Building Governed AI-Powered Hybrid Data Products for Group Insurance and Retirement Platforms

  • Authors

    • Ganesh Pambala Independent Researcher, USA. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2023PII3N8P

    Published 09-08-2023

  • Hybrid Data Products, Agentic AI Solutions, Data Mesh Architecture, Data Fabric Architecture, Financial Services Analytics, Group Insurance Platforms, Retirement Solution Systems, Predictive Modeling, Treatment Effect Models, Scenario Simulation Engines, Personalized Decision Recommendations, Regulatory Compliance Alignment, Data Privacy Governance, Model Bias Mitigation, Scalable Data Sharing, Domain-Oriented Data Products, Digital Data Product Design, Lifecycle Financial Planning, Risk Provisioning Controls, Enterprise Data Usability

    Issue

    Vol. 6 No. 2 (2023)

    Section

    Articles

    How to Cite

    [1]
    G. Pambala, “Building Governed AI-Powered Hybrid Data Products for Group Insurance and Retirement Platforms”, IJADSMC, vol. 6, no. 2, pp. 01–19, Sep. 2023, doi: 10.67228/30713498/IJADSMC-2023PII3N8P.

  • Abstract

    Hybrid data products combine the content, applicative, and consumption components of data sharing in a singular consumable package that any data consumer can leverage with little or no effort. Architectures facilitating hybrid data products, like data mesh and data fabric, address the increasing complexity of data integration and sharing across silos. At the same time, demand for agentic AI solutions—those that act on behalf of the user—is on the rise. Hybrid data products have particular relevance for group insurance and retirement solution platforms, given the availability of predictive and treatment effect models, semantic simulated events for scenario testing, and personalized decision recommendations. Extra care should be taken to ensure that data products in the financial domain do not perpetuate model or sampling bias and that they adhere to industry regulations, from data privacy—where applicable—to risk provisioning. Although clearly Patterned for Financial Services, these concerns are secondary to the stability, accessibility, and usability of hybrid data products at scale. Hybrid Data Products combine the content, applicative, and consumption components imposed by a data-sharing framework in one consumable package designed for the applicable domain. Agentic solutions are on the rise in financial services as companies navigate the uncertainty associated with risk assessment, decision processes, and market positioning through the adoption of Digital Data Products that recommend or simulate. In the context of group insurance and retirement solutions, Hybrid Data Products and the associated data-mesh and data-fabric architectural patterns have particular relevance: predictive models for onboarding and UX enhancement, decision-relevant recommendation at investment-product level, and cash-flow and lifecycle-scenario planning over the customer lifecycle.

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