This report details the experiences of mission-based lenders such as community development financial institutions (CDFIs) and minority depository institutions (MDIs) as they adopt electronic bank account data and lending platforms to increase lending to minority-owned businesses and other underserved entrepreneurs. The paper provides both a practical snapshot of how lenders manage data and technology adoption today and a broader analysis of issues that will shape their ability to serve substantially larger numbers of entrepreneurs going forward.
This empirical paper analyzes the impacts on predictive accuracy and credit access of incorporating electronic cash-flow data into small business underwriting models using data from two fintech lenders that lend to a broad spectrum of customers. The research was conducted in collaboration with Sabrina T. Howell and Siena Matsumoto at the New York University Stern School of Business.
The Future We Make: Leveraging AI in Financial Services” contains four themes and nine key insights from the AI Symposium 2024. The goal of this report is to highlight some of the key messages, ideas, and examples that were discussed throughout the Symposium.
FinRegLab 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.
This report delves into the evolving landscape of small business lending following the pandemic, identifying significant trends that are shaping the lending ecosystem. As the economy improves, lenders will face critical decisions about how to meet the capital needs of more small businesses, including record numbers of minority- and women-owned startups since 2020.
FinRegLab is launching 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.
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.
This additional study of COVID-19 loan forbearances probes the benefits of combining short-term payment relief with longer term assistance plans for the most vulnerable consumers struggling with credit card debt.
This additional study of COVID-19 loan forbearances probes the benefits of combining short-term payment relief with longer term assistance plans for the most vulnerable consumers struggling with credit card debt.
FinRegLab is investigating ways to improve credit access for underserved small businesses through the use of non-traditional data sources and mission-based lenders.