Survival Analysis As A Tool For Better Probability Of Default Prediction

This paper focuses on using survival analysis models in the area of credit risk and on the modelling of the probability of default (i.e. a situation where the debtor is unwilling or unable to repay the loan in time) in particular. Most of the relevant scholarly literature argues that the survival models produce similar results to the commonly used logistic regression models for the development or testing of samples. However, this paper challenges the standard performance criteria measuring precision and performs a comparison using a new prediction-based method. This method gives more weight to the predictive power of the models measured on an ex-ante validation sample rather than the standard precision of the random testing sample. This new scheme shows that the predictive power of the survival model outperforms the logistic regression model in terms of Gini and lift coefficients. This finding opens up the prospect for the survival models to be further studied and considered as relevant alternatives in financial modelling.