Spline based survival model for credit risk modeling

Survival modeling has been adapted in retail banking because of its capability to analyze the censored data. It is an important tool for credit risk scoring, stress testing and credit asset evaluation. In this paper, we introduce a regression spline based discrete time survival model. The flexibility of spline function allows us to model the nonlinear and irregular shape of the hazard functions. By incorporating the regression spline into the multinomial logistic regression, this approach complements the existing Cox model. From a practical perspective, the logistic regression is relatively easy to understand and implement, and the simple parametric form is especially advantageous for predictive scoring. Using a credit card dataset, we demonstrate how to build a cubic regression spline based survival model. We also compare the performance of spline based discrete time survival model with the classical Cox model, our results show the spline based survival model can provide similar statistical explanatory and improve the prediction accuracy for attrition model which has low event rate.