Graph-feature-preprocessed gradient boosting
GFP (graph features) + XGBoost
Part of QSMPC-QKD-QHE-AI-Hybrid,
a quantum-safe orchestration demo. This is the plaintext model for the
finance_aml use case; the encrypted path runs a distilled student, not this model.
Measured performance
| metric | value |
|---|---|
accuracy |
0.9983866140161103 |
auprc |
0.07323010834680115 |
auprc_baseline |
0.0015234617040716665 |
auroc |
0.8980183471873905 |
decision_threshold |
0.928426 |
minority_class_f1 |
0.08077786088257292 |
n_test |
1523504 |
n_train |
3554841 |
positive_rate |
0.00101943 |
wall_clock_s |
73.7 |
Published baselines this is measured against
- Target metric: minority-class F1 (laundering)
- Baseline to beat: 0.6323 - GFP+XGBoost, IBM AMLworld HI-Small, NeurIPS 2023 D&B (63.23 +/- 0.17)
- Published ceiling: 0.6816 - Multi-PNA+EU, AAAI-24 vol.38 no.10 pp.11838-11846 (68.16 +/- 2.65)
- Companion metric shown alongside:
auprc- reported together because the aggregate figure can look healthy while the class that matters is not.
Gap to the published baseline
This reaches minority-class F1 well below the published 0.6323. Two differences account for most of it and both are deliberate. (1) The published baseline uses IBM's Graph Feature Preprocessor, which ships in snapml under a Proprietary licence and is therefore excluded by this project's commercial-OK-only rule; what is reimplemented here is the documented typology set (degree, fan-in/out, reciprocity, k-hop cycle closure, burst degree, scatter-gather). (2) The corpus injects CYCLE attempts of up to 10 hops (HI-Small_Patterns.txt), while reachability here is computed to 4 hops because the boolean sparse powers grow quickly beyond that. The evaluation is also strictly inductive: graph aggregates are fitted on the training window only, so no test-time edge informs a training-time feature. Reported as measured; not adjusted to close the gap.
Training data
- Dataset: IBM AMLworld HI-Small
- Licence: CDLA-Sharing-1.0
- Source: https://www.kaggle.com/datasets/ealtman2019/ibm-transactions-for-anti-money-laundering-aml (licence read 2026-08-03)
5M transactions, 515K accounts, ~1 laundering transaction in 981. NeurIPS 2023 D&B. SYNTHETIC: IBM generates it with a multi-agent virtual-world model and states 'Everything is synthetic'; the paper is titled 'Realistic Synthetic Financial Transactions for AML Models'. Chosen for its calibration to real transaction statistics and its CDLA-Sharing terms, not because it is real data - no commercial-OK corpus of real laundering transactions exists.
Notes and limitations
Stateless at inference, so this is the browser-tier finance model.
Honest scope
This model is published as part of a research proof of concept, not as a production system.
Numbers above are what this repository measured on the split described, with the code in
scripts/train/. Where a figure is carried from the literature rather than measured here,
it is labelled as such.