Credit Card Users Churn Prediction Using Ensemble Techniques

Credit card companies are continuously working to keep hold of their customers due to the tough competition in the financial industry. Churning takes place when a customer leaves one company to join some other company. It is a notable and complicated issue that many businesses, including credit card companies and telecommunications companies face globally, on a daily basis. Devastating a business are the potential effects of a high churn rate, as it not only diminishes income but also hinders company operations. Although a certain degree of turnover is inevitable, it is vital to manage it to avoid adverse consequences. Stakeholders are increasingly dedicating their focus and efforts towards grasping the factors that drive customer churn and predicting if loyal customers will continue to engage with their businesses. These efforts aim to mitigate the potential impact that the loss of valuable customers can have. Leveraging the strengths of multiple algorithms in order to enhance predictive performance for credit card user churn prediction, we present an ensemble method in this study. Utilizing a real-world credit card user dataset, we conducted experiments to determine the effectiveness of our proposed method and the results revealed that it surpassed the individual algorithms in terms of predictive accuracy, precision, and recall. Customer retention strategies can be optimized with insights about what leads to credit card user churn, and our approach yields just that. By reducing churn and creating greater customer satisfaction with this knowledge, credit card companies will improve their overall business performance. Valuable findings from this study can help them achieve these goals.