Projects

Proposed Research: Assessing Federated Machine Learning’s Potential for Transforming KYC/AML


Overview

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 Conferencewhich 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.

We 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.



FinRegLab Events


  • Assessing Federated Machine Learning’s Potential for Transforming KYC/AML

    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.

    Learn More: Assessing Federated Machine Learning’s Potential for Transforming KYC/AML

Related Publications

FinRegLab has published several reports on this project, and is considering additional empirical research to assess the potential use of cash-flow data in facilitating consumers’ and small businesses’ financial recovery from the pandemic. The reports to date are:

  • AI FAQS: The Data Science of Explainability

    This fourth edition of our FAQs focuses on emerging techniques to explain complex models and builds on prior FAQs that covered the use of AI in financial services and the importance of model transparency and explainability in the context of machine learning credit underwriting models.


  • AI FAQs: Federated Machine Learning in Anti-Financial Crime Processes

    This third edition of our FAQs considers the technological, market, and policy implications of using federated machine learning to improve risk identification across anti-financial crime disciplines, including in customer onboarding where it may facilitate more accurate and inclusive customer due diligence.


  • AI FAQS: Explainability in Credit Underwriting

    This second edition of our FAQs considers more deeply issues and debates about model transparency, explainability, and the implications of using machine learning for credit underwriting.


  • AI FAQS: Key Concepts

    This first edition of our FAQs addresses a range of introductory questions about AI and machine learning.



About FinregLab

FinRegLab is an independent, nonprofit organization that conducts research and experiments with new technologies and data to drive the financial sector toward a responsible and inclusive marketplace. The organization also facilitates discourse across the financial ecosystem to inform public policy and market practices. To receive periodic updates on the latest research, subscribe to FRL’s newsletter and visit www.finreglab.org. Follow FinRegLab on LinkedIn and Twitter (X).

FinRegLab.org | 1701 K Street Northwest, Suite 1150, Washington, DC 20006