organisations employ AI to continuously monitor patterns, detect anomalies such as fraud or misstatements, and optimise data extraction for financial reporting, mirroring the way AI analyses virtual economies.
Urban Chaos: GTA V and Real-World Simulation
While Grand Theft Auto (GTA) V is famous for its open-world chaos, researchers have leveraged its traffic systems and non-player character behaviours to train AI for applications such as self-driving cars, crime pattern recognition, and urban planning. At its heart, GTA provides a platform for AI to process vast amounts of unstructured data in real time.
Similarly, financial institutions manage millions of data points from a wide range of sources. Their AI tools must automatically extract insights, classify information, and normalise complex formats. GTA serves as a controlled yet intricate environment for simulating scenarios, enabling AI to optimise for real-world tasks through ongoing feedback loops.
Sandbox Creativity: Minecraft and Adaptive Thinking
Minecraft provides a sandbox environment where AI learns through exploration. OpenAI even trained an AI to play Minecraft by watching YouTube tutorials, closely mimicking the way humans learn. Similarly, any AI used by financial institutions must be able to self-learn from new document types and structures, adapting just as a Minecraft AI learns to survive.
Reinforcement learning, where AI improves based on feedback, is a key element of intelligent document processing. Thanks to its vast scalability and dynamic, hierarchical environments, Minecraft serves as an ideal setting for navigation and repeated feedback loops, helping models develop domain-flexible reasoning.
Multiplayer Mayhem: Dota 2 and the Art of Teamwork
Dota 2 stands out as one of the most complex competitive games ever created, presenting AI with challenges in real-time decision-making, strategic coordination, and adaptability. OpenAI Five, trained on the equivalent of 45,000 years of gameplay within just 10 months, managed to defeat renowned, professional human teams. As anyone who has mastered StarCraft knows, tactical adaptability is essential for gaining the upper hand.
Financial institutions operate in environments that are just as dynamic as the shifting levels of a video game. Market conditions, regulations, and data formats are in constant flux. AI must be able to adjust to new document structures, handle missing information, and navigate edge cases, much like AlphaStar adapts to an opponent’s unpredictable strategies.
From Pixels to Profits: Bringing Game Logic to Finance
Whether to streamline operations, mitigate risks, or make informed decisions in today’s data-intensive financial landscape, AI has the potential to fundamentally transform financial offerings, delivering personalised and evolving experiences that foster understanding and combine seamlessness with regulatory compliance.
Yet AI does not simply require more data from which to learn; it needs better data. Video games offer near limitless, pre-built, highly complex digital worlds where AI can test hypotheses, simulate scenarios, and refine decision-making models. By utilising these unique environments, AI is challenged to enhance its speed, accuracy, and efficiency.
The world of video games has many lessons we can learn when building AI, and given AI’s remarkable ability for transferable learning, it makes sense to leverage these pre-trained models to power essential financial workflows. It is more than just document processing; it is thinking, and the same intelligence that enables AI to defeat world champions in Dota 2 is now driving the next generation of financial AI solutions.
The next chapter of AI-driven finance will be shaped not just by technology, but by creativity. By embracing unconventional data sources such as the immersive complexity of video games, industry leaders will unlock new possibilities for personalisation, security, and customer engagement.
Richard Doherty, Head of Wealth & Asset Management, Publicis Sapient, on how asset managers must redesign their enterprise for AI-driven decision intelligence
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The asset management industry is entering a structural inflexion point. The first wave of AI focused on improving productivity through copilots and automation. The next wave will fundamentally reshape how decisions are made, executed, and governed across the enterprise. This is not a technology upgrade. It is an operating model shift.
Despite significant investment, many firms remain trapped in fragmented AI experimentation. A majority are yet to realise meaningful economic returns from AI, not due to lack of capability, but due to a failure to redesign how intelligence is applied across the organisation. The gap between ambition and outcome is not a technology problem. It is a structural one.
From Automation to Decision Intelligence
The industry conversation has evolved. The question is no longer whether to adopt AI, but how to scale it across the enterprise. However, most firms are still approaching this challenge through the lens of automation, identifying tasks that can be executed faster or at lower cost. This delivers incremental value, but does not address the underlying constraint: the structure of decision-making within the organisation.
Traditional operating models are built around sequential workflows. Work moves from function to function: research, compliance, operations, and distribution, each dependent on the previous stage. This creates latency, duplication, and fragmentation. Agentic operating models shift the focus from tasks to decisions.
Instead of asking “Which processes can we automate?”, leading firms are asking: “Which decisions can be augmented or owned by intelligent systems?”
This shift enables organisations to move from sequential workflows to parallel decision systems; from human-led analysis to AI-assisted reasoning; from periodic insight to continuous intelligence. The result is not a marginal improvement. It is a step-change in how the enterprise operates.
The Pressures Driving Change
This transformation is not happening in a vacuum. Asset managers face mounting structural pressures: margin compression driven by fee pressure and passive competition; rising operational complexity from regulation and product proliferation; and advisor capacity constraints that limit scalable growth. Agentic operating models directly address all three.
By automating complex workflows, rather than individual tasks, firms can significantly increase advisor and analyst capacity without proportional cost increases. Parallel decision systems reduce the time required to launch products, respond to market events, and deliver client insights. This compresses cycles from months to days. Continuous monitoring of guidelines, portfolios, and operational processes reduces exposure to regulatory breaches and operational failures.
These are not theoretical benefits. They represent measurable improvements in cost-to-serve, time-to-market, and operational resilience.
