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.
AI in anti-fraud is often treated as a universal solution. In practice, however, the term covers fundamentally different tasks - from machine learning models built on large datasets to text structuring and anomaly detection. Igor Ermak explains why AI delivers the greatest practical value in detection, how teams work with confirmed fraud patterns and typologies, and where the limits of the technology begin.
