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Machine Learning for Compliance

Breaking the Blackbox Myth
1
Rule-based vs Machine Learning
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.
2
Advantages of ML Systems
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.
3
Breaking down perceptions
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.

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