Quantum Graph Neural Network for Financial Fraud Detection

Financial fraud has become a major challenge with the rapid growth of online banking, digital payments,and e-commerce, causing significant financial losses. Traditional fraud detection methods often analysetransactions independently, making it difficult to identify complex patterns involving multiple entities. Graph Neural Networks (GNNs) address this limitation by representing financial transactions as graphs and learning relationships among accounts, users, and merchants. This work proposes an Adaptive Hybrid Quantum Graph Neural Network (AHQGNN) that combines graph-based learning with quantum machine learning. A classical GNN processes the transaction graph, classifying high-confidence predictions directly, while uncertain predictions are selectively analysed using a Variational Quantum Circuit (VQC). This adaptive strategy reduces unnecessary quantum computation while maintaining effective fraud detection. The framework is implemented using Python, PyTorch Geometric, and Qiskit, and evaluated using Accuracy, Precision, Recall, F1-Score, and ROC-AUC. The integration of GNNs with selective quantum classification provides an efficient and scalable framework for financial fraud detection in modern digital financial systems.