Overview
Our research priorities are informed through engagement with policymakers and with input from industry, academia, and advocate groups. Through our empirical work, market monitoring, and convening activities, we produce timely reports that provide fact-based insights and legal and policy analysis to inform the financial sector.
AI in Financial Services
AI and machine learning are transforming financial services, from underwriting loans and trading securities to customizing products and providing individualized financial advice. These advanced technologies hold promise for enhancing the accuracy and speed of underwriting and other service delivery, expanding access to underserved populations, and reducing losses and other costs. But they also carry significant risks—including reliability, bias, security, privacy, and transparency. FinRegLab’s research is focused on informing both market practice and policy to promote fair, responsive, and inclusive use of AI and machine learning systems across financial services.
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AI-Enabled Personal Financial Agents
Read more: AI-Enabled Personal Financial AgentsFinRegLab has launched a project to assess and shape the next generation of AI-enabled tools designed to help consumers manage and build their financial lives.
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Agentic AI in Financial Services
Read more: Agentic AI in Financial ServicesFinRegLab is tracking the spread of agentic AI in financial services and e-commerce. These dynamic AI systems can be structured to respond to new information and make and execute decisions without ongoing human engagement.
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AI & Data for Identity Proofing & Transaction Monitoring
Read more: AI & Data for Identity Proofing & Transaction MonitoringFinRegLab is assessing the potential value of conducting empirical tests and other research to analyze particular data and technology solutions for identity proofing and transaction monitoring to improve financial inclusion and combat bad actors.
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Machine Learning Underwriting Models & Cash-Flow Data
Read more: Machine Learning Underwriting Models & Cash-Flow DataFinRegLab conducted a ground-breaking comparison of the financial inclusion benefits of using machine learning underwriting models with and without cash-flow data to increase responsible access to credit for consumers who may otherwise find it difficult to obtain safe and affordable loans.
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Explainability and Fairness in Machine Learning for Credit Underwriting
Read more: Explainability and Fairness in Machine Learning for Credit UnderwritingFinRegLab 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…
Alternative Data in Credit Underwriting
Millions of consumers and small businesses struggle to access affordable credit because of gaps and weaknesses in traditional financial information systems. FinRegLab is investigating non-traditional data sources to evaluate their effects on predictiveness, fairness, and inclusion, with a particular focus on additional sources of financial data that can be used for credit underwriting.
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Machine Learning Underwriting Models & Cash-Flow Data
Read more: Machine Learning Underwriting Models & Cash-Flow DataFinRegLab conducted a ground-breaking comparison of the financial inclusion benefits of using machine learning underwriting models with and without cash-flow data to increase responsible access to credit for consumers who may otherwise find it difficult to obtain safe and affordable loans.
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The Use of Cash-Flow Data in Underwriting Credit
Read more: The Use of Cash-Flow Data in Underwriting CreditRecords from consumers’ deposit and card accounts and from small businesses’ accounting software can provide a relatively detailed and comprehensive picture of how applicants manage their finances on an ongoing basis. FinRegLab conducted an empirical assessment of the data’s benefits and risks, as well as market and policy analyses of the challenges to its wider…
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Utility, Telecom & Rental Payment History
Read more: Utility, Telecom & Rental Payment HistoryUtility, telecom, and rental payment history can help to assess how credit applicants manage housing and other recurring expenses. FinRegLab and the Urban Institute have examined available research, historical and recent initiatives to increase data access and use for credit underwriting, and market and policy issues that will determine whether such efforts can reach scale.
Innovations in Small Business Financial Services
We are investigating the potential for technology and non-traditional data sources to improve credit access, measure financial health, and support new financial products and services for small businesses.
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Advances in Small Business Financial Health Measurement
Read more: Advances in Small Business Financial Health MeasurementFinRegLab is evaluating the use of digital banking data to measure small business financial health, evaluate the effects of loan capital on business outcomes, and examine the contribution of entrepreneurship to wealth building in underserved communities.
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Innovations in Underwriting Small Businesses
Read more: Innovations in Underwriting Small BusinessesWe are investigating the potential for technology and non-traditional data sources to improve credit access, measure financial health, and support new financial products and services for small businesses.
Building inclusive recoveries
The COVID-19 pandemic has highlighted how households’ and small businesses’ ability to rebound from financial shocks affects not only their own future financial health, but the nation’s broader racial wealth gaps and economic resiliency. FinRegLab is investigating the use of data and technology innovations and other strategies to promote faster, more inclusive recoveries.
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Debt Management Insights for Distressed Borrowers
Read more: Debt Management Insights for Distressed BorrowersFinRegLab is working with teams at The Ohio State University and Charles River Associates to evaluate new workout structures and data and technology applications for consumers who are struggling with unsecured credit. The project will use data from pilots organized by the National Foundation for Credit Counseling and other sources.
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COVID-19 Triage (Series)
Read more: COVID-19 Triage (Series)Rapidly adjusting credit reporting and underwriting practices and processing small businesses’ Paycheck Protection Program applications posed substantial challenges in the first months of the pandemic. In 2020, FinRegLab produced a series of research briefs highlighting emerging issues and innovations.
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Data for Underwriting MSEs in Emerging Markets
Read more: Data for Underwriting MSEs in Emerging MarketsFinRegLab is investigating the financial inclusion and consumer protection implications of using new data sources for credit underwriting of micro and small enterprises (MSEs) in Kenya with a particular focus on women-owned small businesses.
Recent Publications
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This report lays the foundation for a FinRegLab initiative that will explore opportunities to advance financial health measurement for small businesses. It proposes a framework and set of metrics for defining and measuring small business financial health using digital bank account data. The report also outlines how financial health measurement together with cash flow data…
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This paper reflects discussions with several banks about key considerations and approaches when managing machine learning underwriting models for consumer credit underwriting.
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Just a year after the inaugural FinRegLab AI Symposium highlighted the inflection point facing the sector and the broader U.S. economy, participants in the 2025 Symposium described concrete impacts, evolving risks, and expanding opportunities. This report highlights key themes and insights from the event.
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This empirical white paper assesses the impacts on model predictiveness and credit access of both adopting machine learning techniques and incorporating electronic bank account information (often called cash flow data) in consumer underwriting models.
LEARN MORE: Advancing the Credit Ecosystem: Machine Learning & Cash Flow Data in Consumer Underwriting
FinRegLab Events & Webinars
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AI Symposium 2025
Read more: AI Symposium 2025The FinRegLab AI Symposium 2025 offers a special forum for collaborative dialogue at the nexus of financial innovation, public policy, the market, and society. Attendees include policymakers, technology leaders, banking and fintech executives, consumer advocates, and nonprofits to explore how we can, and should, deploy AI across the financial system to reap its powerful capabilities…
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VIDEO: View from the State Level: Technology Transformation, Innovation, and Consumer Protection with Superintendent, New York State Department of Financial Services
Read more: VIDEO: View from the State Level: Technology Transformation, Innovation, and Consumer Protection with Superintendent, New York State Department of Financial Services
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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