Application of machine learning in predicting credit card customer churn

Abstract

This paper aims to forecast the likelihood of customers leaving bank credit card services using
machine learning methods. The methods used include Random Forest, SVM, Naïve Bayes, Logistic
regression, and a combination of all four methods. The results show that those methods have good
predictive quality with high accuracy. In particular, the prediction results by Random Forest are the best on
all criteria from accuracy, sensitivity, specificity to F Score. In addition, the most important factors affecting
the customer churn probability are indicators related to transaction history, products, and the relationship
between the bank and the customer. This result can provide recommendations for bank managers in
retaining customers who are using credit card services.