Customer Churn Prediction in Neobanking System Using Predictive Analytics and Feature Selection

Context: Customer behavior, including loyalty and satisfaction, is increasingly volatile due to rapid technological and market changes. Neobank startups, in particular, face significant challenges related to customer churn, which can severely impact profitability and reputation. Objective: To develop a predictive model that identifies customers at risk of churning in the neobanking sector, enabling proactive retention strategies. Method: This study employed data mining techniques using three classification algorithms, Logistic Regression, Naïve Bayes, and Decision Tree, implemented in the WEKA platform. Feature selection methods based on accuracy, precision, and correlation were applied to identify key churn indicators. Results: The Decision Tree algorithm outperformed the others, achieving an accuracy of 80.5%. It demonstrated superior performance, particularly when all features were included in the model. Key predictive features were successfully identified through feature selection techniques. Conclusions: The findings confirm that Decision Trees are effective in predicting customer churn in neobanks. Understanding and targeting the key factors influencing churn can help neobanks retain customers and maintain a competitive edge.