Article

    Fintech + AI: Who you need on your team

    UK fintech is short on AI talent. Find out which roles are hardest to fill and how nearshore teams can help you grow faster and smarter.

    Service

    IT Outsourcing Staff Augmentation Global Capability Centers

    Industry

    Financial Services

    The UK is a global AI hub - third only to the US and China - with over 3,700 AI companies, 60,000 employees, and a market on track to reach £230 billion by 2025. Fintech sits at the heart of this growth, and the government is investing heavily to keep it that way. 

    Initiatives like the AI Safety Institute and the Alan Turing Institute, along with long-term funding and regulatory frameworks, prove the UK views AI as a pillar of its economy. Homegrown businesses like Monzo, Starling Bank, and Revolut illustrate how AI can dramatically change the way people interact with money. 

    UK fintech firms have the ideas. The backing. The use cases. So why is it so challenging for so many of them to find the right people to build, grow, and enhance their AI teams?

    In the UK, as in many other countries, skills are in increasingly shorter supply, recruitment takes time, and the gaps aren’t closing quickly enough. As the Bank of England reveals, insufficient access to skills puts a damper on AI plans for 25% of British companies in finance.

    It’s not a lack of ideas holding UK fintechs back - it’s the difficulty of scaling teams fast enough to match their pace of innovation. Join us for a breakdown of how to fix that.

    Leading British AI applications in fintech.

    AI is not merely a five-year plan for UK fintech. It’s already in, changing how money is managed, moved, dispensed, and tracked.

    In the UK financial services, operations, and IT are the greatest beneficiaries of AI, followed by retail banking, general insurance, and risk and compliance. 

    These use cases determine AI-driven fintech solutions that experience the highest adoption rates:

    Fraud detection

    Fraud detection is one of the most advanced UK fintech AI use cases. With real-time anomaly detection and graph-based models, AI spots dubious activity early without flooding teams with false positives. Quantexa, for instance, links billions of data points across siloed systems to help banks uncover hidden risks, especially in complex fraud cases.

    Customer service

    Instead of old-school basic bots, the UK financial clients now see large language model agents holding real conversations, escalating complex queries, and giving human employees more time to explore them in detail. One example is Revolut’s AI assistant, RITA. It now files millions of simple customer queries with hardly any human input.

    Credit risk assessment

    Banks, credit unions, and lenders like Zopa use AI-driven credit risk assessment based on self-learning models to continually improve their decisions. By analysing past behaviour and enhancing it with predictive models and external credit data, they can approve more people, more fairly, and without increasing risks.

    Identity verification (KYC)

    Know Your Customer (KYC) is another leading application for UK fintechs, which both benefit from it (Starling, Tandem or Aviva) and develop it, like Onfido or iProov. Combining computer vision, LLMs, biometrics, and real-time data analysis, these solutions match documents and faces in seconds to validate clients, borrowers, and applicants.

    What makes a strong AI team in fintech?

    Even the best AI projects can stall without the right team. On the surface, building a solid AI team for fintech in the UK looks doable. “The UK’s AI industry employs 50,000 people” (according to the UK government sources). Yet, it’s a glaring oversimplification. 

    Under the broad term ‘AI expert’ is a huge range of skills and tools, often at opposite ends of the talent spectrum. While UK professionals fairly well represent some of these roles, others are seriously underrepresented, making them almost impossible to find locally. 

    A high-performing AI fintech team typically comprises:

    Data scientists & ML engineers

    MLOps engineers

    Cloud architects

    Compliance & ethics officers

    AI product managers

    Change management leads

    Why your fintech’s AI team is not working out

    Even well-funded AI projects can flop if the team setup isn’t right. In fintech, the stakes are even higher: tighter rules, legacy tech, and slimmer margins. And yet, the same traps keep showing up, burning your money, and wasting time.

    Often, the issues aren’t purely technical. They include organisational blind spots like mismatched team structures, hard-to-fill specialist roles, or fragmented execution.

    Lacking a clear business goal

    Here’s a common trap: building AI just to say you’re using AI. Without measurable objectives like improving fraud detection accuracy or cutting onboarding time, projects get abandoned. Gartner found that 30% of AI initiatives fail due to "unclear business value.” Before writing a line of code, define what success looks like. Maybe it’s fewer fraud cases, faster customer onboarding, or better credit scoring. Whatever the case, you need it spelt out in block capitals.

