AI for the firms that underwrite 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 portco operating 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.
Carriers run three lines of defense: underwriting and claims own the workflow, risk and compliance oversee the controls, internal audit tests the evidence. The AI Audit slots into the third line. The deliverable is a structured audit memorandum (opinion, materiality threshold, scope, exceptions, remediation) the audit committee can pass to the external auditor unchanged.
AI in insurance has to prove trust before scale.
The core-modernization conversation is moving quickly. The guide below turns that pressure into a proof matrix for correctness evidence, regulator audit trails, human approval gates, and failure containment.
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.
- 01AI AuditMap use, value, risk, and next move.
- 02AI TransformationShip the priority workflow with measures.
- 03AI GovernanceControl what ships and prove what changed.
- 04AI FluencyTrain owners on the workflows that changed.
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.