Analysis and prediction of bank user churn based on ensemble learning algorithm

In the era of big data, customer churn is a big problem faced by banks in the increasingly competitive market. Therefore, it is very important to establish an efficient early warning system for Customer Churn by mining the information that has an impact on churn from massive customer data. The purpose of this paper is to analyze the quarterly user data of banks, and establish user churn prediction model by using ensemble learning algorithm such as Catboost, Lightgbm, Random Forest, so as to improve the accuracy of prediction, so as to achieve the purpose of helping banks save costs. The experimental results show that the accuracy rate of the model has reached 90%, and the AUC value is more than 80%. The model can be used to predict whether the user may be lost in the future, reserve enough time for the user retention activities, and provide a lot of valuable information to help marketing personnel to formulate feasible user retention scheme, which has a wide range of industry application prospects.