Detecting fraud within financial systems has become important due to the continuous evolution of fraudulent techniques to evade existing detection methods. Credit card fraud is one of the most common forms of payment fraud and is steadily rising globally. In this work, we proposed a framework that leverages three distinct multi-view graphs, capturing relationships between cardholders and merchants, transaction categories, and geospatial merchant clusters. These are integrated with a temporally causal transaction sequence graph to enhance fraud detection in credit card transactions. To effectively learn meaningful edge representations, the framework employs a Graph Attention Network (GAT) for fusing the heterogeneous features derived from these graph views. We also mitigate the challenge of class imbalance using the Synthetic Minority Oversampling Technique (SMOTE), facilitating two experimental settings, one using an undersampled dataset and the other using an oversampled dataset through bootstrapped minority instances. We perform various experiments, and the proposed model demonstrates significantly high performance compared to traditional models and current state-of-the-art approaches, attaining an accuracy of 88.93% across two balanced datasets.