An Intelligent and Privacy-Preserving Architecture for Crypto Payment Systems Using Federated Learning and Explainable AI

Cryptocurrency payment systems face persistent challenges related to fraud detection, regulatory compliance, scalability, and user trust. Existing approaches frequently rely on centralized data aggregation and opaque decision-making models, which conflict with the decentralized nature of blockchain systems and raise significant privacy and auditability concerns. This paper presents the design of an intelligent, privacy-preserving crypto payment architecture that integrates workflow automation, federated learning, and explainable artificial intelligence. The proposed system employs automated orchestration to manage payment workflows, distributed learning to enable collaborative fraud detection without centralized data collection, and transparent reasoning mechanisms to support auditability and regulatory alignment. By combining these components into a cohesive architecture, the system enables adaptive risk assessment, operational scalability, and interpretable decision-making while preserving decentralization principles. The paper focuses on architectural design choices, component interactions, and security considerations, supported by a proof-of-concept prototype and empirical feasibility evaluation.