With fraud costing the UK economy over £200 billion every year, it is no surprise that banks treat suspicious activity with caution. The problem is that caution has a price. For customers, a flagged transaction has historically meant a card that stops working at the worst possible moment, a series of phone calls, a dispute form, and a wait measured on working days. All because they booked a flight somewhere new, spent more than usual, or simply paid from a device their bank did not recognize.
How AI is Changing Fraud Detection
However, that experience is becoming less common. A growing number of banks and fintechs are now using AI-powered fraud detection to spot unusual activity in real time and reduce disruption. Instead of applying a fixed set of rules, AI systems learn continuously from millions of transactions and build an individual picture of what normal looks like for each customer: the shops they use, the amounts they typically spend, the devices they pay with, the times of day they are active. When a transaction fits that picture, it passes. When something is off, the system notices. The judgment is contextual rather than mechanical, and that distinction makes a big difference.
What Are the Advantages of AI-Powered Fraud Detection
One of the clearest wins from bringing fraud detection early into a bank’s AI strategy is fewer unnecessary payment declines, and the benefit runs both ways. A survey by Mastercard and Financial Times Longitude found that 83% of payments industry leaders report AI has significantly reduced the number of legitimate transactions that get blocked and, as a result, the number of customers who switch banks out of frustration. For the clients, the practical benefit is straightforward: paying for things works more reliably, with less friction and fewer unexplained interruptions.
Alongside this, dispute resolution has become considerably faster. A process that once took up to 15 working days – forms, acknowledgement emails, a reviewer working through a backlog – can now be settled in hours. An example is Revolut’s early deployment of AI agents has reduced resolution times by more than eight times compared with traditional support queues, with most issues handled in under five minutes.
Perhaps the most significant shift has been in authorized push payment (APP) fraud where one is manipulated into sending money directly to a fraudster. Because the customer initiates the transfer themselves, these cases were historically almost impossible for banks to catch in time. AI changed the intervention point by analyzing the full context of a payment in real time: the history of the recipient account, the size of the transfer relative to the typical spending behavior, the speed at which the decision was made. When the combination of signals fits a known scam pattern, the system can flag or pause the payment before it completes. The result is APP fraud cases in the UK fell 20% in 2025, reaching their lowest level since 2021, reflecting how meaningfully this layer of detection has matured across the industry.
Why Does AI Can Still Make Mistakes
While these improvements are real, they are not evenly distributed, and the reason is often simpler than you might expect. Banks hold vast amounts of information about account holders: transaction histories, location patterns, spending habits built over the years. The problem is that this information frequently sits in disconnected systems that were never designed to talk to each other. A current account here, card activity there, savings data somewhere else entirely. When an AI fraud detection system pieces together an incomplete or contradictory picture of who you are and how you spend, the results can be surprisingly poor. A payment blocked for no apparent reason. A scam that slipped through. And in more serious cases even AI hallucinations: where the system generates a confident decision that has no reliable basis in reality. While it is reasonable to trust that a bank’s AI is working in the customer’s interest, pushing back when something feels wrong is just as valid.
Can Banks Be Trusted to Be Transparent
Trust presents a separate challenge. When an AI system makes a wrong call, most people receive no explanation and have no straightforward way to question the decision. The FCA and the Information Commissioner’s Office announced in June 2025 that they would create a joint statutory code of practice covering AI automated decision-making in financial services, a sign that regulators are taking the explainability gap seriously. Building transparency from the outset is what will determine whether clients eventually trust these systems as much as the banks deploying them do. Every wrongful decline that goes unexplained makes that goal a little harder to reach.
Final Thoughts
AI is already changing how banks detect fraud, helping payments go through with fewer interruptions. But it’s not perfect. Gaps in data, clunky integrations, and limited transparency still lead to mistakes and those are the moments that erode trust.
The real work is in getting those fundamentals right. That’s what turns AI from something that works in theory into something people can depend on day to day. And for customers, the difference between the two is felt every time they tap their card.
