Establishment of Business Loan Default Prediction Model by Integrating Survival Analysis with Logistic Regression

Abstract

An insufficient amount of capital conservation buffer would cause a financial institution to be unable to withstand fluctuations in the economic cycle; while an excessive amount would reduce the financial institution’s available funds, which would lead to a loss of the capital available for investment. In order to address this issue in an effective manner, the business loan default prediction model is established in this study by integrating survival analysis with logistic regression. In the section of case validation, the reliability of the proposed approach is validated with the information of businesses that have been granted loans by financial institutions in Taiwan, and the proposed approach was also compared with the Cox proportional hazards model approach, which is frequently applied by financial institutions. The empirical results demonstrate that the approach proposed in this study could predict a business loan default state closer to the actual default trend, and provide prediction results superior to that of the Cox proportional hazards model, thus, providing financial institutions with effective and reliable information for reference, which will allow them to prepare an appropriate amount of capital conservation buffer, and improve the capital flexibility of the financial institution.