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18 Yale J.L. & Tech. 148 (2016)
Credit Scoring in the Era of Big Data

handle is hein.journals/yjolt18 and id is 148 raw text is: CREDIT SCORING IN THE ERA OF BIG DATA Mikella Hurley* & Julius Adebayo** 18 YALE J.L. & TECH. 148 (2016) ABSTRACT For most Americans, access to credit is an essential requirement for upward mobility and financial success. A favorable credit rating is necessary to purchase a home or car, to start a new business, to seek higher education, or to pursue other important goals. For many consumers, strong credit is also necessary to gain access to employment, rental housing, and essential services such as insurance. At present, however, individuals have very little control over how they are scored and have even less ability to contest inaccurate, biased, or unfair assessments of their credit. Traditional, automated credit- scoring tools raise longstanding concerns of accuracy and unfairness. The recent advent of new big-data credit-scoring products heightens these concerns. The credit-scoring industry has experienced a recent explosion of start-ups that take an all data is credit data approach, combining conventional credit information with thousands of data points mined from consumers' offline and online activities. Big-data scoring tools may now base credit decisions on where people shop, the purchases they make, their online social media networks, and various other factors that are not intuitively related to creditworthiness. While the details of many of these products remain closely guarded trade secrets, the proponents of big-data credit scoring argue that these tools can reach millions of underserved consumers by using complex algorithms to detect patterns and signals within a vast sea of information. While alternative credit scoring may ultimately benefit some consumers, it also poses significant risks. * Law Clerk to the Honorable Roy W. McLeese III, District of Columbia Court of Appeals, J.D., 2015, Georgetown University Law Center. ** S.M. Computer Science & Technology and Policy, MIT 2016. This views expressed in this article are solely those of the authors and do not represent the opinions of their respective institutions. The authors would like to thank Taesung Lee for helpful comments and suggestions on earlier drafts. The authors also wish to thank Professors David Vladeck and Alvaro Bedoya of Georgetown Law, and Professors Danny Weitzner and Hal Abelson of MIT for their extensive guidance in the development of this article. All errors remain those of the authors alone.

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