摘要: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, whilst survival analysis allows profit to be modelled. The paper explores the application of accelerated failure time and proportional hazard models to the data from the retail card (revolving credit) from three European counties. The predictive performance of three national models is tested for different definitions of default and then compared to that of a single generic model. It is found that survival analysis approach is suitable for building generic models and competitive with the current industry standard - logistic regression. Stratification is investigated as a way of extending proportional hazards models to tackle heterogeneous segments in the population.