<![CDATA[The banking industry has become increasingly dynamic with the emergence of financial technology (fintech) companies that have significantly changed customer behavior and expectations. As competition intensifies, customer churn has become a critical issue because it directly affects a bank’s revenue, reputation, and long-term sustainability. Therefore, banks require effective analytical approaches to identify customers likely to leave and to develop appropriate retention strategies. This study aims to analyze and predict customer churn likelihood using a bank customer dataset by applying supervised machine learning classification techniques. Five algorithms were evaluated, namely Decision Tree, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). The models were trained and evaluated using a hold-out validation approach, and performance was assessed using accuracy as the primary evaluation metric. The experimental results show that Random Forest achieved the highest accuracy of 86%, outperforming the other algorithms, while the MLP model produced the lowest accuracy of 82%. These findings indicate that ensemble-based methods provide better performance for predicting bank customer churn. The results of this study can assist banks in identifying potential churn customers and in developing effective customer retention strategies. Future research may explore additional algorithms, advanced data preprocessing techniques, and larger datasets to further improve prediction performance.]]>
Copyrights © 2026
