Bank Customer Churn Prediction using Machine Learning

The banking sector constantly changes, and the growing customer churn problem forces banks to implement proactive customer retention measures. [1] To predict and lessen the impact of bank customer churn, this term paper conducts a thorough investigation into the application of various machine learning models, such as logistic regression, support vector machines (SVM), decision trees, neural networks, k- nearest neighbours (KNN), and Bayesian classification. Through thorough testing and comparative research, we examine the effectiveness of every model, revealing its unique advantages and disadvantages. Our research reveals the most effective methodology, offering a solid and sophisticated response to the financial industry’s critical problem of client attrition. This study also highlights how early detection of possible churners enables banks to create retention strategies proactively. Institutions may detect clients who are about to leave by using sophisticated analytics, which enables the tactical execution of individualized retention programs. In addition to addressing the rising issue of customer turnover, this proactive strategy gives financial institutions insightful information that they can use to build enduring client relationships in a highly competitive market.