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VynFi/je-fraud-gnn
je-fraud-gnn is a machine learning model from VynFi. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch_geometric. The card lists the license as apache-2.0.
GraphSAGE edge classifier trained on the v5.29 SOTA-mode VynFi/vynfi-journal-entries-1m accounting-network edges. Binary fraud detection at the edge level (single journal entry's debit→credit relation).
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Updated May 26, 2026
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From the Hugging Face model README
GraphSAGE edge classifier trained on the v5.29 SOTA-mode
VynFi/vynfi-journal-entries-1m
accounting-network edges. Binary fraud detection at the edge
level (single journal entry's debit→credit relation).
| metric | v5.27 baseline | v5.29 retrain | Δ |
|---|---|---|---|
| AUC-ROC | 0.909 | 0.9185 | +0.010 |
| AUC-PR | 0.799 | 0.8048 | +0.006 |
| F1 | 0.790 | 0.8010 | +0.011 |
Trained 50 epochs on NVIDIA A10, ~2.5 min. n_test = 234,953 edges, 13,776 positive (5.86 % fraud rate).
| process | n | n_pos | AUC | PR | F1 |
|---|---|---|---|---|---|
| P2P | 70,635 | 4,230 | 0.914 | 0.800 | 0.798 |
| O2C | 82,521 | 4,785 | 0.919 | 0.803 | 0.801 |
| R2R | 46,758 | 2,727 | 0.925 | 0.812 | 0.803 |
| H2R | 23,318 | 1,315 | 0.920 | 0.810 | 0.808 |
| A2R | 11,721 | 719 | 0.915 | 0.807 | 0.802 |
# Build PyG dataset from the v5.29 HF dataset
python3 scripts/ml/build_je_pyg_dataset.py \
--output je_pyg_v2.pt --seed 42
# Train
python3 scripts/ml/train_je_fraud_gnn.py \
--dataset je_pyg_v2.pt \
--output je_fraud_gnn.pt \
--epochs 50 --device cuda --seed 42
Source: mivertowski/SyntheticData @ v5.29.0.
The PR-AUC and F1 lifts over v5.27 reflect the SOTA-N behavioral levers (recurring archetypes, source-conditional rarity tagging, trading-partner pool concentration, etc.) producing sharper fraud-vs-clean separability in the accounting-network substrate.