Scalable GenAI-Powered Medical Insurance Analytics with Multi-Cloud Data Engineering

• Author(s): Syed Ahad Murtaza Alvi; Radha Raman Chandan
• Paper ID: 1707331
• Page: 437-449
• Published Date: 14-04-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 10 April-2025

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, vol. 8, no. 10, Apr. 2025

APA:
Syed Ahad Murtaza Alvi, Radha Raman Chandan (2025). Scalable GenAI-Powered Medical Insurance Analytics with Multi-Cloud Data Engineering. Iconic Research And Engineering Journals, 8(10).

MLA:
Syed Ahad Murtaza Alvi, Radha Raman Chandan "Scalable GenAI-Powered Medical Insurance Analytics with Multi-Cloud Data Engineering" Iconic Research And Engineering Journals, vol. 8, no. 10, Apr. 2025.

BibTeX

@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}
}