Fintech + AI: Who you need on your team

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.

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

If you need to train models to improve decision-making and decrease credit risks based on transaction history and behavioural patterns, or justify with data why a loan was declined, these are your people. They train models, tune them, and extract signals from cluttered financial data across all fintech. But supply can’t meet demand. Fewer than 10,000 graduate from UK universities each year, against roughly 180,000 open roles in data-focused positions.

MLOps engineers

These experts with a rare skillset make AI models work day-to-day, acting like DevOps engineers but for machine learning. They use tools like MLFlow, AWS SageMaker or Kubeflow Pipelines to deploy models, automate machine learning workflows, and monitor model performance. There aren’t many of them around, but the demand keeps growing, way faster than new talent can keep up.

Cloud architects

AI needs fast, secure, and regulation-friendly infrastructure. Cloud architects build systems that handle demand, protect data, and keep costs down. Besides designing and delivering cloud-based infrastructures, they also secure data handling and storing, encrypting data at rest and in transit for payment apps, digital banks, lending platforms, buy now, pay later providers, and virtually any fintech-related software. With UK public compute capacity set to expand 20x by 2030, this role is becoming one of the most sought-after in the UK.

Compliance & ethics officers

AI decisions affect customers directly. These people ensure that this impact stays safe, fair, and legal. Apart from ensuring data governance and enforcing policies and laws, they also audit ML and AI outputs to protect users from bias and ensure fair access to financial services. Industry analyses confirm compliance experts are already in short supply in the UK, with those specialised in AI being even more difficult to find.

AI product managers

PMs keep AI grounded in reality. They balance what AI can do with what users need, managing teams and making sure products get launched on time. Without them, teams lose focus or overcomplicate things, yet half of the project managers in technology in the UK are complaining about the persisting skills gap in their profession.

Change management leads

In modern fintech, change occurs daily, and AI redefines roles and routines even more dynamically. Change leads help teams adapt to new tools and processes, communicate risk, and reduce friction. Let’s say you’re moving to a modern banking platform, want to embed a new finance offering, or integrate a smart fraud detection system - change management leads will oversee all that. With 97% of financial services leaders expecting AI to reshape their business, their role is vital.

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 Pwrteams, 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.

AI team & tech stack checklist

The odds are against you: up to 80% of AI projects might fail, and 30% don’t even make it past the proof-of-concept stage. That's why we've put together the essential tools, frameworks, and roles you need.

• 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 nearly 20 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 of30 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:

*Add a dedicated MLOps engineer as soon as models are customer-facing or fall under regulatory oversight.

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 Pwrteams, 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 120 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.

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 Pwrteams, 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.