By using new machine learning technologies, we are entering a new future in which gaining security doesn’t mean sacrificing privacy.
Imagine a world in the not so distant future where banks share information with law enforcement and government agencies share back with banks, all while protecting privacy and only focusing on behaviors of interest, like money laundering and human trafficking.
An effective AI/AML platform has three attributes: It filters data via a classification method (i.e. “money launderer” versus “not-money launderer”). It learns without sharing Personal Identifiable Information (PII). And it produces human-interpretable results so that bias is easily detected and prevented.
Banks generally seek to comply with regulations and have started to engage with new technologies in order to address emerging risks and improve the effectiveness of their AML programs.
In December 2018, the Board of Governors of the Federal Reserve System, the Federal Deposit Insurance Corp., the Financial Crimes Enforcement Network, the National Credit Union Administration, and the Office of the Comptroller of the Currency issued a joint statement encouraging banks to use innovative technology to combat money laundering and terrorist financing. In September 2020, FinCEN issued a request for public comment on the best way to improve the effectiveness of the financial institutions’ AML programs.
This technology can have remarkable results. In just the past two years, I have personally seen search technology help banks uncover customers’ nefarious links to Russian crime rings; connections with Mexican drug cartels; and money laundering in the music industry.
The story of the FinCEN Files reminds industry, law enforcement, and the regulators of the urgent need to reform the AML system. The good news is, with innovative technology, we’re on the right track.
