Deep Dive Into Churn Prediction in the Banking Sector: The Challenge of Hyperparameter Selection and Imbalanced Learning

Abstract

Forecasting customer churn has long been a major issue in the banking sector because the early identification of customer exit is crucial for the sustainability of banks. However, modeling customer churn is hampered by imbalanced data between classification classes, where the churn class is typically significantly smaller than the no-churn class. In this study, we examine the performance of deep neural networks for predicting customer churn in the banking sector, while incorporating various resampling techniques to overcome the challenges posed by imbalanced datasets. In this work we propose the utilization of the APTx activation function to enhance our model’s forecasting ability. In addition, we compare the effectiveness of different combinations of activation functions, optimizers, and resampling techniques to identify configurations that yield promising results for predicting customer churn. Our results offer dual insights, enriching the existing literature in the field of hyperparameter selection, imbalanced learning, and churn prediction, while also revealing that APTx can be a promising component in the field of neural networks.