Edge Computing for Real-Time Financial Analytics

Abstract

The rapid growth of digital financial services has increased the demand for low-latency, secure, and scalable financial analytics. Traditional cloud-based architectures often face challenges related to latency, bandwidth consumption, privacy, and real-time responsiveness. Edge computing addresses these limitations by processing financial data closer to its source, enabling faster decision-making and reducing communication overhead. This paper proposes an AI-powered edge computing framework for real-time financial analytics that integrates IoT-enabled financial devices, distributed edge servers, intelligent data preprocessing, AI-based prediction engines, and cloud-based centralized model management. Machine learning models deployed at edge nodes perform real-time transaction analysis, fraud detection, credit risk assessment, market trend forecasting, and customer behavior analysis while minimizing data transfer to the cloud. The framework also incorporates federated learning and privacy-preserving AI techniques to enable collaborative model training while protecting sensitive financial data and supporting regulatory compliance. Performance is evaluated using computation latency, prediction accuracy, network utilization, transaction processing efficiency, bandwidth consumption, and system reliability. Experimental findings indicate that the proposed framework significantly improves fraud detection, reduces response time, enhances scalability, and optimizes resource utilization compared with conventional cloud-centric financial analytics systems. The integration of edge computing and artificial intelligence provides a secure, intelligent, and scalable solution for real-time financial decision-making, supporting advanced applications such as automated compliance, risk management, smart investment advisory, and next-generation digital financial services.