Publications
Machine Learning Explainability & Fairness: Insights from Consumer Lending
Empirical White Paper (Updated July 2023)
Report Summary
This empirical white paper is part of a broader research project on the explainability and fairness of machine learning in credit underwriting. The empirical research was conducted in collaboration with Professors Laura Blattner and Jann Spiess at the Stanford Graduate School of Business.
This empirical white paper is part of a broader research project on the explainability and fairness of machine learning in credit underwriting. The empirical research was conducted in collaboration with Professors Laura Blattner and Jann Spiess at the Stanford Graduate School of Business.
The evaluation analyzes model diagnostic tools from seven technology companies–Arthur, H2O.ai, Fiddler, RelationalAI, SolasAI, Stratyfy, and Zest AI–as well as several open-source tools.
This white paper is an update of research published in April 2022.
Acknowledgments
Principal Investigators and Other Contributors:
Contributing Authors:
Data Science Team:
FinRegLab would also like to recognize the presenters and members of our project Advisory Board who contributed to productive discussion of the development of the design, execution, and interpretation of this research. The Advisory Board consists of subject matter experts from computer science, economics, financial services, and regulatory backgrounds and includes representatives from approximately 30 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 meetings.
We would also like to thank the following individuals who provided valuable feedback on this report:
We would like to acknowledge the FinRegLab team who worked on convenings and reports related to this project:
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
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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
Events
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Machine Learning Explained: Evidence on the Explainability and Fairness of Machine Learning Credit Models
Read more: Machine Learning Explained: Evidence on the Explainability and Fairness of Machine Learning Credit ModelsFinRegLab 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.
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Artificial Intelligence and the Economy: Charting a Path for Responsible and Inclusive AI
Read more: Artificial Intelligence and the Economy: Charting a Path for Responsible and Inclusive AIThe U.S. Department of Commerce and National Institute of Standards and Technology, FinRegLab, and the Stanford Institute for Human-Centered Artificial Intelligence (HAI), hosted a virtual conference, “Artificial Intelligence and the Economy: Charting a Path for Responsible and Inclusive AI.”
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.
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