FinRegLab Deputy Director Kelly Cochran joins RegFi cohosts Jerry Buckley and Caroline Stapleton for a conversation about how machine learning – including generative artificial intelligence – is used by consumer lenders and the evolving regulatory response. Kelly begins with a helpful distinction between the technologies commonly included under the broad “AI” moniker, noting that many of these algorithmic models are not new to credit underwriting. The discussion then pivots to the growing focus on generative AI and the need to balance its potential benefits with consumer protection, data privacy and explainability considerations. Kelly suggests that existing rules in the financial services regulatory framework can be adapted to address many of these issues and serve as a model for AI policymaking in other sectors.

“Melissa discusses her organization’s work in evaluating the explainability of complex machine learning algorithms in credit underwriting, model governance and adverse action notices. The conversation covers the CFPB’s recent guidance on credit denial by lenders using artificial intelligence as well as the role explainability plays in promoting fairness and inclusion in lending, methods and tools emerging for lenders to explain credit-related decisions based on AI models, and the ongoing work required to adapt to the digital economy and evolving regulatory landscape.”

Consumer Reports released a video series exploring biases in machine learning algorithms and data sets and the resulting unfair practices faced by communities of color. The series is designed to educate consumers on the risks hidden in seemingly “neutral” technologies. FinRegLab CEO Melissa Koide is featured in the Mortgage Lending episode of the series.

“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,”

“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.”

“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.”

“The report “examines the central role of finance in the economic recovery from COVID-19. Based on an in-depth look at the consequences of the crisis most likely to affect low- and middle-income economies, it advocates a set of policies and measures to mitigate the interconnected economic risks stemming from the pandemic—risks that may become more acute as stimulus measures are withdrawn at both the domestic and global levels.”