Projects
AI & Data for Identity Proofing & Transaction Monitoring
Overview
At the same time that gaps in traditional approaches to identity proofing and transaction monitoring are creating roadblocks to financial inclusion for millions of consumers and small businesses, large data breaches and other identity challenges are also making it difficult for financial institutions to detect and protect against truly bad actors.
The rapidly increasing costs of frauds and scams is prompting financial services providers to make substantial investments in new data sources, AI tools, and data sharing infrastructure. These initiatives have injected new momentum into improving identity proofing and transaction monitoring solutions at a time when efforts to develop comprehensive frameworks for digital identity systems and data protection regulations in the US have been struggling to move forward.
However, while data and technology innovations could create opportunities to advance financial inclusion and consumer privacy in addition to combating bad actors, they could also create risks of unintended consequences particularly for vulnerable and historically underserved consumers. FinRegLab is exploring these issues to assess the potential value of conducting empirical tests and other research to analyze particular data and technology solutions for identity proofing and transaction monitoring.
Related Publications
Innovations for Identity Proofing and Transaction Monitoring: Advancing Financial Inclusion Through Data & Technology
This landscape paper details gaps in traditional approaches to identity proofing and transaction monitoring that both make it difficult for millions of consumers to access financial services and for financial institutions to detect and protect against bad actors.
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. 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.
Assessing Federated Machine Learning‘s Potential for Transforming KYC/AML: A Proposal to the Central Bank of the Future Project
This proposal describes an interdisciplinary research approach to assess the potential of using federated machine learning models to both improve the effectiveness of anti-financial crimes (AFC) risk management and to enhance the inclusiveness of the global financial system.
AI FAQs: Federated Machine Learning in Anti-Financial Crime Processes Future Project
We have prepared an edition of our AI FAQs to consider the technological, market, and policy 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.