Explainability & Fairness in Machine Learning for Credit Underwriting: Policy & Empirical Findings Overview

Overview Paper

Report Summary

This overview summarizes our empirical findings and describes emerging policy dialogues across several regulatory areas as lenders, advocates, policymakers, and stakeholders consider differences between the information and techniques used to develop, manage, and validate traditional underwriting models as compared to machine learning models.

It draws on the discussions of three policy working groups that FinRegLab convened in 2022 as well as other interviews, convenings, and analysis. The paper highlights topics on which regulatory guidance could encourage more consistent, responsible use of machine learning models and explainability and fairness techniques, even as the underlying technologies and research are continuing to evolve.


FinRegLab would like to recognize the presenters and members of our project Advisory Board and policy working groups who contributed to productive discussion of the development of the design, execution, and interpretation of this research and the policy insights reflected in this document. The Advisory Board and policy working groups consisted of subject matter experts from computer science, economics, financial services, and regulatory backgrounds and included representatives from approximately 45 major institutions including bank and nonbank financial institutions, technology firms, advocacy and civil society organizations, and academic institutions. State and federal regulators participated as observers in Advisory Board and policy working group meetings.

We would like to thank the following individuals who provided feedback on this report:

Brad Blower

General Counsel
National Community Reinvestment Coalition (NCRC)

Nat Hoops


Irene Meyer


John Morgan

Managing Vice President

David Moskowitz

Burning Tree Advisors

David Silberman


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

Sarah Davies


Colin Foos


Gillous Harris

Research Analyst

Mashrur Khan


Kerrigan Molland

Senior Finance & Business Operations Manager

Zishun Zhao

Senior Data Scientist

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

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.

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    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 Follow FinRegLab on LinkedIn and Twitter (X). | 1701 K Street Northwest, Suite 1150, Washington, DC 20006