Predicting Banking Customer Churn based on Artificial Neural Network

Customer churn has become one of the major issues in the banking industry. Because it is difficult to gain new clients, the major focus of customer relationship management is on existing clients. Customer Churn is defined as when customers switch to another provider due to their low prices and better offers. There are many research papers that found solutions to solve the customer churn problem with the help of the techniques of machine learning. In this research paper, we have suggested a framework that introduces a solution to the problem of customer churn in the banking industry. We used the techniques of deep learning namely the artificial neural network to analyze bank customer data and predict the customer churn. The experiment was conducted on a dataset called churn modeling and the results reveal that we were able to attain an accuracy of 87 % for bank customer data by using the ANN algorithm. The proposed framework presented a costeffective option for maintaining bank customers, which increases bank profits by retaining customers.