Autonomous Insurance Analytics Using Agentic AI for Personalized Coverage and Predictive Risk Intelligence
DOI:
https://doi.org/10.15662/IJARCST.2026.0903007Keywords:
Agentic Insurance Analytics, Personalized Premium Models, Predictive Claims Intelligence, Autonomous Decision Systems, Insurance Risk Modeling, Customer Segmentation Analytics, Proactive Coverage Recommendations, AI-Driven Underwriting, Ethical AI Governance, Predictive Insurance PlatformsAbstract
Autonomous insurance analytics through agentic AI enable personalized premium models, proactive coverage recommendations, risk-averse product feature enhancement, and predictive claims intelligence. Agentic AI exhibits decision-making autonomy, pursues user-centric goals, and interacts with external environments, serving insurance analytics where historical patterns alone remain insufficient. Empirical methods, including data collection, feature engineering, predictive modeling, personalization pipelines, and customer segmentation, operationalize the theoretical foundations. Data ecosystems and ethical considerations encompass sources, quality assessment, bias mitigation, and stakeholder responsibility. System architecture describes components, workflows, and integration within existing analytical platforms. Operating in highly data-driven sectors characterized by large volumes of historical-time series data—spanning product features and pricing, demand-supply interactions, resource and capacity deployment, and claims, loss, fraud, and warranty patterns—traditional analytical methods can produce high-fidelity predictive models. However, such environments can also require proactive actions and decision-making for customers and companies. In insurance underwriting, pricing, and claims domains, developing such proactive actions—one of the principal tenets of agentic AI—constitutes a significant challenge because of the catastrophic and highly risk-averse nature of the sector. The available historical-time-series data for specific products, geographies, and time periods are usually limited by the infrequent occurrence of catastrophic events such as floods and earthquakes. Consequently, merely relying on historical patterns to predict future behavior is rarely adequate for standard insurance companies, especially the ones implementing conservative risk-management strategies or seeking consolidation in the market.
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