Smart MDM for Insurance & Retirement Platforms

Authors

  • Sophia Martinez Author
    Competing Interests

    AI,ML

Keywords:

Cloud-Native Architecture,Artificial Intelligence in Insurance,Master Data Management (MDM),Retirement Data Products,AI-Driven Data Governance,Insurance Digital Transformation,Scalable Data Platforms,Intelligent Data Integration,Hybrid Cloud Computing,Enterprise Data Modernization.

Abstract

Ubiquitous digitalization and increasing regulatory scrutiny are creating unprecedented business opportunities and risks for insurance carriers and retirement service providers. Developing next-generation insurance and retirement data products that leverage the cloud, deploy advanced capabilities, adopt a data product mindset, and implement robust data governance can enhance business direction, decision-making speed, performance, and responsiveness to newcomers and disruptors. Integrating these elements into a single master data management (MDM) framework for insurance and retirement domains is essential for orchestrating quality, reliable, consistent, and trustworthy data assets and delivering seamless advanced data services to the organization.

Mission-critical business operations depending on data assets in these domains have a long history of data management challenges. As a result, many organizations have addressed the need for structured enterprise-wide data governance, investment in scalable and secure AI/ML platforms for strategical and operational functions, and creation of sharable data products. Growing investment in next-generation data and analytics solutions has informed the integration of cloud-native architecture, AI/ML enablement, and data product enablement into a reference architecture and MDM framework validated with industry benchmarks. Cloud-native architecture is increasingly essential to support the deployment of scalable enterprise-level data solutions. AI/ML investments have expanded well beyond research functions, creating mission-critical production workloads required for business operations or providing customer-facing services. The data product enablement paradigm drives the creation of enterprise-wide MDM capabilities with shared data product lifecycles governed by business need rather than technology requirements.

Downloads

Download data is not yet available.

References

1. Hikmawati, S., Santosa, P. I., & Hidayah, I. (2021). Improving data quality and data governance using master data management: A review. International Journal of Information Technology and Electrical Engineering, 5(3).

2. Pansara, R. (2021). Master data management importance in today’s organization. International Journal of Management, 12(10), 55–59.

3. Dawadi, D., Yandamuri, U. S., V, S. K., Stalin, J. L. A., & Naveenkumar, R. (2026). Privacy-Aware Edge-Based Intelligent Video Analytics for Scalable Crowd Management in Smart Cities. In 2026 3rd International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1–7). IEEE. 2026 3rd International Conference on Integrated Intelligence and Communication Systems (ICIICS). https://doi.org/10.1109/iciics67880.2026.11483444

4. Taleb, I., Serhani, M. A., Bouhaddioui, C., & Dssouli, R. (2021). Big data quality framework: A holistic approach to continuous quality management. Journal of Big Data, 8, Article 76.

5. Wook, M., Hasbullah, N. A., Zainudin, N. M., Jabar, Z. Z. A., Ramli, S., Razali, N. A. M., & Yusop, N. M. M. (2021). Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling. Journal of Big Data, 8, Article 49.

6. Noshad, M., Choi, J., Sun, Y., Hero, A. O., & Dinov, I. D. (2021). A data value metric for quantifying information content and utility. Journal of Big Data, 8, Article 82.

7. Nandan, B. P., Kumar, M. V. K., Garapati, R. S., Bandi, V. D. V. K., Davuluri, P. S. L. N., & Mangalampalli, B. M. (2026). AI-Enhanced Semiconductor Yield Optimization Using Hybrid Deep Learning and Edge Data Analytics. In 2026 IEEE International Conference on AI Engineering and Innovations (AIEI) (pp. 1–6). IEEE. 2026 IEEE International Conference on AI Engineering and Innovations (AIEI). https://doi.org/10.1109/aiei69164.2026.11497190

8. Mullins, M., Holland, C. P., & Cunneen, M. (2021). Creating ethics guidelines for artificial intelligence and big data analytics customers: The case of the consumer European insurance market. Patterns, 2(10), Article 100362.

9. Eling, M., Nuessle, D., & Staubli, J. (2022). The impact of artificial intelligence along the insurance value chain and on the insurability of risks. The Geneva Papers on Risk and Insurance—Issues and Practice, 47, 205–241.

10. Zhang, Q., Sun, X., & Zhang, M. (2022). Data matters: A strategic action framework for data governance. Information & Management, 59(4), Article 103642.

11. Nadal, S., Jovanovic, P., Bilalli, B., & Romero, O. (2022). Operationalizing and automating data governance. Journal of Big Data, 9, Article 117.

12. Kolla, S. K., Khaparkar, S., Kumar, D., Yadav, S., Shankar, S., & Shamila, M. (2026, June). Deep Learning Based Real-Time Threat Monitoring for Cloud and IoT-Enabled Healthcare Systems. In 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1-6). IEEE.

13. Karkošková, S. (2023). Data governance model to enhance data quality in financial institutions. Information Systems Management, 40(1), 90–110.

14. Taha, A., Cosgrave, B., & McKeever, S. (2022). Using feature selection with machine learning for generation of insurance insights. Applied Sciences, 12(6), Article 3209.

15. Owens, E., Sheehan, B., Mullins, M., Cunneen, M., Ressel, J., & Castignani, G. (2022). Explainable artificial intelligence (XAI) in insurance. Risks, 10(12), Article 230.