Not all Intelligence is the Same
To scale AI effectively, organisations must recognise that not all problems require the same type of intelligence. Enterprise AI operates across three distinct layers, and conflating them is one of the primary reasons AI initiatives fail to scale.
Deterministic systems execute predefined rules with complete consistency. They are essential for functions where there is zero tolerance for error, trade validation, settlement processing, and regulatory reporting. If a business outcome must be identical every time, deterministic logic remains the correct approach.
Predictive systems use historical data to forecast outcomes. Applied in areas such as portfolio risk modelling, fraud detection, and client churn prediction, they generate probabilities and insights, but they do not interpret context or make decisions independently.
Agentic systems operate where problems require interpretation, judgment, and contextual understanding, investment guideline interpretation, regulatory document analysis, portfolio insights, and client communication. These systems can reason across complex information, generate insights, and take action within defined boundaries.
The ‘Different but Valid’ Dilemma
A critical challenge in adopting agentic systems is understanding how they behave. Traditional software produces identical outputs. Agentic systems produce reasoned outputs.
This introduces what I call the ‘different but valid’ dilemma. An agent may take a different reasoning path from a human and arrive at a different, but still correct, conclusion. This variability is not an error. It is inherent to reasoning systems.
The real risk lies in hallucination, outputs that are not grounded in data or evidence. Managing this requires organisations to clearly define where variability is acceptable. All AI-driven processes sit on a spectrum: deterministic actions with no variability (trade execution), predictive actions with controlled variability (risk scoring), and agentic actions with higher variability (investment insights).
Leading firms design systems where agents perform reasoning, deterministic systems enforce execution, and humans retain oversight on high-consequence decisions. This balance enables both flexibility and control.
The Operating Model Shift
The most significant change is not technological; it is organisational. Traditional models are built on functional workflows. Agentic models are built on coordinated decision systems.
Consider what launching a new investment product looks like under each model. In a traditional model, it involves sequential handoffs between teams, compliance reviews the guidelines, operations configures the systems, and distribution drafts the client narrative. Each stage waits for the last.
In an agentic model, intelligent systems operate in parallel: compliance agents interpret guidelines, operations agents configure constraints, distribution agents generate client narratives, and governance agents validate outputs. This orchestration compresses timelines, reduces friction, and enables continuous decision-making. It represents a fundamental redesign of how work is performed.
Governance: the Foundation for Trust
Trust is the prerequisite for scaling AI. Without it, adoption stalls, not because the technology fails, but because the organisation cannot adequately explain or defend the decisions it makes.
Leading firms implement governance models built on three principles. First, explainability: every decision must be traceable and auditable. Second, authority boundaries: agents operate within clearly defined limits. Third, human oversight: high-consequence decisions remain under human control.
Regulatory expectations will continue to evolve, but one principle remains constant: organisations must be able to explain how decisions are made.
Scaling AI is a Leadership Challenge
Executives must take a deliberate approach across four areas:
Define the intelligence model: map business problems to deterministic, predictive, or agentic systems.
Build the foundation: invest in data, infrastructure, and orchestration capabilities.
Redesign the operating model: shift from workflows to decision systems.
Implement governance to ensure transparency, control, and compliance.
Start with high-value use cases and expand rapidly across the enterprise. The firms that act now will establish a structural advantage in cost, speed, and decision quality. Those that do not risk being constrained by legacy operating models that cannot scale with the demands of modern markets.
The Question is not if, it is Who
The industry is not simply adopting new technology. It is redefining how decisions are made. The firms that succeed will not be those that deploy AI tools in isolation. They will be those who design the right form of intelligence for each problem, redesign their operating models around intelligent systems, and scale agentic capabilities across the enterprise.
This shift is already underway. The question is no longer whether it will happen. The question is which firms will lead, and which will be forced to follow.
Hampshire Trust Bank (HTB) is using artificial intelligence (AI) to act faster on customer concerns. It is empowering its teams…
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Hampshire Trust Bank (HTB) is using artificial intelligence (AI) to act faster on customer concerns. It is empowering its teams to identify and respond quickly, whilst also meeting regulatory timeframes for handling complaints and supporting vulnerable customers.
Netcall: AI-Powered Sentiment
The specialist bank has worked with Netcall to deploy AI-powered sentiment analysis using Netcall’s Liberty Create platform. The solution reduces manual effort and improves operational efficiency by bringing customer emails from multiple mailboxes into a single interface. Incoming messages are automatically analysed to identify dissatisfaction, highlighting cases that may require faster intervention. This allows urgent cases to be prioritised, helping HTB to resolve issues before they escalate and improve the customer experience.
“Our AI-powered sentiment analysis solution rapidly processes vast amounts of email data. Its efficiency allows our team to focus on resolving customer enquiries and issues rather than sorting priorities. The streamlined process ensures swifter responses and better customer outcomes, upholding our reputation for exceptional customer service.” Ed Eames, Head of Customer Savings Operations at Hampshire Trust Bank.
The application was built by the Hampshire Trust Bank development team using Liberty Create. It worked closely with Netcall to integrate AI sentiment analysis into existing processes. Customer-facing teams were involved throughout to ensure the solution aligned with established workflows and regulatory requirements.
Customer Service Control
A key benefit of the approach is the level of control it gives internal teams. Keywords, sentiment thresholds, and classifications can be adjusted directly. This allows rapid refinement as customer behaviour changes or new regulatory considerations emerge, without waiting for development cycles.
