Suspicious Activity Reports and Analytics
Staying ahead of the compliance curve
For years banks and other financial institutions have operated sophisticated systems for monitoring, investigating, and reporting suspected money laundering/terrorist financing (AML/TF) transactions. In compliance with a number of U.S. regulations under the Bank Secrecy Act of 1970 (BSA), the USA PATRIOT Act of 2002, and other U.S. laws, these systems sift through significant amounts of data and, as a result, flag many transactions that require investigation as possible suspicious activities.
The regulatory expectations for finding suspicious activity have escalated commensurately as financial institutions have improved their detection of bad actors. Every new round of improvement carries with it a better understanding of how to find suspicious activity, and each subsequent round seeks to apply the lessons of the one before it. Analytics helps financial institutions discern the lessons from each round efficiently and transition to an improved set of scenarios and thresholds, while reducing the burden of testing out new processes. Applying analytics to the acquired knowledge contained in a SAR database – leveraging existing information – can provide the insight needed to regularly refine internal processes and improve quality and effectiveness.
Institutions can benefit from taking steps to structure their data-gathering – ideally as part of their business-as-usual procedures – so analytics can be readily applied to SAR databases. Collecting the data in a structured manner allows analytics to become an integral part of ongoing compliance operations – not simply a snapshot of a system state at a certain point in time, but a perpetual process of mining the data and providing feedback on the system’s rate of success and failure.
Analytics can provide compliance executives with an opportunity that helps them leverage information they already have to meet an unrelenting imperative they face every day: fulfilling their regulatory duty by running high quality, dynamic, and effective compliance programs.
This paper suggests that financial institutions should use analytics to improve AML systems, which can result in more efficient, effective, and insightful operations.
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