Deciphering big data in consumer credit evaluation

Jinglin Jiang, Li Liao, Xi Lu, Zhengwei Wang and Hongyu Xiang

Journal of Empirical Finance, 2021, vol. 62, issue C, 28-45

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 (search for similar items in EconPapers)
JEL-codes: G10 G21 G23 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:62:y:2021:i:c:p:28-45

DOI: 10.1016/j.jempfin.2021.01.009

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