Publications

Explainability & Fairness in Machine Learning for Credit Underwriting: Policy Analysis


Overview Paper

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.

Acknowledgments


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 at https://finreglab.org/ai-machinelearning/ explainability-and-fairness-of- machine-learning-in-credit-underwriting. 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 (NFHA)

Brad Blower

General Counsel
National Community Reinvestment Coalition (NCRC)

Dr. Marsha Courchane

Vice President, Practice Leader of Financial Economics
Charles River Associates

Steve Dickerson

Discover Financial Services

Stephen Hayes

Partner
Relman Colfax PLLC

Eric Sublett

Partner
Relman Colfax PLLC

John Morgan

Managing Vice President

David Moskowitz

Burning Tree Advisors

Conrod Robinson

Risk Management Executive

Michael Umlauf

Senior Vice President, Data Science & Analytics
TransUnion

Gene Volcheck

TransUnion

Scott Zoldi

Chief Analytics Officer
FICO

Delicia Hand

Consumer Reports

We would also like to acknowledge the FinRegLab team who worked on various elements of the research project:

Natalia Bailey

Research Manager

Alex Bloomfield

Communications and Development Manager

Kelly Thompson Cochran

Deputy Director and Chief Program Officer
FinRegLab

Sarah Davies

Advisor
FinRegLab

Colin Foos

FinRegLab

Gillous Harris

Research Analyst
FinRegLab

Mashrur Khan

FinRegLab

Kerrigan Molland

Senior Finance & Business Operations Manager
FinRegLab

Zishun Zhao

Senior Data Scientist
FinRegLab

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.

(NYSE: JPM) is a leading financial services firm based in the United States of America (“U.S.”), with operations worldwide. JPMorgan Chase had $3.9 trillion in assets and $313 billion in stockholders’ equity as of June 30, 2023. The Firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing and asset management. Under the J.P. Morgan and Chase brands, the Firm serves millions of customers in the U.S., and many of the world’s most prominent corporate, institutional and government clients globally. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com.

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.

Related Publications

Events


  • 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… Learn More


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).

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