Abstract
The increasing complexity and volume of medical insurance data require scalable, efficient, and intelligent data processing solutions. This paper presents a multi-cloud data engineering framework for scalable GenAI-driven medical insurance analytics. Our approach leverages distributed cloud infrastructure, automated data pipelines, and foundation models to enhance data ingestion, transformation, and predictive analytics. We integrate multi-cloud storage, serverless computing, and federated learning to optimize real-time claims processing, fraud detection, and risk assessment. The proposed architecture ensures data security, regulatory compliance, and cost efficiency while enabling seamless AI-driven insights across diverse healthcare datasets. Experimental results demonstrate significant improvements in scalability, processing speed, and predictive accuracy compared to traditional single-cloud architectures. This work highlights the potential of multi-cloud AI ecosystems in revolutionizing medical insurance analytics with enhanced efficiency and intelligence.
Keywords
Multi-Cloud, Data Engineering, GenAI Analytics, Scalability and Medical Insurance.
Citations
IRE Journals:
Syed Ahad Murtaza Alvi , Radha Raman Chandan
"Scalable GenAI-Powered Medical Insurance Analytics with Multi-Cloud Data Engineering" Iconic Research And Engineering Journals Volume 8 Issue 10 2025 Page 437-449
IEEE:
Syed Ahad Murtaza Alvi , Radha Raman Chandan
"Scalable GenAI-Powered Medical Insurance Analytics with Multi-Cloud Data Engineering" Iconic Research And Engineering Journals, 8(10)
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