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|z 9781513557618
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|a 1018-5941
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|a BD-DhAAL
|c BD-DhAAL
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|a Huang, Yiping.
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|a Fintech Credit Risk Assessment for SMEs :
|b Evidence from China /
|c Yiping Huang, Longmei Zhang, Zhenhua Li, Han Qiu.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2020.
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| 300 |
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|a 1 online resource (42 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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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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|a Mode of access: Internet
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|a Li, Zhenhua.
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|a Qiu, Han.
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|a Zhang, Longmei.
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|a IMF Working Papers; Working Paper ;
|v No. 2020/193
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| 856 |
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|z Full text available on IMF
|u http://elibrary.imf.org/view/journals/001/2020/193/001.2020.issue-193-en.xml
|z IMF e-Library
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