Can Big Data Defeat Traditional Credit Rating?

59 Pages Posted: 11 Jan 2019 Last revised: 7 Nov 2019

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: September 25, 2019

Abstract

This paper examines the impact of large-scale alternative data, or big data, on predicting consumer loan delinquency for a traditional lender. Based on a unique proprietary dataset containing 700 million individuals and 20,000 variables, we construct a big data credit score by applying machine learning techniques to deal with high dimensionality and massive missing values. We find that incorporating the big data credit score improves the lender’s accuracy in predicting a borrower’s delinquency likelihood by 22.6%. We identify two possible ways through which big data contributes: providing more information for those without public credit records and correcting financial misreporting.

Keywords: Big Data, FinTech, Personal Credit, 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, Can Big Data Defeat Traditional Credit Rating? (September 25, 2019). Available at SSRN: https://ssrn.com/abstract=3312163 or http://dx.doi.org/10.2139/ssrn.3312163

Jinglin Jiang

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 (Contact Author)

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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