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    "schema": "fraud-ieee/1",
    "archetype": "claim_vs_effect",
    "issued_at": "2026-09-16T20:14:20.223122+00:00",
    "subject": {
      "model": "xgboost (scale_pos_weight), interpretable features only",
      "dataset": "IEEE-CIS (Vesta) \u2014 590,540 real card-not-present e-commerce transactions, 3.5% fraud",
      "rows": 590540,
      "fraud_rate": 0.035,
      "split": "time-sorted 70/30"
    },
    "claim": "a fraud model detects fraud at its claimed accuracy",
    "verdict": "falsified",
    "finding": {
      "gap": "low_and_slow_evasion",
      "effect": "94.4% accurate yet catches only 23.9% of fraud \u2014 below the 96.5% 'always legitimate' baseline",
      "detail": "fraud is 'low-and-slow': fraudsters structure transactions to mimic legitimate behaviour, so the naive interpretable signals (amount, card, address, email domain) barely discriminate and the model misses most fraud. The accuracy is an artifact of the class imbalance; the real signal is behavioural/relational (device, account relationships) \u2014 a separate, harder surface."
    },
    "metrics": {
      "accuracy": 0.9442,
      "recall": 0.2392,
      "precision": 0.219,
      "auc": 0.7689,
      "trivial_accuracy": 0.9654
    },
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