The New Data of Student Debt

76 Pages Posted: 9 Apr 2019 Last revised: 8 Dec 2019

Date Written: December 8, 2019

Abstract

Where you go to college and what you choose to study has always been important, but, with the help of data science, it may now determine whether you get a student loan. Silicon Valley is increasingly setting its sights on student lending. Financial technology (“fintech”) firms such as SoFi, CommonBond, and Upstart are ever-expanding their online lending activities to help students finance or refinance educational expenses. These online companies are using a wide array of alternative, education-based data points—ranging from applicants’ chosen majors, assessment scores, the college or university they attend, job history, and cohort default rates—to determine creditworthiness. Fintech firms argue that through their low overhead and innovative approaches to lending they are able to widen access to credit for underserved Americans. Indeed, there is much to recommend regarding the use of different kinds of information about young consumers in order assess their financial ability. Student borrowers are notoriously disadvantaged by the extant scoring system that heavily favors having a past credit history. Yet there are also downsides to the use of education-based, alternative data by private lenders. This Article critiques the use of this education-based information, arguing that while it can have a positive effect in promoting social mobility, it could also have significant downsides. Chief among these are reifying existing credit barriers along lines of wealth and class and further contributing to discriminatory lending practices that harm women, black and Latino Americans, and other minority groups. The discrimination issue is particularly salient because of the novel and opaque underwriting algorithms that facilitate these online loans. This Article concludes by proposing three-pillared regulatory guidance for private student lenders to use in designing, implementing, and monitoring their education-based data lending programs.

Keywords: algorithms, fintech, commercial law, law and finance, finance law, financial technology, underwriting, discrimination, credit, loans, online loans, marketplace lenders, college, student debt, student loans, SoFi, CommonBond, Upstart, alternative data, social mobility, ECOA, class, race, income

Suggested Citation

Odinet, Christopher K., The New Data of Student Debt (December 8, 2019). 92 Southern California Law Review 1617 (2019). Available at SSRN: https://ssrn.com/abstract=3349478

Christopher K. Odinet (Contact Author)

University of Oklahoma - College of Law ( email )

300 Timberdell Road
Norman, OK 73019
United States

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