{"ID":2890572,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.19402","arxiv_id":"2507.19402","title":"FD4QC: Application of Classical and Quantum-Hybrid Machine Learning for Financial Fraud Detection A Technical Report","abstract":"The increasing complexity and volume of financial transactions pose significant challenges to traditional fraud detection systems. This technical report investigates and compares the efficacy of classical, quantum, and quantum-hybrid machine learning models for the binary classification of fraudulent financial activities. As of our methodology, first, we develop a comprehensive behavioural feature engineering framework to transform raw transactional data into a rich, descriptive feature set. Second, we implement and evaluate a range of models on the IBM Anti-Money Laundering (AML) dataset. The classical baseline models include Logistic Regression, Decision Tree, Random Forest, and XGBoost. These are compared against three hybrid classic quantum algorithms architectures: a Quantum Support Vector Machine (QSVM), a Variational Quantum Classifier (VQC), and a Hybrid Quantum Neural Network (HQNN). Furthermore, we propose Fraud Detection for Quantum Computing (FD4QC), a practical, API-driven system architecture designed for real-world deployment, featuring a classical-first, quantum-enhanced philosophy with robust fallback mechanisms. Our results demonstrate that classical tree-based models, particularly \\textit{Random Forest}, significantly outperform the quantum counterparts in the current setup, achieving high accuracy (\\(97.34\\%\\)) and F-measure (\\(86.95\\%\\)). Among the quantum models, \\textbf{QSVM} shows the most promise, delivering high precision (\\(77.15\\%\\)) and a low false-positive rate (\\(1.36\\%\\)), albeit with lower recall and significant computational overhead. This report provides a benchmark for a real-world financial application, highlights the current limitations of quantum machine learning in this domain, and outlines promising directions for future research.","short_abstract":"The increasing complexity and volume of financial transactions pose significant challenges to traditional fraud detection systems. This technical report investigates and compares the efficacy of classical, quantum, and quantum-hybrid machine learning models for the binary classification of fraudulent financial activiti...","url_abs":"https://arxiv.org/abs/2507.19402","url_pdf":"https://arxiv.org/pdf/2507.19402v1","authors":"[\"Matteo Cardaioli\",\"Luca Marangoni\",\"Giada Martini\",\"Francesco Mazzolin\",\"Luca Pajola\",\"Andrea Ferretto Parodi\",\"Alessandra Saitta\",\"Maria Chiara Vernillo\"]","published":"2025-07-25T16:08:22Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CE\"]","methods":"[]","has_code":false}