AI for the firms that underwrite and pay risk.

Every underwriting and claims decision your AI touches has to be defensible: to a state regulator, to the policyholder, and to your own actuaries. The next wave of automation has to survive that scrutiny on day one.

We do two things. We put AI on the underwriting and claims workflows that move loss ratio and cycle time, and make the output reliable enough to act on. And we are the independent read that proves each decision holds, with evidence your model-risk function and your DOI examiner can rely on.

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The output has to be defensible.

A faster claims decision is worthless if you cannot defend how the model reached it. In insurance, the value of AI and the defensibility of its output are the same conversation.

• Value captureAI upsideWhich underwriting and claims workflows AI is actually moving: loss-ratio accuracy, cycle time, fraud catch rate, severity scoring. Ranked by what the actuary and the business can stand behind.

• Risk controlAI riskThe outputs that became a coverage decision before anyone reviewed them, vendor model drift, and the disparate-impact exposure a regulator will test.

• Defensible decisionsRegulator-ready evidenceCorrectness evidence, human-approval gates on consequential decisions, and the audit trail a state DOI examiner will expect.

• Operating readinessWorkforce fluencyWhether underwriters, adjusters, and brokers can operate the AI, and where role redesign is the bottleneck, not the model.

What we are sized for.

NDA-respecting framing: we describe what we solve for, not which customer we solved it with. The patterns below are illustrative until the first insurance customer authorizes a named flag.

• ◎ IllustrativeClaims triageAutomated severity scoring, fast-track routing, early fraud flagging at intake.

• ◎ IllustrativeUnderwriting assistDocument ingestion, risk-scoring augmentation, prior-loss analysis.

• ◎ IllustrativePolicy review + endorsementLanguage-consistency checks, exposure detection across endorsement chains.

• ◎ IllustrativeFraud detectionPattern recognition across claims history, network-effect anomaly detection.

• ◎ IllustrativeCustomer-facing AIBroker self-service and agent copilots, with strict policy boundaries and human-in-the-loop review on consequential decisions.

• ◎ IllustrativeVendor model governanceThird-party model evaluation, ongoing audit, evidence-pipeline output for regulators.

The buyer by sub-sector.

Banking-shaped discipline. Insurance-specific perimeter.

Insurance overlaps banking on model risk (SR 11-7-equivalent practices) and adds state DOI oversight, NAIC model laws, and the EU AI Act for global carriers. Same evidence pipeline as our regulated-finance work.

Carriers run three lines of defense: underwriting and claims own the workflow, risk and compliance oversee the controls, internal audit tests the evidence. The audit slots into the third line. The deliverable is a structured memorandum (opinion, materiality threshold, scope, exceptions, remediation) the audit committee can review with its external auditor. Per-state DOI requirements are configured at engagement kickoff.

Two doors. Same read.

Pick the door that matches what you need first. Both run off one operating read of what AI is doing across underwriting, claims, and customer-facing automation.

Capture value from AI

Put AI on the underwriting and claims workflows that move loss ratio and cycle time, and make the output reliable enough that the team acts on it instead of re-checking it.

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Prove control, independently

Production evals plus an independent audit. Show that each consequential decision is correct, reviewed, and defensible, with the memorandum your risk, compliance, and DOI examiner can rely on.

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Start the read.

Discovery call. Calendar link within 60 seconds.

Frequently asked.

We don't publish customer-specific vignettes here. We do not publish a named insurance case here. The first named pattern will be added once the first insurance engagement closes. Adjacent finance work shares the regulatory posture and the methodology.

The evidence pipeline is framework-agnostic. Per-state DOI requirements are configured at engagement kickoff, and the trail maps to whichever supervisor is in scope.

Yes. The evidence artifacts feed your model-risk team, the same shape as regulated banking work. We do not substitute for actuarial or legal judgment; we feed the team that owns it.

Related links and sources

• Read the insurance AI trust guide →