“Credit underwriting with cash-flow data involves using financial data insights from a bank account or other types of transaction accounts to evaluate consumers and small businesses for credit,”

The National Institute for Standards and Technology has released the first version of a voluntary framework to help incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, systems, and services. The framework and various supplemental materials were developed after more than 18 months of public engagement. Comments are due February 27, 2023.

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.

“Some fintechs think including more data and analyzing it with more advanced algorithms could solve the problem. Others say it’s time to build whole new systems.”

“The report finds that, while concentration among federally insured banks is growing, new entrant non-bank firms, in particular ‘fintech’ firms, are adding significantly to the number of firms and business models competing in core consumer finance markets and appear to be contributing to competitive pressure. While these fintech firms are enabling new capabilities, they are also creating new risks to consumer protection and market integrity, such as risks related to data privacy and regulatory arbitrage.”

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.

“The use of Machine Learning (ML) models is gaining traction in finance due to their better predictive capacity compared to traditional statistical techniques…One of the use cases with greater potential is its application to credit underwriting and scoring, since by having better predictive capacity, ML models allow better estimates of the probability of default and therefore could result in more accurate credit scores. But this improvement in predictive performance does not come without risk.”

“Some fintechs think including more data and analyzing it with more advanced algorithms could solve the problem. Others say it’s time to build whole new systems.”

“Artificial intelligence and machine learning analyses are driving critical decisions impacting our lives and the economic structure of our society. These complex analytical techniques—powered by sophisticated math, computational power, and often vast amounts of data—are deployed in a variety of critical applications, from making healthcare decisions to evaluating job applications to informing parole and probation decisions to determining eligibility and pricing for insurance and other financial services.”