Comparative Analysis of Deep Learning Architectures for Customer Churn Prediction in the Banking Sector

Abstract

Customers’ churn is a critical problem for the banking industry since the prediction of customers’ attrition impacts the business’s key outcomes and decision-making. As such, the research aim of this study is to assess the performance of different deep learning networks in the context of customer churn rate estimation with banking datasets. This study specifically compares feedforward neural networks, long short-term memory networks, convolutional neural networks, and multi-layer perceptrons. We train and test the models on a data set that includes customer details such as credit score, age, balance, and tenure. Hence, we measure each model’s ability depending on factors like accuracy, precision, recall, F1 score, and the ROC-AUC. However, for the evaluation of the models, we use various visualizations, including confusion matrix, receiving operating characteristic curve, precision-recall curve, and learning curves. The results show that all models can achieve comparable performance, but there are some models with specific edges. For example, long short-term memory networks, a type of RNN, excel at modeling sequential relationships in the data, whereas convolutional neural networks craft intricate structures within the data input. This work's main ideas include examining the characteristics and potential of various deep learning designs in the context of customer churn prediction while comparing the architectures. The primary goal of this study is to analyze and identify the most effective deep learning model for customer churn prediction, as well as provide recommendations for banks to improve customer retention. Thus, the results emphasize the importance of selecting an adequate model based on the data's characteristics and prediction goals. It is in this vein that the study proposes to advance the understanding of the deep learning models above with a view to informing banking institutions on how best to address customer churn problems.