“Liberty Create has enabled my development team to work with remarkable agility. The ability to rapidly create and refine applications to meet ever-evolving business needs has significantly enhanced our efficiency. This allows us to deliver a wealth of new features to end users and customers with speed. With the integration of AI, we’ve been able to advance our processes while ensuring exceptional customer service. Our Sentiment Analysis application launch is a prime example of this.” Trina Burnett, Head of Engineering at Hampshire Trust Bank.
The sentiment analysis system also supports automated and ad-hoc reporting. This provides a single source of insight into customer interactions and actions taken. This helps reduce manual effort, supports audit and compliance activity, and enables teams to continuously improve customer service operations.
“As scrutiny around customer experience and accountability increases across UK financial services, the ability to listen, adapt and respond at pace is becoming a defining capability for banks seeking to maintain trust and service standards,” said Alex Ballingall, Key Account Manager at Netcall.
“HTB’s approach shows how banks can use AI-driven insight practically. Turning customer communications into faster action without adding operational complexity,” Ballingall concluded.
About Netcall
Netcall is a leading provider of low-code and customer engagement solutions. A UK company quoted on the AIM market of the London Stock Exchange. By enabling customer-facing and IT talent to collaborate, Netcall takes the pain out of big change projects. It helps businesses dramatically improve the customer experience, while lowering costs. Over 600 organisations in financial services, insurance, local government and healthcare use the Netcall Liberty platform to make life easier for the people they serve. Netcall aims to help organisations radically improve customer experience through collaborative CX.
Patrick Cooney, CFO at Version 1, on why, in an AI-driven operating environment, financial discipline is more important than ever
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Over the last decade, digital transformation has become part of the CFO’s remit. As organisations invested in automation, cloud and data platforms, finance leaders were well placed to oversee spend, drive efficiency and ensure technology investments delivered measurable returns. As artificial intelligence (AI) moves from experimentation into the core of how organisations operate, that model is beginning to evolve. Primarily because AI-scale transformation demands a different balance of expertise.
A recent move by Coca-Cola illustrates this shift. The decision to take digital strategy out of CFO John Murphy’s remit and appoint Sedef Salingan Sahin as the company’s first Chief Digital Officer is not a rejection of finance-led transformation. It reflects a practical reality. While strong financial discipline remains essential, the architectural complexity and technical depth required to embed AI across an enterprise now go beyond traditional finance capabilities alone.
In all industries, AI is no longer confined to innovation labs or isolated pilots. It is increasingly embedded in how organisations operate, make decisions and manage risk. At Version 1, our earliest focus on AI was external: helping partners use AI to transform their own businesses. We have quickly turned the lens inward. Over the past quarter, we have accelerated the use of AI across our own finance and operational functions, implementing a wide range of practical use cases that fundamentally change how work gets done.
Some of these are relatively simple but have had a significant impact. Using AI to summarise documents, generate meeting notes or surface insights from large volumes of information has become normal and is already saving time across the organisation. Others are more structural. In finance, we are applying AI to areas such as accounts payable, accounts receivable and general ledger reconciliations, where large datasets and repetitive processes create natural opportunities for automation and acceleration.
We are also rethinking reporting itself. Rather than manually producing variance analyses each month, we are developing standardised prompts that allow AI to highlight key trends, explain deviations from budget and surface insights that would traditionally take hours to compile. These are not abstract efficiencies. Rather, they directly affect the speed, quality and value of financial decision-making.
What is striking though is the pace of change. Even over the past few months, usage has increased exponentially as people find new ways to integrate AI into their daily work. This is no longer an optional experiment. AI is reshaping how organisations function from the inside out.
Modern CFOs Deliver Stewardship and Governance
One of the biggest challenges CFOs face with AI is that traditional ROI models struggle to capture its true impact. Unlike earlier waves of digital transformation, AI does not deliver value solely through cost reduction or headcount optimisation. Increasingly, its value lies in better planning, faster decision-making, improved risk management and higher-quality outputs.
I see this clearly in how we use AI for planning. Recently, we fed a combination of internal data, previous plans and external consultancy material into a large language model and spent time crafting a detailed prompt. The output was a first-pass design for a major simplification programme (including workstreams, resourcing requirements and sequencing) that would previously have taken weeks to develop.
It is worth noting that this new process didn’t replace human judgement – it dramatically accelerated it. We are using similar approaches to shape annual finance priorities, drawing on historic plans and organisational context to generate structured, actionable starting points. This kind of value is real, but it does not always show up neatly in short-term financial metrics.
At the same time, the risks associated with AI are increasing. Model drift, regulatory scrutiny, data security and vendor dependency all carry financial implications. This is why governance matters as much as innovation. At Version 1, we have put formal structures in place, including an AI oversight committee that reviews and approves new tools, ensures appropriate controls are in place and sets clear boundaries around responsible use. We tightly manage which platforms can be used and how data is protected, recognising that public, uncontrolled tools pose unacceptable risks in an enterprise environment.
This combination of accelerating value and growing risk is precisely why ownership models are changing. Many CFOs continue to play a leading role in digital transformation, with research showing that around three-quarters of finance leaders now prioritise digital strategies at the highest levels of the organisation.
People Remain at the Heart of AI Adoption
As AI scales, the CFO’s role is shifting from delivery ownership to strategic stewardship. Finance leaders are uniquely positioned to connect technology ambition with financial reality, ensuring AI investments are governed properly, aligned to business outcomes and measured over time.
This aligns closely with how we think about our own operating model at Version 1. We use what we call a ‘strength in balance’ business model, built around three equally important pillars: customers, people and a strong organisation. That final pillar includes financial performance, risk management, cybersecurity and governance, all areas that become more critical, not less, as AI adoption accelerates.
