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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.

Banks and Banking --- Money and Monetary Policy --- Industries: Financial Services --- Intelligence (AI) & Semantics --- Financial Forecasting and Simulation --- General Financial Markets: Government Policy and Regulation --- Pension Funds --- Non-bank Financial Institutions --- Financial Instruments --- Institutional Investors --- Financing Policy --- Financial Risk and Risk Management --- Capital and Ownership Structure --- Value of Firms --- Goodwill --- Monetary Systems --- Standards --- Regimes --- Government and the Monetary System --- Payment Systems --- Technological Change: Choices and Consequences --- Diffusion Processes --- Monetary Policy, Central Banking, and the Supply of Money and Credit: General --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Computer applications in industry & technology --- Machine learning --- Monetary economics --- Finance --- Financial services law & regulation --- Fintech --- Bank credit --- Loans --- Credit risk --- Technology --- Financial regulation and supervision --- Financial institutions --- Money --- Financial services industry --- Technological innovations --- Credit --- Financial risk management --- China, People's Republic of

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

China, People's Republic of