Financial fraud detection is a critical challenge requiring accurate identification of anomalous patterns in complex transaction networks. Graph Neural Networks (GNNs) have emerged as powerful tools for fraud detection by capturing relational structures among entities. Meanwhile, quantum computing offers new possibilities to enhance machine learning through high-dimensional Hilbert spaces and parallelism. In this paper, we propose a hybrid classical-quantum model called QCTGNN (Quantum Chebyshev Transform-based Graph Neural Network) for financial fraud detection. The QCTGNN integrates a classical graph neural network component based on Simplified Graph Convolutions (SGConv) with a quantum component that performs a Chebyshev polynomial-based transform via variational quantum circuits. The quantum component employs angle encoding of node features, Chebyshev polynomial embedding to enrich feature representations, multiple layers of parameterized RY rotations with entangling gates, and Pauli-Z measurements for readout. To address the noise and instability of Noisy Intermediate-Scale Quantum (NISQ) devices, we incorporate Noise-Aware Quantum Calibration (NQC) that adjusts for decoherence and readout bias in the quantum outputs. Our system also supports adaptive graph construction using correlation thresholds and temporal windows to capture dynamic transaction patterns, and uses SMOTE oversampling to rebalance class distributions in training data only. We present the architecture and algorithmic design of QCTGNN and provide an experimental evaluation on four financial transaction datasets. Across these benchmarks, QCTGNN matches or exceeds classical GNN baselines in ROC-AUC and PR-AUC, demonstrating the potential of quantum-enhanced graph learning for financial fraud analytics.
Financial fraud detection is a critical challenge requiring accurate identification of anomalous patterns in complex transaction networks. Graph Neural Networks (GNNs) have emerged as powerful tools for fraud detection by capturing relational structures among entities. Meanwhile, quantum computing offers new possibilities to enhance machine learning through high-dimensional Hilbert spaces and parallelism. In this paper, we propose a hybrid classical-quantum model called QCTGNN (Quantum Chebyshev Transform-based Graph Neural Network) for financial fraud detection. The QCTGNN integrates a classical graph neural network component based on Simplified Graph Convolutions (SGConv) with a quantum component that performs a Chebyshev polynomial-based transform via variational quantum circuits. The quantum component employs angle encoding of node features, Chebyshev polynomial embedding to enrich feature representations, multiple layers of parameterized RY rotations with entangling gates, and Pauli-Z measurements for readout. To address the noise and instability of Noisy Intermediate-Scale Quantum (NISQ) devices, we incorporate Noise-Aware Quantum Calibration (NQC) that adjusts for decoherence and readout bias in the quantum outputs. Our system also supports adaptive graph construction using correlation thresholds and temporal windows to capture dynamic transaction patterns, and uses SMOTE oversampling to rebalance class distributions in training data only. We present the architecture and algorithmic design of QCTGNN and provide an experimental evaluation on four financial transaction datasets. Across these benchmarks, QCTGNN matches or exceeds classical GNN baselines in ROC-AUC and PR-AUC, demonstrating the potential of quantum-enhanced graph learning for financial fraud analytics.
We propose a novel QTGNN framework for detecting fraudulent transactions in large-scale financial networks. By integrating quantum embedding, variational graph convolutions, and topological data analysis, QTGNN captures complex transaction dynamics and structural anomalies indicative of fraud. The methodology includes quantum data embedding with entanglement enhancement, variational quantum graph convolutions with non-linear dynamics, extraction of higher-order topological invariants, hybrid quantum-classical anomaly learning with adaptive optimization, and interpretable decision-making via topological attribution. Rigorous convergence guarantees ensure stable training on noisy intermediate-scale quantum (NISQ) devices, while stability of topological signatures provides robust fraud detection. Optimized for NISQ hardware with circuit simplifications and graph sampling, the framework scales to large transaction networks. Simulations on financial datasets, such as PaySim and Elliptic, benchmark QTGNN against classical and quantum baselines, using metrics like ROC-AUC, precision, and false positive rate. An ablation study evaluates the contributions of quantum embeddings, topological features, non-linear channels, and hybrid learning. QTGNN offers a theoretically sound, interpretable, and practical solution for financial fraud detection, bridging quantum machine learning, graph theory, and topological analysis.
