<![CDATA[The use of credit cards in Indonesia has increased significantly, creating complex challenges for financial institutions in understanding user behavior and meeting their needs. This growth poses a higher risk of fraud, customer dissatisfaction due to unmet expectations, and financial instability for both consumers and banks. These issues highlight the urgency of conducting research to segment customers based on their usage behavior. The analyzed dataset includes information from 8,950 credit card users, covering transaction frequency, account balance, and transaction types. This study aims to segment customers using K-Means, DBSCAN, and Hierarchical Clustering algorithms. K-Means groups customers with similar behavioral patterns, DBSCAN identifies irregular clusters and outliers, while Hierarchical Clustering provides insights into relationships between clusters. The analysis results reveal four main segments, each with unique characteristics. For instance, the active user segment exhibits high transaction frequency and large balances, whereas new users demonstrate lower transaction frequency. These findings offer valuable insights for financial institutions to enhance their services and product offerings. By understanding the characteristics of each segment, financial institutions can tailor their marketing strategies and products to improve customer satisfaction and loyalty]]>
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