Modelling of Credit Risk: Random Forests versus Cox Proportional Hazard Regression

Abstract

In survival analysis several regression modeling strategies can be applied to predict the risk of future events. Often, however, the default choice of analysis tends to rely on Cox regression modeling due to its convenience. Extensions of the random forest approach to survival analysis provide an alternative way to build a risk prediction model. This paper discusses the two approaches in reference to credit management and compares the impact and results of both methods. The Cox Proportional Hazard model displayed a better performance than that of Random Survival Forest when estimating credit risk.