AI-Driven Cloud-Native Platforms for Health, Insurance, and Urban Automation with Robust Anomaly Detection and Optimized QA
DOI:
https://doi.org/10.15662/IJARCST.2025.0805009Keywords:
AI-driven platforms, Cloud-native architecture, Health automation, Insurance systems, Urban automation, Anomaly detection, Quality assurance, Machine learning, Predictive analytics, Data securityAbstract
This paper presents AI-driven cloud-native platforms designed for health, insurance, and urban automation domains, emphasizing robust anomaly detection and optimized quality assurance (QA). These sectors generate massive volumes of heterogeneous and sensitive data, requiring secure, scalable, and intelligent solutions to maintain operational efficiency, compliance, and service quality. The proposed framework integrates AI and machine learning models within cloud-native architectures to monitor processes in real time, detect anomalies across data streams, and optimize QA workflows. By leveraging predictive analytics, automated alerts, and cross-domain coordination, the system enhances decision-making, reduces operational risks, and ensures regulatory compliance. Experimental results demonstrate improved anomaly detection accuracy, efficient QA management, and resilient platform performance, highlighting the transformative potential of AI-powered cloud-native solutions for secure, intelligent, and adaptive operations in health, insurance, and urban automation ecosystems.
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