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220928 ||| eng |
| 020 |
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|a 9781513557618
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| 100 |
1 |
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|a Huang, Yiping
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| 245 |
0 |
0 |
|a Fintech Credit Risk Assessment for SMEs: Evidence from China
|c Yiping Huang, Longmei Zhang, Zhenhua Li, Han Qiu, Tao Sun, Xue Wang
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| 260 |
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|a Washington, D.C.
|b International Monetary Fund
|c 2020
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| 300 |
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|a 42 pages
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| 651 |
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4 |
|a China, People's Republic of
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| 653 |
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|a Credit
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| 653 |
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|a Payment Systems
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| 653 |
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|a Banks
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| 653 |
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|a Finance
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| 653 |
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|a Financial services industry; Technological innovations
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| 653 |
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|a Industries: Financial Services
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| 653 |
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|a Regimes
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| 653 |
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|a Financial Forecasting and Simulation
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| 653 |
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|a Fintech
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| 653 |
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|a Mortgages
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| 653 |
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|a Money
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| 653 |
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|a Standards
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| 653 |
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|a Financial risk management
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| 653 |
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|a Credit risk
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| 653 |
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|a Capital and Ownership Structure
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| 653 |
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|a Goodwill
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| 653 |
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|a Bank credit
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| 653 |
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|a Financial Risk and Risk Management
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| 653 |
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|a Financing Policy
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| 653 |
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|a Depository Institutions
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| 653 |
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|a Machine learning
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| 653 |
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|a Government and the Monetary System
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| 653 |
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|a Technological Change: Choices and Consequences
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| 653 |
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|a Institutional Investors
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| 653 |
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|a Pension Funds
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| 653 |
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|a Technology
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| 653 |
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|a Monetary economics
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| 653 |
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|a Financial institutions
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| 653 |
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|a General Financial Markets: Government Policy and Regulation
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| 653 |
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|a Financial Instruments
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| 653 |
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|a Value of Firms
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| 653 |
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|a Monetary Policy, Central Banking, and the Supply of Money and Credit: General
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| 653 |
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|a Micro Finance Institutions
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| 653 |
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|a Intelligence (AI) & Semantics
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| 653 |
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|a Diffusion Processes
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| 653 |
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|a Non-bank Financial Institutions
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| 653 |
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|a Loans
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| 653 |
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|a Banks and Banking
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| 653 |
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|a Computer applications in industry & technology
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| 653 |
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|a Monetary Systems
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| 653 |
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|a Financial regulation and supervision
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| 653 |
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|a Money and Monetary Policy
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| 653 |
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|a Financial services law & regulation
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| 700 |
1 |
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|a Zhang, Longmei
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| 700 |
1 |
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|a Li, Zhenhua
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| 700 |
1 |
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|a Qiu, Han
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b IMF
|a International Monetary Fund
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| 490 |
0 |
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|a IMF Working Papers
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| 028 |
5 |
0 |
|a 10.5089/9781513557618.001
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| 856 |
4 |
0 |
|u https://elibrary.imf.org/view/journals/001/2020/193/001.2020.issue-193-en.xml?cid=49742-com-dsp-marc
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 330
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| 520 |
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|a 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
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