Overview
AI and machine learning are transforming financial services, from underwriting loans and trading securities to customizing products and answering customer questions. These advanced modeling techniques may enhance the accuracy and speed of models used to identify potential customers and assess their risks, expand access to underserved populations, and reduce losses and other costs. But they also carry significant risks—of enhancing bias, eroding data privacy, and obscuring oversight of model’s behavior. FinRegLab’s research is focused on informing both market practice and policy to promote fair, responsive, and inclusive use of AI and machine learning systems across financial services.
Research
Explainability and Fairness in Machine Learning for Credit Underwriting
FinRegLab worked with a team of researchers from the Stanford Graduate School of Business to evaluate the explainability and fairness of machine learning for credit underwriting. We focused on measuring the ability of currently available model diagnostic tools to provide information about the performance and capabilities of machine learning underwriting models. This research helps stakeholders assess how machine learning models can be developed and used in compliance with regulatory expectations regarding model risk management, anti-discrimination, and adverse action reporting.
Learn MoreProposed Research: Assessing Federated Learning for BSA/AML
FinRegLab presented a research proposal for research on the use of federated machine learning in Bank Secrecy Act/Anti-Money laundering compliance 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 also convened a panel conversation to highlight the potential inclusion and efficiency benefits of using federated learning for BSA/AML.
Learn MorePublications
Explainability & Fairness in Machine Learning for Credit Underwriting: Policy Analysis (December 2023)
This policy analysis explores the regulatory and public policy implications of the increasing use of machine learning models and explainability and fairness techniques for credit underwriting in depth, particularly for model risk management, consumer disclosures, and fair lending compliance.
Read MoreExplainability & Fairness in Machine Learning for Credit Underwriting: Policy & Empirical Findings Overview (July 2023)
This paper summarizes the machine learning project’s key empirical research findings and discusses the regulatory and public policy implications to be considered with the increasing use of machine learning models and explainability and fairness techniques.
Read MoreMachine Learning Explainability & Fairness: Insights from Consumer Lending
(Updated July 2023)
This empirical white paper assesses the capabilities and limitations of available model diagnostic tools in helping lenders manage machine learning underwriting models. It focuses on the tools’ production of information relevant to adverse action, fair lending, and model risk management requirements.
Read MoreThe Use of Machine Learning for Credit Underwriting: Market & Data Science Context (Sept. 2021)
This report surveys market practice with respect to the use of machine learning underwriting models and provides an overview of the current questions, debates, and regulatory frameworks that are shaping adoption and use.
Read MoreWebinar
Getting Ahead of the Curve: Emerging Issues in the Use of AI and Machine Learning in Financial Services
January 17, 2024 – 2:30 PM ET
FinRegLab hosted a webinar with senior federal financial regulators to discuss the growing use of artificial intelligence and machine learning in financial services, including credit underwriting.
Learn MoreVirtual Conferences
Artificial Intelligence and the Economy: Charting a Path for Responsible and Inclusive AI
The U.S. Department of Commerce and National Institute of Standards and Technology, FinRegLab, and the Stanford Institute for Human-Centered Artificial Intelligence (HAI), hosted an April 2022 virtual conference, “Artificial Intelligence and the Economy: Charting a Path for Responsible and Inclusive AI.”
Learn MoreMachine Learning Explained: Evidence on the Explainability and Fairness of ML Credit Models
FinRegLab hosted a virtual conference in April 2022 featuring research being conducted by FinRegLab and Professors Laura Blattner and Jann Spiess of the Stanford Graduate School of Business on the use of machine learning in credit underwriting, with a particular focus on their potential implications for explainability and fairness.
Learn MoreContact us
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