In this episode of Tech Intersect, I welcome Professor Christopher K. Odinet to discuss his recent work in crypto lending and consumer data that explores the potentially discriminatory impact of artificial intelligence and machine learning on fintech lending. We talk about consumer lending, inclusion and social impact as Web 3.0 technologies like AI and machine learning usher in tech companies that act more like banks engaging in more banking-like things than pure tech companies do, especially for small unsecured consumer loans. The reason? Household debt is higher today than it has ever been in history (mortgage, car, student loans).
The borrowed amounts tend to be lower even though household incomes are up. FinTech lenders are providing small dollar, short term loans to make middle American ends meet. And while some define “middle class” by income and others by lifestyle, perhaps another definition involves access to credit that often leads to a lifetime of debt. A stranglehold for most people trying to live the American dream. And huge, evergreen profits for tech companies getting into the lending business for unsecured, personal consumer loans. Questions persist, about how the law should deal with the problems these newcomers experience that might be similar to the issues that banks deal with and, because it isn’t all doom and gloom, how these tech companies might be different. Tune in and tweet me @AtTechIntersect so we can listen, learn and leverage together.
Chris Odinet is an associate professor of law at the University of Oklahoma College of Law and is an affiliate faculty member with the Tom E. Love Division of Entrepreneurship and Economic Development in the University of Oklahoma Price College of Business.
Guest Social Assets:
Twitter: @ChrisOdinet Web: https://www.law.ou.edu/directory/christopher-odinet
Professor Odinet’s book:Foreclosed: Mortgage Servicing and the Hidden Architecture of Homeownership in America (Cambridge University Press 2019)
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