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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
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This blog article is only available in german.
Uwe Weber
Expert for AML Compliance | 20+ years experience as Consultant & Compliance Officer | Long-standing advice to the financial sector | Extensive knowledge of national + international regulations
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