FD4QC: Application of Classical and Quantum-Hybrid Machine Learning for Financial Fraud Detection A Technical Report

2025-07-25

AI Summary

To address the challenge of modeling high-dimensional, dynamic financial transaction data in fraud detection, this paper proposes FD4QC—a “classical-first, quantum-enhanced” hybrid architecture integrating behavioral feature engineering and API-driven design for deployable binary classification. Methodologically, we systematically benchmark classical models (logistic regression, random forest, XGBoost) against quantum/hybrid algorithms (QSVM, VQC, HQNN) on the IBM AML dataset. Results show that random forest achieves the highest performance (accuracy: 97.34%, F1-score: 86.95%), while QSVM—though lower in accuracy (77.15%)—exhibits an exceptionally low false positive rate (1.36%), highlighting its promise for low-FPR operational settings. This work establishes the first quantum computing benchmark tailored to financial fraud detection, providing both an architectural paradigm and empirical evidence for the incremental integration of quantum machine learning into real-world risk management systems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 extit{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, extbf{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.

Problem

• Compares classical and quantum ML for fraud detection
• Develops feature engineering for financial transaction data
• Proposes FD4QC system for real-world quantum-enhanced deployment

Innovation

• Hybrid classical-quantum ML models for fraud detection
• Behavioural feature engineering framework for transactions
• API-driven FD4QC system with quantum-enhanced fallback