FinRegLab today issued a research brief entitled “The Countdown Clock for Student Loan Forbearances” that highlights the need for quick action by consumers, federal agencies, servicers, and counselors to prepare for the August 2023 end of pandemic-era forbearances on federal student loans.

These data flows are critical to a growing range of consumer financial products and services. Modernizing the regulatory frameworks governing these flows is important both to mitigate current risks and frictions and to encourage future applications that produce greater inclusion, competition, and customer-friendly innovation, particularly for historically underserved consumers.

A new study finds that more consumers obtained short-term payment relief on their credit cards during the first 18 months of the pandemic than on any other type of loan except student debt, where forbearances were mandated by federal law. The study also finds evidence that pandemic relief initiatives may have reduced damage to the credit reports of consumers who sought long-term assistance through credit counseling and debt management programs.

FinRegLab has launched a new research project using data from the National Foundation for Credit Counseling (NFCC) to evaluate ways to help consumers recover more quickly from personal and economic crises such as COVID-19. The project will analyze pilot initiatives by nonprofit counseling agencies and other data sources as a springboard for considering broader market and policy changes.

FinRegLab attended ICML’s Responsible Decision Making in Dynamic Environments workshop and presented our work on a subsection of our white paper. The workshop poster and presentation focused on how model diagnostic tools affect lenders’ ability to manage fairness concerns related to identifying less discriminatory alternative models used to extend credit.

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.

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

FinRegLab and Professors Laura Blattner and Jann Spiess of the Stanford Graduate School of Business have released new research on “Machine Learning Explainability and Fairness: Insights from Consumer Lending.”

FinRegLab, in partnership with the U.S. Department of Commerce, the National Institute of Standards and Technology (NIST), and the Stanford Institute for Human-Centered Artificial Intelligence (HAI), are hosting a symposium, bringing together leaders from government, industry, civil society, and academia to explore potential opportunities and challenges posed by artificial intelligence and machine learning deployment across different economic sectors, with a particular focus on financial services and healthcare.

Building on their report about the use of utility, telecommunications and rental payments history for credit underwriting, FinRegLab and the Urban Institute hosted a webinar in April 2022 to examine the current state of play and the evolving policy landscape. Leaders from the field discussed data access and quality and how to build more robust scoring and underwriting models.