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

arXiv: https://arxiv.org/abs/2308.05237 · HTML: https://mlsanity.com/paper/2308.05237/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 41 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection](https://mlsanity.com/paper/2208.07963/) (2022)

## Built on by (most cited first, 19 of 19)

- [FedQNN: Federated Learning using Quantum Neural Networks](https://mlsanity.com/paper/2403.10861/) (2024)
- [QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection](https://mlsanity.com/paper/2404.02595/) (2024)
- [Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection](https://mlsanity.com/paper/2412.19441/) (2024)
- [QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction](https://mlsanity.com/paper/2408.03088/) (2024)
- [LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networks](https://mlsanity.com/paper/2412.03158/) (2024)
- [A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection](https://mlsanity.com/paper/2507.22908/) (2025)
- [Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogates](https://mlsanity.com/paper/2512.09586/) (2025)
- [RobQFL: Robust Quantum Federated Learning in Adversarial Environment](https://mlsanity.com/paper/2509.04914/) (2025)
- [Towards Quantum-Ready Blockchain Fraud Detection via Ensemble Graph Neural Networks](https://mlsanity.com/paper/2509.23101/) (2025)
- [Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Prediction](https://mlsanity.com/paper/2601.03802/) (2026)
- [Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease](https://mlsanity.com/paper/2604.13608/) (2026)
- [PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier](https://mlsanity.com/paper/2503.01799/) (2025)
- [IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring](https://mlsanity.com/paper/2510.15044/) (2025)
- [QUPID: A Partitioned Quantum Neural Network for Anomaly Detection in Smart Grid](https://mlsanity.com/paper/2601.11500/) (2026)
- [Hybrid Quantum-Classical Neural Architecture Search](https://mlsanity.com/paper/2605.18345/) (2026)
- [Quantum Algorithms: A New Frontier in Financial Crime Prevention](https://mlsanity.com/paper/2403.18322/) (2024)
- [Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region](https://mlsanity.com/paper/2411.18141/) (2024)
- [Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection](https://mlsanity.com/paper/2605.21164/) (2026)
- [QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation](https://mlsanity.com/paper/2606.04689/) (2026)

## 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.