Credit card fraud detection is a critical task in financial security, as fraudulent transactions are rare, highly imbalanced, and often resemble legitimate ones. A wide range of classical machine learning methods, as well as more recent quantum machine learning approaches, have been investigated to address this challenge, each providing valuable progress but also leaving open questions regarding scalability, robustness, and adaptability to evolving fraud patterns. In this work, we introduce the Fidelity-based Quantum Autoencoder (FiD-QAE), a quantum architecture that employs fidelity estimation as the decision criterion for anomaly detection. Transactions are encoded into quantum states, compressed through a variational quantum circuit, and evaluated using the SWAP test to distinguish legitimate from fraudulent transactions. We conduct a comprehensive evaluation of FiD-QAE, including statistical analyses, multiple performance metrics, and robustness tests under quantum noise models. The results show that FiD-QAE maintains consistent performance across different imbalance levels and preserves robustness in noisy conditions. Moreover, validation on IBM Quantum hardware backends confirms the feasibility of our approach on real devices, with outcomes consistent with simulation. These findings position quantum fidelity as a powerful criterion for anomaly detection and highlight FiD-QAE as a promising direction that complements existing classical and quantum approaches, offering robustness and generalizability for financial fraud detection in realistic environments.
Credit card fraud detection is a critical task in financial security, as fraudulent transactions are rare, highly imbalanced, and often resemble legitimate ones. A wide range of classical machine learning methods, as well as more recent quantum machine learning approaches, have been investigated to address this challenge, each providing valuable progress but also leaving open questions regarding scalability, robustness, and adaptability to evolving fraud patterns. In this work, we introduce the Fidelity-based Quantum Autoencoder (FiD-QAE), a quantum architecture that employs fidelity estimation as the decision criterion for anomaly detection. Transactions are encoded into quantum states, compressed through a variational quantum circuit, and evaluated using the SWAP test to distinguish legitimate from fraudulent transactions. We conduct a comprehensive evaluation of FiD-QAE, including statistical analyses, multiple performance metrics, and robustness tests under quantum noise models. The results show that FiD-QAE maintains consistent performance across different imbalance levels and preserves robustness in noisy conditions. Moreover, validation on IBM Quantum hardware backends confirms the feasibility of our approach on real devices, with outcomes consistent with simulation. These findings position quantum fidelity as a powerful criterion for anomaly detection and highlight FiD-QAE as a promising direction that complements existing classical and quantum approaches, offering robustness and generalizability for financial fraud detection in realistic environments.
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.
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.
Quantum Auto-Encoders (QAEs) represent a transformative approach to anomaly detection, leveraging quantum computational power to address challenges in domains such as healthcare and finance. In this study, we focus on detecting anomalies in breast cancer diagnosis and fraudulent financial transactions. QAEs are trained exclusively on non-anomalous data, enabling anomaly detection through their inability to faithfully reconstruct anomalous points. Reconstruction quality is evaluated using fidelity tests for simulations and SWAP tests for quantum hardware, ensuring reliable and robust performance across diverse environments. Our work introduces QEAFAD: Quantum Enhanced Autoencoders for Anomaly Detection, incorporating enhanced and patch-based architectures that demonstrate significant advancements in precision. By integrating quantum entanglement into the encoding and decoding processes, via shared Bell pairs and direct qubit entanglement. Our framework improves correlations and delivers statistically significant performance gains. Benchmarks on the MNIST dataset validate the scalability of our approach, while JAX-accelerated optimization reduces training time on financial datasets. Experiments on IBM and Amazon quantum hardware confirm the practical feasibility of QAEs. These advancements highlight the efficiency of QAEs, achieving superior accuracy with reduced computational resources, making them a compelling solution for real-world anomaly detection challenges in critical applications.
Detecting fraudulent financial transactions is inherently challenging due to extreme class imbalance, complex high-dimensional feature spaces, and the adaptive strategies employed by fraudsters.To overcome these limitations, this work presents a hybrid quantum-classical fraud detection framework that strategically combines conventional machine learning models with quantum computing techniques to enhance predictive reliability.Rich feature representations are constructed by integrating transactional, behavioral, and graph-based attributes to capture both local and relational fraud characteristics.A Quantum Autoencoder (QAE) is utilized to perform efficient dimensionality reduction, enabling compact feature encoding while retaining essential discriminatory patterns.The reduced feature set is subsequently processed using Cat-Boost, alongside Quantum Neural Networks (QNNs) or Variation Quantum Classifiers (VQCs) to leverage complementary learning capabilities.Model outputs are aggregated through a stacking-based meta-classification strategy, resulting in improved generalization and decision stability.Performance evaluation is carried out using fraud-oriented metrics, including recall, precision, accuracy, and false positive rate.Experimental analysis indicates that the proposed hybrid approach consistently surpasses standalone classical and quantum models, achieving superior fraud recall with notably fewer false alarms.These results demonstrate the robustness, scalability, and practical viability of quantum-classical integration, positioning the proposed framework as a promising foundation for next-generation financial fraud detection systems.