People are central to this conversation. AI inevitably raises questions about job impact and cost optimisation, and organisations have a responsibility to approach this responsibly. That means clear communication, strong change management and treating people fairly where roles evolve. It also means investing in training and enablement. We have rolled out organisation-wide AI training focused on responsible use, and we are developing a network of AI champions with deeper skills who can identify and build use cases without relying solely on central teams.
The most effective model I see emerging is a shared one. Specialist digital leaders focus on building and embedding AI capabilities at scale. CFOs retain accountability for financial discipline, data governance and value realisation. When these roles work in partnership, organisations are far more likely to capture the value they expect from AI.
Financially Guided Value Delivery
As AI becomes a baseline capability rather than a differentiator, debates about who ‘owns’ digital strategy are becoming less relevant. The more important question is how organisations ensure AI investments deliver measurable, sustainable value. For CFOs, AI is now undeniably a balance sheet issue.
Investment in the latest technology affects cost structures, risk exposure, governance and long-term resilience. Those who engage proactively, shape governance and demand disciplined value creation will help their organisations unlock lasting advantage. Those who remain passive risk inheriting complexity, cost and compliance challenges that are far harder to unwind later.
In an AI-driven operating environment, financial discipline is not diminished. It is more important than ever.
Dr. George Papamargaritis & Dr. Konstantia Barmpatsalou
Published
26 February 2026
Estimated Read time
4Mins
Obrela’s Dr. George Papamargaritis (EVP MSS) and Dr. Konstantia Barmpatsalou, (Blue Team Support Manager) on why embracing a risk-led cybersecurity model will leave financial organisations better positioned not just to meet regulatory requirements but to strengthen resilience, protect customers and uphold the trust that is so essential to the future of financial systems
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Cybersecurity in the financial sector was once viewed as a compliance-driven discipline. But as attackers have increasingly targeted institutions with sophisticated, persistent and often internally driven campaigns, it has become a strategic priority.
According to the Digital Universe Report H1 2025, financial services were the second most targeted industry globally, accounting for 19% of all observed cyberattacks. This reflects both the sector’s value to adversaries and the complexity of the digital ecosystems it now operates within.
Regulatory frameworks such as the FCA and PRA’s operational resilience rules, the EU’s Digital Operational Resilience Act (DORA) and NIS2 have strengthened baseline protections. However, the report’s findings demonstrate that regulation alone cannot deliver true cyber resilience. Institutions must adopt a strategic, risk-led approach that looks beyond compliance to understand real threats, behaviours and operational dependencies.
Tailored, Internal and Stealthier Threats
One of the most striking insights from the report is how targeted financial sector attacks have become. Industry-specific security risks now represent 32% of all incidents in the sector. This is an indication that adversaries are designing attacks using detailed knowledge of financial operations, from trading workflows to payment systems.
Internal activity is also a major concern. Suspicious internal activity accounts for 26% of detections across financial services, reflecting the frequency of compromised accounts, misused privileges and lateral movement. For a sector historically focused on defending the perimeter, this shift highlights the need for deeper visibility into user behaviour and identity-driven risks.
The wider threat landscape reveals adversaries are moving away from overt, signature-based attacks. In H1 2025, brute force activity made up 27% of global alerts, while vulnerability scanning accounted for 22% and known malicious indicators for 20%. Notably, direct malware payloads dropped to 0% of trending alerts, replaced by fileless techniques and living-off-the-land methods that bypass traditional defences.
For financial institutions, this is a challenge. Many compliance requirements still centre on endpoint protection, patching and malware controls. These will of course, remain important, but they cannot address threats that are increasingly behavioural, stealth-driven and identity-focused.
Operational Complexity
The financial sector’s cyber risk is intensified by its expanding operational footprint. Cloud adoption, open banking, digital identity models and extensive third-party ecosystems have all created new points of exposure. Financial services operate within a global digital infrastructure that is both vast and increasingly interconnected. This level of complexity cannot be effectively protected through compliance checklists alone.
Regulators are recognising these realities. DORA’s emphasis on ICT third-party risk, operational resilience testing and continuous oversight reflects the need for more proactive, intelligence-driven approaches. But DORA still only sets a minimum standard. True resilience requires institutions to move beyond regulatory expectations and embed cybersecurity into broader business strategy.
Strategic, Risk-Led Cybersecurity
A risk-led approach begins with understanding the threats that pose the greatest risk to operations and customers. Financial institutions remain priority targets for groups such as FIN7, TA505, Cobalt Group and various state-backed actors. Their tactics, such as credential harvesting, remote access tools, web-injection frameworks and lateral movement, are specifically designed to exploit the digital fabric of financial services.
This evolving threat profile puts identity and behaviour at the heart of cyber defence. With credential-driven and internal threats so prevalent, institutions must prioritise behavioural analytics, continuous authentication and zero-trust models that verify users and devices contextually rather than relying on static controls.
Strategic cyber resilience also needs to have continuous assurance. Traditional audits, annual testing and scheduled penetration exercises cannot keep pace with rapidly evolving threats. Leading institutions are shifting toward continuous control monitoring, automated attack simulation and persistent adversarial testing. These practices align with the Bank of England’s CBEST framework and demonstrate a sector-wide move toward ongoing, intelligence-led assurance.
Crucially, cyber risk must be treated as an operational issue, not just a technical one. Embedding cybersecurity into enterprise risk management, financial planning, product development and board oversight is essential. This integrated approach also mirrors the direction of FCA and PRA regulation, which increasingly emphasises governance, accountability, and resilience across the entire organisation.
