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    "python": "3.14.7",
    "platform": "Windows-11-10.0.26300-SP0",
    "machine": "AMD64",
    "cpu_count": 8,
    "training_threads": 4,
    "packages": {
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      "pandas": "3.1.0",
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      "xgboost": "3.4.1",
      "joblib": "1.6.0"
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  "source_hashes": {
    "fraudshield/__init__.py": "2ecf118f587dd8723fb27a391dedbdcc0184a2014043b17e5756dc29fe145c75",
    "fraudshield/data.py": "97b689bb88f74e537f25cff6fb9e069ad4e69828d15b3871f8a7b7a0816ba9bb",
    "fraudshield/metrics.py": "7db83979844176f515c965a18cfa8e88ddfa0498eb5c6b5e55fafd289f9606c0",
    "fraudshield/server.py": "69a3c2b56fa1afa160569522e39798ed7c057603edeff323c45ab80d1fd7ec58",
    "fraudshield/training.py": "7e5acbf2709c42b01f7c3ac5e7471b1367c2f30352de964169eff3469d35ec86"
  },
  "benchmark_protocol": {
    "split": "Ordered time quantiles: train 50%, tune 15%, calibration 15%, test 20%; tied timestamps kept together.",
    "calibration": "Sigmoid fit on independent calibration period. Threshold from same period targets 1% alerts; actual test alert rate can differ.",
    "uncertainty": "150 fixed non-overlapping-block resamples (40 contiguous blocks), percentile 95% AP interval; conditional on this fitted model.",
    "latency": "12 local repetitions of a 256-row batch; includes feature validation and calibration; excludes network and disk.",
    "average_precision": "scikit-learn average_precision_score; step-weighted AP, not trapezoidal PR area."
  },
  "limitations": [
    "Two days of European transactions from 2013 do not establish current bank or Indian payment performance.",
    "Original PCA fitting provenance is outside this experiment; entity identifiers and label delay are unavailable.",
    "PCA components cannot be translated into merchant, device, customer or behavioral explanations.",
    "Fraud labels are historical outcomes, not proof of criminal intent; scores require human review.",
    "Exposure is the sum of historical fraud transaction amounts; it is not realized savings or recoverable loss.",
    "Local results are not directly comparable to published random-split or Kaggle hidden-test scores.",
    "Block bootstrap intervals describe this short test period only; few frauds and dependent events limit precision.",
    "Protected attributes are unavailable; demographic fairness cannot be measured on this dataset."
  ],
  "roadmap": [
    {
      "name": "Card transactions",
      "status": "Measured in this run",
      "dataset": "ULB / Worldline"
    },
    {
      "name": "Online payments",
      "status": "Next: rule acceptance and dataset acquisition required",
      "dataset": "IEEE-CIS / Vesta"
    },
    {
      "name": "Account opening",
      "status": "Next: separate task and fairness protocol",
      "dataset": "Feedzai BAF (synthetic, derived from real data)"
    }
  ]
}