One of the biggest challenges that financial crime programs face today involves anti-money laundering (AML) alert volumes.
At many organizations, AML transaction monitoring and sanctions models generate far more alerts than reviewers can handle. This disparity leads to an ever-growing backlog of alerts – many of which are false positive or unproductive. When each alert must be reviewed manually, AML specialists can feel like they’re using snow shovels to dig out from an avalanche that never ends.
AML program leaders shouldn’t lose hope. The range of data analysis techniques known as advanced AML analytics can help solve this challenge and transform an organization’s financial crime prevention program in the process.
Advanced analytics, including techniques such as machine learning, can reduce operational workloads in a financial crime prevention and AML compliance program to sustainable levels. And the efficiency gains from analytics can improve the program’s ability to satisfy regulators and identify truly suspicious activity.