The client needed to update their Anti-Money Laundering (AML) Scenario and Sanctions Screening Thresholds to comply with New York regulations requiring regular threshold updates. The aim was to align these thresholds with current transaction behaviors, enhancing the effectiveness of their AML and Sanctions Screening processes and ensuring regulatory compliance.
Our analysis began with a comprehensive review of the bank's existing BSA/AML program methodology and scenario documentation, coupled with the collection of relevant historical transaction data. This foundational step enabled us to perform a detailed gap analysis to identify key areas for enhancement in the monitoring framework.
We then collaborated directly with the bank's compliance experts to refine existing detection scenarios and develop new, targeted ones. A core component of this work involved meticulously calibrating scenario thresholds to optimize the system's performance. This process aimed to strike an operational balance between sensitivity and specificity, ultimately reducing false positives while ensuring the effective detection of genuinely suspicious activity. These measures resulted in improved detection accuracy, strengthened regulatory compliance, and more efficient investigation workflows.
By optimizing its transaction monitoring and Sanctions Screening program, the bank significantly reduced false positives, enhanced detection accuracy, and improved overall compliance with regulatory requirements.
Our Python based dashboard development revolutionized the client's AML Scenario and Sanctions Screening Thresholds tuning. It empowered the client to actively participate in the process with clear visualizations of transaction data and proposed adjustments, improving their understanding and risk-based input. The dashboard'ss automation reduced the risk of manual errors, enhancing accuracy and reliability, while timely results enabled quick responses to transaction behavior changes.