Machine Learning for Compliance

Many organizations still use traditional rule-based systems for compliance, especially in Financial Crime detection, due to their explainability. While easy to understand, these systems have high false-positive rates and require extensive manual effort to define rules.

The complexity and rapidly evolving nature of financial crimes make rule-based systems increasingly inadequate. Machine learning (ML) models provide a powerful alternative, detecting complex patterns, improving accuracy, and reducing false positives.

A key barrier to ML adoption in compliance is the perception that these models are “black boxes” lacking transparency. However, this is not entirely accurate.