We study the adoption of automated credit scoring at a large auto
nance company and the changes it enabled in lending practices. Credit scoring appears to have increased pro
ts by roughly a thousand dollars per loan. We identify two distinct bene
ts of risk classi
cation: the ability to screen high-risk borrowers and the ability to target more generous loans to lower-risk borrowers. We show that these had e¤ects of similar magnitude. We also explore whether increased reliance on hard information led to convergence in pro
tability across dealerships by substituting for varying qualities of soft local information. We
nd that all dealerships appear to have become more pro
table, but little evidence that pro
ts converged. Keywords: Information technology, Credit scoring, Consumer lending. JEL classi
cation: D82, G21, L86. We thank Luke Stein for excellent research assistance, Will Adams for his contributions to this project, and Chris Knittel, Ulrike Malmendier, and participants in IO Fest, the AEA meetings, and the NBER IO Summer meeting for helpful comments. We acknowledge support from the Stanford Institute for Economic Policy Research, the National Science Foundation (Einav and Levin), and the Center for Advanced Study in the Behavioral Sciences (Levin). yEinav and Levin: Department of Economics, Stanford University, and NBER; Jenkins: Wharton School, University of Pennsylvania. E-mail addresses: leinav@stanford.edu; mjenk@wharton.upenn.edu; jdlevin@stanford.edu.
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