Leveraging Artificial Intelligence for Improved Customer Retention in the Banking Industry

Abstract

This paper examines the use of artificial intelligence technologies to promote customer loyalty in the banking industry. An analysis of customer exit patterns shows that three characteristics - age, projected income, and credit rating - are consistently among the most important predictors of attrition. Several different classification algorithms were benchmarked including Logistic Regression, Random Forest, AdaBoost, and Gradient Boosting, and these algorithms were assessed in terms of accuracy, precision, recall, and F1-score. Experimental results show that ensemble methods, especially Random Forest and Gradient Boosting, are more effective than baseline models for predicting client departure and more focused and proactive retention strategies.