Market & Data Science Context
Report Summary:
The Market & Data Science Context 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. It is designed to provide a resource to stakeholders, especially non-technical ones, that explains lenders’ decisions that can promote fair and responsible use of machine learning underwriting models.
The report focuses in particular on model transparency as a critical ingredient in allowing firms, regulators, and other stakeholders to evaluate whether and in what circumstances these models meet pre-existing requirements regarding reliability and fairness. Without sufficient transparency, neither firms nor their regulators can evaluate whether individual models are making credit decisions based on strong, intuitive, and fair relationships between an applicant’s behavior and creditworthiness. Yet the same complexity that fuels the accuracy of machine learning underwriting models can make it more difficult to understand how a model was developed and how it assessed a particular applicant’s creditworthiness.
This report lays the foundation for FinRegLab’s empirical research with researchers from the Stanford Graduate School of Business.
Download the Market & Data Science Context report Download the Report Overview here Explainability and Fairness of Machine Learning in Credit Underwriting: Empirical ResearchVirtual 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 a 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 on April 28 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 MoreAcknowledgments
This report is part of a broader research project on the explainability and fairness of machine learning in credit underwriting. This initiative includes empirical research being conducted with Laura Blattner and Jann Spiess at the Stanford Graduate School of Business. This report lays a foundation for that empirical research, as well as a publication which will investigate how law, regulation, and market practices may need to evolve to promote fair and responsible use of these new technologies. Many of the insights in this report were derived from interviews with stakeholders in the financial services, technology, and civil society sectors
FinRegLab would also like to recognize members of our project Advisory Board who contributed to discussions of the issues covered in this report. The Advisory Board consists of subject matter experts from computer science, economics, finance, and regulatory backgrounds. The group represents more than 40 major entities from the worlds of bank and nonbank financial services, technology, policy, advocacy, and academia. State and federal regulators participate as observers.
We would also like to thank the following individuals who provided feedback on this report.
- Michael Akinwumi, National Fair Housing Alliance
- Brad Blower, National Community Reinvestment Coalition
- Rohit Chauhan & Nitendra Rajput, Mastercard
- Marsha Courchane & Peter Zorn, Charles River Associates
- Steve Dickerson & Raghu Kulkarni, Discover Financial Services
- Talia Gillis, Columbia Law School
- Patrick Hall, bnh.ai
- Stephen Hayes, Relman Colfax PLLC
- Deborah Hellman, University of Virginia Law School
- Scott Lundberg, Microsoft Research
- Kevin Moss, Kevin Moss Consulting LLC
- Beju Rao, Amruta Inc.
- David Silberman
- Scott Zoldi
Finally, we would like to acknowledge the FinRegLab team who worked on the development of this report. They include: Kelly Thompson Cochran, Colin Foos, Hilary Griggs, Tess Johnson, Mashrur Khan, Duncan McElfresh, Kerrigan Molland, P-R Stark, YaYa Sun, and Sormeh Yazdi.
With support from
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.
The 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 helping to advance racial equity and drive an inclusive recovery through business expertise and resources, policy advocacy, data and philanthropic investments. For more information and impact stories, visit www.jpmorganchase.com/impact.
