Run a Regulatory Assessment

A transaction is the set of facts the carrier records about one AI-influenced decision: which system fired, what it produced, what it consumed, whether a person reviewed it, and what the consumer was told. Raw personal data never enters; only a tok_ reference does.

Showcase cases

TX-CLM-0417	TX · claims · auto	Rear-end collision claim; vendor fraud score 0.93 on social-media and location signals; denial letter queued automatically.
TX-UR-0052	TX · utilization review · health	Prior authorization for inpatient rehabilitation; system recommended adverse determination; nurse reviewer concurred; no physician review recorded.
CO-LIFE-0088	CO · underwriting · life	Term life application declined on vendor mortality score built from prescription history and credit attributes.
NY-RATE-0231	NY · rating · life	Rated up 18% over standard; notice gave specific reasons but did not disclose that external data and an AI system were used.
CO-AUTO-0144	CO · renewal · auto	Colorado auto renewal priced with telematics and credit-based score; the same transaction dated before 15 October 2025 falls outside Regulation 10-1-1's amended scope.
TX-UW-0311	TX · underwriting · auto	Standard new-business approval with a consumer report and underwriter sign-off.
CT-LIFE-0075	CT · underwriting · life	Connecticut adopted the NAIC bulletin (MC-25, 26 February 2024), so the bulletin's third-party and testing expectations apply here and not in Texas.
TX-RNW-0199	TX · renewal · home	Homeowners non-renewal driven by an aerial-imagery roof-condition score; no notice generated; no human review.

Try changing the facts

Set human_involvement.reviewed to true with reviewer_role: "physician" on the utilization-review case.
Add a notice_sent_at timestamp and reasons_provided: true to a denial.
Change jurisdiction.state from TX to CT and watch the NAIC bulletin rules appear.
Set an "as of" date before 15 October 2025 on the Colorado auto case: the amended regulation did not yet apply.

Transaction (JSON)

{
  "txn_id": "TX-CLM-0417-LIVE-974",
  "tenant_id": "bluebonnet",
  "occurred_at": "2026-10-21T09:00:00Z",
  "stage": "claims",
  "line_of_business": "auto",
  "jurisdiction": {
    "state": "TX",
    "filing_state": null
  },
  "ai_system": {
    "system_id": "fnol-fraud",
    "model_version": "3.1",
    "vendor": "third_party",
    "vendor_name": "Sentinel Analytics (fictional)",
    "high_risk_class": "fraud",
    "material_financial_impact": false,
    "model_doc_ref": null,
    "governance_attested": false,
    "testing_report_ref": null
  },
  "inputs": {
    "feature_classes": [
      "claims_history",
      "geography",
      "social_media"
    ],
    "protected_factors_used": [],
    "actuarial_support_ref": null,
    "ecdis_present": false,
    "consumer_report_used": false,
    "credit_used": false,
    "pii_token": "tok_showcase_0417",
    "data_source_refs": [
      "vendor://sentinel/fnol"
    ]
  },
  "output": {
    "decision": "deny",
    "score": 0.93,
    "reason_codes": [
      "F07",
      "F12"
    ],
    "premium_delta_pct": null
  },
  "human_involvement": {
    "reviewed": false,
    "overridden": false,
    "reviewer_role": null
  },
  "adverse_action": {
    "is_adverse": true,
    "notice_sent_at": null,
    "reasons_provided": false,
    "specific_reasons": false,
    "ecdis_disclosed": false
  },
  "consumer": {
    "appeal_channel": "claims@bluebonnet.example",
    "proxy_group": "B"
  },
  "notes": "Rear-end collision claim; vendor fraud score 0.93 on social-media and location signals; denial letter queued automatically."
}

Mode

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