AI for the firms that price and pay risk.
Carriers, MGAs, brokers, claims tech, insurtech. AI investment here has to clear model risk management practices that predate the LLM era, and the next wave of underwriting and claims automation has to survive regulatory scrutiny on day one.
Built for insurance teams that need AI evidence across underwriting, claims, vendor models, and customer-facing automation.
AI for insurance has to be defensible across underwriting, claims, policy review, vendor models, and customer-facing automation. The AI Audit shows what is running, what can move faster, what must be controlled, and what evidence regulators will expect.
The buyer profile.
| Sub-sector | Buyer titles |
|---|---|
| P&C carriers | Chief Underwriting Officer, Head of Claims, Chief Actuary. |
| MGAs | CEO, Head of Technology. |
| Brokerages | COO, Head of Distribution Tech. |
| Claims tech / insurtech | CTO, VP Engineering. |
| PE-backed insurance services | CIO (mirror PE-backed banking-ops shape). |
Six illustrative patterns we're sized for.
NDA-respecting framing: we describe what we solve for, not which customer we solved it with. All six patterns below are illustrative until the first insurance customer authorizes a 'we've shipped this' flag.
- ◎ Illustrative
Claims triage
Automated severity scoring, fast-track routing, early fraud flagging at intake.
- ◎ Illustrative
Underwriting assist
Document ingestion, risk-scoring augmentation, prior-loss analysis.
- ◎ Illustrative
Policy review + endorsement
Language-consistency checks, exposure detection across endorsement chains.
- ◎ Illustrative
Fraud detection
Pattern recognition across claims history, network-effect anomaly detection.
- ◎ Illustrative
Customer-facing AI
Broker self-service and agent copilots, with strict policy boundaries and human-in-the-loop review on consequential decisions.
- ◎ Illustrative
Vendor model governance
Third-party model evaluation, ongoing audit, evidence-pipeline output for regulators.
Banking-shaped discipline. Insurance-specific perimeter.
Insurance brings regulatory overlap with banking (model risk management, SR 11-7-equivalent practices) plus state DOI oversight, NAIC model laws, and the EU AI Act for global carriers. Same evidence pipeline as our regulated-finance work; see the finance compliance posture for the canonical taxonomy.
No named vignette by design.
We don't publish customer-specific vignettes here. Insurance engagements run under tight confidentiality. The use-case patterns above describe what we solve for; the discovery call is where the customer-specific shape gets discussed.
Start with Audit. Sequence the workstreams.
One order, applied across the engagement. The AI Audit produces the operating read, then AI Transformation, AI Governance, and AI Fluency sequence per the customer's priority.
- AI AuditSee use, value, and risk.
- AI TransformationShip value workflows.
- AI GovernanceProduce audit evidence.
- AI FluencyRaise role-level capability.
Start with an AI Audit baseline.
Discovery call. Calendar link within 60 seconds.
Frequently asked.
The first vignette will be added once the first insurance engagement closes. Adjacent finance work shares regulatory posture and methodology.
The evidence pipeline is framework-agnostic; per-state DOI requirements are configured at engagement kickoff.
Yes. The evidence artifacts feed your model risk team. Same shape as regulated banking work.