Financial Fraud Detection using Quantum Graph Neural Networks

TL;DR

This paper introduces Quantum Graph Neural Networks (QGNNs) enhanced with Variational Quantum Circuits for financial fraud detection, demonstrating improved performance over classical GNNs on real-world data.

Contribution

It presents a novel quantum neural network architecture for graph data, combining quantum computing with GNNs to enhance fraud detection capabilities.

Findings

QGNNs achieved an AUC of 0.85 on real-world dataset

QGNNs outperformed classical GNNs in detection accuracy

Quantum-enhanced GNNs show promise for financial fraud detection

Abstract

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.