Deep Learning-Driven Customer Segmentation in Banking: A Comparative Analysis for Real-Time Decision Support

In this study, we investigate the effectiveness of various deep learning algorithms for customer segmentation in the banking sector, aiming to enhance targeted service delivery and customer experience. We employ a comprehensive pipeline encompassing data collection, preprocessing, feature selection, feature extraction, model development, and rigorous evaluation. Our dataset, derived from real-world banking customer profiles, was processed using normalization, encoding, and dimensionality reduction techniques. We implemented and compared eight models: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), CNN-LSTM Hybrid, Autoencoder-Based Network, and Deep Neural Network (DNN). Among them, the Autoencoder-Based model achieved the highest accuracy of 91.56%, outperforming others in terms of segmentation clarity and computational efficiency. These findings suggest that deep learning methods, particularly Autoencoder-Based architectures, offer robust solutions for real-time banking customer segmentation, enabling institutions to tailor products and services more effectively.