European generic scoring models using survival analysis

Abstract

Credit scoring discriminates between ‘good’ and ‘bad’ credit risks to assist credit-grantors in making lending decisions. Such discrimination may not be a good indicator of profit, while survival analysis allows profit to be modelled. The paper explores the application of parametric accelerated failure time and proportional hazards models and Cox non-parametric model to the data from the retail card (revolving credit) from three European countries. The predictive performance of three national models is tested for different timescales of default and then compared to that of a single generic model for a timescale of 25 months. It is found that survival analysis national and generic models produce predictive quality, which is very close to the current industry standard—logistic regression. Stratification is investigated as a way of extending Cox non-parametric proportional hazards model to tackle heterogeneous segments in the population.

Keywords:

• credit scoring
• regression analysis
• risk
• banking

Acknowledgements

The author is grateful to ESRC for funding the dissemination of this research (PTA-026-27-0216), to Jonathan Crook and Jake Ansell (University of Edinburgh) for their guidance and advice, to the anonymous referee for his/her useful and constructive comments on the paper.