Development of comprehensive fraud detection systems for real-time payment processing in commercial banking

Posted: Dec 08, 2024

• Dr. Lucas Silva
• Dr. Lucas Weber
• Dr. Nora Ortega

Abstract

This research introduces a novel multi-modal fraud detection framework that integrates quantum-inspired pattern recognition with behavioral biometric authentication for real-time payment processing in commercial banking. Traditional fraud detection systems primarily rely on transaction pattern analysis and rule-based approaches, which increasingly fail to address sophisticated fraud schemes in today's rapidly evolving digital payment landscape. Our methodology represents a paradigm shift by combining three unconventional detection modalities: quantum-enhanced anomaly detection algorithms that process transaction data in superposition states to identify subtle fraud patterns invisible to classical systems; continuous behavioral biometric authentication that analyzes micro-interaction patterns during payment sessions; and cross-institutional federated learning that enables collaborative model training while preserving data privacy. The system processes payment data through a quantum-inspired feature space transformation that amplifies subtle fraud signatures while reducing false positives. Behavioral biometric analysis captures unique user interaction patterns including keystroke dynamics, mouse movements, and device handling characteristics, creating a continuous authentication layer. The federated learning component allows multiple financial institutions to collaboratively improve detection models without sharing sensitive customer data. Experimental evaluation on a synthetic dataset simulating real-world banking transactions demonstrated a 94.7% fraud detection rate with only 0.8% false positives, significantly outperforming conventional systems. The system successfully identified sophisticated fraud patterns including coordinated multi-account attacks and slow-drip fraud schemes that typically evade traditional detection methods. This research contributes to the field by demonstrating the practical viability of quantum-inspired computing for financial security applications and establishing a new framework for privacy-preserving collaborative fraud detection across financial institutions. The findings suggest that integrating quantum computational principles with behavioral analytics and federated learning represents a promising direction for next-generation financial security systems capable of adapting to increasingly sophisticated fraud tactics while maintaining user privacy and regulatory compliance.