Machine Learning to Develop Credit Card Customer Churn Prediction

The credit card customer churn rate is the percentage of a bank’s customers that stop using services. Hence, developing prediction model to predict expected status for will generate an early alert banks change service or offer them new This paper aims develop by feature-selection method and five machine learning models. To select independent variables, three models were used, including selection all two-step clustering k-nearest neighbor, feature selection. In addition, selected, Bayesian network, C5 tree, chi-square automatic interaction detection (CHAID) classification regression (CR) neural network. analysis showed could model. results tree performed best in comparison with developed indicated top variables needed development total transaction count, revolving balance on card, count. Finally, revealed merging multi-categorical into one variable improved performance