AI-Driven Predictive Modeling for Banking Customer Churn: Insights for the US Financial Sector

Abstract

The US banking sector is operating within a very dynamic and competitive environment, providing a wide array of services under the pressure of increasingly demanding customers. Customer churn in the context of financial institutions is defined as the phenomenon of customers terminating their relationship with a bank. The central tenet of this research project was to devise and develop predictive models of artificial intelligence that can help address the issue of customer churn from the banking perspective. The dataset of banking customer churn prediction used for this analysis comprises a comprehensive set of data about customers from a leading financial institution. It includes extensive customer records, each described by features representing different dimensions of customer behavior and demographics. The three most influential algorithms were selected for this study: Logistic Regression, Random Forest, and XG-Boost. Each model has different strengths that are quite appropriate for the intrinsic complexities of the customer churn forecast. Random Forest was the best in terms of accuracy among the models, with a relative accuracy, which may indicate that this algorithm fits the underlying pattern in the data best. The integration of AI-driven churn prediction models in the US financial sector has far-reaching implications for banks, enhancing their operational efficiency and customer relationship management. First and foremost, it can identify at-risk customers with a high degree of accuracy, thus helping the banks to implement focused retention strategies that can bring down the churn rate significantly.