1 Masters in Engineering Management, University- Lamar University, Beaumont, Texas.
2 Department of Industrial Engineering, University- Lamar University, Beaumont, Texas.
World Journal of Advanced Engineering Technology and Sciences, 2026, 18(01), 089-104
Article DOI: 10.30574/wjaets.2026.18.1.1582
Received on 23 November 2025; revised on 11 January 2026; accepted on 14 January 2026
The rapid digital transformation of retail ecosystems has accelerated the adoption of Internet of Things (IoT) technologies alongside fintech driven payment and financial management systems. Smart point-of-sale terminals, connected inventory systems, and sensor enabled retail environments continuously generate large volumes of heterogeneous, high velocity data. However, traditional on premise analytics infrastructures face significant limitations in handling the scale, real time processing requirements, and integration complexity associated with these data streams. As a result, retailers often experience delayed financial insights, limited fraud detection capabilities, and inefficient operational decision-making. To address these challenges, this paper proposes a cloud native fintech analytics platform specifically designed for IoT-enabled retail networks. The proposed architecture integrates real time data ingestion pipelines, scalable cloud-based analytics services, and intelligent financial insight generation within a unified framework. By leveraging cloud-native design principles such as microservices, container orchestration, and event driven processing, the platform enables elastic scalability, high availability, and fault tolerance while reducing infrastructure and maintenance overhead. The system supports real time transaction monitoring, contextual fraud detection through IoT data correlation, and advanced business intelligence for retail operations. Experimental evaluation using simulated retail workloads demonstrates significant improvements in transaction processing latency, anomaly detection accuracy, and system resilience when compared with conventional monolithic retail analytics systems. The results highlight the effectiveness of cloud-native approaches in supporting data intensive fintech applications and confirm their suitability for next-generation smart retail environments that demand agility, scalability, and real-time financial intelligence.
Cloud computing; Fintech analytics; Internet of Things; Retail networks; Microservices; Real-time data processing; Smart retail
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S M Mobasshir Islam Sharan, Akiful Islam Fahim and Hamza Farooq. Cloud Native Fintech Analytics Platform for IoT Enabled Retail Networks. World Journal of Advanced Engineering Technology and Sciences, 2026, 18(01), 089-104. Article DOI: https://doi.org/10.30574/wjaets.2026.18.1.1582