<![CDATA[Credit cards are non-cash payment tools that use cards issued by banks. Fraud in credit card payment transactions is a significant issue for the banking and financial industry. With the increasing number of digital transactions, early detection of potential fraud is essential to protect consumers and financial institutions. One of the techniques that can be used is clustering, which allows data to be grouped based on similar characteristics without requiring specific labels. This study aims to analyze potential fraud in credit card bill payments using the K-Means Clustering approach, with model evaluation results including a Silhouette Score of 0.5211 and a Davies-Bouldin Index (DBI) score of 0.8293. This research is expected to provide deeper insights into the use of K-Means Clustering for detecting potential fraud. The study is not limited to identifying fraud in bank data alone but can also be applied in various sectors that are vulnerable to unauthorized or suspicious transactions.]]>
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