Use cases for emerging quantum computing platforms become economically relevant as the efficiency of processing and availability of quantum computers increase. We assess the performance of Restricted Boltzmann Machines (RBM) assisted by quantum computing, running on real quantum hardware and simulators, using a real dataset containing 145 million transactions provided by Stone, a leading Brazilian fintech, for credit card fraud detection. The results suggest that the quantum-assisted RBM method is able to achieve superior performance in most figures of merit in comparison to classical approaches, even using current noisy quantum annealers. Our study paves the way for implementing quantum-assisted RBMs for general fault detection in financial systems.
Use cases for emerging quantum computing platforms become economically relevant as the efficiency of processing and availability of quantum computers increase. We assess the performance of Restricted Boltzmann Machines (RBM) assisted by quantum computing, running on real quantum hardware and simulators, using a real dataset containing 145 million transactions provided by Stone, a leading Brazilian fintech, for credit card fraud detection. The results suggest that the quantum-assisted RBM method is able to achieve superior performance in most figures of merit in comparison to classical approaches, even using current noisy quantum annealers. Our study paves the way for implementing quantum-assisted RBMs for general fault detection in financial systems.
Use cases for emerging quantum computing platforms become economically relevant as the efficiency of processing and availability of quantum computers increase. We assess the performance of Restricted Boltzmann Machines (RBM) assisted by quantum computing, running on real quantum hardware and simulators, using a real dataset containing 145 million transactions provided by Stone, a leading Brazilian fintech, for credit card fraud detection. The results suggest that the quantum-assisted RBM method is able to achieve superior performance in most figures of merit in comparison to classical approaches, even using current noisy quantum annealers. Our study paves the way for implementing quantum-assisted RBMs for general fault detection in financial systems.
Credit Card Fraud Detection (CCFD) is a major concern for the financial industry, requiring rapid and accurate identification of fraudulent transactions amidst vast datasets. The rapid evolution of quantum computing presents transformative opportunities for enhancing Machine Learning (ML) applications, particularly in the domain of financial security. This paper explores the implementation of Quantum Machine Learning (QML) techniques for CCFD, a critical challenge in the financial industry characterized by imbalanced datasets. Traditional ML models, while effective, often struggle with scalability and computational efficiency when processing large-scale transactional data. QML leverages quantum phenomenon namely entanglement and superposition to potentially outperform classical algorithms in feature extraction and pattern recognition. Our study proposes a hybrid quantumclassical framework, integrating Variational Quantum Circuits (VQC) with classical Neural Networks (NN) to classify fraudulent transactions. Using a simulated quantum environment, we preprocess a publicly available credit card transaction dataset, encoding features into quantum states for processing through a parameterized quantum circuit. The model is trained to improves its ability to detect fraud accurately with fewer false positives, addressing the critical issue of customer inconvenience.
Credit card fraud detection is crucial for financial security which entails identifying unauthorized transactions that can result in significant financial losses. Detection is inherently challenging due to the rarity and indistinguishability of fraudulent transactions from genuine ones, which makes it an anomaly detection problem. Traditional detection systems struggle with the highly imbalanced nature of transaction datasets, where genuine transactions vastly outnumber fraudulent cases. In response to these challenges, we propose a novel detection model utilizing Quantum AutoEncoders-based Fraud Detection (QAE-FD). Our approach leverages quantum computing principles to enhance anomaly detection capabilities by encoding transaction data into compressed quantum states and optimizing the model against a loss function that evaluates the fidelity in flagging fraudulent transactions. The efficacy of the QAE-FD model is tested on a real-world credit card transaction dataset, achieving a G-mean of 0.946 and an AUC of 0.947 which demonstrates superior performance compared to existing models. Our results indicate that QAE-FD has not only higher accuracy in fraud detection but also better computational efficiency. The integration of quantum autoencoders is a promising advancement in the field of anomaly detection for credit card fraud, addressing the limitations of imbalanced datasets and offering a scalable solution for real-time detection systems.
A Deep Belief Network (DBN) is a generative model stacking multiple Restricted Boltzmann Machine (RBM) layers to learn hierarchical data representations. While effective for feature extraction, classical DBNs struggle with high-order patterns in complex, imbalanced datasets, such as credit card fraud data. To overcome this, we integrate quantum-inspired RBMs (QRBMs) into the DBN framework. This study compares four 3-layer DBN configurations on the Credit Card Fraud Detection dataset: (i) classical DBN (all RBM layers), (ii) 1-Quantum DBN (1 QRBM layer), (iii) 2-Quantum DBN (2 QRBM layers), and (iv) full Quantum DBN (all QRBM layers). Models were trained via contrastive divergence and assessed using precision, recall, and F1-score. Results show the full Quantum DBN outperforming others: precision 0.581, recall 0.637, F1-score 0.602—yielding a 34.4% F1 improvement over classical DBN (precision 0.319, recall 0.755, F1 0.448). Hybrids ranked intermediately. Quantum advantages stem from entanglement and superposition, fostering complex pattern capture and faster convergence (fewer epochs). These findings highlight quantum-enhanced DBNs’ potential for scalable anomaly detection in financial fraud systems, paving the way for hybrid quantum-classical ML applications.
A Deep Belief Network (DBN) is a generative model stacking multiple Restricted Boltzmann Machine (RBM) layers to learn hierarchical data representations. While effective for feature extraction, classical DBNs struggle with high-order patterns in complex, imbalanced datasets, such as credit card fraud data. To overcome this, we integrate quantum-inspired RBMs (QRBMs) into the DBN framework. This study compares four 3-layer DBN configurations on the Credit Card Fraud Detection dataset: (i) classical DBN (all RBM layers), (ii) 1-Quantum DBN (1 QRBM layer), (iii) 2-Quantum DBN (2 QRBM layers), and (iv) full Quantum DBN (all QRBM layers). Models were trained via contrastive divergence and assessed using precision, recall, and F1-score. Results show the full Quantum DBN outperforming others: precision 0.581, recall 0.637, F1-score 0.602—yielding a 34.4% F1 improvement over classical DBN (precision 0.319, recall 0.755, F1 0.448). Hybrids ranked intermediately. Quantum advantages stem from entanglement and superposition, fostering complex pattern capture and faster convergence (fewer epochs). These findings highlight quantum-enhanced DBNs’ potential for scalable anomaly detection in financial fraud systems, paving the way for hybrid quantum-classical ML 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.