Report Summary:
This policy analysis focuses on compliance issues under federal laws governing consumer disclosures, fair lending analyses, and risk management of machine learning credit underwriting models. This report builds on FinRegLab’s earlier empirical analysis and elaborates on the Policy & Empirical Findings Overview released in the summer of 2023.
The analysis stresses that rigorous research, thoughtful deployment, and proactive regulatory engagement are critical to ensuring that machine learning innovations benefit borrowers and financial service providers alike. While there are still many questions to be answered, the paper highlights the importance of beginning to update regulatory frameworks to foster the responsible use of these technologies, such as by articulating what qualities are important for explainability techniques and regulators’ expectations on how and when lenders should search for fairer alternative models.
Download the Policy Analysis Paper Download the Fact SheetAcknowledgments
This Policy Analysis is part of a broader research project on the explainability and fairness of machine learning in credit underwriting. Other reports in this series, including an overview of our empirical and policy findings, are available here. Support for this publication and other aspects of FinRegLab’s Machine Learning for Credit Underwriting project was provided by the Mastercard Center for Inclusive Growth, JPMorgan Chase, and Flourish Ventures. Detailed information on our funders and additional acknowledgments can be found on the inside back cover.
The empirical research described herein was conducted in collaboration with Professors Laura Blattner and Jann Spiess at the Stanford Graduate School of Business. We would also like to thank the companies that participated in the research–Arthur AI, H20.ai, Fiddler AI, Relational AI, Solas AI, Stratyfy, and Zest AI—for their time and commitment throughout this project.
We would like to extend a special note of appreciation for their leadership and facilitation of FinRegLab’s policy working groups to Adam Gailey, Charles River Associates; John Morgan, Capital One; Yogesh Mudgal, Citi; Eric Sublett and Ken Scott, Relman Colfax PLLC; and Stephen Van Meter. Thanks also to the U.S. Commerce Department, National Institute of Standards and Technology, and the Stanford Institute for Human-Centered Artificial Intelligence for co-hosting a conference, “Artificial Intelligence and the Economy: Charting a Path for Responsible and Inclusive AI.”
FinRegLab would also like to recognize the individuals and organizations who participated in the policy working groups and/or our project Advisory Board as listed in Appendix A, the participants of the Artificial Intelligence and the Economy conference, and stakeholders who participated in interviews.
We would like to thank the following individuals who provided feedback on portions of this report:
- Michael Akinwumi, National Fair Housing Alliance
- Brad Blower
- Nick Bourke
- Jay Budzik
- Marsha Courchane, Charles River Associates
- Steve Dickerson
- Delicia Hand, Consumer Reports
- Stephen Hayes, Ken Scott, & Eric Sublett, Relman Colfax PLLC
- Jeremy Hochberg
- Irene Meyer, Craig Warrington, and Kristin Williams, Upstart
- John Morgan, Capital One
- David Moskowitz, Burning Tree Advisors
- Anthony Penta
- Conrod Robinson
- David Silberman
- Bryce Stephens
- Michael Umlauf and Gene Volcheck, TransUnion
- Stephen Van Meter
- Scott Zoldi
We would also like to acknowledge the FinRegLab team who worked on various elements of the research project:
Natalia Bailey, Alex Bloomfield, Kelly Thompson Cochran, Sarah Davies, Colin Foos, Saurab Guatam, Hilary Griggs, Gillous Harris, Tess Johnson, Mashrur Khan, Duncan McElfresh, Kerrigan Molland, P-R Stark, YaYa Sun, Sormeh Yazdi, and Zishun Zhao.
With support from
Mastercard Center for Inclusive Growth
The Mastercard Center for Inclusive Growth advances equitable and sustainable economic growth and financial inclusion around the world. The Center leverages the company’s core assets and competencies, including data insights, expertise, and technology, while administering the philanthropic Mastercard Impact Fund, to produce independent research, scale global programs and empower a community of thinkers, leaders, and doers on the front lines of inclusive growth. The Center has provided funding to support this research.
JPMorgan Chase & Co.
JPMorgan Chase is committed to advancing an inclusive economy and racial equity. The firm uses its expertise in business, public policy and philanthropy, as well as its global presence, expertise and resources, to focus on four areas to drive opportunity: careers & skills, financial health and wealth creation, business growth & entrepreneurship, and community development.
Flourish Ventures
Flourish, a venture of the Omidyar Group, has provided operating support to FinRegLab since its inception. Flourish is an evergreen fund investing in entrepreneurs whose innovations help people achieve financial health and prosperity. Established in 2019, Flourish is funded by Pam and Pierre Omidyar. Pierre is the founder of eBay. Managed by a global team, Flourish makes impact-oriented investments in challenger banks, personal finance, insurtech, regtech, and other technologies that empower people and foster a fairer, more inclusive economy.
Explainability and Fairness in Machine Learning for Credit Underwriting: Empirical Research
FinRegLab is working with a team of researchers from the Stanford Graduate School of Business to evaluate the explainability and fairness of machine learning for credit underwriting. Our focus is 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 will help 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 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 MoreRelated Publications
Explainability & 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 MoreAI FAQs (Series)
To provide a resource for financial services stakeholders, we have prepared FAQs on the use of AI in financial services. Individual entries highlight technological, strategic, and ethical debates relevant to the use of advanced modelling and analytical techniques in financial services, and many explain concepts relevant to our research on machine learning credit underwriting.
Read More