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CASE STUDY — Large Banking Organization

Optimized AML transaction monitoring to cut false positives without compromising risk coverage

The Challenge

The bank's rule-based AML engine was struggling with false positives, clogging up transaction monitoring, stretching turnaround times for MLROs, and burning compliance bandwidth without proportional risk coverage.

The Impact

The bank achieved leaner, faster AML operations: fewer false alerts, quicker adjudication, same robust risk posture. Compliance became sharper, not just busier.

What We Built

We aggregated and standardized the bank's data, then built simulators that mirrored their existing AML rule sets. This allowed us to stress-test, recommend, and deploy optimizations to the rule-based engine, surgically reducing false positives and improving MLRO adjudication TAT, all without loosening the bank's risk coverage by even a thread.