Deciphering Big Data in Consumer Credit Evaluation

Abstract

This paper examines the impact of large-scale alternative data on predicting consumer delinquency. Using a proprietary double-blinded test from a traditional lender, we find that the big data credit score predicts an individual’s likelihood of defaulting on a loan with 18.4% greater accuracy than the lender’s internal score. Moreover, the impact of the big data credit score is more significant when evaluating borrowers without public credit records. We also provide evidence that big data have the potential to correct financial misreporting.

Keywords: Big Data, FinTech, Personal Credit, Large-scale Alternative Data, Income Exaggeration

JEL Classification: G10, G21, G23