Detecting financial fraud is a critical aspect of modern intelligent financial systems. Despite the advances brought by deep learning in predictive accuracy, challenges persist-particularly in capturing complex, high-dimensional nonlinear features. This study introduces a novel hybrid quantum recurrent neural network for fraud detection (HQRNN-FD). The model utilizes variational quantum circuits (VQCs) incorporating angle encoding, data reuploading, and hierarchical entanglement to project transaction features into quantum state spaces, thereby facilitating quantum-enhanced feature extraction. For sequential analysis, the model integrates a recurrent neural network (RNN) with a self-attention mechanism to effectively capture temporal dependencies and uncover latent fraudulent patterns. To mitigate class imbalance, the synthetic minority over-sampling technique (SMOTE) is employed during preprocessing, enhancing both class representation and model generalizability. Experimental evaluations reveal that HQRNN-FD attains an accuracy of 0.972 on publicly available fraud detection datasets, outperforming conventional models by 2.4%. In addition, the framework exhibits robustness against quantum noise and improved predictive performance with increasing qubit numbers, validating its efficacy and scalability for imbalanced financial classification tasks.
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.
Financial transaction systems are becoming increasingly complex and large, in this scenario, fraud detection patterns have become more sophisticated than the conventional detection mechanisms by themselves. While machine learning models have been developed for operational purposes, very few of them are interpretable, sensitive to class imbalance or do not generalize to new fraudulent behaviors. Research on quantum machine learning (QML) for fraud detection is still nascent, with many efforts concentrating on classification without addressing explainability or even attempting to deal with graph-structured and sequential data comprehensively. This study aims to fill this gap by presenting a new explainable quantum machine learning architecture to detect fraud from financial transaction datasets. Three core quantum models are developed and validated: (1) Hybrid Quantum-Loss Adaptive Variational Autoencoder (HQLAVAE) for anomaly detection-has a fidelity aware, class-adaptive loss function that enhances reconstruction of typical transaction patterns and sensitively detects anomalies; (2) The third is Quantum Feature Relevance Graph Attribution (Q-FRGA): an explainable quantum graph neural network particularly capturing transactional relationships with respect to node-level fraud-attributed predictions from Hilbert-space-based relevance scoring; and (3) Quantum Environment-Adaptive Policy Refinement (Q-EAPR), a reinforcement learning model integrating distributional quantum Bellman updates with a novel tracking mechanism for policies to enable online processing of fraud behavior changes. Moreover, two cross-model innovations for enhanced explainability and model integration include Cross-Modal Quantum Counterfactual Explainer (CQCE), generating explainable perturbation-based rationales for single predictions as well as Quantum Residual Explainability-Integrated Ensemble Framework (Q-REIEF) using an ensemble of all models with residual error corrections and meta-explanation tracing sets. This work further advances quantum explainable AI in the areas of transparent, adaptive and high-accuracy fraud detection-all critical gaps in interpretability, data imbalance and behavioral drift on financial systems.
With Deep learning and quantum computing are emerging technologies with transformative potential in financial risk assessment, optimization, and fraud detection. The integration of these fields can significantly enhance the efficiency and accuracy of financial decision-making processes. However, existing fraud detection methods often suffer from high false positives, slow processing times, and limited adaptability to complex fraud patterns. Traditional machine learning models struggle with scalability and computational efficiency when dealing with large-scale financial data. To address these issues, we propose the Quantum-Inspired Neural Networks for Fraud Detection (Q-INN-FD) framework, which leverages quantum-inspired computing principles to enhance neural network architectures. The framework incorporates quantum-inspired feature encoding, variational quantum circuits, and hybrid classical-quantum learning mechanisms to improve pattern recognition in financial transactions. These techniques enable more efficient fraud detection by enhancing model robustness and reducing computational overhead. The proposed method is applied to financial datasets to detect fraudulent transactions, optimize risk assessment models, and improve decision-making in real-time scenarios. Our findings demonstrate that Q-INN-FD achieves higher accuracy, faster processing, and reduced false positive rates compared to traditional deep learning approaches. This paper highlights the potential of quantum-inspired deep learning for revolutionizing financial security and risk management.
Graph Neural Networks (GNNs) are now one of the key tools to model relational and non-Euclidean data in multiagent systems, infrastructure networks and even molecular modeling applications. Nevertheless, in very dense or noisy graph topologies, classical message-passing architectures may experience scalability and representational degeneration, especially due to over-smoothing and insufficient higher-order dependency information. The recent developments in quantum machine learning indicate that hybrid quantum-classical models can also provide an alternative feature representation by projecting relational data to parameterized quantum circuits. This article presents a Quantum-Enhanced Graph Neural Network (QGNN) architecture that extends variational quantum circuits with graph learning pipelines, based on classical learning. Parameters are represented as parameterized qubits, and relational dependencies are represented as structured entanglement mappings. We benchmark the regimes where quantum-enhanced relational encoding proves to be more robust and stable through the swarm coordination, molecular property prediction, and financial transaction network. We also elaborate on the issue of scalability, hardware issues, and practical aspects of near-term hybrid implementations and give a critical view of the changing role of quantum methods in computational intelligence systems.