Development of real-time fraud detection algorithms for credit card transaction monitoring systems

Posted: Apr 30, 2023

• Jacob Baker
• Jacob Clark
• Jacob Harris

Abstract

The escalating sophistication of financial fraud represents one of the most pressing challenges in modern digital commerce, with global losses exceeding $32 billion annually. Traditional fraud detection systems have reached a performance plateau, constrained by their reliance on classical computational models and conventional pattern recognition techniques. This research addresses the fundamental limitations of existing approaches by introducing a quantum-inspired neural architecture that operates on principles fundamentally different from current industry standards. The novelty of our approach lies not in incremental improvements to existing methods, but in a complete re-conceptualization of how transaction data should be processed and analyzed for fraudulent patterns. Our work is motivated by the observation that fraudulent activities often manifest as subtle perturbations in complex transaction networks, patterns that classical systems struggle to detect due to their inherent computational constraints. We hypothesize that by representing transaction features as quantum state vectors and processing them through quantum-inspired transformations, we can uncover fraud signatures that remain hidden from conventional detection systems. This paper makes several groundbreaking contributions to the field of financial security. First, we introduce the Quantum-Inspired Recurrent Neural Network (QI-RNN), a novel architecture that processes financial transactions through quantum state representations. Second, we develop a quantum-enhanced feature transformation methodology that maps traditional transaction attributes into quantum feature spaces. Third, we implement a real-time streaming quantum Fourier transform that enables efficient processing of high-volume transaction data. Finally, we demonstrate through extensive experimentation that our approach achieves unprecedented detection accuracy while maintaining the low latency requirements essential for real-time financial systems.