EVALUATING MACHINE LEARNING MODELS FOR OPTIMAL CUSTOMER SEGMENTATION IN BANKING: A COMPARATIVE STUDY

Abstract

This study presents a comparative analysis of machine learning algorithms for customer segmentation in the banking sector, utilizing a comprehensive dataset that includes transactional, demographic, and engagement attributes. Various clustering models, including K-Means, Gaussian Mixture Models (GMM), Hierarchical Clustering, DBSCAN, and Spectral Clustering, were evaluated to identify the most effective approach in terms of segmentation accuracy, scalability, and interpretability. The results revealed that Spectral Clustering consistently outperformed other models, offering superior accuracy and valuable insights into customer interactions across multiple banking touchpoints. While K-Means delivered fast and scalable segmentation, it lacked the flexibility needed for non-spherical clusters. The study also highlighted the benefits of a multi-dimensional dataset approach, which provided deeper insights into customer behavior, engagement, and loyalty. Although limitations such as computational complexity and scalability challenges remain, future research should focus on real-time data integration and multi-channel interactions across banking operations. This research not only contributes to machine learning applications in banking but also offers actionable strategies for targeted marketing, personalized customer engagement, risk management, and overall service optimization.