This study aims to build a model predicting the churn rate of customers using mobile banking services in Vietnam by applying data mining techniques. Customer churn is an issue that any service provider must pay attention to because it is decisive to the development of the business. The competition between banks is getting tougher, hence customer churn prediction has become of great concern to banking service companies. It is necessary for banks to collect colossal data and establish a valued model for classifying types of customers. In this study, three supervised statistical learning methods which are KNN, Random Forest, and Gradient Boosting are applied to the churn prediction model using the data source of VIB’s customers. In addition to selecting models belonging to the group of weak single learners such as Neural Networks, Naïve Bayes Classifier, and K-nearest Neighbor..., this paper utilizes Random Forest and Gradient Boosting which are assessed as better models because they can combine weak learners for improving model efficiency and capable of classification. The results exhibited that Gradient Boosting is the best performance in the three above classifiers with a 79.71% of accuracy rate, and 86.23% of ROC (Receiver Operating Characteristic) curve graph. Moreover, the decision tree algorithm generates readable rules for churner and non-churner classification which are potentially helpful to managers. Finally, this study suggests a proper model that can be used to forecast churners of mobile banking services in Vietnam.