Multi-Level Machine Learning Model to Improve the Effectiveness of Predicting Customers Churn Banks

Abstract This study presents a novel multi-level Stacking model designed to enhance the accuracy of customer churn prediction in the banking sector, a critical aspect for improving customer retention. Our approach integrates four distinct machine-learning algorithms – K-Nearest Neighbor (KNN), XGBoost, Random Forest (RF), and Support Vector Machine (SVM) – at the first level (Level 0). These algorithms generate initial predictions, which are then combined and fed into higher-level models (Level 1) comprising Logistic Regression, Recurrent Neural Network (RNN), and Deep Neural Network (DNN). We evaluated the model through three scenarios: Scenario 1 uses Logistic Regression at Level 1, Scenario 2 employs a Deep Convolutional Neural Network (DNN), and Scenario 3 utilizes a Deep Recurrent Neural Network (RNN). Our experiments on multiple datasets demonstrate significant improvements over traditional methods. In particular, Scenario 1 achieved an accuracy of 91.08%, a ROC-AUC of 98%, and an AUC-PR of 98.15%. Comparisons with existing research further underscore the enhanced performance of our proposed model.