Financial fraud detection is essential for preventing significant financial
losses and maintaining the reputation of financial institutions. However,
conventional methods of detecting financial fraud have limited effectiveness,
necessitating the need for new approaches to improve detection rates. In this
paper, we propose a novel approach for detecting financial fraud using Quantum
Graph Neural Networks (QGNNs). QGNNs are a type of neural network that can
process graph-structured data and leverage the power of Quantum Computing (QC)
to perform computations more efficiently than classical neural networks. Our
approach uses Variational Quantum Circuits (VQC) to enhance the performance of
the QGNN. In order to evaluate the efficiency of our proposed method, we
compared the performance of QGNNs to Classical Graph Neural Networks using a
real-world financial fraud detection dataset. The results of our experiments
showed that QGNNs achieved an AUC of $0.85$, which outperformed classical GNNs.
Our research highlights the potential of QGNNs and suggests that QGNNs are a
promising new approach for improving financial fraud detection.

提出一种利用量子图神经网络和变分量子电路检测金融欺诈的新方法，通过与经典图神经网络的比较实验证明该方法在金融欺诈检测方面具有更高的性能。