Can the AI make coverage decisions?

No. The AI supports the claims adjuster and underwriter — document extraction, policy retrieval, pattern flagging — not the coverage determination. Every decision is made by a licensed professional.

How is policyholder data protected?

All training runs on de-identified or synthetic data within your infrastructure. Live policyholder data is never sent to an external AI service. Deployed models run on-premise or in your private cloud.

Can the system handle multiple lines of business with different data structures?

Yes. Different lines of business are modeled separately, each with their own training data and knowledge index. The shared infrastructure handles the common plumbing.

What does the actuarial audit trail look like?

Every model has a documented manifest: training data version, evaluation metrics, and the held-out dataset used to validate performance. Model outputs trace to input features.

How do you handle state-specific regulatory variation?

State-specific regulatory requirements — approved forms, rate adequacy standards — are encoded in the knowledge layer and referenced during retrieval, so responses reflect the governing regulatory context.

Insurance & Underwriting

Underwriting AI built on your book of business, not the industry average.

Claims history, actuarial tables, and regulatory filings — structured into knowledge infrastructure that improves with every policy cycle.

Insurance and underwriting decisions are made from historical data, regulatory constraints, and domain expertise that no general-purpose model carries. RonanLabs builds the knowledge and model infrastructure that lets actuarial and underwriting teams apply AI to their own book of business rather than an industry-average approximation.

The data problem in insurance & underwriting

Underwriting decisions depend on historical claims patterns specific to each carrier's book of business. A model trained on industry-wide data will regress to industry averages, masking the risk profile signals that distinguish one carrier's experience from another. Generic AI underwriting tools produce generic risk assessments.

Insurance data is subject to a layered regulatory regime that varies by line of business and jurisdiction. Rate filings, policy form approvals, and claims handling requirements differ across states and product lines. A compliance-unaware AI tool creates regulatory exposure that scales with adoption.

Claims review involves structured data, document interpretation, and policy language analysis — three fundamentally different information types that most AI systems handle separately. The integration of those data types, grounded in the policy as written, is where analytical value lives.

Actuarial models require reproducible, auditable outputs. 'The model said so' is not a defensible basis for rate changes or reserve adjustments. Infrastructure that cannot trace its outputs to training data and explicit assumptions creates actuarial standards compliance problems.

What we deliver

Synthetic data

Synthetic claims and policy datasets mirroring your book's distribution characteristics — for model testing, scenario planning, and staff training without exposing live policyholder data.

Custom models

Underwriting and claims models trained on your own historical data, capturing the risk signals specific to your lines of business, distribution channels, and geographic concentration.

Knowledge & retrieval

A structured index of your policy forms, state filings, and actuarial assumptions, queryable by claims and underwriting staff with traceability to the governing document.

See the full architecture for how these layers fit together.

Common deployments

Underwriting triage scoring

Model trained on the carrier's historical book to score incoming submissions against observed loss experience, surfacing the signals most predictive in that specific portfolio.

Claims document extraction

Structured extraction of coverage-relevant facts from loss notices, medical records, and repair estimates, mapped to the policy fields that govern the claim.

Policy form cross-reference

Staff-facing retrieval tool for policy language, endorsements, and exclusion interpretation, with citations to the specific form and filing version.

Reserve adequacy modeling

Pattern models trained on the carrier's development triangles to surface claims with reserve patterns inconsistent with historical development on similar claim types.

Frequently asked

Discuss your insurance & underwriting deployment

Tell us about your data, your constraints, and your workflows. We'll design the layers around them.

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