{
  "id": "workflow:fraud-detection-model-review",
  "_kind": "Workflow",
  "_file": "workflows/workflows/workflows-thin-domains-fill.yaml",
  "_cluster": "workflows",
  "attributes": {
    "displayName": "Fraud Detection Model Review",
    "workflowKind": "governance",
    "triggerType": "scheduled",
    "typicalCadence": "monthly",
    "complexity": "cross-team",
    "description": "Reviews fraud detection model performance and drift -- analyzing\nprecision-recall tradeoffs across transaction segments and payment\nmethods, evaluating model drift by comparing feature distribution\nshifts between training and production data, auditing false positive\nrates and their impact on customer friction and operational review\ncosts, reviewing rule engine threshold calibration against emerging\nfraud typologies, assessing model fairness across demographic and\ngeographic cohorts to prevent discriminatory blocking, validating\nchallenger model A/B test results against champion performance, and\nreviewing fraud loss ratios against industry benchmarks. Produces\nmodel performance report, drift analysis, fairness audit summary, and\nthreshold tuning recommendations. Excludes model retraining\nexecution.\n"
  },
  "outgoingEdges": [
    {
      "from": "workflow:fraud-detection-model-review",
      "to": "role:ml-engineer",
      "kind": "involves_role",
      "attributes": {}
    },
    {
      "from": "workflow:fraud-detection-model-review",
      "to": "role:data-scientist",
      "kind": "involves_role",
      "attributes": {}
    },
    {
      "from": "workflow:fraud-detection-model-review",
      "to": "role:security-reviewer",
      "kind": "involves_role",
      "attributes": {}
    },
    {
      "from": "workflow:fraud-detection-model-review",
      "to": "skill-area:ml-fine-tuning",
      "kind": "requires_skill_area",
      "attributes": {}
    },
    {
      "from": "workflow:fraud-detection-model-review",
      "to": "skill-area:data-quality",
      "kind": "requires_skill_area",
      "attributes": {}
    },
    {
      "from": "workflow:fraud-detection-model-review",
      "to": "domain:fintech",
      "kind": "applies_to_domain",
      "attributes": {}
    },
    {
      "from": "workflow:fraud-detection-model-review",
      "to": "domain:security",
      "kind": "applies_to_domain",
      "attributes": {}
    },
    {
      "from": "workflow:fraud-detection-model-review",
      "to": "domain:data-science",
      "kind": "applies_to_domain",
      "attributes": {}
    },
    {
      "from": "workflow:fraud-detection-model-review",
      "to": "responsibility:data-quality-monitoring",
      "kind": "triggers_responsibility",
      "attributes": {}
    },
    {
      "from": "workflow:fraud-detection-model-review",
      "to": "responsibility:security-review",
      "kind": "triggers_responsibility",
      "attributes": {}
    },
    {
      "from": "workflow:fraud-detection-model-review",
      "to": "org-unit:ml-platform-team",
      "kind": "performed_by_org_unit",
      "attributes": {}
    },
    {
      "from": "workflow:fraud-detection-model-review",
      "to": "org-unit:risk-management-team",
      "kind": "performed_by_org_unit",
      "attributes": {}
    },
    {
      "from": "workflow:fraud-detection-model-review",
      "to": "org-unit:security-team",
      "kind": "performed_by_org_unit",
      "attributes": {}
    }
  ],
  "incomingEdges": []
}