Financial fraud continues to pose a formidable challenge to institutions and governments globally, costing billions annually and threatening the integrity of digital financial ecosystems. Traditional rule-based systems and isolated machine learning models, while useful, often fall short in capturing the complex, non-linear, and relational patterns that characterize modern fraud schemes. This paper introduces a hybrid artificial intelligence (AI) approach that combines Graph Neural Networks (GNNs) with transactional behavior pattern analysis to detect financial fraud more effectively. By leveraging GNNs, we are able to model the intricate network of relationships among entities accounts, merchants, and devices uncovering anomalous link structures that are not evident through tabular analysis alone. Complementing this, transactional pattern analysis extracts temporal features such as frequency, value distribution, and merchant categories to identify deviations from normative financial behavior. The fusion of graph embeddings and behavior-driven features enables a multi-dimensional understanding of fraud, allowing for early detection of complex scams such as synthetic identities, layering in money laundering, and collusive fraud rings. The study utilizes both real-world financial transaction datasets and simulated synthetic fraud scenarios to evaluate the hybrid model. Performance benchmarks show significant improvement in precision, recall, and Area Under the ROC Curve (AUC) compared to baseline logistic regression, random forest, and standalone GNN models. Additionally, the system demonstrates adaptability across different financial sectors including retail banking, digital wallets, and cryptocurrency exchanges. This research provides a scalable, explainable, and domain-agnostic framework for institutions seeking to enhance their fraud detection systems through AI, particularly in an era of rapidly evolving fraud typologies and expanding digital finance infrastructures.