mlsanityData to 7 Oct 2026

Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models

arXiv 2308.05237 ↗First posted 9 Aug 2023quant-ph cs.LG q-fin.GNcited by 19

Not on the map. Why?

Abstract (arXiv)

In this research, a comparative study of four Quantum Machine Learning (QML) models was conducted for fraud detection in finance. We proved that the Quantum Support Vector Classifier model achieved the highest performance, with F1 scores of 0.98 for fraud and non-fraud classes. Other models like the Variational Quantum Classifier, Estimator Quantum Neural Network (QNN), and Sampler QNN demonstrate promising results, propelling the potential of QML classification for financial applications. While they exhibit certain limitations, the insights attained pave the way for future enhancements and optimisation strategies. However, challenges exist, including the need for more efficient Quantum algorithms and larger and more complex datasets. The article provides solutions to overcome current limitations and contributes new insights to the field of Quantum Machine Learning in fraud detection, with important implications for its future development.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 2 of its 41 references that are in the map. First posted on arXiv 9 Aug 2023.

Builds on

2 of 41 references.

References · 2

  1. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  2. 2022Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selectioncited by 15

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 6; 2026: 6.

19 papers.

Sort:

  1. 2024FedQNN: Federated Learning using Quantum Neural Networkscited by 20
  2. 2024QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detectioncited by 18
  3. 2024Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detectioncited by 10
  4. 2024QADQN: Quantum Attention Deep Q-Network for Financial Market Predictioncited by 8
  5. 2024LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networkscited by 8
  6. 2025A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detectioncited by 7
  7. 2025Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogatescited by 3
  8. 2025RobQFL: Robust Quantum Federated Learning in Adversarial Environmentcited by 2
  9. 2025Towards Quantum-Ready Blockchain Fraud Detection via Ensemble Graph Neural Networkscited by 2
  10. 2026Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Predictioncited by 2
  11. 2026Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Diseasecited by 2
  12. 2025PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifiercited by 1
7 more
  1. 2025IQNN-CS: Interpretable Quantum Neural Network for Credit Scoringcited by 1
  2. 2026QUPID: A Partitioned Quantum Neural Network for Anomaly Detection in Smart Gridcited by 1
  3. 2026Hybrid Quantum-Classical Neural Architecture Searchcited by 1
  4. 2024Quantum Algorithms: A New Frontier in Financial Crime Preventioncited by 0
  5. 2024Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Regioncited by 0
  6. 2026Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detectioncited by 0
  7. 2026QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generationcited by 0