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| title: VynFi Fraud-GNN Demo | |
| emoji: 🛡️ | |
| colorFrom: red | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 5.5.0 | |
| python_version: '3.11' | |
| app_file: app.py | |
| pinned: true | |
| license: apache-2.0 | |
| short_description: GraphSAGE fraud + GAE anomaly on synthetic JE network | |
| tags: | |
| - vynfi | |
| - graph-neural-network | |
| - fraud-detection | |
| - anomaly-detection | |
| - synthetic-data | |
| # 🛡️ VynFi Fraud-GNN Demo | |
| Interactive inference Space for the | |
| [`VynFi/je-fraud-gnn`](https://huggingface.co/VynFi/je-fraud-gnn) | |
| model bundle. | |
| ## Three tabs | |
| * **Edge fraud predictor** — pick a curated sample (clear fraud / clear | |
| normal / borderline) or build your own edge from any of the 499 GL | |
| accounts in the published COA. Returns fraud probability + anomaly MSE. | |
| * **Node anomaly explorer** — top-K accounts ranked by GAE | |
| reconstruction error on a 5,000-edge sample; surfaces accounts whose | |
| attribute patterns don't fit the structural prior. | |
| * **Live evaluation** — sample N edges from | |
| [`VynFi/vynfi-journal-entries-1m`](https://huggingface.co/datasets/VynFi/vynfi-journal-entries-1m), | |
| run the classifier, render confusion matrix + ROC against ground truth. | |
| ## Tech | |
| * Gradio + torch-geometric + pandas + matplotlib | |
| * Loads model bundle from `VynFi/je-fraud-gnn` at cold-start (cached after). | |
| * Loads dataset slices from `VynFi/vynfi-journal-entries-1m` on demand. | |
| ## Source | |
| * [Engine repo (`spaces/fraud-gnn-demo/`)](https://github.com/mivertowski/SyntheticData/tree/main/spaces/fraud-gnn-demo) | |
| * [Model card](https://huggingface.co/VynFi/je-fraud-gnn) — full training details, metrics, and honest discussion of where GNN helps vs LR baseline. | |
| * [Companion paper (SSRN)](https://ssrn.com/abstract=6538639) | |
| ## License | |
| Apache-2.0. | |