https://www.frontlinejournals.org/journals/index.php/gs-indexing <p>American Research Index Library</p> en-US American Research Index Library https://www.frontlinejournals.org/journals/index.php/gs-indexing/article/view/701 <p>This study evaluates three machine learning clustering algorithms—K-Means, DBSCAN, and Hierarchical Clustering—for customer segmentation in the banking sector. Using a dataset of customer demographic, financial, and transactional data, we compare the algorithms based on the Silhouette score and Davies-Bouldin index. Hierarchical Clustering performed best, achieving the highest Silhouette score (0.68) and the lowest Davies-Bouldin index (1.15), indicating well-defined and compact clusters. K-Means showed reliable performance with a Silhouette score of 0.62 but required predefined clusters. DBSCAN identified noise effectively but resulted in lower cluster compactness, with a Silhouette score of 0.55 and a Davies-Bouldin index of 1.50. The findings highlight Hierarchical Clustering as the most effective method for customer segmentation in banking, with flexibility depending on the data and objectives.</p> Md Amran Hossen Pabel Biswanath Bhattacharjee Sonjoy Kumar Dey Sakib Salam Jamee Md Omar Obaid Md Sakib Mia Sajidul Islam Khan Mohammad Kawsur Sharif Copyright (c) 2025 Md Amran Hossen Pabel, Biswanath Bhattacharjee, Sonjoy Kumar Dey, Sakib Salam Jamee, Md Omar Obaid, Md Sakib Mia, Sajidul Islam Khan, Mohammad Kawsur Sharif https://creativecommons.org/licenses/by/4.0 2025-03-06 2025-03-06 1 13