Research On Customer Loss Early Warning Model Based On XGBoost Algorithm

Today,competition between banks is becoming more intense.In recent years,with the support of national policies,many local banks and Internet finance are gradually occupying the market.Due to their low quota,flexible trading and high returns,they have quickly attracted a large number of young and high-quality customer groups.Therefore,accurately predicting whether customers are about to lose and adopting appropriate retention plans in a timely manner are the focus of banks to improve their overall competitiveness.This paper mainly discusses the customer churn warning model based on the tree model.Based on the basic information and transaction data provided by a bank to predict whether customers are about to lose,so that banks can better take positive measures against customers who are predicted to be losing soon and reduce customer churn.This article creatively uses the XGBoost model to predict the probability of customer churn,the prediction effect is significantly better than the decision tree and random forest model used by previous scholars.Firstly,this paper introduces the shortcomings of banks in customer management and the need to prevent customer loss,also introduces the development of the customer churn early warning model based on the theory of data mining technology.Then,the paper elaborates the theoretical principles of decision tree,random forest and XGBoost three tree models.Then,this paper introduced several key steps in the data processing process: processing outliers and missing values,calculating derived variables,filtering variables,and dealing with data imbalances.Finally,the processed data is substituted into the model to train and adjust the important parameters.According to the AUC value and other model evaluation indicators,the XGBoost model is selected to predict the customer churn probability.In the end,there are several suggestions for how to effectively recover lost users.