    And yet, even this step is often overlooked. When we looked at why so many AI projects go wrong, one issue kept coming up: missing leadership. Tech teams don't need someone who can “run a project.” An effective AI Lead in fintech must bring more than technical knowledge; they need business insight, regulatory awareness, and hands-on delivery experience. They must understand compliance constraints (like GDPR, PSD2, and AML/KYC regulations) and the commercial objectives driving the adoption. Such a vast skillset is often hard to find.

    At Nortal, we connect you with vetted PMs who’ve worked across multiple fintech deployments, including cross-border rollouts. We typically match the right person to your project in just a few weeks, so you don’t have to delay delivery while trying to hire.

    Ignoring data privacy concerns

    In UK financial services, launching a model without regulatory safeguards is often illegal. Under GDPR and FCA rules, firms can’t rely on black-box models to make decisions that materially affect users. Article 22 gives individuals the right to challenge automated decisions. Under its Consumer Duty framework, the FCA also expects explainability, fairness, and clear customer outcomes. Fail here, and you may need to shut the project down.

    Unfortunately, too many teams treat compliance like a final check before launch. That’s a mistake, especially in AI-driven fintech. Regulatory requirements shouldn’t be an afterthought. The rules need to shape what you’re building right from the start. That’s why fintech compliance managers need more than regulatory know-how. They’ve got to keep up with shifting laws and understand AI. Otherwise, your product and licence may take a serious hit if they fall behind.

    To avoid that, we bring in sharp, experienced compliance managers and AI governance experts who’ve worked in financial services and know the tech and the regulatory aspects inside out. And because of our vast talent network and a framework for fast engagement and onboarding, they’re here to support your projects from day one, not week twelve.

    Chaotic infrastructure

    Fintech data is all over the place, spread across payment systems, credit platforms, CRMs, and spreadsheets. Almost half of enterprises cite poor data readiness as the top reason AI projects get delayed or fail. RAND calls underinvestment in infrastructure one of the most common AI failure points.

    This isn’t something you can fix with one hire or a bunch of freelancers. You need a cohesive team of data engineers, cloud architects, MLOps and other roles, with shared context, not scattered contractors or a team that takes months to assemble. But many of these roles are notoriously difficult to fill in the UK. That’s why many British fintechs choose to work with us. 

    Instead of piecing together hires, we deliver a fully formed, tightly aligned team drawn from a talent network we’ve been growing for over 25 years. Without onboarding delays and without team-building from scratch.

    Breaking at scale

    A proof-of-concept that works for 1,000 records might crash at 100,000. Yet, many AI teams don’t plan beyond the pilot phase, often due to a lack of direction. That’s when infrastructure, monitoring, or even model accuracy breaks down.

    Traditional hiring compounds the problem—it's slow, rigid, and poorly suited for dynamic AI projects. In-house recruiting takes months, while scaling up or down remains costly and inflexible.

    We’ve seen what works better. For one fintech client, Funding Circle, we built a team of 40 engineers and support professionals to power a machine learning platform from the ground up. The project moved fast, but so did the demands. Because the team was fully managed and nearshore, they could scale as needed, without operational bottlenecks or long-term commitments. When the workload spiked, we added capacity.

    Piecemeal AI teams 

    Even with good infrastructure, AI fails when the team behind it isn’t aligned. When leads, compliance, product, and engineering operate in siloes, the AI-driven outcome likely won’t meet the core business and customer needs. Only well-coordinated teams can jump the hurdles of overlooked regulatory requirements or features without user input. 

    In our project for C-Quilibrium, we quickly delivered a blended team that included core developers, a database engineer, DevOps support, QA, and a dedicated innovation unit working on new product features. They weren’t scattered freelancers; they came as a unit, with shared tools, context, and goals. The result? Faster delivery, fewer bottlenecks, and a system built around the real operational, with the added benefit of staying 25% under budget.

    AI team tuning for fintech growth - getting the right mix

    Knowing which roles and skills you need to reinforce your AI lineup is half the success. Ramping your team up quickly and in a scalable way - that’s the hard yards.

    Whatever team mix worked in the early run will likely be insufficient to deal with the challenge of AI implementation. A solo Data Scientist or a PM who’s stretched thin by now might have taken your project off the ground, but they won’t suffice to push the project across the line, especially if it involves AI tech.

