ASE-Bagging: An Embedded approach for imbalanced customer credit risk assessment

For the problem of customer credit risk assessment, the training data are often imbalanced in class distribution practically, which significantly influences the performance of assessment. Nevertheless, in this study, we propose an embedded approach, Advanced Sampling Embedded Bagging (ASE-Bagging), for the imbalanced customer credit risk assessment. ASE-Bagging integrates Synthetic Minority Over-sampling Technique (SMOTE) with bagging in order to solve the imbalanced data problem. Two real-world credit data sets are used for evaluating the proposed method. And the empirical results reveal that the proposed ASE-Bagging is a very promising approach for the imbalanced customer credit risk assessment. © 2011 ISSN 2185-2766.