WT Model & Applications in Loan Platform Customer Default Prediction Based on Decision Tree Algorithms

Abstract

Due to the huge losses caused by the bad credit customers, loan platforms attach great attention to testing and forecasting of bad loans. From perspectives of both loan customer type identification and loan default prediction, we initially constructed a WT early warning model for loan default client prewarning based on C5.0 decision tree, CART decision tree and CHAID decision tree in this paper. WT model is set with weighted calculating algorithms. Considering the data characteristics of loan platform, we designed a posteriori combination algorithm of three sub-models: C5.0, CART, CHAID, and performance test indicators: sensitivity, accuracy, warning rate, false alarm rate. In empirical research, we used the real loan transaction dataset of a bank in Taiwan to construct the WT model of the bank, and found that WT model overcomes the shortcomings of each sub-model respectively and achieves effective prewarning of customer default. The experimental results show that the alarm rate of test data set is 26.93% and the false alarm rate is 18.33% and the accuracy rate is 81.67% when applying the WT model. Loan platforms can acquire both high customer default prediction accuracy and high alarm rate by applying WT prewarning model. Both the research method and experiment results in this paper are meaningful to loan platform operation.