| --- |
| library_name: xgboost |
| tags: |
| - fraud-detection |
| - phishing |
| - xgboost |
| - nexus |
| license: mit |
| --- |
| |
| # NEXUS Fraud Detection — model artifacts |
|
|
| Trained heads for [NEXUS Fraud Detection](https://github.com/Deepnar/NEXUS-Fraud-Detection) |
| (forked from [Rishit1769/NEXUS-Fraud-Detection](https://github.com/Rishit1769/NEXUS-Fraud-Detection)). |
| Each folder is a drop-in `ml/artifacts/<task>/<version>/` directory: copy it into the |
| repo's `ml/artifacts/` (or point `MODEL_ARTIFACT_ROOT` at this repo) and the |
| FastAPI model service (`ml/service.py`) picks it up with no code changes. |
|
|
| | Head | Folder | Model | Unseen-data result | |
| |---|---|---|---| |
| | Transaction fraud (XGBoost + isotonic, Optuna-tuned) | `transaction/` | `model.json` + `calibration_model.joblib` | Fresh-seed PR-AUC 0.689; future-years slice 0.847 | |
| | Message phishing (TF-IDF char+word + lexical, XGB) | `message-tfidf/` | `model.json` + `vectorizer.joblib` | 18/18 hand-crafted probes | |
| | Message count baseline (XGB, fallback) | `message/` | `model.json` | advisory-only fallback | |
| | URL phishing (lexical XGB) | `url/` | `model.json` | 19/20 probes | |
|
|
| `eval_unseen.json` is the full unseen-data ledger. Per-head `metrics.json`, |
| `thresholds.json` (recall>=0.80 operating points), `feature_manifest.json`, |
| `split_manifest.json`, and `training_manifest.json` document provenance. |
|
|
| **Note:** deterministic rules stay authoritative in NEXUS — these heads are |
| advisory and never override a critical rule. Transaction data is synthetic |
| (IBM); retrain on real adjudicated cases before production use. |
|
|
