<![CDATA[Credit card fraud detection faces two major challenges: severe class imbalance and the limited ability of conventional feature-based models to capture relational patterns among transactions. This study proposes a graph-based Hybrid GNN-Transformer architecture for imbalanced credit card fraud detection by integrating transaction-level relational learning through k-nearest neighbor graph construction and feature-interaction learning through multi-head self-attention. The novelty of this study lies in combining graph-based transaction modeling and Transformer-based feature interaction within a unified architecture. Using the selected graph configuration and validation-based threshold tuning, the proposed model achieved 79.71% precision, 74.32% recall, 76.92% F1-score, 96.06% ROC-AUC, and 68.65% PR-AUC. Compared with Logistic Regression, Random Forest, and Gradient Boosting baselines, the hybrid model showed competitive fraud detection sensitivity, although the baseline classifiers still achieved stronger overall F1-score and PR-AUC. Ablation results show that the hybrid architecture improves minority-class detection compared with single-branch variants by combining relational transaction information from the GNN branch and feature-interaction information from the Transformer branch. These findings indicate that graph-based hybrid representation learning is a promising direction for imbalanced fraud detection, while further optimization is still required to improve precision-recall balance and competitiveness against strong feature-based baselines.]]>
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