Fintech Credit Risk Assessment for SMEs: Evidence from China

By Yiping Huang, Longmei Zhang, Zhenhua Li, Han Qiu, Tao Sun, Xue Wang

September 25, 2020

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Yiping Huang, Longmei Zhang, Zhenhua Li, Han Qiu, Tao Sun, and Xue Wang. Fintech Credit Risk Assessment for SMEs: Evidence from China, (USA: International Monetary Fund, 2020) accessed November 8, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

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.

Subject: Bank credit, Credit risk, Financial institutions, Financial regulation and supervision, Fintech, Loans, Machine learning, Money, Technology

Keywords: Adverse selection, B. firm location, Bank credit, Big data, BigTech company, Cash flows, Credit history, Credit risk, Credit risk assessment, Fintech, Fintech approach, Fintech firm, Fintech lending, Firm size, Firms' access, Firm's real-time customer rating, Fixed cost, House ownership, Information advantage, Internet company, Loans, Machine learning, Medium-size enterprise, Micro firm, MYbank loan, Real-time customer rating, WP

Publication Details

Pages:

42
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2020/193
Stock No:

WPIEA2020193
ISBN:

9781513557618
ISSN:

1018-5941

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