The client needed to tune thresholds for AML scenarios implemented globally to mitigate risks associated with failed segments and reduce false positives.

Applied threshold tuning methodology and validated scenarios.

1. Data Quality Analysis: Defined transformation rules, performed automated data cleansing, validated data sources, and resolved data issues.

2. Validation of Scenarios and Segments: Mapped jurisdiction codes and segments, validated scenario codes with vendor solution, removed redundant conditions.

3. Threshold Tuning and Optimization: Applied threshold tuning methodology, implemented obtained thresholds, identified and revised threshold mismatches across segments using ATL & BTL methods.

4. Scenario Validation and Reconciliation: Compared risk events generated with vendor system, investigated mismatches during reconciliation.

Reduced false positives and enhanced scenario effectiveness.

- Helped the bank mitigate risks associated with failed segments.

- Minimized false positives leading to operational efficiency gains.

- The development of reusable tools facilitated future validations.