Insurance scenarios
Insurance industry scenarios — claims triage, underwriting, policy Q&A, fraud detection, subrogation. Industry patterns, not customer case studies.
Five canonical scenarios where insurance carriers and InsurTech teams use Stratix evaluation to gate AI features into production. Patterns, not specific customers.
1. Claims triage and processing
Setup: First-notice-of-loss assistant ingests claim narrative, photos, prior policy data; produces severity bucket, coverage-applicability flag, and recommended next action (assign adjuster, pay-in-place, deny-with-reason).
Why it's hard:
Severity must be calibrated — under-triage delays critical claims; over-triage clogs senior adjusters with low-value work
Coverage-applicability requires reading a policy document the model has likely never seen
Photos add modality risk (occluded damage, poor lighting, staged-loss indicators)
Stakes:
Industry estimates put insurance fraud at ~$80B/year across P&C lines
Average mis-estimation cost on commercial property claims: $50K-$500K per incident
State DOI complaint rates rise sharply when triage routes legitimate claims to denial paths
Stratix evaluation criteria:
Severity calibration
Buckets vs. ground-truth final paid amount
Deterministic rule on bucket-vs-actual delta
Coverage citation
Cited policy section exists and supports decision
Citation-existence + judge for citation-supports-claim
Escalation rule
Severity ≥ "major" auto-routes to senior adjuster
Hard rule — zero violations tolerated
Photo evidence handling
Claim doesn't proceed past triage without photos when policy requires
Deterministic rule
Disparate impact
Approval rate parity across demographic proxies (zip code, age band)
Group fairness scorer
2. Underwriting risk assessment
Setup: AI co-pilot helps underwriter price a new policy. Inputs include applicant data, third-party risk signals, prior claims history. Output: recommended premium, risk class, exclusions to attach.
Why it's hard:
Proxy discrimination — zip codes, occupation codes, even credit-tier features can encode protected class
State-by-state rate filing rules diverge
Recommendation must be explainable to a regulator post-hoc
Stakes:
Most state DOIs flag underwriting decisions when premium varies by more than ~1.25× across otherwise similar applicants without documented basis
DOJ and state AG investigations have followed proxy-discrimination findings in algorithmic underwriting
Stratix evaluation criteria:
Disparate impact ratio — Fairness scorer; ratio < 0.8 fails (four-fifths rule analog)
Premium-band justification — Each premium decision must cite the rate filing factor that drove it; citation-existence rule
State-rule applicability — Hard rule that state-specific exclusions match the policyholder state
Explainability — Judge scores whether a non-technical regulator could follow the reasoning
3. Policy Q&A and customer self-service
Setup: Customer-facing chatbot answers questions about an active policy: "Am I covered for water damage from the supply line in my dishwasher?" Response must reference the actual policy document version on file, not generic policy language.
Why it's hard:
Stale-policy interpretation — model trained on yesterday's policy form misreads today's renewal
Coverage edge cases (deductibles, sub-limits, exclusion endorsements) are legally precise and don't tolerate paraphrase
Stakes:
Carriers face state DOI complaints and class-action exposure when chatbot output contradicts the actual policy
A miscommunicated coverage decision can trigger bad-faith claims handling allegations
Stratix evaluation criteria:
Policy-document grounding — Retrieval-faithfulness judge against the customer's specific policy version
Document-version match — Hard rule: cited document version equals customer's active version
Sub-limit accuracy — Deterministic rule: any dollar amount cited matches the policy file
Hedge language enforcement — Output must include "this is informational, refer to your policy / contact a licensed agent" caveat for non-trivial coverage questions
4. Fraud detection and special-investigations referral
Setup: Model scores claims for fraud-investigation referral. Output: risk score 0-100, top three contributing factors, recommended action (close, investigate, refer to SIU).
Why it's hard:
False positives drag legitimate claimants into invasive investigations and create complaint exposure
False negatives cost the carrier directly
Demographic disparity in referral rates is a regulator hot button
Stakes:
Industry-wide P&C fraud cost ~$80B annually; SIU efficiency drives loss-ratio meaningfully
Disparate referral rates have been the subject of state DOI consent orders
Stratix evaluation criteria:
Precision at top decile — Deterministic; tracks whether the high-score band matches confirmed-fraud rate
Recall on labeled known-fraud set — Deterministic
Group-fairness scorer — Referral rate ratio across demographic proxies
Reason-code coverage — Hard rule: every referral must cite ≥ 3 contributing factors
Audit-trail completeness — Every score logged with model version, feature inputs, threshold decision
5. Subrogation and recovery
Setup: AI assistant reads a claim file and identifies subrogation potential — the third party potentially liable for the loss the carrier just paid. Output includes liable-party identification, dollar-recovery estimate, statute-of-limitations deadline.
Why it's hard:
Liability identification requires reading police reports, third-party policies, and jurisdictional law
Statute-of-limitations errors directly bar recovery
False-positive subrogation pursuits annoy customers and damage agency relationships
Stakes:
Subrogation recovery typically returns 1-3% of P&C loss expense — material to combined ratio
Missed-deadline claims are unrecoverable and an E&O exposure for the carrier
Stratix evaluation criteria:
Liability-party identification accuracy — Judge scores against ground-truth liability findings
Statute-of-limitations rule — Hard deterministic rule: every subrogation file must include the SoL date and jurisdiction
Recovery-estimate calibration — Deterministic; estimated vs. actual recovery on closed files
Document-citation completeness — Every liability claim must cite the supporting document section
See also
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