16. Bedi, B., Yandamuri, U. S., Kummari, D. N., Nagubandi, A. R., Amistapuram, K., & D, A. (2026). AI-Driven Sentiment and Behavior Analysis for Sustainable Business Growth. In 2026 International Conference on Emerging Research in Smart Electronics and Machine Informatics (ECMI) (pp. 1–11). IEEE. 2026 International Conference on Emerging Research in Smart Electronics and Machine Informatics (ECMI). https://doi.org/10.1109/ecmi68341.2026.11603189

17. Oletzky, T., & Reinhardt, A. (2022). Herausforderungen der Regulierung von und der Aufsicht über den Einsatz künstlicher Intelligenz in der Versicherungswirtschaft [Challenges of regulating and supervising the use of artificial intelligence in the insurance industry]. Zeitschrift für die gesamte Versicherungswissenschaft, 111, 495–513.

18. Amerirad, B., Cattaneo, M., Kenett, R. S., & Luciano, E. (2023). Adversarial artificial intelligence in insurance: From an example to some potential remedies. Risks, 11(1), Article 20.

19. Vial, G. (2023). Data governance and digital innovation: A translational account of practitioner issues for IS research. Information and Organization, 33(1), Article 100450.

20. Suprith, M., Davuluri, P. N., Paramasamy, S., Yandamuri, U. S., & Motamary, S. (2026, June). AI-Enabled Business Process Reengineering for Agile and Data-Driven Organizations. In 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1-6). IEEE.

21. Jarvenpaa, S. L., & Essén, A. (2023). Data sustainability: Data governance in data infrastructures across technological and human generations. Information and Organization, 33(1), Article 100449.

22. Abraham, R., Schneider, J., & vom Brocke, J. (2023). A taxonomy of data governance decision domains in data marketplaces. Electronic Markets, 33(1), 1–13.

23. Ducuing, C., & Reich, R. H. (2023). Data governance: Digital product passports as a case study. Journal of Law, Technology & Policy, 2023.

24. Goel, K., Martin, N., & ter Hofstede, A. H. M. (2024). Demystifying data governance for process mining: Insights from a Delphi study. Information & Management, 61(5), Article 103973.

25. Eling, M., Gemmo, I., Guxha, D., & Schmeiser, H. (2024). Big data, risk classification, and privacy in insurance markets. The Geneva Risk and Insurance Review, 49, 75–126.

26. Singh, K. U., Gupta, S. K., Dubey, Y., & Mangalampalli, B. M. (2026, June). Enhancing Healthcare Cyber Defense with Machine Learning-Based Attack Prediction and Prevention. In 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1-6). IEEE.

27. Cosma, S., & Rimo, G. (2024). Redefining insurance through technology: Achievements and perspectives in Insurtech. Research in International Business and Finance, 70, Article 102301.

28. Januarita, R., Alamsyah, I. F., & Perdana, A. (2024). Guardians of data: TruMe Life’s continuous quest for data protection. SAGE Open, 15(2).

29. Qin, Z., Zhang, Y., Li, W., Yang, Y., Wang, B., & Li, Y. (2024). Data quality management in smart governance: Core elements, mechanism construction, and support guarantee. Journal of University of Electronic Science and Technology of China, 2024.

30. Kalluri, R. R., Surasani, V. R., Rellu, N. S. H., & Devarakonda, N. (2025). Evolution of master data management and data governance: A two-decade review of advancements and innovations. Journal of Informatics Education and Research.

31. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).

32. Bhat, A. K. (2025). Unlocking the power of data: The critical role of master data management in insurance. SSRN Electronic Journal.

33. Shah, B., & Vashishtha, S. (2025). AI-driven governance for master and reference data management in insurance. International Research Journal of Modernization in Engineering Technology and Science.

34. Zheng, Z. (2025). The dark side of AI in insurance: A systematic review of mechanisms linking AI design features to consumer harm. Journal of Consumer Affairs, 59(4), Article e70034.

35. Yang, X., Xia, T., Zhang, E., & Zhou, X. (2025). Empowering consumers: An experimental study of human and AI intermediary in insurance decision-making. Journal of Behavioral and Experimental Finance, 47, Article 101096.

36. Sanku, R., Kolla, S. H., Karri, S., & AS, Y. (2026, February). An Intelligent Analytics-Aware Cloud-Cluster Framework for Large-Scale Data Analytics. In 2026 3rd International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-7). IEEE.

37. Li, S., & Faure, M. (2025). The insurability of AI-related risks: Implications from the recent legislation in the European Union. The Geneva Papers on Risk and Insurance—Issues and Practice, 50(3), 524–544.

38. de Almeida, F. L., et al. (2025). AI, insurance, discrimination and unfair differentiation: An overview and research agenda. International Journal of Law and Information Technology, 33, 177–204.

Additional Files

Published

2026-03-23

Data Availability Statement

None

How to Cite

Smart MDM for Insurance & Retirement Platforms. (2026). Global Research Development(GRD), 4(01). https://grdjournals.org/index.php/grd/article/view/29

Most read articles by the same author(s)

Similar Articles

1-10 of 26

You may also start an advanced similarity search for this article.