Deciphering Big Data in Consumer Credit Evaluation

55 Pages Posted: 11 Jan 2019 Last revised: 12 Apr 2021

See all articles by Jinglin Jiang

Jinglin Jiang

Tsinghua University - PBC School of Finance

Li Liao

Tsinghua University - PBC School of Finance

Xi Lu

BaiRong Financial Information Service Co., Ltd

Zhengwei Wang

Tsinghua University - PBC School of Finance

Hongyu Xiang

Tsinghua University - PBC School of Finance

Date Written: December 2, 2020

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

Suggested Citation

Jiang, Jinglin and Liao, Li and Lu, Xi and Wang, Zhengwei and Xiang, Hongyu, Deciphering Big Data in Consumer Credit Evaluation (December 2, 2020). PBCSF-NIFR Research Paper, Journal of Empirical Finance, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3312163 or http://dx.doi.org/10.2139/ssrn.3312163

Jinglin Jiang (Contact Author)

Tsinghua University - PBC School of Finance ( email )

No. 43, Chengdu Road
Haidian District
Beijing 100083
China

Li Liao

Tsinghua University - PBC School of Finance ( email )

No. 43, Chengdu Road
Haidian District
Beijing 100083
China

Xi Lu

BaiRong Financial Information Service Co., Ltd

Zhengwei Wang

Tsinghua University - PBC School of Finance ( email )

No. 43, Chengfu Road
Haidian District
Beijing 100083
China

Hongyu Xiang

Tsinghua University - PBC School of Finance ( email )

No. 43, Chengdu Road
Haidian District
Beijing 100083
China

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