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Quantum-Enhanced Financial Crime Detection with Hybrid Transformers, Entity Resolution, and Risk-Aware Evidence Graph Discovery

GODWIN OLUWAFEMI OLAOYE

Published Jun 16, 2026• 0 citations
Publisher
DOI
10.5281/zenodo.20718258

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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.

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Abstract Financial crimes’ fast proliferation and sophistication require novel approaches that provide robust and effective solutions. This paper explores the potential of quantum algorithms in combating financial crimes. It highlights the advantages of quantum computing by examining traditional and Machine Learning (ML) techniques alongside quantum approaches. The study showcases advanced methodologies such as Quantum Machine Learning (QML) and Quantum Artificial Intelligence (QAI) as potential solutions for detecting and preventing financial crimes, including money laundering, financial crime detection, cryptocurrency attacks, and market manipulation. These quantum approaches may leverage the inherent computational capabilities of quantum computers to overcome limitations faced by classical methods, contingent on continued advances in hardware and error correction. Furthermore, the paper illustrates how quantum computing could support enhanced financial risk management analysis. Financial institutions may improve their ability to identify and mitigate risks through quantum technologies as the field matures. The paper’s primary contribution is a structured three-layer mapping framework connecting financial crime typologies with classical, machine learning, and quantum countermeasures, accompanied by a feasibility assessment of key quantum algorithms. All capability claims are grounded in an explicit discussion of current NISQ-era limitations and a phased roadmap for future experimental validation.
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.