Predicting non performing loan of business bank with data mining techniques

The non-performing loans (NPL) prediction plays an important role in business bank. However, there is still a large gap between the requirement of prediction performance and current techniques. In this paper data mining approaches is used to predict the NPL. Both macroeconomic and bank-specific variables are collected to form the feature set firstly. Based on selected features, the study firstly applies single basic classifiers such as decision tree, k nearest neighbors and support vector machine (SVM) to model the problem of NPL. Bagging and AdaBoost are described in this paper as two different methods of multiple classifier fusion, to build prediction models. In this experiment, non-performing loans data with 96 features and 10415 instances of a business bank is collected. F-mean and The Area under the ROC Curve (AUC) are considered as metrics of classification performances. The results illustrate that multiple classifier fusion algorithms outperform single basic classifier. The model built by multiple classifiers fusion can produce better prediction results. Furthermore, the AdaBoost method performs much better than bagging method in processing NPL.