Advancing Customer Segmentation in Banking: Harnessing Machine Learning and H2O for Personalized Insights

In the contemporary banking landscape, understanding customer behavior is paramount for delivering personalized experiences and driving business growth. Customer segmentation, the process of categorizing customers into distinct groups based on shared characteristics, lies at the heart of this endeavor. Traditional segmentation approaches often fall short in capturing the nuanced and evolving nature of customer behavior. Therefore, this paper proposes a data-driven approach to advance customer segmentation in banking using state-of-the-art machine learning algorithms and techniques. In this paper, a comprehensive H2O-based framework for processing, cleaning, and clustering a big dataset of banking transactions and customer information. Through the implementation of H2O, K-Means, Hierarchical, and Gaussian Mixture Models (GMM) clustering algorithms, distinct customer segments are identified based on transactional and demographic data. The effectiveness of each clustering algorithm is evaluated using silhouette scores, with the best-performing model selected for further analysis. Finally, the implications of our findings are discussed and recommendations for leveraging customer segmentation to enhance banking services and drive business growth are presented.