From AI-powered trading to smarter customer engagement, it’s changing how Fintech companies operate, scale, and compete in their markets. But while the technology is racing ahead, the talent pool isn’t keeping up.
In this blog, we explore the growing gap between AI innovation and available AI talent across the fintech space—why new roles are so difficult to fill, where the most exciting AI use cases are emerging, and how leading firms are building long-term AI talent strategies. We also sit down with our EMEA Tech specialist Rebecca Brennan to unpack what’s really driving the AI talent crisis, and what forward-thinking FinTechs are doing to close the gap.
Speaking with CTOs and engineering leads every day, I keep hearing the same challenge: “We’ve got big plans with AI to improve our products, but we’re struggling to find the people to help us build and integrate”. For some, their roles have been open for months before they look into recruitment support, a clear challenge that needs to be addressed. As a result, some teams are now leaning more heavily on contractors, leading to more liquidity and reducing the opportunities for a permanent, well-structured team build. Demand’s going up, but the number of candidates with the right experience is still too low.
So what’s holding things back, and what can FinTechs do about it?
The New Wave of AI Roles (and Why They’re So Hard to Fill)
AI in fintech used to mean hiring a data scientist or two. Now? We’re seeing demand for AI product architects, ML ops engineers, behavioural AI specialists – you name it.
These roles aren’t just technical. They need people who understand the financial side of things too – how markets behave, how products are built, and how risk is managed. You can’t just train a model; you need to know what you’re training it for. And that combination of skills is rare.
AI Use Cases That Are Already Shaking Things Up
Here’s where the opportunities are endless and exciting opportunities are arising. AI is already making a big difference in fintech, and the use cases are only getting more advanced:
- AI-Powered Product Design
Designing complex financial products used to take months. Today, AI simulates client needs, market conditions, and risk appetite to build bespoke products in days. This is where innovation in financial services has been turbocharged, with banks even replying on Fintech partnerships to improve their customer experiences.
- Predictive Market Making
In terms of market making, AI predicts moves, spots liquidity gaps, and uncovers hidden arbitrage quicker than anyone else does. Traders here can get a speed boost, and the market is moving faster and smarter.
- Cross-Asset Correlation Engines
AI doesn’t only look at one market or one asset class. It connects dots across assets and countries, revealing sneaky correlations and domino effects that traditional risk systems miss.
- Next-Level Customer Support
Modern AI chatbots go beyond scripted replies to provide context-aware, empathetic customer interactions. They can identify signs of customer disengagement or financial distress, enabling proactive outreach and improved client retention.
- Behavioural AI in Trading
Something cool as well, which I read about recently, is advanced AI models analysing micro-behaviours in traders. They notice things like hesitation times, decision patterns, and even voice tone during trading calls to infer trader stress and cognitive biases. This helps them avoid making mistakes or errors that could cost them a lot of time/money. They can look back at reviews and learn and adapt, which only improves services! In such a stressful environment, these tiny changes implemented can be the reason they are a cut above their competitors.
These use cases aren’t just exciting, they’re already being rolled out across the fintech space. But the tech is only half the battle. You need the people to build and manage it. That’s where things are stuck.
Upskilling: It’s Not Just a Buzzword
If you can’t hire ready-made AI specialists (and right now, that’s a big ask), you’ve got to build from within. And the smart FinTech’s? They’re already on it:
- Launching internal AI learning programmes to upskill engineers.
- Partnering with universities to tap into fresh talent and sponsor further research.
- Cross-training their existing product and risk teams in data and model literacy.
In the UK, we’ve seen £100m of government funding go into AI skills programmes – everything from retraining to postgraduate research centres. That’s great, but it’s going to take time. In the meantime, FinTechs need to move faster to continue innovation in the market.
What Will Set the Leaders Apart?
It’s easy to lean on contractors or outsource to vendors when the talent isn’t there. But when AI becomes core to how your products and platforms work, that’s risky.
The fintech’s that will come out on top aren’t just building smarter models; they’re building smarter teams. That means:
- Creating long-term talent pipelines.
- Focusing on potential, not just previous experience.
- Investing in continuous, inclusive upskilling.
Final Thought
The pace of AI innovation in fintech is incredible, but it won’t mean much if the people aren’t there to deliver it. That’s the real challenge.
Whether it’s behavioural trading models, smarter customer support, or next-gen product design, we’ve already seen what AI can do. Now it’s time to make sure the talent can keep up.
Looking for more insights on tech hiring trends?
If you’d like to dive deeper into the current demands for tech and AI talent in fintech, don’t hesitate to get in touch with our regional Tech recruitment specialists.
Whether you’re building out your AI capabilities or scaling an engineering function, our team can offer tailored advice and market insight to support your hiring strategy.
