AI for the insurance industry, deployed without the bias surprise.
Claims, underwriting, fraud detection, broker assistants, customer service. NAIC AI model bulletin compliant. OSFI E-23 ready. State DOI documentation included. Bias testing designed in — not added later.
The regulators care. The class actions care more.
Insurance AI lives at the intersection of three pressures: consumer-protection regulators (state DOIs, the NAIC, OSFI in Canada), data-protection regimes (GLBA, state privacy acts, PIPEDA), and class-action plaintiffs who treat AI-driven adverse decisions as fair-lending-equivalent claims.
The technology that addresses all three at once is not a clever model — it's a documented architecture. Bias testing before deployment. Decision logging during operation. Human review on adverse actions. Consumer disclosure where it matters. We design every insurance AI deployment with those as primary constraints, not afterthoughts.
Six insurance workflows where AI agents earn the regulatory cost.
Claims processing agents
First-notice-of-loss triage, document extraction, coverage analysis, adjuster copilots. Adverse decisions stay human-controlled with documented escalation.
Underwriting copilots
Risk-factor extraction from broker submissions, policy structure suggestions, pricing model integration. Bias-tested, audit-logged, fair-lending-aware.
Fraud detection RAG
Pattern matching against known fraud schemes, cross-claim correlation, language analysis on adjuster notes. Investigative copilot, not autonomous decision-maker.
Conversational AI for insurance
Insured-facing chat for FAQ, policy explanation, simple endorsements. Broker-facing copilots for product expertise. Disclosure-rule-aware.
Broker enablement copilots
Product knowledge agents, illustration helpers, application-stage RAG over carrier appetites and underwriting guides. Faster, more accurate broker placements.
Customer service automation
Routine policy questions, document requests, change-of-address. Escalation to humans on coverage interpretation and adverse-action questions.
The regulatory map for insurance AI.
- NAIC AI model bulletin — governance, risk management, outcomes monitoring.
- State DOI guidance — California, New York, Colorado, and others have specific AI-in-insurance rules.
- OSFI E-23 for Canadian federally-regulated insurers.
- GLBA Privacy and Safeguards Rules for non-public personal information.
- PIPEDA + Quebec privacy law for Canadian personal data.
- SOC 2 — expected by carrier customers and reinsurance partners.
- Fair lending / unfair discrimination standards applied analogously to AI underwriting and claims decisions.
Full compliance hub: /ai-compliance/.
Frequently asked questions
How does the NAIC AI model bulletin apply to our deployment?
The NAIC model bulletin sets expectations around governance, risk management, and outcomes monitoring for AI used in insurance — particularly underwriting, claims, and rate-setting. Most US state insurance regulators have adopted versions of it. We map each AI workflow against the bulletin's requirements at the Discovery Sprint stage: governance committee structure, model documentation, bias testing, decision logging, consumer disclosure. The compliance map your CISO signs covers the bulletin explicitly.
Can we use AI in underwriting without bias liability?
You can — with the right controls. Bias testing has to be designed in, not added later. Every underwriting AI we ship includes: protected-class drift monitoring, decision-explanation generation, human-in-the-loop for adverse actions, and audit logs supporting fair-lending-equivalent review. State DOIs and class-action plaintiffs both ask the same questions; we make sure you have answers.
What about OSFI guidance for Canadian carriers?
OSFI's guidance (E-23 and related) is broadly aligned with NAIC principles but adds Canadian-specific data residency and supervisor-notification expectations. For Canadian carriers, we add a PIPEDA layer and ensure model deployment satisfies provincial insurance regulator expectations (especially in Quebec, which has its own data and AI regime).
Do you work with MGAs and broker networks too?
Yes — about a third of our insurance pipeline is MGAs and broker networks, who often have less compliance infrastructure than carriers but the same fundamental data and regulatory pressures. Engagements for MGAs are typically smaller and faster than carrier-scale work.
Bring us a claims or underwriting workflow.
30 minutes. Engineer + compliance lead. Bias testing on the agenda from minute one.