Credit Risk Reduction Effect on Small and Medium-Sized Enterprise Finance through the Use of Bank Account Information
We suggest a model for analyzing credit risk more easily without past financial information, especially for small enterprises.
We verify the impact of bank account information, such as information on deposits and withdrawals, that is not necessarily fully accounted for in conventional internal ratings and that can affect the accuracy of the default predictions of small and medium-sized enterprises (SMEs). Our analysis demonstrates that the accuracy of default predictions improves when a model based on bank account information is used in addition to the default prediction model based on traditional financial information. We also show that the degree of improvement increases when the size of the company is small. For small companies, the quality of financial data is generally assumed to be low, but the bank account information model can complement the incomplete data. In addition, for small firms, the bank account information model shows better default prediction capability compared to the financial model, which implies the possibility that banks could extend loans even if only the bank account information is available. The correlation coefficients of the financial model and the bank account model are higher than 50% but not very high, suggesting that these models evaluate borrowers from different perspectives.