It takes the average reader and 42 minutes to read Fintech Credit Risk Assessment for SMEs: Evidence from China by Yiping Huang
Assuming a reading speed of 250 words per minute. Learn more
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.
Fintech Credit Risk Assessment for SMEs: Evidence from China by Yiping Huang is 42 pages long, and a total of 10,584 words.
This makes it 14% the length of the average book. It also has 13% more words than the average book.
The average oral reading speed is 183 words per minute. This means it takes and 57 minutes to read Fintech Credit Risk Assessment for SMEs: Evidence from China aloud.
Fintech Credit Risk Assessment for SMEs: Evidence from China is suitable for students ages 8 and up.
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