Quantum-Resistant Cryptographic Protocols in AI-Optimized Payment Reconciliation Systems - Aparna Thakur (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
This article presents an innovative hybrid architecture that integrates quantum-resistant cryptography with artificial intelligence to revolutionize global payment and reconciliation systems. The proposed article framework addresses the dual challenges of potential quantum computing threats and current inefficiencies in financial operations. By leveraging lattice-based cryptography, the system ensures long-term security against quantum attacks, while AI-driven algorithms, including reinforcement learning and neural networks, optimize payment reconciliation processes. The architecture incorporates quantum-safe signatures into SWIFT messaging protocols and introduces a federated learning approach for enhanced fraud detection across financial institutions. This collaborative model, underpinned by homomorphic encryption, enables the sharing of machine learning insights without compromising data confidentiality. The article provides a comprehensive analysis of the system’s performance, demonstrating significant improvements in reconciliation efficiency, robustness against quantum threats, and scalability compared to traditional systems. By offering a future-proof solution that addresses current pain points while preparing for emerging technological challenges, this framework represents a significant advancement in securing and streamlining global financial infrastructure.