S. Law AI and Other People's Money

This seminar will explore the challenges of regulating emerging consumer finance products and services through the lens of algorithmic credit pricing. Traditionally, lenders priced consumer credit by using a small set of borrower and loan characteristics, sometimes with the assistance of loan officers. Today, lenders increasingly use big data and advanced prediction technologies, such as machine-learning, to set the terms of credit. These modern underwriting practices have the potential to increase access to credit, but may also raise important question regarding whether consumers are adequately protected and whether these practices potentially give rise to violations of anti-discrimination laws. The seminar will begin with an overview of new underwriting practices and how they differ from traditional underwriting. We will discuss the move towards reliance on algorithms to predict creditworthiness and price credit accordingly. In addition, we will examine “new entrants” to the market of consumer credit and discuss how their business models may differ from traditional lender, like banks and mortgage originators. Next, we will discuss the various legal and regulatory challenges that arise in this context. We will cover a broad range of issues, from discrimination concerns, to privacy challenges. Our last topic will examine current regulatory attempts around modern underwriting practices in the US and other countries. We will critically assess whether these approaches are sensitive to the technological changes and meet the regulatory challenges that currently exist. Class format: As long as 10 seminar participants or 75% of seminar participants (the lower of the two) want to meet in-person, the seminar will be hybrid. If there are not enough students who wish to attend in-person, the seminar will be fully online.