Quantum computing greatly benefit a wide range of fields, including the detection of credit card fraud, through its twin characteristics of improved computational power and efficiency. Quantum algorithms are used to enhance data analysis, pattern recognition and optimization properly with increased speed and efficiency. In the proposed model, we investigate how quantum machine learnings algorithms (QML) are used to spot credit card fraud using a data set that includes transactions from European cardholders. Various models such as neural network activation functions, epochs, and other factors employed in a hybrid quantum-classical method. The results from the best model employing a quantum hybrid technique, a conventional neural network, and an ML approach are compared. For equivalent prediction performance, we find that the quantum models train substantially more effectively than the conventional models. In order to implement Quantum algorithms, penny lane is used for enabling differentiable scripting of quantum computers that is cross-platform. Similar to how a neural network is trained, a quantum computer is trained. Overall, this in-depth investigation of the application of hybrid QML in credit card fraud detection shows significant promise.Quantum computing greatly benefit a wide range of fields, including the detection of credit card fraud, through its twin characteristics of improved computational power and efficiency. Quantum algorithms are used to enhance data analysis, pattern recognition and optimization properly with increased speed and efficiency. In the proposed model, we investigate how quantum machine learnings algorithms (QML) are used to spot credit card fraud using a data set that includes transactions from European cardholders. Various models such as neural network activation functions, epochs, and other factors employed in a hybrid quantum-classical method. The results from the best model employing a quantum hybrid technique, a conventional neural network, and an ML approach are compared. For equivalent prediction performance, we find that the quantum models train substantially more effectively than the conventional models. In order to implement Quantum algorithms, penny lane is used for enabling differentiable scripting of quantum computers that is cross-platform. Similar to how a neural network is trained, a quantum computer is trained. Overall, this in-depth investigation of the application of hybrid QML in credit card fraud detection shows significant promise.

International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)Oct 23, 20244 citations

A Mixture-of-Experts Framework for Practical Hybrid-Quantum Models in Credit Card Fraud Detection

Rodrigo Chaves, Kunal Kumar, Bruno Chagas +4 more

This paper investigates whether hybrid quantum-classical machine learning can deliver practical improvements in financial fraud detection performance for card-based and other payment transactions. Building on a Guided Quantum Compressor architecture, the approach integrates an autoencoder, a variational quantum circuit, and a classical neural head, and then embeds this hybrid model into a mixture-of-experts framework including a state-of-the-art gradient-boosted tree classifier. Using a European credit card dataset with severe class imbalance, the routed hybrid architecture with 0.6 threshold achieves average precision scores of $0.793\pm0.085$ compared to $0.770\pm0.096$ of XGBoost on 3 repeated 5-fold cross-validation benchmarks. Precision and recall comparisons reveals a possible trade-off of fraud and nominal detections with a reduction in false positives at the cost of a small reduction in fraud detections. The improvements are achieved while adding only 7 to 21 minutes of extra inference time depending on the choice of hyperparameters. These results indicate that selectively routing transactions to quantum-classical models can enhance fraud detection while remaining compatible with the latency and operational constraints of modern financial institutions.This paper investigates whether hybrid quantum-classical machine learning can deliver practical improvements in financial fraud detection performance for card-based and other payment transactions. Building on a Guided Quantum Compressor architecture, the approach integrates an autoencoder, a variational quantum circuit, and a classical neural head, and then embeds this hybrid model into a mixture-of-experts framework including a state-of-the-art gradient-boosted tree classifier. Using a European credit card dataset with severe class imbalance, the routed hybrid architecture with 0.6 threshold achieves average precision scores of $0.793\pm0.085$ compared to $0.770\pm0.096$ of XGBoost on 3 repeated 5-fold cross-validation benchmarks. Precision and recall comparisons reveals a possible trade-off of fraud and nominal detections with a reduction in false positives at the cost of a small reduction in fraud detections. The improvements are achieved while adding only 7 to 21 minutes of extra inference time depending on the choice of hyperparameters. These results indicate that selectively routing transactions to quantum-classical models can enhance fraud detection while remaining compatible with the latency and operational constraints of modern financial institutions.

quant-pharXiv:2603.06473Mar 9, 20260 citations

CREDIT CARD FRAUD DETECTION USING QUANTUM MACHINE LEARNING: A HYBRID CLASSICAL-QUANTUM APPROACH

Akanksha Reddy, A. Sripriya, J. Nihanth Reddy +3 more

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.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.

Research Digest on Engineering Management and Social InnovationsApr 24, 20260 citations

0.793\pm0.085

compared to

0.770\pm0.096

of XGBoost on 3 repeated 5-fold cross-validation benchmarks. Precision and recall comparisons reveals a possible trade-off of fraud and nominal detections with a reduction in false positives at the cost of a small reduction in fraud detections. The improvements are achieved while adding only 7 to 21 minutes of extra inference time depending on the choice of hyperparameters. These results indicate that selectively routing transactions to quantum-classical models can enhance fraud detection while remaining compatible with the latency and operational constraints of modern financial institutions.