{
 "provenance": "IBM AMLworld HI-Small (Altman et al., Kaggle ealtman2019/ibm-transactions-for-anti-money-laundering-aml, CDLA-Sharing-1.0). The corpus is entirely SYNTHETIC by IBM's own statement -- generated by a multi-agent virtual world, not real customer data. An earlier revision of this line opened with the word 'real' and then said synthetic in its next sentence; what is real is the provenance, not the transactions. These are unmodified rows of the published corpus, carrying the engineered features finance_aml.gbm was actually trained on, unlike the 8-feature PaySim sample this page uses for the FHE/MPC pipeline.",
 "input": {
  "shape": [
   1,
   32
  ],
  "dtype": "float32",
  "preprocessing": [],
  "layout": "samples[i] is one row of the 32 features below"
 },
 "feature_names": [
  "amount_paid_log",
  "amount_ratio",
  "currency_mismatch",
  "is_self_transfer",
  "hour_of_day",
  "day_of_week",
  "is_round_amount",
  "format_risk",
  "src_out_degree",
  "src_unique_dst",
  "dst_in_degree",
  "dst_unique_src",
  "src_fan_out_ratio",
  "dst_fan_in_ratio",
  "src_amount_mean_log",
  "src_amount_max_log",
  "dst_amount_mean_log",
  "amount_z_src",
  "amount_share_of_src_total",
  "is_reciprocal",
  "two_cycle_count",
  "scatter_gather_src",
  "scatter_gather_dst",
  "src_bank_diversity",
  "dst_bank_diversity",
  "is_cross_bank",
  "closes_cycle_2",
  "closes_cycle_3",
  "closes_cycle_4",
  "onward_hops",
  "src_burst_fanout",
  "dst_burst_fanin"
 ],
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 "expected": {
  "graph": "model.onnx",
  "accuracy": 0.9873046875,
  "majority_baseline": 0.97998046875,
  "minority_class_f1": 0.543859649122807,
  "note": "Stratified to contain positives -- AMLworld's real prevalence is ~1 in 981, so an unstratified draw would be all-negative and accuracy would be vacuous."
 }
}