Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10323356 | Expert Systems with Applications | 2005 | 10 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Hyun Ju Noh, Tae Hyup Roh, Ingoo Han,