Explainability and Fairness in Machine Learning for Credit Underwriting

Overview

Publications

Other Resources

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.

Research SummaryProject Structure

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.

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Explainability & Fairness in Machine Learning for Credit Underwriting: Policy & Empirical Findings Overview (July 2023)

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
(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.

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The Use of Machine Learning for Credit Underwriting: Market & Data Science Context (Sept. 2021)

The first report in this project 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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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.

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Virtual 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 an April 2022 virtual conference, “Artificial Intelligence and the Economy: Charting a Path for Responsible and Inclusive AI.”

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Machine 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.

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Recommended Reads

Podcasts & Webinars

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