As the health insurance industry digitizes at a rapid pace, data engineering and analytics are upheld within the industry as indispensable tools for better policies and claims service operations along with more effective compliance management. This article illustrates the problems that data engineers and analysts must solve so as to ease the operation of health insurance. Securing heterotic sources of information can be interfaced with illumination filters. The computing of work queues will become a thing heretofore poorly conceived. It is possible to find out Overflows and make them disappear. And that approach leads to decision- making optimization. In particular, responsibilities include debug requests from 587s, model data flows, clean datasets, and run production automatic-jobs as well as coordinating deployment. Nor can health insurance providers manage the policy changes. How can they do so when this takes more time, indeed very many cycles longer than ever before? So how do they adapt? As health insurers offering customers with services in a data-driven era networks and insurers of alliances among stakeholders do better. In education organization for this type of world–is needed too. For insurance market today and tomorrow, life insurance companies are already starting to face innovation and change: data own technologies, long-term health goal setting, early warning fragmented experience reconstruction of medical practices industry has brought us. It is these information-based systems that will change how people bought life policies next year.

Health Insurance Data Analytics, Data Engineering in Insurance, Insurance Claims Processing

Takeuchi HHärting RYamamoto S(2025)Method for Identifying Business Goals for Generative Artificial Intelligence Applications Based on Knowledge Distribution Models and GQM+StrategiesHuman Centred Intelligent Systems10.1007/978-981-97-8598-8_17(191-201)Online publication date: 17-Jan-2025

Wang ZHuang CYao X(2024)A Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?ACM Transactions on Autonomous and Adaptive Systems10.1145/370200419:4(1-40)Online publication date: 24-Nov-2024

Razzaq ABuckley JLai QYu TBotterweck G(2024)A Systematic Literature Review on the Influence of Enhanced Developer Experience on Developers' Productivity: Factors, Practices, and RecommendationsACM Computing Surveys10.1145/368729957:1(1-46)Online publication date: 7-Oct-2024

D. Patil, Building Data Science Teams, O'Reilly, 2011.

S. Kandel, A. Paepcke, J. Hellerstein and J. Heer, "Enterprise Data Analysis and Visualization: An Interview Study," in IEEE Visual Analytics Science & Technology (VAST), 2012.

A. E. Hassan and T. Xie, "Software intelligence: the future of mining software engineering data," in FOSER '10: Proceedings of the Workshop on Future of Software Engineering Research, 2010.

A. Begel and T. Zimmermann, "Analyze This! 145 Questions for Data Scientists in Software Engineering," in ICSE'14: Proceedings of the 36th International Conference on Software Engineering, Hyderabad, India, 2014.