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.
Download the Overview Paper Download the Fact SheetAcknowledgments
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
- Nick Bourke
- Jay Budzik
- Annie Delgado
- Nat Hoops, and Irene Meyer, Upstart
- John Morgan, Capital One
- David Moskowitz, Burning Tree Advisors
- David Silberman
- Stephen VanMeter
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 MoreVirtual Conferences
Webinar: 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 MoreArtificial 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 Analysis (December 2023)
This policy analysis explores the regulatory and public policy implications of the increasing use of machine learning models and explainability and fairness techniques for credit underwriting in depth, particularly for model risk management, consumer disclosures, and fair lending compliance.
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