Prediction and Feature Importance Investigation for Bank Churn Based on Machine Learning

Abstract

Since bank customers are one of the main sources of bank income, preventing the loss of bank customers has always been the primary and struggle problem for banks. This paper chooses three machine learning methods, random forest, decision tree and logistic regression should focus to predict the leaving customers. In order to more accurately determine the factors affecting the departure of bank customers, this paper grouped the data set according to the age of over and under 40. The results shows that the prediction performance of random forest is the best one in both groups, and the logistic regression is the worst one. The precision of this model is higher in younger group than in older group, the accuracy in each group is about 90% and 76% respectively. Then the random forest method is used to return the important features for two groups. For people older than 40 years old, whether to continue to stay in the bank to buy its products is greatly affected by their Balance and Age factors. Having more balance and being younger, the more possibility to keep purchasing. While for under 40 years old customers, their counterpart behaviors are more determined by the Estimated Salary and Credit Score. Thus, when banks managers tackle customer management, they should focus more on the above factors to better prevent the loss of customers.