Financial Fraud Detection using Quantum Graph Neural Networks
Nouhaila Innan Abhishek Sawaika Ashim Dhor Siddhant Dutta Sairupa Thota Husayn Gokal Nandan Patel Muhammad Al-Zafar Khan Ioannis Theodonis Mohamed Bennai
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.