| --- |
| license: other |
| license_name: cdla-sharing-1.0 |
| library_name: onnx |
| pipeline_tag: tabular-classification |
| tags: |
| - quantum-safe |
| - privacy-preserving |
| - finance-aml |
| - onnx |
| - onnxruntime-web |
| --- |
| |
| # Graph-feature-preprocessed gradient boosting |
|
|
| GFP (graph features) + XGBoost |
|
|
| Part of [QSMPC-QKD-QHE-AI-Hybrid](https://github.com/thedaemon-wizard/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. |
| |
