Detecting fraudulent activity in credit card transactions poses a serious challenge for financial institutions, which requires robust techniques that can accurately pinpoint fraudulent occurrences while minimizing false positives. In this study, we introduce an innovative strategy to enhance Credit Card Fraud Detection (CCFD) by utilizing Knowledge Graphs and Centrality measures. We propose creating a Knowledge Graph (KG) representing the credit card transaction network so as to capture connections and correlations between the transactions, and analyzing the KG to evaluate centrality measures that capture the importance of nodes and relationships within the graph. These centrality measures are utilized to enhance the input features that are used to train Machine Learning classifiers for fraud detection. Our experiments show that using the enhanced features significantly improved classification performance, providing better identification of fraudulent transactions, especially through the combination of HITS and degree centrality.