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1707331 Vol 8 · Issue 10 Download Paper

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

Syed Ahad Murtaza Alvi Radha Raman Chandan

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.

References

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[49] Khan, Shakir, and Arun Sharma. "Moodle Based LMS and Open Source Software (OSS) Efficiency in E-Learning." International Journal of Computer Science & Engineering Technology 3.4 (2012): 50-60.

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[51] AlAjmi, Mohamed F., Shakir Khan, and Arun Sharma. "Studying data mining and data warehousing with different e-learning system." International Journal of Advanced Computer Science and Applications 4.1 (2013).

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[59] Eldosoky, Mahmoud A., Jian Ping Li, Amin Ul Haq, Fanyu Zeng, Mao Xu, Shakir Khan, and Inayat Khan. "WallNet: Hierarchical Visual Attention-Based Model for Putty Bulge Terminal Points Detection." The Visual Computer (2024): 1-16.

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[81] Khan, Shakir. "Visual Data Analysis and Simulation Prediction for COVID-19 in Saudi Arabia Using SEIR Prediction Model." International Journal of Online & Biomedical Engineering 17.8 (2021).

[82] Khan, Shakir, and Mohammed Altayar. "Industrial internet of things: Investigation of the applications, issues, and challenges." Int. J. Adv. Appl. Sci 8.1 (2021): 104-113.

[83] S. Khan, "Study Factors for Student Performance Applying Data Mining Regression Model Approach," International Journal of Computer Science Network Security, vol. 21, no. 2, pp. 188-192, 2021.

[84] Khan, Shakir, and Amani Alfaifi. "Modeling of coronavirus behavior to predict it’s spread." International Journal of Advanced Computer Science and Applications 11.5 (2020): 394-399.

[85] S. Khan and M. Alshara, "Development of Arabic evaluations in information retrieval," International Journal of Advanced Applied Sciences, vol. 6, no. 12, pp. 92-98, 2019.

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How to cite this paper

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
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
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).
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. 
@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},
  }