---
license: apache-2.0
library_name: pytorch_geometric
tags: [graph-neural-network, fraud-detection, accounting, vynfi, v5.29]
---

# VynFi JE Fraud GNN — v5.29 retrain

GraphSAGE edge classifier trained on the v5.29 SOTA-mode
[`VynFi/vynfi-journal-entries-1m`](https://huggingface.co/datasets/VynFi/vynfi-journal-entries-1m)
accounting-network edges. Binary fraud detection at the edge
level (single journal entry's debit→credit relation).

## Metrics on test split (held-out)

| 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).

### Per-process breakdown (test split)

| 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 |

## Repro

```bash
# 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.