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Traditional AML segmentation methods often fall short when it comes to truly understanding customer behavior. Regulatory expectations are shifting toward more refined and explainable approaches that go beyond simple thresholds. By applying statistical clustering techniques, compliance teams can detect risks with greater precision, reduce false positives, and strengthen their AML programs – all without relying on AI.
The use of models has moved to the forefront of supervisory law as part of the current MaRisk amendment. In particular, the model understanding of analytical procedures is of central importance in order to establish appropriate risk management processes. The following article takes a closer look at the new requirements using the variance-covariance approach as an example. This article is available in German only
Stay ahead in compliance with AFC Trend Radar. Assess regulatory and technological changes, identify risks, and develop proactive strategies for a future-proof compliance system.
Automate your backtesting process for anti-money laundering compliance, ensuring effective monitoring and optimal system tuning to reduce false positives and enhance audit readiness.