Beyond Compliance
Financial services underpin national economies and public confidence. As digital ecosystems grow and adversaries become more sophisticated, the sector faces a dual challenge: meeting rising regulatory expectations while defending against complex, targeted attacks. It is clear that cybersecurity must evolve from compliance-driven activity to a strategic capability built on intelligence, continuous assurance and behavioural insight.
Institutions that embrace this risk-led model will be better positioned not just to meet regulatory requirements but to strengthen resilience, protect customers and uphold the trust that is so essential to the future of financial systems.
Gregory Mostyn, CEO and co-founder of Wexler, on why the era of generalist AI tools is over, and how the future will focus on high-precision AI designed for specific industries
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For decades, the UK’s professional services sector, including areas such as Law, Insurance, and Wealth Management, has argued that its business value is locked in its access to proprietary data and the specialised labour required to navigate it. Investors, lured by the moat of institutional knowledge, priced these companies accordingly. However, the first quarter of 2026 has seen significant AI disruption within the professional services market. The catalyst wasn’t a single event, but rather a move by foundational model providers that turned the industry’s most defensible assets into commodities.
When Anthropic launched its specialised legal AI plugin, OpenAI integrated a real-time insurance underwriting engine directly into its interface, and Alturist Corp automated bespoke tax strategies, the market reacted harshly. As professional services titans such as RELX, MoneySuperMarket, and St James’s Place saw their share prices decline by more than 10% in a matter of hours, the message became clear: the era of treating AI as a ‘future risk’ is over.
The market has been awoken to the fact that foundational AI models are no longer just plugins or nice ‘add-on’ tools; they are competitors. The move by foundation-model providers into professional services – like the legal sector – is not a one-off shock, but rather an inevitability.
The Proliferation of Information
Historically, a law firm’s competitive advantage was its access to information – repositories of case law, proprietary research, and historical contracts. Investors and clients valued these companies on the assumption that this data constituted an impenetrable barrier to competitors. Before AI entered the mainstream, the cost of extracting actionable information from thousands of pages of data required a small army of junior associates and hundreds of billable hours.
In 2026, that moat has mostly evaporated. Recent benchmarks show that frontier models now achieve 80% accuracy on complex documents, compared with the 71% average of a human associate. More importantly, they do it at a fraction of the cost. It is now estimated that the inference cost for a system at the level of GPT-3.5 dropped by more than 280-fold between November 2022 and October 2024. It’s predicted that UK law firms will reduce their chargeable hours by 16% through the implementation of AI.
The narrative that AI would be able to handle only ‘low-level’ tasks, such as NDAs or simple contract summaries, has all but evaporated. Anthropic’s move into high-stakes litigation support validates this trend.
AI – From Swiss Army Knives to Scalpels
An error made by many law firms when AI became entrenched within the market was to treat it as a ‘plug-in’, a nice-to-have built onto existing internal software. Many adopted general-purpose tools, often referred to as ‘Swiss Army knife’ solutions, that covered the breadth of legal work but lacked the precision, jurisdictional nuance, and risk-weighted requirements for high-stakes professional services.
The 2026 market reaction highlighted the needs of a ‘scalpel’ approach – those that go deep in a specialised vertical within a legal workflow. For example, instead of a junior associate spending billable hours searching through case files to establish the facts of a case, they could use a ‘fact intelligence’ platform that can automate that process into minutes, whilst increasing accuracy by 95% versus 78% for human reviewers and up to 90% savings in large-scale litigation. The market is no longer rewarding firms for having information. Rather, it rewards those who can apply it at the lowest possible cost and friction.
Reallocating Capital Across Professional Services
We’re already seeing investors withdrawing from the traditional software market and reallocating that capital into specialised AI firms. However, the risk for legacy players is that they are being disrupted from both ends. From the bottom, they are losing the efficiency game to generalist foundation models from companies such as OpenAI and Google, which are commoditising the ‘knowledge’ aspect of professional services, including basic advice and contract drafting. At the top, they are losing the expertise game to specialised firms that use AI as a precision instrument; their overhead would be lower than that of a traditional Magic Circle firm, allowing them to undercut prices while maintaining profit margins.
The result is a massive reallocation of capital. Investments into vertical AI (AI built for one specific industry) are expected to surge to $115 billion by 2034. The market no longer bets on labour with tools, but on autonomous workflows. Investors have realised that the value lies in the middle layer – the software that sits between a general foundation model and a specific industry’s needs.
Innovation or Obsolescence
So far, the first market fluctuation of 2026 has taught us that you cannot outrun new technologies. To survive, firms must stop treating AI as an add-on and treat it as a foundation for their core business infrastructure.
For UK professional services, the choice is no longer whether to adopt AI, but whether they can evolve quickly enough to avoid becoming the training data for companies building foundational models. The firms that remain in 2030 will recognise that the competitive landscape has changed. You’re not just competing with your peers, but with the compute cycles of the world’s most powerful AI labs.
The era of generalist AI tools is over, and the future will focus on high-precision AI designed for specific industries.
Trilliam Jeong, CEO at WealthBlock on why pairing credit discipline with real-time reporting will deliver a better position to hold onto investor confidence
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There’s no shortage of noise around the direct lending market right now. On one hand, deal activity remains strong, capital continues to flow in and investor appetite hasn’t wavered. On the other, competition is fierce, rates are edging down and macro conditions are less forgiving than they were a year ago.
But strip out the headlines and the fundamentals still look solid. The demand is there, both from borrowers looking for speed and flexibility and from investors chasing yield and consistency. That puts direct lenders in a strong position, provided they’re prepared to adapt.
