FinRegLab Events
Federal Regulatory Considerations for the Use of Artificial Intelligence in Credit Underwriting
FinRegLab Webinar – December 10, 2020
FinRegLab presents a webinar with diverse leading voices from the federal regulatory community on the use of artificial intelligence in credit underwriting. The event focuses on regulators’ perspectives in relation to explainability and fairness in consumer and small business credit underwriting models where all credit stakeholders – lenders, advocates, and policymakers – are considering the implications of a broad transition to AI-based underwriting.
The webinar is moderated by FinRegLab CEO Melissa Koide and includes the following participants:
Regulators are having to both shape and adapt to the new technical and economic realities of AI in financial services. Although regulators have extensive pre-existing frameworks and guardrails to help ensure these technologies are safely and fairly applied in uses like credit underwriting, questions remain over how technologies like machine learning models operate, how they treat data, and how they may affect financial inclusion and fairness. In this session, we explore regulatory perspectives on both the opportunities and risks AI in financial services represents and what technical and market-oriented questions remain, especially from the standpoint of leveraging technology to be more inclusive of underserved populations in the formal economy through access to credit.
Related Publications
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Explainability & Fairness in Machine Learning for Credit Underwriting: Policy Analysis
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
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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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
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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