NEXUS Fraud Detection — model artifacts

Trained heads for NEXUS Fraud Detection (forked from 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.

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