Insurance Analytics Engine

Scaling Insurance Analytics with Palantir

A large, multi-line insurance carrier set out to accelerate underwriting operations while laying the groundwork for scalable AI adoption. Underwriters were spending significant time manually reviewing submissions, extracting critical data, and filling gaps through external research. The result was slower decision-making, higher operational cost, and limited consistency in risk evaluation.

PremiumIQ partnered with the insurer to design and implement a generative AI–powered underwriting assistance solution that automated submission intake, enriched risk profiles, and surfaced prioritized insights directly within underwriting workflows. The outcome was a production-ready GenAI capability embedded into the carrier’s existing environment.

The Challenges: Manual Intake, Fragmented Data, and Slow Decisions

Although the insurer had access to substantial submission data, the intake and evaluation process remained inefficient, including:

• Manual extraction of insured details, broker information, and location data

• Inconsistent data quality across submissions and lines of business

• Heavy reliance on underwriters to perform ad hoc external research

• Limited visibility into relative risk, profitability, and likelihood to bind

• Early AI experimentation without a clear path to operational scale

The organization needed a faster, more consistent way to ingest data and support underwriting decisions without disrupting core systems.

The Solution: AI-Powered Underwriting Assistance with Embedded Governance

PremiumIQ provided architectural leadership and hands-on technical support to design a scalable GenAI solution built on AWS, Palantir, and Anthropic’s Claude LLM. Rather than replacing underwriter expertise, the solution focused on accelerating intake and augmenting decision-making.

The approach delivered:

• AI-driven extraction of critical submission data at intake

• Automated enrichment using external intelligence sources such as Google, Dun & Bradstreet, and Pitchbook

• Risk-based prioritization and assessments aligned to profitability and propensity to bind

• A flexible, cloud-based architecture designed to support future AI use cases

• Governance built into extraction logic and data handling from the start

• Direct integration into existing underwriting workflows and systems

PremiumIQ also validated model performance and accuracy to ensure the solution was ready for production use.

The Impact: Faster Underwriting and Measurable Cost Reduction

The solution delivered measurable operational and financial improvements while proving that generative AI could be safely embedded into core underwriting workflows at scale.

Results included:

• 50% reduction in average case processing time

• 30% decrease in operational costs driven by reduced manual review

• Accuracy exceeding 85% across more than 15 AI data extractors

• Faster policy approvals and smoother collaboration across teams

• A scalable AI foundation that supports continued expansion

Underwriters shifted focus from data collection to risk evaluation, while leadership gained confidence in the reliability, governance, and scalability of AI-driven workflows.

Why It Matters

Many insurers stall at proof-of-concept when adopting GenAI. This initiative demonstrated how AI can deliver measurable underwriting value when paired with disciplined architecture and governance. By embedding data quality and integration into the solution from the outset, the insurer moved beyond experimentation and established a repeatable model for future AI-enabled capabilities.

“Our priority was to make AI operational, not experimental. Automating submission intake and embedding governance directly into the workflow allowed the client to achieve real efficiency gains while creating a foundation they can build on.”
— PremiumIQ Engagement Lead