Projects
Explainability and Fairness in Machine Learning for Credit Underwriting
Overview
Lending decisions informed by credit underwriting models affect the lives of hundreds of millions of Americans. The emerging use of machine learning models has the potential to increase credit access by more accurately identifying applicants who are likely to repay loans and to reduce the number of people given loans that they are unlikely to repay. However, the “black box” nature of many machine learning models has focused attention on model transparency as a critical threshold question for both lenders and regulators.
FinRegLab joined forces with researchers from the Stanford Graduate School of Business to evaluate the capabilities, limitations, and performance of proprietary and open-source tools designed to help lenders address these transparency challenges.
Related Publications
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Explainability & Fairness in Machine Learning for Credit Underwriting: Policy & Empirical Findings Overview
This paper summarizes the machine learning project’s key empirical research findings and discusses the regulatory and public policy implications to be considered with the increasing use of machine learning models and explainability and fairness techniques.
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Machine Learning Explainability & Fairness: Insights from Consumer Lending
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.
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The Use of Machine Learning for Credit Underwriting: Market & Data Science Context
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.
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AI FAQS: The Data Science of Explainability
This fourth edition of our FAQs focuses on emerging techniques to explain complex models and builds on prior FAQs that covered the use of AI in financial services and the importance of model transparency and explainability in the context of machine learning credit underwriting models.
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AI FAQs: Federated Machine Learning in Anti-Financial Crime Processes
This third edition of our FAQs considers the technological, market, and policy implications of using federated machine learning to improve risk identification across anti-financial crime disciplines, including in customer onboarding where it may facilitate more accurate and inclusive customer due diligence.
FinRegLab Events
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Getting Ahead of the Curve: Emerging Issues in the Use of Artificial Intelligence and Machine Learning in Credit Underwriting
Learn More: Getting Ahead of the Curve: Emerging Issues in the Use of Artificial Intelligence and Machine Learning in Credit UnderwritingFinRegLab hosted a webinar with senior federal financial regulators in January 2024 to discuss the growing use of artificial intelligence and machine learning in financial services, including credit underwriting.
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Machine Learning Explained: Evidence on the Explainability and Fairness of Machine Learning Credit Models
Learn 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.
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 safe and responsible 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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