Hybrid Quantum-Classical Algorithms for Enhanced Fraud Detection in E-commerce Transactions

Abstract

Abstract
E-commerce fraud detection is a critical component of transaction risk management for digital commerce platforms and payment systems. As fraud tactics grow in sophistication and dataset volumes escalate, conventional machine learning (ML) approaches face challenges in scalability, feature complexity, real-time detection and adversarial resilience. Meanwhile, quantum computing and hybrid quantum-classical machine learning (QML) algorithms have emerged as a promising frontier. This paper proposes a comprehensive framework for leveraging hybrid quantum-classical algorithms in the context of e-commerce fraud detection, combining classical feature engineering and supervised/unsupervised ML with quantum‐enabled feature encoding, variational quantum circuits (VQCs) and ensemble decision architectures. We present full mathematical formulations of classical and quantum models, specify a benchmarking methodology for e-commerce transaction data (imbalanced classes, high dimensional features, streaming environment), and show how hybrid algorithms can yield performance and efficiency gains (e.g., enhanced feature-space expressivity, parameter-efficiency, shorter training sometimes). We also discuss practical industry adoption aspects—including data pipeline integration, latency, quantum hardware constraints (NISQ era), regulatory/compliance issues, interpretability, adversarial resilience—and present a roadmap for e-commerce platforms seeking quantum-augmented fraud detection. The result is a theoretically grounded yet operationally oriented article, designed to assist both academic researchers and industry practitioners planning for hybrid quantum-classical fraud-detection solutions.