Customer Churn Prediction Model Using Artificial Neural Network: A Case Study in Banking

The customer Churn problem has a great impact on banking industries as it accelerates a loss of revenue and customer loyalty. The focus of the research is to create a model for the banking sector using Artificial Neural Networks (ANNs) which can predict whether the customer is churned or not. The prediction is based on the input features and the independent variable of the trained dataset. The hyperparameters are altered during model training using the forward propagation algorithm and cross-validation techniques which enable the model to perform well with respect to accuracy and precision rate. The results showed that the suggested ANN model was 86% accurate at predicting customer attrition. In comparison to the performance of the logistic regression model, ANN models are more effective for predicting customer churn in the banking industry. The research offers important insights into how to use machine learning techniques to increase client retention and decrease churn. Banks can use this model to spot clients who are at risk of churning and take proactive measures to keep them.