Fintech Credit Risk Assessment for SMEs: Evidence from China

43 Pages Posted: 30 Oct 2020

See all articles by Yiping Huang

Yiping Huang

Peking University

Longmei Zhang

International Monetary Fund (IMF)

Zhenhua Li

Independent

Han Qiu

Independent

Tao Sun

International Monetary Fund (IMF)

Xue Wang

Independent

Date Written: September 1, 2020

Abstract

Promoting credit services to small and medium-size enterprises (SMEs) has been a perennial challenge for policy makers globally due to high information costs. Recent fintech developments may be able to mitigate this problem. By leveraging big data or digital footprints on existing platforms, some big technology (BigTech) firms have extended short-term loans to millions of small firms. By analyzing 1.8 million loan transactions of a leading Chinese online bank, this paper compares the fintech approach to assessing credit risk using big data and machine learning models with the bank approach using traditional financial data and scorecard models. The study shows that the fintech approach yields better prediction of loan defaults during normal times and periods of large exogenous shocks, reflecting information and modeling advantages. BigTech's proprietary information can complement or, where necessary, substitute credit history in risk assessment, allowing unbanked firms to borrow. Furthermore, the fintech approach benefits SMEs that are smaller and in smaller cities, hence complementing the role of banks by reaching underserved customers. With more effective and balanced policy support, BigTech lenders could help promote financial inclusion worldwide.

Keywords: Fintech, Machine learning, Bank credit, Loans, Credit risk, WP, credit history, Fintech firm, house ownership, internet company, real-time customer rating

JEL Classification: G17, G18, G23, G32, E42, O33, E50, G21

Suggested Citation

Huang, Yiping and Zhang, Longmei and Li, Zhenhua and Qiu, Han and Sun, Tao and Wang, Xue, Fintech Credit Risk Assessment for SMEs: Evidence from China (September 1, 2020). IMF Working Paper No. 20/193, Available at SSRN: https://ssrn.com/abstract=3721218

Yiping Huang (Contact Author)

Peking University ( email )

Beijing, 100871
China

Longmei Zhang

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

Zhenhua Li

Independent ( email )

Han Qiu

Independent ( email )

Tao Sun

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

Xue Wang

Independent ( email )

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