Customer churn analysis in banking sector: Evidence from explainable machine learning models

Although large companies try to gain new customers, they also want retain their old customers. Therefore, customer churn analysis is important for identifying customers without loss and developing products making strategic decisions retaining This study focuses on the analysis, that a significant topic in banks relationship management. Identifying will helps management classification who are likely early target using promotions, as well provide insight into which factors should be considered when different models used literature, this especially explainable Machine Learning uses SHapely Additive exPlanations (SHAP) values support machine learning model evaluation interpretability analysis. The goal of research estimate real data from banking evaluate many test data. According results, XgBoost outperformed other methods classifying