Credit card fraud detection is still one of the significant challenges in the digital economy, and most traditional machine learning methods appear to be inadequate fordealing with highly imbalanced datasets and intricate non-linear fraud patterns. This paper introduces and tests Q-fusion enhanced hybrid models, which exploit Quantum-Inspired FeatureMaps to embed classical transaction data in high-dimensional Hilbert spaces with the aim of increasing class distinguishability and performance. Five quantum-boosting classifiers were performed includingand described: Quantum Support Vector Machine (QSVM), Quantum Logistic Regression (QLR), Quantum K-Nearest Neighbors (QKNN), Quantum Gaussian Naive Bayes (QGNB) and Quantum Multi-Layer Perceptron (QMLP). The results show that the Quantum MLP model provided the best performance and reached an accuracy of 99.35%, precision of 0.96, recall of0.92, F1-score of 0.94 and ROC-AUC score of 0.97 which outperformed all quantum-enhanced models as well as classical ANN model.. Quantum feature mapping greatly improved feature separability, especially for minority class fraudulent transactionsand remained compatible with classical infrastructure. These results identify quantum-inspired machine learning as a promising route for enhancing fraud detection in severely imbalanced data sets and can berealized without deploying full-scale quantum hardware. implementation faces hardware limitations ( 27), algorithmic complexity (28), and infrastructure integration challenges (29). Quantum-inspired approaches provide a pragmatic pathway, offering quantum-like advantages through classical simulation (30; 31). Research Problem and Contribution Credit card fraud detection faces interconnected challenges: (1) extreme class imbalance creates severe classifier bias where models achieve high accuracy by predicting all transactions as legitimate while missing fraud (32; 33); (2) feature engineering requires domain expertise to identify transaction attributes, temporal patterns, and behavioral anomalies (14; 34); (3) real-world systems require millisecond classification (35); (4) regulatory compliance demands transparent, explainable systems (36; 37). Despite growing QML interest, empirical evaluations for fraud detection remain limited. Most studies focus either on purely classical methods (8; 32) or theoretical quantum algorithms requiring full hardware (30; 38), creating a gap in practical quantum-inspired implementations. Recent surveys identify this as a critical frontier (26; 39; 40). This research contributions: (1) Implementation and evaluation of five quantum-enhanced hybrid models (QSVM, QLR, QKNN, QGNB, QMLP); (2) Demonstration that quantum-inspired transformations project data into higher-dimensional Hilbert spaces, improving fraud class separability; (3) Empirical evidence of substantial gains (99.35% accuracy, 0.97 ROC-AUC) on classical hardware; (4) Rigorous comparative evaluation with consistent preprocessing and comprehensive metrics; (5) Identification that Quantum MLP achieves synergistic performance combining quantum features with deep learning. Research Objectives This research aims to: (1) develop quantum-inspired feature mapping transforming transaction data into Hilbert space representations; (2) design and evaluate five quantum-enhanced classifiers; (3) conduct comprehensive performance evaluation using imbalanced classification metrics; (4) quantify performance improvements from quantum-inspired transformations; (5) assess computational feasibility for production deployment; (6) provide theoretical explanations based on Hilbert space geometry. Paper Organization Section 2 reviews related work in machine learning fraud detection, quantum computing, and quantum machine learning. Section 3 describes methodology including preprocessing, quantum feature mapping, classifier architectures, and evaluation metrics. Section 4 presents experimental results with performance analysis. Section 5 concludes with key findings and future directions. 2 Related Work 2.1 Classical Machine Learning for Fraud Detection Credit card fraud detection has been extensively studied using classical approaches. Early work established cost-sensitive learning to address class imbalance (41). Logistic Regression provides interpretability and probabilistic outputs (3) but struggles with nonlinear patterns (7). Support Vector Machines identify maximum-margin hyperplanes (5) but face computational challenges (O(n 2 )-O(n 3 )) (42) and performance degradation on imbalanced data (8). Ensemble methods including Random Forest (4) and Gradient Boosting (43; 44) achieve improved stability. Deep learning approaches-Multi-Layer Perceptrons with modern optimization (ReLU (45), batch normalization (46), dropout (47), Adam (48))-achieve state-of-the-art performance with preprocessing (49). Recurrent architectures (LSTM (50), GRU (51)) capture temporal patterns (52), while Autoencoders frame fraud as anomaly detection (53; 54). Imbalance Handling Techniques Class imbalance (¡0.2% fraud) (13) necessitates specialized techniques. SMOTE generates synthetic minority samples through interpolation (6), while SMOTEENN combines SMOTE with Edited Nearest Neighbors for noise reduction (55; 56). Cost-sensitive learning assigns different misclassification penalties (33; 57). Ensemble methods train multiple classifiers on balanced subsets (58; 59).
Social financial technology focuses on trust, sustainability, and social responsibility, which require advanced technologies to address complex financial tasks in the digital era. With the rapid growth in online transactions, automating credit card fraud detection and loan eligibility prediction has become increasingly challenging. Classical machine learning (ML) models have been used to solve these challenges; however, these approaches often encounter scalability, overfitting, and high computational costs due to complexity and high-dimensional financial data. Quantum computing (QC) and quantum machine learning (QML) provide a promising solution to efficiently processing high-dimensional datasets and enabling real-time identification of subtle fraud patterns. However, existing quantum algorithms lack robustness in noisy environments and fail to optimize performance with reduced feature sets. To address these limitations, we propose a quantum feature deep neural network (QFDNN), a novel, resource efficient, and noise-resilient quantum model that optimizes feature representation while requiring fewer qubits and simpler variational circuits. The model is evaluated using credit card fraud detection and loan eligibility prediction datasets, achieving competitive accuracies of 82.2% and 74.4%, respectively, with reduced computational overhead. Furthermore, we test QFDNN against six noise models, demonstrating its robustness across various error conditions. Our findings highlight QFDNN’s potential to enhance trust and security in social financial technology by accurately detecting fraudulent transactions while supporting sustainability through its resource-efficient design and minimal computational overhead.