Time to default in credit scoring using survival analysis: a benchmark study

Lore Dirick,Gerda Claeskens,Bart Baesens

doi:10.1057/s41274-016-0128-9

Abstract

We investigate the performance of various survival analysis techniques applied to ten actual credit data sets from Belgian and UK financial institutions. In the comparison we consider classical survival analysis techniques, namely the accelerated failure time models and Cox proportional hazards regression models, as well as Cox proportional hazards regression models with splines in the hazard function. Mixture cure models for single and multiple events were more recently introduced in the credit risk context. The performance of these models is evaluated using both a statistical evaluation and an economic approach through the use of annuity theory. It is found that spline-based methods and the single event mixture cure model perform well in the credit risk context.

Highlights

With the introduction of compliance guidelines such as Basel II and Basel III, and the resulting higher need for more accurate credit risk calculations, survival analysis gained more importance over the recent years
Many authors followed the example of Narain (1992) and started to use more advanced methods as compared to the parametric accelerated failure time (AFT) survival methods used in this first work
Accelerated failure time (AFT) and Cox proportional hazards modeling is possible through the use of the R-package survival (Therneau, 2015), with additional use of

Summary

Introduction

With the introduction of compliance guidelines such as Basel II and Basel III, and the resulting higher need for more accurate credit risk calculations, survival analysis gained more importance over the recent years. Survival analysis is mainly used in the medical context as well as in engineering, where the time duration until an event is analyzed, for example the time until death or machine failure (see Kalbfleisch and Prentice, 2002; Collett, 2003; Cox and Oakes, 1984). As an alternative to logistic regression, Narain (1992) first introduced the idea of using survival analysis in the credit risk context. The advantage of using survival analysis in this context is that the time to default can be modeled, and not just whether an applicant will default or not (Thomas et al, 2002). Many authors followed the example of Narain (1992) and started to use more advanced methods as compared to the parametric accelerated failure time (AFT) survival methods used in this first work. With its flexible nonparametric baseline hazard, the Cox proportional hazards (Cox PH) model was an obvious first alternative to the AFT model (Banasik et al, 1999), and subsequent contributions extended both Cox PH and AFT models by using, among others, coarse classification (Stepanova and Thomas, 2002) and time-varying covariates

Methods

Results

Conclusion