AI fraud defense is becoming a fixed cost of modern banking. Across the region, financial institutions are using AI systems to analyze behavior, context, and risk in real time, anticipating threats before they materialize. The harder part is what happens around the model: who trusts it, who challenges it, and who owns the final call.
Predictive fraud systems only work when organizations move from a “tool mindset” to a “judgment mindset.” The model can flag risk, but the institution still has to decide what to do with that signal. At Mashreq, that discipline is structural: fraud, cyber, and business teams continuously validate, challenge, refine, and feed back into the models so they stay relevant. That shared loop matters because fraud patterns change quickly, and a model left alone can become yesterday’s defense. AI treated as a black box creates risk. AI used with governance gives teams better judgment under pressure.
The customer experience depends on that judgment. Predictive fraud prevention moves banks beyond blunt “approve or block" logic and toward adaptive, risk-driven decisioning. Low-risk activity should keep moving. Controls should appear when confidence drops. That is the point of “friction right”: applying the minimum effective control once risk rises, so customers are protected without feeling punished.
The same logic gives product teams room to move: when fraud controls are built into digital journeys from the start, banks can launch features with greater confidence instead of adding security after the fact. That gives growth teams room to move faster without treating risk as someone else’s problem.
The test for any bank using AI in fraud prevention is simple: when the model intervenes, can the team explain why — and stand behind the decision? The banks that get the most from AI security will be the ones whose people keep the model explainable and accountable long after launch.
Olivier Busolini, Group Head of Information Security, Mashreq