Survival Analysis Applied To The Research In The Default Risk Of Credit Card Holder

Abstract
Survival Analysis was originally adopted for in Biological and Actuarial sciences. In the recent decades this method is also used to perform the company failure prediction model. However, it is still in the early stage to apply survival analysis to predict credit risk in the personal credit card market while it is the main purpose of this study. Thank to the dataset provided by Joint Credit Information Center, there are demographic information as well as the credit card holder’s usage histories and the consuming behavior difference between short-term and medium term. By applying Principal Component Analysis, the 82 Principal Components are therefore obtained to be the inputs in the Cox Model.
The Cox Model provides advantage that the parameters can be directly estimated without specifying distribution of data. The result of the proposed method shows that the accuracy ratio of classification is around 80% when the cut-point of survival probability is made as 0.98. If the cut-point is lower, there will be lower accuracy ratio in the default sample but higher in the normal sample. By using Cox Model, one can also get the information of when will credit card holder defaults. The ability of default timing prediction makes the Cox Model different from other models and it can also improve the ability of credit risk management.





Keyword: Survival Analysis, Cox Model, Personal Credit Risk