# Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection

arXiv: https://arxiv.org/abs/2208.07963 · HTML: https://mlsanity.com/paper/2208.07963/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 16 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [A rigorous and robust quantum speed-up in supervised machine learning](https://mlsanity.com/paper/2010.02174/) (2020)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [Quantum Ensemble for Classification](https://mlsanity.com/paper/2007.01028/) (2020)

## Built on by (most cited first, 15 of 15)

- [Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models](https://mlsanity.com/paper/2308.05237/) (2023)
- [QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection](https://mlsanity.com/paper/2404.02595/) (2024)
- [Benchmarking quantum machine learning kernel training for classification tasks](https://mlsanity.com/paper/2408.10274/) (2024)
- [Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection](https://mlsanity.com/paper/2412.19441/) (2024)
- [In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methods](https://mlsanity.com/paper/2407.15776/) (2024)
- [From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks](https://mlsanity.com/paper/2408.06524/) (2024)
- [Several fitness functions and entanglement gates in quantum kernel generation](https://mlsanity.com/paper/2309.03307/) (2023)
- [Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection](https://mlsanity.com/paper/2505.00137/) (2025)
- [Towards Transparency in Coreference Resolution: A Quantum-Inspired Approach](https://mlsanity.com/paper/2312.00688/) (2023)
- [Quantum feature-map learning with reduced resource overhead](https://mlsanity.com/paper/2510.03389/) (2025)
- [Quantum Algorithms: A New Frontier in Financial Crime Prevention](https://mlsanity.com/paper/2403.18322/) (2024)
- [Machine Learning for Quantum Computing Specialists](https://mlsanity.com/paper/2404.18555/) (2024)
- [Hybrid Heuristic Algorithms for Adiabatic Quantum Machine Learning Models](https://mlsanity.com/paper/2407.21062/) (2024)
- [Quantum feature encoding optimization](https://mlsanity.com/paper/2512.02422/) (2025)
- [Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection](https://mlsanity.com/paper/2605.21164/) (2026)

## Abstract (arXiv)

This paper presents a first end-to-end application of a Quantum Support Vector Machine (QSVM) algorithm for a classification problem in the financial payment industry using the IBM Safer Payments and IBM Quantum Computers via the Qiskit software stack. Based on real card payment data, a thorough comparison is performed to assess the complementary impact brought in by the current state-of-the-art Quantum Machine Learning algorithms with respect to the Classical Approach. A new method to search for best features is explored using the Quantum Support Vector Machine's feature map characteristics. The results are compared using fraud specific key performance indicators: Accuracy, Recall, and False Positive Rate, extracted from analyses based on human expertise (rule decisions), classical machine learning algorithms (Random Forest, XGBoost) and quantum based machine learning algorithms using QSVM. In addition, a hybrid classical-quantum approach is explored by using an ensemble model that combines classical and quantum algorithms to better improve the fraud prevention decision. We found, as expected, that the results highly depend on feature selections and algorithms that are used to select them. The QSVM provides a complementary exploration of the feature space which led to an improved accuracy of the mixed quantum-classical method for fraud detection, on a drastically reduced data set to fit current state of Quantum Hardware.