Operational Shift
One of the most significant shifts underway is operational. We’re seeing real adoption of technology across the mid-market from AI-assisted onboarding to fully digitised investor dashboards. This isn’t just cosmetic. Faster processes and clearer visibility mean capital can move more quickly, investors stay better informed and managers have more room to protect margins, even in a tightening spread environment.
LP expectations are shifting too. Many now expect a consumer-grade digital experience from the platforms they commit capital to. They want real-time access to reports, frictionless communication and clarity around how their money is being deployed. That shift in expectations is accelerating the tech arms race across the mid-market. It’s no longer about who can show the best deck but rather can deliver the best infrastructure. And as investor sophistication grows, that infrastructure is becoming a non-negotiable.
Digital Infrastructure
That shift is also influencing how mandates are awarded. Institutional investors increasingly view digital infrastructure not as a bonus, but as a sign of long-term readiness. Questions that once focused solely on deal pipeline and past performance now extend to data availability, reporting cadence and system resilience. It’s not just about what a manager can deliver but how transparently and reliably they can do it. As more allocators run tighter operational due diligence processes, digital maturity is quietly becoming a competitive edge. Platforms that can demonstrate consistent, tech-enabled processes are better positioned to win, and keep, capital.
That matters, because rates may not stay where they are. Increased competition is already putting pressure on pricing. But firms with strong digital infrastructure are better placed to absorb it. Operational leverage, not just headline yield, is becoming a key differentiator.
Scaling Up
There’s also the issue of scale. Consolidation is real and it’s reshaping the market. The biggest managers are only getting bigger and their resources are hard to match. But size alone isn’t the whole story. Technology is giving smaller and mid-sized players a way to compete on experience even if not on balance sheet. A seamless, professional, tech-forward investor journey can carry real weight with LPs, particularly those who value speed and clarity over brand.
That’s especially relevant for new entrants. There’s no shortage of managers in direct lending and standing out requires more than just a different strategy. Yes, some are carving out a niche in NAV lending, venture debt or structured credit but what really earns attention is trust. That comes from clear communication, repeatable processes and a level of transparency that goes beyond the marketing deck.
The Outlook for Lending
The macro outlook is part of the equation too. With corporate defaults expected to rise, discipline is going to matter more than it has in recent years. Underwriting strength, sponsor alignment and proactive portfolio monitoring are back in focus. Investors will be watching for signals that managers are prepared for downside risk. The tougher the environment, the more exposed weaker systems become. Inconsistent reporting, vague valuation logic or delayed updates might have been tolerated in a bull market – but not now. Allocators want to know how a manager will behave under stress, not just how they perform when everything’s going to plan. That makes operational maturity as important as deal-level returns.
Firms that pair credit discipline with real-time reporting will be in a better position to hold onto investor confidence. Allocators are already asking more pointed questions and looking for managers who can back up claims with data. There’s still plenty of room to grow in direct lending, but it won’t be enough to rely on past performance or broad market tailwinds. The firms that outperform from here will need to be efficient, responsive and trusted. In a more competitive, more transparent and more regulated market, those are the traits that will endure.
New research from myPOS, the European payments provider for small and medium-sized businesses, reveals that Britain’s shift toward tap-to-pay is leaving…
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New research from myPOS, the European payments provider for small and medium-sized businesses, reveals that Britain’s shift toward tap-to-pay is leaving traditional PIN codes behind. As contactless becomes the country’s top payment preference, almost a third of young adults now admit they can’t remember the four digits once central to everyday spending.
myPOS data reveals 29% of Gen Z struggle to remember, or have completely forgotten, their PIN. Highlighting how digital-first habits are shaping consumer behaviour. However, it isn’t just younger groups that are feeling the effects. One in five Boomers (20%) say they face the same issue as reliance on physical cards significantly declines.
Contactless Payments
This shift has been driven largely by the dominance of contactless card and mobile payments. Over two-thirds of Brits (69%) say tapping, via card, mobile phone, or smartwatch, is now their primary method of payment. In contrast, just 16% rely mainly on chip and PIN, and only 14% primarily use cash. A further 10 % of Brits now live entirely wallet-free, using only their mobile or smartwatch for day-to-day spending.
Convenience-led behaviours are accelerating the decline of PIN usage across the UK. Nearly half of British consumers (47%) say they would happily go completely contactless if it meant shorter queues in shops and venues. Flexibility and convenience (42%) and speed (34%) remain the largest drivers behind the rise of tap-to-pay.
“As the UK embraces contactless and mobile payments, it’s clear that the traditional PIN is becoming less central to everyday transactions. Businesses and payment providers should ensure security and convenience go hand-in-hand, while recognising that consumer habits are evolving rapidly.”
Michael Ault, Country Manager at integrated payments specialists myPOS, offers strategic advice for SMEs looking to scale through digital transformation and diversification
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Scaling a small business is one of the most rewarding, yet complex journeys for any entrepreneur. While growth brings opportunities for greater reach, higher revenue, and stronger market presence, it also demands foresight, discipline, and the ability to manage risk strategically. Securely integrating new technology is the main obstacle for 47% of SME’s, even though 76% of these businesses intend to expand their IT investment. This underscores a key point of tension, as many businesses want to grow through digital transformation but struggle to do so securely and sustainably.
The business landscape continues to evolve with changing customer expectations, technology, and economic conditions. For UK SMEs, the key to long-term success lies in achieving growth but also in building resilience. Sustainable scaling comes down to three principles: embracing technology pragmatically, diversifying intelligently, and investing in people and partnerships that strengthen resilience.
