Straight-through claims
FNOL triage, photo-estimate review and parametric-trigger evidence. Never pays or settles a claim.
Insurance agentic AI governance
Five insurance workflows. One governed control chain.
Plan straight-through claims, intelligent underwriting, multimodal fraud review, conversational resolution and continuous compliance without pretending that high-impact insurance decisions should run without accountable people.
- Synthetic selections only
- No uploads
- No customer data
- No automated decisions
- Human approval required
Reference experience, not a live insurer system. This public lab runs entirely in your browser. It does not connect to Microsoft Foundry, Azure OpenAI, an insurer, a policy system, payment rail, identity provider, telematics feed or claims database.
The five tracks
What the lab governs
Real-time underwriting
Document extraction, risk-signal review and quote-to-bind readiness. Never prices or binds coverage.
Multimodal fraud review
Identity, image, voice and behavioral anomaly signals. Never labels a person guilty or auto-denies.
Conversational resolution
Distress-aware routing and omnichannel handoff. Never changes coverage or makes legal representations.
Continuous compliance
Decision logs, policy tests, drift alerts and approval evidence. Never certifies compliance.
Azure reference architecture
Shared governance spine
- 1Bounded intakeData minimization, consent and purpose checks before orchestration.
- 2Microsoft FoundryVersioned agent orchestration and tool allowlists.
- 3Azure OpenAI + RAGAzure AI Search grounding with source citations and freshness metadata.
- 4Content SafetyPrompt-attack, unsafe-content and unsupported-action screening.
- 5Entra ID + Azure RBACSeparate analyst, reviewer and authorized-approver permissions.
- 6Evaluation gatesGroundedness, citation coverage, safety and human-oversight metrics.
- 7Azure MonitorPrivacy-minimized traces, latency, failures, drift and override monitoring.
- 8Human approvalPending by default; auditable decision, reason and accountable owner.
Governance result
Required controls
Human approval path
Evidence to retain
Monitoring signals
Approval state:
First-pass control, not a safety guarantee. The result is not insurance, actuarial, legal, fraud, medical or compliance advice and must not be used to decide a real case.
Primary-source basis
Controls before autonomy
The control design was reviewed on 24 August 2026 against current primary sources. The NAIC expects insurers using AI to maintain a risk-based governance program and documentation; EIOPA highlights data governance, record-keeping, fairness, cyber security, explainability and human oversight; Microsoft documents identity, RBAC, content filters, evaluations, tracing and monitoring for Foundry agents.
Questions buyers ask
Can this make a straight-through payout?
No. It plans the evidence and approval gate. A real payout requires insurer-owned systems, verified triggers, fraud and sanctions controls, legal review, payment controls and explicit authorization.
Can it detect deepfakes?
No. The public lab models how image, voice and identity-integrity signals should be reviewed. It does not inspect media and must never be treated as proof of fraud.
Can an approver role auto-approve?
No. RBAC makes a person eligible to act; it does not replace case review, separation of duties, a reasoned decision or an audit record.
What is needed for a live Azure implementation?
Customer-owned Azure resources, Entra identity and RBAC, private networking, approved knowledge sources, retention controls, evaluated models, content-safety policy, monitoring thresholds, cost ceilings and named human approvers.