Credit scoring is one of the most successful applications of quantitative analysis that helps organizations decide whether or not to grant credit to consumers who apply to them. However, standard credit risk models based on binary classifying approaches appear to have missed several important time-varying factors and censoring information. This paper looks at the extensions of the survival analysis model to analyze personal credit risk. Survival analysis has mainly been used in the clinical domain, which can handle the above issues. This paper investigates the ability of the survival-based approach to predict the probability of personal default. The proposed method can give a prediction of `time' as well as `probability' of personal default. We develop a survival-based credit risk model and assess the relative importance of different variables in predicting default. Standard binary classifying models are also developed for assessing a new way in the context of classifying power. These models are applied to personal credit card accounts dataset. According to the experiment results, survival-based credit risk modeling is a more useful approach for classifying risky customers than others. The survival-based approach is a useful alternative and a complement in view of personal credit risk. (c) 2005 Elsevier Ltd. All rights reserved.