Fraud is becoming more sophisticated, while traditional rules and fragmented systems are struggling to keep pace. AI can improve detection by identifying anomalies earlier, reducing false positives and uncovering connections between accounts, devices and people involved in fraud. But its success depends on three foundations: high-quality data, explainable models and effective information sharing & collaboration between institutions.
Fraud prevention in digital banking can no longer begin with the payment itself. Warning signs often appear earlier, during onboarding, login, a device change or an account recovery attempt. The UniCredit NV/SA case shows how connecting AML, device intelligence and transaction monitoring can help a bank detect the sequence before money leaves the account.
Fraud schemes often evolve faster than internal controls. A mature anti-fraud function does more than investigate losses. It identifies weaknesses early, informs product design, connects signals across accounts and measures whether controls are reducing risk without creating unnecessary friction.
