How Do Machine Learning and Non-Traditional Data Affect Credit Scoring? New Evidence from a Chinese Fintech Firm

24 Pages Posted: 15 Jan 2020

See all articles by Leonardo Gambacorta

Leonardo Gambacorta

Bank for International Settlements (BIS); Centre for Economic Policy Research (CEPR)

Yiping Huang

Peking University

Han Qiu

Peking University - National School of Development

Jingyi Wang

Peking University - National School of Development

Multiple version iconThere are 2 versions of this paper

Date Written: December 19, 2019

Abstract

This paper compares the predictive power of credit scoring models based on machine learning techniques with that of traditional loss and default models. Using proprietary transaction-level data from a leading fintech company in China for the period between May and September 2017, we test the performance of different models to predict losses and defaults both in normal times and when the economy is subject to a shock. In particular, we analyse the case of an (exogenous) change in regulation policy on shadow banking in China that caused lending to decline and credit conditions to deteriorate. We find that the model based on machine learning and non-traditional data is better able to predict losses and defaults than traditional models in the presence of a negative shock to the aggregate credit supply. One possible reason for this is that machine learning can better mine the non-linear relationship between variables in a period of stress. Finally, the comparative advantage of the model that uses the fintech credit scoring technique based on machine learning and big data tends to decline for borrowers with a longer credit history.

Keywords: fintech, credit scoring, non-traditional information, machine learning, credit risk

JEL Classification: G17, G18, G23, G32

Suggested Citation

Gambacorta, Leonardo and Huang, Yiping and Qiu, Han and Wang, Jingyi, How Do Machine Learning and Non-Traditional Data Affect Credit Scoring? New Evidence from a Chinese Fintech Firm (December 19, 2019). BIS Working Paper No. 834, Available at SSRN: https://ssrn.com/abstract=3506945

Leonardo Gambacorta (Contact Author)

Bank for International Settlements (BIS) ( email )

Centralbahnplatz 2
Basel, Basel-Stadt 4002
Switzerland

Centre for Economic Policy Research (CEPR)

London
United Kingdom

Yiping Huang

Peking University ( email )

Beijing, 100871
China

Han Qiu

Peking University - National School of Development

Beijing, 100871
China

Jingyi Wang

Peking University - National School of Development ( email )

Beijing, 100871
China

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