Leveraging Digital Transformation
Digital transformation is the foundation of business growth, especially for small business. Cloud-based solutions, automation, and data analytics help to streamline operations, reduce inefficiencies, and create better customer experiences. However, transformation must be purposeful, not performative.
The smartest approach is to scale technology investment incrementally, integrating flexible, modular systems that evolve with business needs. This approach not only lowers risk but also helps ensure digital maturity evolve over time. When SMEs use modular, cloud-based technology, operations run more smoothly and changes can be effectively analysed. Ultimately, resilience is not built through one-time upgrades but through a culture of continuous digital evolution.
Diversifying Revenue Streams
Depending on a single product, service, or market leaves a business vulnerable to sudden changes in demand. Diversification, when guided by customer insight and data can turn volatility into opportunity. Expanding into online sales, introducing subscription models, or targeting fresh customer segments can make income streams much more stable and sustainable.
At myPOS, we know that even simple changes based on data, such as adding additional payment options or tapping into cross-border e-commerce, can help cash flow and protect against market shocks. The goal of technology is to mitigate specific challenges without adding layers of complexity.
Investing in Employee Development
Your people are pivotal to your ability to grow as a business; empowered teams are the engine of sustainable scale. A team that feels supported and motivated will bring fresh ideas, adapt to challenges, and push the business forward. Investing in training, mentoring, and development opportunities builds skills that pay back in the form of innovation and improved performance.
In fast-changing industries, having employees who are confident in learning and adapting can make the difference between struggling through disruption and taking advantage of it. Equally, strong partnerships extend this resilience beyond the organisation. Building resilience at the team level creates resilience for the whole business, so fostering a culture of continuous learning and celebrating employee contributions is key to maintaining motivation.
Focusing on Financial Health and Flexibility
Financial resilience underpins sustainable growth. Scaling often requires upfront investment, and without healthy cash flow or reserves, opportunities can be lost. Monitoring income and expenses closely, cutting unnecessary costs, and preparing for seasonal fluctuations gives businesses more control.
Having flexible financing options, like credit lines, small business loans, or even crowdfunding, provides a level of agility. Instead of being caught off guard by unexpected challenges, businesses with financial flexibility are positioned to respond quickly and strategically.
Financial management software can make it easier to track performance, spot issues early, and forecast future needs. When you can see your finances in real time, you can make proactive, data-driven decisions instead of waiting for problems to happen. In markets that change quickly, this kind of financial management helps small firms plan with confidence, stay flexible, and establish a stronger base for long-term growth.
Prioritising Customer Relationships and Feedback
Your customers are not just buyers; they are advocates, sources of insight, and the foundation of repeat business and brand loyalty. Businesses that scale successfully often place customer relationships at the heart of their strategy by actively gathering feedback, responding quickly to issues, and personalising interactions, which shows customers they are valued.
This loyalty becomes a form of resilience. In periods of uncertainty, a base of satisfied, returning customers provides more stability than constantly chasing new ones. Successful businesses use CRM tools to track customer preferences and automate follow-ups so no opportunity to strengthen a relationship is missed.
Building Strategic Partnerships
Partnerships can accelerate growth while also spreading risk. Working with other businesses, organisations, or influencers can provide access to new audiences, shared expertise, or additional resources. Collaboration can also create opportunities for joint marketing, co-branded initiatives, or innovative product and service offerings.
In times of uncertainty, strong partnerships act as a support network. By aligning with others who share your values and vision, you create opportunities that are mutually beneficial and more resilient than going it alone. It is important to find partners whose goals and audiences complement your own for the best long-term impact.
The next stage of small business success will be defined by resilience rather than speed, the ability to adapt, recover, and continue to create value in the fact of uncertainty. For SMEs, this means developing adaptable growth plans that include flexible technology, diverse models and empowered employees.
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.
Can Taner, Chief Product Officer at Bitpace, analyses the most important shifts in the crypto and payments landscape
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The crypto industry has entered a phase of unbundling. Instead of one-size-fits-all platforms that try to do everything, businesses are looking to specialised providers that solve real-world problems with focus and precision. This shift defines how leading firms now build products: client-first, agile, and compliance-ready by design.
Solving Real Problems with Real Products
The key to building effective crypto payment solutions is understanding what businesses actually need. Payments should help companies operate faster, more efficiently, and at lower cost. Rather than chasing every trend, the focus should be on creating tools that remove friction and add measurable value.
That’s why many providers now offer modular solutions designed to work seamlessly across industries:
Payment gateway – enabling merchants to accept crypto securely, with instant conversion to fiat if needed, reducing volatility risk.
Global settlements – allowing businesses to move funds cross-border quickly and cost-effectively, bypassing traditional bottlenecks.
API integration –giving partners the tools to embed crypto payment functions directly into their platforms, delivering a frictionless experience for end-users.
OTC services –providing access to large-scale crypto trades, executed with discretion, high liquidity, and competitive pricing.
Each product is tailored to solve a specific pain point. Instead of bundling everything into a rigid system, we focus on flexible modules that businesses can adopt individually or together.
Agility and Expertise in Product Development
For providers, being specialised also means being agile. Every client problem requires a different approach, and in-house expertise allows them to respond quickly without compromising quality. From compliance to sales to product development, teams must collaborate to find creative solutions that meet the highest regulatory and technical standards.
This agility is only possible if they invest in deep domain knowledge. Product and engineering teams that understand the nuances of payments, crypto, and regulation can adapt quickly to market changes while keeping compliance at the core of every decision.
How to Launch New Products Effectively
Launching a new product in crypto, or any fast-evolving sector, demands structure and discipline. The most successful teams follow a process that balances creativity with rigour.
