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IEEE International Conference on AI and Data Analytics (ICAD)

Quantum-Hybrid Deep Learning Ensemble for Credit Card Fraud Detection

Tanay Kamlesh Patel, Todd M.W. Hodges, Glen S. Uehara, Sutapa Samanta, Dagen Wang, Andras Ferenczi, Andreas Spanias

Published Jun 1, 2026• 1 citation
Publisher
DOI
10.1109/icad69378.2026.11609125

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This paper introduces a novel hybrid quantum-classical approach to credit card fraud detection using CVQBoost, a hybrid quantum-classical boosting algorithm executed on the photonic Dirac-3 processor from Quantum Computing Inc. (QCi). By integrating a diverse set of weak classifiers, which includes K-nearest neighbours (KNN), linear discriminant analysis, logistic regression, and XGBoost, within a hybrid quantum-classical ensemble, the proposed method demonstrates significant improvements over the latest published classical benchmarks. Experiments on a Kaggle credit card fraud dataset show that the quantum-enhanced model achieves a mean AUC-PR score of over 0.8, corresponding to an approximately 9% relative improvement over the best published classical baseline. This indicates an improved precision–recall trade-off which can reduce false positives at a fixed recall in operational settings. The study also highlights the trade-off between training runtime and detection performance, with KNN-based ensembles offering superior accuracy at higher computational cost. Results indicate that quantum machine learning pipelines leveraging photonic processors can deliver tangible advantages in rare-event detection tasks, suggesting a promising direction for operational fraud analytics in finance.
With the rapid growth of digital financial transactions, credit card fraud detection has become a critical challenge in the banking and finance sector. Traditional rule-based systems and classical machine learning (ML) models often struggle to detect complex and evolving fraud patterns, especially in highly imbalanced datasets. This paper proposes a novel hybrid approach to credit card fraud detection using Quantum Machine Learning (QML) techniques combined with classical methods. The system applies Principal Component Analysis (PCA) for dimensionality reduction and Synthetic Minority Oversampling Technique (SMOTE) to handle class imbalance. A classical Random Forest model is implemented as a baseline, while a QML model based on Variational Quantum Circuits (VQC) is developed using PennyLane. A hybrid decision engine combining predictions of both models is designed for improved performance. Experimental results demonstrate that the hybrid model achieves 96.9% accuracy, outperforming individual models by achieving better detection rates and reducing false predictions. The system is deployed via an interactive Streamlit-based dashboard. This work highlights the potential of Quantum Machine Learning in enhancing fraud detection systems in financial security applications.