Transaction Monitoring Optimization Transaction Monitoring Optimization

Identification and reporting of suspicious transactions is one of the most important components of a Financial Institution’s (FI) compliance program. Transaction Monitoring Systems (TMS) play a crucial role in the detection of suspicious transactions. In the face of a more stringent regulatory environment, FIs are faced with challenges of relying on a ‘Black-box’ TMS.

Our professionals have significant experience performing Transaction Monitoring Optimization and have delivered engagements across multiple countries in the Asia Pacific region as well as globally. KPMG is well positioned to assist you in Transaction Monitoring Optimization. Our work typically involves the following key steps, leveraging a consistent and methodological approach:

 

Procedures and Documents Review
Perform TMS system walkthroughs and review the functional specifications of TMS’ existing rule logic to understand the current system architecture. This will identify gaps between the current system parameters and industry best practices

 

Data Extraction and Rules Replication
Perform sandbox testing of historical transactions and reference data extracted by replicating current rule logics. The results are compared to the FI’s actual historical results for completeness and accuracy.

 

Data Analysis – Initial Calibration
Assess the effectiveness of existing scenarios by analyzing the alerts triggered and transactions for which Suspicious Transactions Reports (STRs) were filed.

 

Review and Discuss
Conduct workshop to identify and discuss:

• Money laundering risks specific to the bank
• Money laundering risks based on past true hits
• Effectiveness of existing algorithms
• Potential ways to reduce false positive hits
• Money laundering risks not covered by existing algorithms
• Potential ways to monitor money laundering risks not covered byexisting algorithms

 

Data Analysis – Re-calibration
Calibrate the replicated rules in the sandbox database server to test agreed upon changes to the scenarios. Identify the root causes if the testing results deviate significantly from the expected results. Propose and agree upon the changes to the algorithms with stakeholders.