    As your fintech scales and risk ramps up, your AI team needs a rethink. Here’s a quick breakdown of how fintech leaders may expand their AI teams, stage by stage:

    Role

    Seed
    (Prototype, months 0-12)

    Series A
    (Product-market fit, months 12-30)

    Growth
    (Series B/C, scaling)

    Mature
    (Post-IPO/multi-product)

    AI product manager

    0.5 FTE senior (shared with founder)

    1 full-time

    1 lead + 1 associate

    1 per AI product line

    Data scientist/ML engineer

    1 generalist

    2-3 core model owners

    4-6 incl. domain specialists (NLP, graph, time-series)

    Embedded in each domain squad

    MLOps engineer

    Ad hoc by ML engineer*

    1

    2-3

    4-5 plus 24/7 support rotation

    Data engineer

    Contractor to build the first pipelines

    1

    2

    3-4

    Cloud architect

    External advisor (1-2 days/month)

    0.5 FTE (shared)

    1 dedicated

    Architecture guild/centre of excellence

    Compliance & ethics lead

    Part-time consultant

    1 internal or on retainer

    1 senior + analyst

    Full risk & compliance function

    QA/validation

    n/a (founder-led)

    1

    2 incl. test automation

    Dedicated test-engineering team

    Change management lead

    n/a (founder-led)

    Consultant on a project basis

    1 full-time

    Integrated into PMO

     

    In fintech and AI, delay means defeat, so time plays a critical role. Your teams must evolve fast, but smart, always leaving space to course-correct. Tightly aligned, cross-functional teams that you can grow or slim down on demand give you the edge. They help you close AI skills gaps quickly, reliably, and with minimal risk.

    Sourcing hard-to-find AI talent with Pwrteams

     

    The UK is facing one of the most intense tech talent shortages in over 15 years. Roles like MLOps engineers, cloud architects, AI compliance officers, and ML specialists are among the hardest to find and the most urgent to fill. With fintech job vacancies up 44% in 2024, hiring locally is a long game.

    We help you move faster, building dedicated, nearshore teams that integrate seamlessly with your roadmap and start delivering in just 4-8 weeks, not months. Each team is tailored to your stack, culture, and compliance needs, with AI, cloud, blockchain, and financial regulation experts.

    With Nortal, you get:

    - Speed: Full teams, ready fast, while local hiring drags on.
    - Savings: Up to 50% lower costs than UK-based hires, thanks to our Eastern European talent network.
    - Stability: 95.7% retention rate, compared to 20-30% churn in tech recruiting.
    - Fintech-ready skills: We’ve filled over 150 roles across finance and fintech, delivering talent fluent in regulatory frameworks and secure AI deployment.

    Whether you need to expand quickly, backfill a gap, or reinforce your internal team, we’ll help you get the right people in place, right when you need them.

    The consequences of human error are costly.  According to IBM’s Cost of a Data Breach Report, businesses lose an average of €3.9 million per breach, with phishing and stolen credentials being the top initial attack vectors. Furthermore, 60% of small and medium-sized businesses go out of business within six months of experiencing a cyberattack. 

    Cyber exercising:
    The cornerstone of
    cyber resilience

    Cyber exercises must be integrated into our security strategy to truly strengthen cyber resilience. These structured simulations test an organisation’s readiness against real-world cyber threats. They help teams practice incident response, refine decision-making processes, clarify communications channels, assure roles and responsibilities, test assumptions, hone tactics, techniques, and procedures (TTPs), and build confidence in crises.

    From critical infrastructure to corporate enterprises, cyber exercising equips teams with the practical experience to respond with clarity and speed. Whether defending national infrastructure or safeguarding sensitive customer data, these exercises transform static response plans into living capabilities. 

    Why cyber exercising matters

    • Reveals critical gaps in technical controls, escalation paths, and decision-making workflows.
    • Fosters organisation-wide collaboration, improving coordaination and communication across all roles, functions, and levels. Builds confidence under pressure, giving participants, groups, and organisations muscle memory they can rely on.
    • Exposes participants to real-world attack techniques, improving detection, containment, and familiarity.
    • Strengthens regulatory and stakeholder alignment by stress-testing notification and reporting procedures in a simulated environment.
    • Fosters a culture of continuous improvement by turning lessons from exercises into actionable changes across people, processes, and technologies. 

    Building AI is hard. Building AI teams doesn’t have to be

    AI is already delivering value in UK fintech, but finding the people to build and scale those systems is the difficult part.

    We help solve that. At Nortal, we build high-performing, nearshore AI teams in weeks, not months. Our specialists are vetted, embedded, and built for fintech, from data science to compliance.

    You focus on your roadmap. We’ll bring the team to build it.

    Get in touch to see how we can help.

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