Edge Computing for Financial Data Processing

Authors

Mahaboobsubani Shaik

Abstract

The Edge computing is staking its claim in financial data processing by mitigating the inherent latency, bandwidth, and security issues of centralized computing systems. The article presents a comprehensive analysis of different edge computing frameworks applied in real-time analytics over financial data. This article discusses a state-of-the-art architecture for decentralizing data processing, wherein computations would be closer to sources like stock exchanges, banking systems, and payment networks. By reducing data over-distance travel, edge solutions significantly reduce latency and improve response times, hence driving operational efficiency. Benchmark tests of edge computing against traditional centralized systems have demonstrated as much as a 40% improvement in real-time transaction processing and fraud detection capabilities. Additionally, the paper talks about the scalability and fault tolerance of edge systems for energy efficiency, which would position them well for high-frequency trading, risk assessment, and personalized financial services. Attention is also given to the security discussion, where localized processing ensures less chance of data breaches and helps organizations maintain compliance with regulations. Use cases from banking, trading, and insurance illustrate how edge computing will transform financial ecosystems.

Keywords

Edge computing, financial data processing, real-time analytics, latency reduction, decentralized architecture, high-frequency trading, fraud detection, scalability, efficiency of operation, regulatory compliances, fintech, frameworks of edge, benchmark analysis, low latency solutions, banking systems

Edge Computing for Financial Data Processing. Mahaboobsubani Shaik. 2017. IJIRCT, Volume 3, Issue 3. Pages 1-9. https://www.ijirct.org/viewPaper.php?paperId=2412031