Cloud-Native AI and MDM Framework for Next-Generation Insurance and Retirement Data Products
DOI:
https://doi.org/10.15662/IJEETR.2026.0803006Keywords:
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 ModernizationAbstract
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.
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