The Auto Insurance sector in the DecisioQ Decision Catalog is identified by AUTO-INS. It supports decision support for claims, underwriting, repair approvals, risk scoring, compliance, analytics, and customer service operations across automotive insurance workflows.

DecisioQ integrations should load sector, category, decision, Decision Preparation Model, and scenario metadata from the Decision Catalog. Do not hard-code legacy template names or old decision payload structures.

Recommended first deployment: start with a high-volume advisory workflow such as claim intake prioritization, adjuster assignment, rental extension approval, or coverage validation. Compare DecisioQ recommendations against existing claim handling decisions before automating downstream actions.

Auto Insurance client applications should use the same discovery flow as the Business Decision Studio:

1. Load GET /decisioncatalog through the approved server-side Decision Catalog gateway.
2. Populate Sector from catalog.sectors[] and select AUTO-INS for Auto Insurance.
3. Populate Category from selectedSector.categories[]. Example categories include Claims Intake & Triage, Coverage & Liability, Customer Mobility, Compliance & Regulatory, and Analytics & Reporting.
4. Populate Decision from selectedCategory.decisions[] when available, or call /decisioncatalog/sectors/{sectorCode}/categories/{categoryCode}/decisions.
5. Display Decision Name only in the dropdown while keeping decisionId as the option value.
6. Load full decision detail from /decisioncatalog/decisions/{decisionId} before rendering Guided Data Collection.

Decision detail is the source of truth for criterion names, data guidance, scale, direction, profile options, scenarios, and overview metadata.

For regional deployments, keep DecisioQ technical identifiers stable and localize only user-facing labels and guidance. Unless the selected endpoint and decision schema explicitly support unit-qualified input, convert mileage, distance, pressure, temperature, mass, volume, speed, fuel consumption, and other measurements to the required canonical units before submission. Do not infer units or currency from locale.

Auto Insurance integrations should map operational claim, policy, customer, vehicle, repair, and service data into decision-specific Business Data. The selected decision detail defines which criteria are required and how each value should be measured.

Do not invent criterion IDs. Use the canonical criteria returned by /decisioncatalog/decisions/{decisionId}, including criterion overview metadata, unit, direction, scale, and data guidance.

Production integrations should keep credentials and bearer tokens on trusted servers or server-side gateways. Browser pages should use same-origin proxy handlers or their own backend service.

1. Trusted backend obtains a JWT token from identity.vinquery.com.
2. Application loads catalog and decision detail metadata.
3. User or system prepares Business Data for candidate claims, policies, repair cases, or operational actions.
4. Call POST /api/v1/validate to validate and prepare the decision input before execution.
5. Call POST /api/v1/decide to return the authoritative Decision Result.
6. Display the recommendation, ranked alternatives, warnings, assumptions, and Explanation of Decision Result.
7. Keep execution trace and diagnostics available to integrators, but collapsed or hidden by default for business users.

Use /api/v1/decide for both Prepared Criteria Mode and business-data integrations that submit a businessData object. New public onboarding should prefer Preview Decision and Execute Decision terminology.

The exact fields depend on the selected Auto Insurance decision. The example below shows the integration style rather than a guaranteed schema for every decision.

{
  "decisionId": "AUTO-INS-011",
  "decisionPreparationModelId": "AUTO-INS-PROFILE-STANDARD",
  "scenarioId": "AUTO-INS-SCENARIO-CLAIMS-TRIAGE",
  "businessData": {
    "candidates": [
      {
        "optionId": "CLAIM-78421",
        "name": "2019 Honda Civic rear-end collision",
        "values": {
          "claimSeverityScore": 82,
          "coverageConfidenceScore": 94,
          "customerImpactScore": 76,
          "slaUrgencyScore": 88,
          "fraudRiskScore": 18,
          "estimatedLossAmount": 7400
        }
      },
      {
        "optionId": "CLAIM-78433",
        "name": "2022 Ford F-150 hail damage",
        "values": {
          "claimSeverityScore": 61,
          "coverageConfidenceScore": 89,
          "customerImpactScore": 58,
          "slaUrgencyScore": 64,
          "fraudRiskScore": 12,
          "estimatedLossAmount": 5200
        }
      }
    ]
  },
  "requestContext": {
    "correlationId": "claims-workbench-20260717-001"
  }
}

This pattern belongs in a trusted backend, not directly in browser JavaScript.

async function executeAutoInsuranceDecision(decisionInput, jwtToken) {
  const response = await fetch(`${process.env.DECISIOQ_BASE_URL}/api/v1/decide`, {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${jwtToken}`,
      "Content-Type": "application/json"
    },
    body: JSON.stringify(decisionInput)
  });

  if (!response.ok) {
    throw new Error(`DecisioQ execution failed: ${response.status} ${await response.text()}`);
  }

  return await response.json();
}

• Store identity credentials, Decision API base URL, and proxy configuration in trusted server configuration or a secret manager.
• Do not expose bearer tokens or integration credentials in browser code.
• Use catalog metadata rather than hard-coded categories, decisions, criteria, profiles, or scenarios.
• Validate Business Data before execution and show user-friendly validation messages.
• Persist correlation ID, claim ID, policy ID, decision ID, profile ID, scenario ID, selected option, score, and explanation for audit.
• Retain the successful decision response requestId with the host business transaction, then call POST /api/v1/decision-outcomes when the action and realized result are known.
• Require an overrideReason for overridden recommendations; never infer acceptance merely because a result was displayed.
• Monitor GET /api/v1/decision-outcomes/summary for acceptance rate, override rate, and realized-value aggregates. Treat outcome feedback as reporting evidence, not automatic model training.
• Redact claim notes, customer identifiers, policy details, medical details, and payment data in logs and diagnostics.
• Monitor catalog load failures, token refresh failures, API latency, validation failures, account verification failures, and unusual exclusion rates.
• Define fallback behavior so claims workflows remain usable when DecisioQ is unavailable.
• Review Decision Preparation Models and scenario assumptions with claims, compliance, and product owners when policy rules or regulatory requirements change.