AI Driven Payment System Security Improvement and User Privacy Protection Mechanism

Abstract

With the continuous expansion of electronic payment systems and the rapid evolution of sophisticated cyber-attack methodologies, traditional security measures are increasingly finding it difficult to address the multifaceted risk challenges of the modern era. Leveraging the high-dimensional feature extraction and adaptive learning characteristics of artificial intelligence, this paper establishes a comprehensive AI-driven payment security and privacy protection framework. In terms of system security, the proposed architecture utilizes a residual attention mechanism for precise anomaly detection, while incorporating graph neural networks to analyze and cluster complex account relationship topologies. Furthermore, reinforcement learning is integrated to dynamically adjust risk control strategies in real-time, facilitating the construction of a collaborative defense system through the fusion of multi-source information. Regarding data privacy and integrity, the system adopts homomorphic encryption to enable complex model operations within an encrypted state, which is further combined with blockchain technology to ensure the rigorous traceability and immutability of the entire data flow. The implementation of this integrated technological architecture significantly enhances the intelligent defense capabilities of payment systems, providing a robust and scalable solution for safeguarding digital transactions and sensitive information in high-risk environments. This research not only offers a theoretical advancement in payment security but also provides a practical implementation roadmap for developing next-generation resilient financial information systems.