Start with ideation. Listen closely to client feedback, analyse emerging trends, and identify where the market still falls short. Great products don’t begin with technology, but with a clear problem to solve.
Do the research. Test assumptions early, model potential use cases, and validate compliance requirements before writing a single line of code. A strong evidence base prevents costly pivots later.
Plan collaboratively. Bring product, legal, compliance, sales, and technology teams together from the outset. Aligning goals across functions ensures that innovation doesn’t come at the expense of security or scalability.
Build with resilience in mind. Security, interoperability, and performance should be built into the product from day one, not retrofitted at the end.
Test thoroughly. Create safe environments to simulate real-world conditions and identify weaknesses before launch. Testing isn’t just a single step, but an ongoing cycle.
Launch deliberately. Roll out in phases, gather user feedback, and support early adopters closely. A careful launch builds trust and sets the stage for sustainable growth.
Each of these stages is designed to reduce risk, accelerate learning, and maximise long-term value, principles that define successful product development in today’s crypto landscape.
How Specialisation Wins
Launching products in crypto is about precision and collaboration. The great unbundling of crypto is rewarding those who specialise, focusing on solutions that solve real business challenges. Specialised providers win because they put the client first. That focus on expertise and flexibility is what defines success in the new era of crypto payments.
Ben Francis, Insurance Lead at Risk Ledger, on navigating cyber threats by reinforcing security from the inside out
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Cyber insurance has evolved from a straightforward risk transfer mechanism into an integral component of enterprise risk strategy. As a result, the conversation has shifted beyond simply securing coverage to embracing three foundational elements: transparency in risk exposure, accountability for security measures, and active collaboration throughout the digital ecosystem.
Rather than asking ‘are you covered?’, the more pertinent question has become ‘can you demonstrate measurable risk reduction?’. Insurers and insureds alike are recognising that what matters now is how well an organisation understands and manages its digital exposure, especially across its extended supply chain. Recent data reveals that 46% of organisations experienced at least two separate supply chain-related cyber incidents in the past year, a clear sign that exposure often lies beyond direct control.
From Risk Transfer to Risk Visibility
In recent years, the cyber insurance market has matured significantly. Once viewed as a reactive safety net to cushion the financial impact of attacks, it is now becoming a proactive tool for managing and mitigating risk. This shift is partly driven by insurers, who increasingly expect and work with organisations to demonstrate strong security practices and a nuanced understanding of their threat landscape, including risks deep within their digital supply chains; an area where many businesses still fall short.
At the same time, the industry faces a growing challenge from systemic cyber risk within their portfolios, as many businesses rely on the same cloud providers, payment systems and digital platforms, increasing the chance of a single point of failure. Insurers must gain visibility into how policyholders are connected, not only to suppliers but to each other. Tools and frameworks that map and monitor these interconnections will be essential to avoid underestimating the wider impact of seemingly isolated cyber events.
Mapping Beyond Third Parties
It is no secret that cyber attackers often target the weakest link in a supply chain. These are not always direct suppliers, but fourth, fifth or even sixth-tier vendors that have indirect but critical access to systems and data. Unfortunately, many organisations lack visibility beyond their first tier, creating blind spots that attackers can easily exploit. From an insurance perspective, this presents a clear challenge. If an organisation cannot account for who it is connected to, it cannot adequately quantify its risk and neither can its insurer. Mapping these extended connections is more than just a technical exercise; it means actively practiced risk governance and responsibility. Insurers increasingly want to know how their policyholders are identifying and managing indirect dependencies, particularly in sectors like financial services and retail where disruption can ripple across entire markets.
Collaboration as a Risk Strategy
One of the more underappreciated aspects of cyber resilience is the role of peer collaboration. Unlike physical incidents, cyber threats rarely exist in isolation. A single compromised vendor can impact multiple organisations simultaneously, a fact that has been highlighted by high-profile supply chain attacks such as SolarWinds and MOVEit.
As a result, businesses need to think beyond their own perimeters and adopt a more collective mindset. This includes building relationships with industry peers, sharing threat intelligence and participating in sector-wide initiatives aimed at improving visibility and preparedness.
In highly regulated sectors, such as insurance, this collaboration is increasingly being encouraged by oversight bodies. Frameworks like the Digital Operational Resilience Act (DORA) in the EU and initiatives from the Prudential Regulation Authority (PRA) and the Financial Conduct Authority (FCA) in the UK are pushing for more transparency around third-party risk. In this context, openness is no longer optional; it will be a regulatory expectation.
For insurance providers, greater collaboration between policyholders also means better data on emerging threats and more accurate portfolio management. For businesses, it offers a chance to anticipate vulnerabilities that may not yet have hit their own networks but are affecting others in their industry.
Proactive Transparency Builds Trust
Organisations that take a proactive, transparent approach to cyber risk management are more likely to secure cover and potentially favourable terms, not just in terms of premiums, but also in access to additional services such as forensic support, incident response sources and legal counsel.
Demonstrating a mature cyber posture is not about claiming perfection. No organisation is immune to breaches. What insurers are looking for is evidence of a structured approach: the existence of incident response plans, robust governance, effective supply chain risk management, and above all, an honest view of risk.
A Shift in Mindset
Ultimately, our understanding of cyber insurance must keep evolving. It should not be treated as a simple checkbox exercise, but as a collaborative relationship between insurers and the organisations they support – one built on shared insight, clear communication, and a drive for continuous improvement.
The organisations best equipped to navigate today’s threats will be those that prioritise transparency. Not only does it lead to stronger protection, but it also builds a culture of accountability that reinforces security from the inside out.
