Sarcouncil Journal of Multidisciplinary

Abstract: The geometric increase in the number of transactions carried out through digital payments has made it necessary to completely transform the conventional cloud-based monitoring systems to edge-cloud hybrid monitoring systems that can use artificial intelligence to identify and authenticate transactions in real time. This article highlights the architecture of the development of payment monitoring systems and discusses how the deployment of edge AI changes the security of transactions, latency in processing, and efficiency in operations in global financial networks. The article shows the integration of distributed intelligence frameworks with existing cloud infrastructure, analyzing the deployment of machine learning models directly on payment terminals to enable localized decision-making while maintaining centralized oversight capabilities. Through article implementation strategies across financial institutions worldwide, this study demonstrates how edge computing addresses critical challenges, including bandwidth constraints, data sovereignty requirements, and network reliability issues that plague traditional centralized approaches. The proposed edge-cloud hybrid framework presents a hierarchical processing model that optimizes resource utilization through intelligent workload distribution, federated learning protocols, and advanced model compression techniques. The important results obtained include a high degree of accuracy in fraud detection, high availability of the system, and customer satisfaction, overcoming the limitations of computational resources and security issues that are exhibited in distributed systems. The article outlines the key missing links in the existing deployments and outlines future research avenues to develop edge AI potential in financial services, with a particular view on the transformative nature of this technology as having the potential to redefine payment processing paradigms and open up new business models in the digital economy.