Predicting Credit Card Churn: Application of XGBoost Tuned by Modified Sine Cosine Algorithm

Abstract

Retaining customers is of great importance for all subscription-based financial institutions. Having in mind that even a small change in customer churn can have a significant impact on a company's profits and overall value of the company, proper customer churn management is a prerequisite. When it comes to banks, the key issue is identifying reasons (factors) that lead to contract termination between a customer and a bank. This paper offers a new model for forecasting customer churn and determines the contribution of variables that could lead to losing a customer. This work presents a novel artificial intelligence approach for predicting churn using the XGboost methods. A novel metaheuristic algorithm is proposed and tasked with se-lecting optimal hyperparameters for the XGBoost algorithm. The performance of the algorithm has been evaluated on real-world data and compared to several cutting-edge algorithms, attaining the best performance, with the highest accuracy of approximately 97%, which proves presumption that customer credit card churn could be forecast with high precision. Additionally, the best models have been subjected to SHAP analysis to determine feature impact. Attained results show that features that belong to customer account information have the strongest impact on customer turnover, while personal customer information does not have or has little contribution. Features with the highest SHAP values are total transaction count and amount over the last 12 months and total revolving card balance.