Insurance AI Trust
AI Trust for claims AI, pricing algorithms, and NAIC AI compliance.
Built for insurance AI risk
Insurance-AI lives under NAIC AI Model Bulletin, state-level AI insurance laws (NY DFS, CO Reg 5-1-09, CA AB 2930), Colorado AI Act, and the inevitable rate-discrimination scrutiny that follows ML-driven pricing. Infrarails scores claims-AI + pricing models + fraud-detection for fairness; Infrarails Consulting builds the model-risk-management evidence that regulators now expect.
- Every AI interaction scored against insurance-specific failure modes, not generic safety checks
- Mapped directly to the regulations your auditors cite — NAIC AI Model Bulletin and NY DFS Circular Letter No. 7 (2024), among others
- Signed, replayable evidence built for the exact review your regulator or carrier will run
Discriminatory pricing models
ML pricing that produces statistically disparate outcomes by protected class — direct NAIC + state-DOI exposure under unfair-trade-practice rules.
Claims-AI false denials at scale
Automated claims-handling that systematically denies legitimate claims; bad-faith and class-action exposure.
Fraud-detection model bias
Fraud-flagging AI that disproportionately flags claims from protected groups; civil-rights + market-conduct exposure.
Lack of decision explainability
Adverse underwriting / claims decisions without the documentation regulators now require for AI-driven outcomes.
How insurance teams run Infrarails
One policy, scoped to your insurance obligations — evaluated on every AI interaction, not just at audit time.
curl -X POST https://api.infrarails.ai/v1/evaluate \
-H "Authorization: Bearer YOUR_KEY" \
-d '{"policy": "insurance_compliance", "prompt": "...", "response": "..."}'Evaluate
Continuous fairness + bias scoring across pricing / underwriting / claims-AI models
Govern
NAIC AI Model Bulletin operationalisation + state-level insurance AI law mapping
Monitor
Drift detection on adverse outcomes by protected class + by line of business
Guard
PII redaction at claims-AI gateway; adverse-action review queues
Regulatory Mapping
Every AI system in your insurance estate mapped against the frameworks your regulators actually cite — kept current as the rules change.
Audit-Ready Evidence
Every evaluation produces a signed, tamper-evident record — the artifact your auditors, examiners, or carrier will ask to see.
NAIC AI Model BulletinNY DFS Circular Letter No. 7 (2024)Colorado Reg 5-1-09 / 10CA AB 2930EU AI ActUnfair Trade Practice Acts
What a typical insurance engagement looks like
Anonymised profile drawn from sector patterns — not a specific client.
Profile
Tier-1 US property + casualty insurer using AI across underwriting + pricing + claims-handling
Drivers
- NAIC AI Model Bulletin adoption by your domiciliary state
- DOI market-conduct exam request for AI governance evidence
- Internal model-risk-committee mandate following an adverse-outcome trend
Bundle
AI Governance Framework Design (8 weeks, NAIC-aligned) → Regulatory Readiness Program against NAIC Bulletin + NY DFS + Colorado Reg 5-1-09 (5 months) → AI Red Team Engagement on the claims-AI endpoint (6 weeks)
Outcome
NAIC-aligned model risk management framework operationalised on Infrarails; state-by-state AI governance evidence package; documented bias-detection + adverse-outcome controls in production.
Talk to the Insurance practice
60-90 minute scoping call, routed to the insurance practice lead. Free.