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

• Published in: Cybernetics and information technologies : CIT Vol. 24; no. 3; pp. 3 - 20
• Main Authors: Ngo, Van-Binh, Vu, Van-Hieu
• Format: Journal Article
• Language: English
• Published: Sciendo 01-09-2024
• Subjects: Banking sector; Customer churn; Machine learning; Stacking model
• ISSN: 1314-4081; 1314-4081
• DOI: 10.2478/cait-2024-0022

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.