mlsanityData to 14 Oct 2026

Financial Fraud Detection using Quantum Graph Neural Networks

arXiv 2309.01127 ↗First posted 3 Sep 2023quant-ph cs.AI cs.LGcited by 35

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Abstract (arXiv)

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.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). Builds on 6 of its 23 references that are in the map. First posted on arXiv 3 Sep 2023.

Builds on

6 of 23 references.

References · 6

  1. 2017An introduction to Topological Data Analysis: fundamental and practical aspects for data scientistscited by 172
  2. 2021E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTcited by 56
  3. 2021Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstructioncited by 20
  4. 2021Stability of Graph Convolutional Neural Networks to Stochastic Perturbationscited by 18
  5. 2021Quantum Graph Convolutional Neural Networkscited by 18
  6. 2021Training Stable Graph Neural Networks Through Constrained Learningcited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 11; 2025: 14; 2026: 10.

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  1. 2024Graph Neural Networks for Financial Fraud Detection: A Reviewcited by 25
  2. 2024FedQNN: Federated Learning using Quantum Neural Networkscited by 20
  3. 2024QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detectioncited by 18
  4. 2024Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detectioncited by 10
  5. 2025HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Predictioncited by 10
  6. 2024QADQN: Quantum Attention Deep Q-Network for Financial Market Predictioncited by 9
  7. 2024LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networkscited by 9
  8. 2025A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAGcited by 9
  9. 2025A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detectioncited by 7
  10. 2024From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networkscited by 6
  11. 2025Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Reviewcited by 4
  12. 2025QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolutioncited by 2
23 more
  1. 2025Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detectioncited by 2
  2. 2025RobQFL: Robust Quantum Federated Learning in Adversarial Environmentcited by 2
  3. 2025Quantum Federated Learning: Architectural Elements and Future Directionscited by 2
  4. 2026Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Predictioncited by 2
  5. 2026Q-AGNN: Quantum-Enhanced Attentive Graph Neural Network for Intrusion Detectioncited by 2
  6. 2026Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Diseasecited by 2
  7. 2024A unifying primary framework for quantum graph neural networks from quantum graph statescited by 1
  8. 2024Cybercrime Prediction via Geographically Weighted Learningcited by 1
  9. 2025Learnable quantum spectral filters for hybrid graph neural networkscited by 1
  10. 2025IQNN-CS: Interpretable Quantum Neural Network for Credit Scoringcited by 1
  11. 2026Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learningcited by 1
  12. 2026How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representationscited by 1
  13. 2024High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel Approachcited by 0
  14. 2024Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Regioncited by 0
  15. 2025QFDNN: A Resource-Efficient Variational Quantum Feature Deep Neural Networks for Fraud Detection and Loan Predictioncited by 0
  16. 2025QSearchNet: A Quantum Walk Search Framework for Link Predictioncited by 0
  17. 2025Quantum Topological Graph Neural Networks for Detecting Complex Fraud Patternscited by 0
  18. 2025A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platformscited by 0
  19. 2026MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environmentscited by 0
  20. 2026Quantum inspired qubit qutrit neural networks for real time financial forecastingcited by 0
  21. 2026Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detectioncited by 0
  22. 2026A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Predictioncited by 0
  23. 2026QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generationcited by 0