Credit scoring with macroeconomic variables using survival analysis

Abstract

Survival analysis can be applied to build models for time to default on debt. In this paper, we report an application of survival analysis to model default on a large data set of credit card accounts. We explore the hypothesis that probability of default (PD) is affected by general conditions in the economy over time. These macroeconomic variables (MVs) cannot readily be included in logistic regression models. However, survival analysis provides a framework for their inclusion as time-varying covariates. Various MVs, such as interest rate and unemployment rate, are included in the analysis. We show that inclusion of these indicators improves model fit and affects PD yielding a modest improvement in predictions of default on an independent test set.

Keywords:

• credit scoring
• survival analysis
• time-varying covariates
• risk
• banking
• macroeconomic variables

Acknowledgements

We thank Professors David Hand, Lyn Thomas and other members of the Quantitative Financial Risk Management Centre for discussion of survival analysis and credit scoring during the course of this research. We are grateful for funding through an EPSRC grant EP/D505380/1. We also thank our anonymous referees for their useful comments that allowed us to make significant improvements to this paper.