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Scalable GenAI-Powered Medical Insurance Analytics with Multi-Cloud Data Engineering
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
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.
How to cite this paper
@article{1707331,
author = {Syed Ahad Murtaza Alvi, Radha Raman Chandan},
title = {Scalable GenAI-Powered Medical Insurance Analytics with Multi-Cloud Data Engineering},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {10},
pages = {437-449},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707331.pdf},
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.},
month = {April},
}