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Maintaining both effective and efficient AML programs has proven elusive for financial institutions due to a reliance on legacy technology solutions and a seemingly ever-increasing investigator case workload. The new technologies of data science, including artificial intelligence (AI) and machine learning, however, hold the key to helping institutions reduce regulatory risk, and improve the AML investigation process. This paper explains how transaction monitoring systems (TMS) can be enhanced with AI and machine learning to reduce risk and drive down AML costs.

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CASE STUDY: Reducing AML Risk for a Leading Retail Bank in Europe

Serving almost 20 million customers, the bank was concerned about the risks associated with false negatives that its current AML compliance technology was missing. Intent on driving financial crime out of its operation, the bank began searching for a solution that could enhance its existing rules-based transaction monitoring system (TMS) and minimize the risk related to undiscovered financial crime.

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435 Devon Park Dr. Bldg. 700
Wayne, PA 19087
610.465.7320

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