The insurance industry is experiencing a profound transformation as AI technologies increasingly power core functions such as underwriting, claims triage, and fraud detection. Delivering these capabilities at scale requires not just advanced models, but high-performance analytics infrastructure capable of supporting real-time, low-latency decisioning across massive volumes of structured and semi-structured data. This session explores how leading insurers are leveraging StarRocks to enable high-speed analytics and intelligent automation across their enterprise data ecosystem.

Attendees will learn how StarRocks is used to unify streaming and batch data sources, support real-time dashboards, and deliver sub-second analytical queries that feed directly into AI decision engines. These capabilities power AI platforms that process thousands of applications per hour, auto-triage 85% of claims on first pass, and improve fraud detection rates by over 60%. The session will showcase how columnar storage, vectorized execution, and materialized views in StarRocks accelerate time-to-insight and support mission-critical AI use cases with millisecond-level responsiveness.

We’ll also discuss how StarRocks fits within a modern insurance data architecture—supporting scalable data ingestion, integration with feature stores, and downstream API delivery for predictive workflows. With strong support for SQL and real-time OLAP performance, StarRocks enables data teams to operationalize analytics and embed intelligence into decision systems at scale.

Ideal for data architects, AI engineers, and analytics leaders, this session delivers a blueprint for implementing StarRocks in AI-native environments to unlock speed, scale, and precision in the modern insurance enterprise.