The Bank Secrecy Act (BSA)/Anti-Money Laundering (AML) regime serves vital national interests in preventing those intent on doing harm – through terrorism, money laundering, fraud, and human trafficking – from using the global financial system for illegal purposes.
But not withstanding enormous investment in and attention to BSA/AML risk detection, the system is broken:
- Firms spend $181 billion annually on financial crime compliance, including $26 billion by U.S. firms.
- Firms get little feedback on the quality of their risk reporting and face sanctions and reputational risk when those processes falter.
- Less than 1% of the estimated $2 trillion laundered globally every year is successfully identified.
These costs have led many financial institutions to manage their risk exposure by simply ceasing to operate in areas where there is too much BSA/AML risk and too little reward or where traditional methods cannot be used to reliably evaluate certain customers and transactions. This “de-risking” has dramatically reduced the availability of banking channels that handle both remittance transfers and foreign direct investment – two key drivers of asset building and economic development in many parts of the world – and approximately 30% of the adult population globally does not have an account from which they can safely transact, save, or access credit.
FinRegLab proudly presented a proposal for research on the use of federated machine learning in BSA/AML to the Central Bank of the Future Conference, which was hosted by the Federal Reserve Bank of San Francisco and the University of Michigan’s Center on Finance, Law & Policy. The 2020 edition of this conference explored the changing role of central banks and their potential to foster more inclusive economies in the United States and around the world. We invite you to read our proposal and watch a panel discussion about the proposal featuring Dr. Gary M. Shiffman, CEO, Giant Oak, Inc., and The Honorable Juan C. Zarate, Chairman & Co-Founder, The Financial Integrity Network.
Read Our ProposalView PanelWe have also prepared an installment of our AI FAQs to consider the policy, technology, and market implications of using federated machine learning to improve risk identification across BSA/AML disciplines, including in customer onboarding where it may facilitate more accurate and inclusive customer due diligence. We also prepared a short video that explains how federated machine learning works in the context of transaction monitoring.
Read AI FAQ’sVideo: Federated Machine Learning
Contact us
If you are involved in research on federated learning or BSA/AML technology-enablement and want to discuss this proposal, please reach out to us with the contact form below.