Churn Prediction in Credit Customers Using Random Forest and XGBoost Methods
Abstract
Introduction: Customer churn in the credit card industry presents a significant challenge for financial institutions, potentially resulting in substantial revenue loss. This study aims to develop predictive models for identifying credit card customers likely to churn, thereby enabling proactive retention strategies. Methods: A dataset of 5,000 credit card customer records was used, including 800 churn and 4,200 non-churn instances, reflecting a class imbalance addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Two machine learning models—Random Forest and XGBoost—were implemented. Data pre-processing involved feature scaling, categorical encoding, and class balancing. Key predictive features included age, marital status, education level, transaction count, and total transaction value. Both models underwent hyperparameter tuning to optimize performance. Results: The Random Forest model achieved a baseline accuracy of 95%, improving to 96% after tuning, with an F1-score of 88% for the churn class. XGBoost demonstrated consistent accuracy of 96% before and after tuning but outperformed in minority class detection with an F1-score of 87%, precision of 86%, and recall of 89%. Analysis revealed that customers aged 40–55 were more likely to churn, influenced by behavioral and demographic factors. Conclusions: Both Random Forest and XGBoost models showed excellent performance in churn prediction. However, XGBoost proved more effective in identifying minority class instances, making it the preferred model for credit customer churn prediction. These findings support the integration of predictive analytics in customer retention strategies within the banking sector.
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