Assess the governance tier, flag jurisdiction-specific review questions, check coverage readiness, and compare the rules against messy insurance scenarios.
Step 1
Build the use-case description from choices instead of starting with a blank text box.
Add specifics such as who reviews it, where it appears, or what system it supports.
Step 2
Pricing, coverage, and claims answers are mutually exclusive with none or not sure.
Step 3
Pick any jurisdictions that may apply. This does not change the deterministic tier yet; it adds review prompts to the packet.
Step 4
Coverage readiness
This does not interpret policy language. It surfaces the broker, carrier, vendor, and coverage-counsel questions that should be resolved before launch or renewal.
Select what exists today or what should be reviewed for this use case.
Scenario lab
These scenarios make the framework testable. They are not a substitute for practitioner validation; each one needs compliance, coverage, or regulatory review before it can be marked validated.
Scenarios
10
Engine aligned
10/10
Validation status
Needs review
Generative AI drafts claim denial letters for adjusters to review before sending to claimants.
Reviewer note: Should remain High because the output influences claims and reaches claimants, even with human review.
Coverage question: Would E&O, CGL, or any claims-handling endorsement respond if the letter is inaccurate or discriminatory?
A model automatically denies claims below a dollar threshold when it predicts low coverage likelihood.
Reviewer note: Autonomous AI decisioning on claims should be blocked until meaningful pre-effect review exists.
Coverage question: Would claim-handling errors, bad faith allegations, or regulatory claims be excluded if AI made the decision?
Employees use generative AI to summarize long policy documents for internal research.
Reviewer note: Internal generative use is not Low because business users may rely on generated summaries.
Coverage question: If an employee relies on an incorrect summary, is the resulting professional error covered or excluded?
A classifier scores claims for fraud referral and investigators decide whether to open a case.
Reviewer note: The model materially influences claims handling and should be governed like regulated decision support.
Coverage question: Could a wrongful fraud referral trigger E&O, defamation, privacy, or regulatory coverage issues?
A customer chatbot answers policyholder questions about whether a loss appears covered.
Reviewer note: Direct coverage explanations to policyholders create high external communication risk.
Coverage question: Would misstatements by a chatbot be treated as professional services, customer communication, or excluded AI output?
A predictive model recommends price changes for underwriters, who approve before quote issuance.
Reviewer note: Pricing recommendations are high impact even when an underwriter approves the final quote.
Coverage question: Does the organization have coverage for algorithmic discrimination or rating-practice allegations?
Generative AI creates insurance marketing copy from public web data and a producer reviews it before use.
Reviewer note: Public data and reviewed external content should trigger at least Moderate controls.
Coverage question: Are copyright, advertising injury, and media liability exposures covered or excluded for AI-generated copy?
An internal classifier prioritizes back-office work, but the team cannot confirm its training data.
Reviewer note: Unknown data provenance should not fall below public unknown data.
Coverage question: If the classifier causes operational loss, does any policy respond when data provenance is undocumented?
A classifier routes policyholder service requests to queues and shows the queue choice to the customer.
Reviewer note: Client-facing classifier output needs monitoring and escalation, even if it is not a regulated decision.
Coverage question: Would misrouting, delay, or customer harm be covered under E&O, cyber, or customer communication coverage?
A pricing model recommends rate actions, but the workflow does not log prompts, outputs, reviewer decisions, or model versions.
Reviewer note: A high-risk model without logging cannot support audit, inquiry, or remediation.
Coverage question: Would the lack of audit evidence weaken defense, indemnity, or regulatory response coverage?