Credit Risk Prediction of Telecom Users based on Model Fusion
Du Juan, Jian Zhang, Shuoshuo Wu · 2021
With the development of information technology and communication industry, there is a phenomenon of malicious arrears of some telecom users, which brings economic losses to telecom operators. To solve the above problems, this paper proposes a credit risk prediction method for telecom users based on model fusion. We utilized three popular and diverse classifiers as the individual learner, including k-nearest neighbor (KNN), random forest and extreme gradient boosting (XGBoost), the results of the three classifiers were then fitted using logistic regression. To verify this model, real data from Chinese telecom users were used in a test. According to the experimental results, the proposed fusion model significantly outperforms the compared methods in terms of recall rate, F1 value and AUC value, which can effectively predict the credit risk of telecom users.