Add CanFraudBench Track A dataset (LFS) and card

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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- tabular-classification
+language:
+- en
+tags:
+- fraud-detection
+- synthetic-identity
+- model-risk-management
+- osfi-e23
+- responsible-ai
+- canada
+- financial-services
+pretty_name: CanFraudBench — Track A (Synthetic Identity)
+size_categories:
+- 10K<n<100K
+---
+
+# CanFraudBench — Track A (Synthetic Identity)
+
+**A public, reproducible benchmark for synthetic-identity fraud detection in a
+Canadian financial-onboarding context — where every metric maps to OSFI
+Guideline E-23 model-risk expectations.**
+
+This is the Track A (synthetic identity) dataset of CanFraudBench. The full
+benchmark, including the metrics library, the OSFI E-23 governance mapping, the
+Track B document/presentation-attack harness, the leaderboard, and reference
+baselines, lives in the code repository:
+
+➡️ **Code & full benchmark: https://github.com/CrillyPienaah/canfraudbench**
+
+---
+
+## Why this exists
+
+OSFI Guideline E-23 (Model Risk Management) takes effect **1 May 2027** and
+applies to **all models at all federally regulated financial institutions**,
+including AI/ML and third-party models. Yet there is **no public Canadian
+benchmark** for identity-fraud detection — the strong open corpora that exist
+(MIDV-2020, SIDTD, IDNet) are all US/European and none is framed against
+Canadian regulatory expectations.
+
+CanFraudBench fills that gap, and adds what a pure-ML leaderboard lacks: every
+score is paired with the E-23 evidence dimension it speaks to (discrimination,
+calibration, stability/drift, fairness, explainability). A submission produces a
+**validation evidence pack**, not just an AUC.
+
+> **The benchmark's core lesson:** the reference baseline scores **0.969 AUC**
+> and still **FAILS overall**, because its Adverse Impact Ratio (0.59) breaches
+> the four-fifths fairness rule. Discrimination without governance is not a
+> passing model.
+
+---
+
+## What's in this dataset
+
+A reproducible, **fully synthetic** set of Canadian onboarding applications.
+**No real person's data is present.** (See *Privacy & Ethics* below.)
+
+| | |
+|---|---|
+| Records | 20,000 |
+| Legitimate | 16,000 |
+| Fraud | 4,000 (20%) |
+| Seed | 23 (deterministic) |
+| Format | JSON Lines (`.jsonl`) |
+
+### Fraud typology breakdown
+
+Fraud labels are grounded in documented synthetic-identity typologies so that
+performance can be sliced by fraud type (not just an aggregate AUC):
+
+| Typology | Count | Description |
+|---|---|---|
+| `fabricated` | 1,200 | Wholly invented identity; no real underlying person |
+| `blended` | 1,200 | "Frankenstein" — real structural identifier + fabricated name/DOB |
+| `file_aged` | 800 | Thin file artificially aged (nominee/piggyback tradelines) |
+| `linked_cluster` | 600 | Member of an application cluster (device/address reuse) |
+| `inconsistent` | 200 | Internally contradictory fields a univariate rule would miss |
+| `legitimate` | 16,000 | Internally consistent legitimate applicant |
+
+### Record schema
+
+Each line is a JSON object:
+
+```json
+{
+  "id": "can_0000001",
+  "raw": { "first_name": "...", "last_name": "...", "dob": "...",
+           "address": {...}, "sin": "...", "...": "..." },
+  "features": {
+    "f_sin_luhn_valid": 0,
+    "f_name_struct_anomaly": 0.0,
+    "f_dob_doc_inconsistency": 0.0,
+    "f_tenure_vs_age_gap": 0.0,
+    "f_cluster_link_score": 0.0,
+    "f_field_entropy": 0.51,
+    "f_thin_file": 0,
+    "f_province_group": 0
+  },
+  "protected_group": 0,
+  "typology": "legitimate",
+  "label": 0
+}
+```
+
+`features` is a ready-to-use numeric vector; `raw` is provided for anyone who
+wants to engineer their own features. `protected_group` is a synthetic region
+grouping included **solely** so fairness metrics (Adverse Impact Ratio, equal
+opportunity) are computable — it encodes no real demographic fact.
+
+---
+
+## Usage
+
+```python
+import json, urllib.request
+
+URL = "https://huggingface.co/datasets/CrillyPienaah/CanFraudBench/resolve/main/canfraudbench_synthid_n20000_seed23.jsonl"
+records = [json.loads(l) for l in urllib.request.urlopen(URL)]
+
+X = [list(r["features"].values()) for r in records]
+y = [r["label"] for r in records]
+groups = [r["protected_group"] for r in records]
+# ... train your model, then evaluate with the CanFraudBench metrics + E-23
+#     governance mapping from the code repo to produce an evidence pack.
+```
+
+To reproduce this exact file from scratch:
+
+```bash
+git clone https://github.com/CrillyPienaah/canfraudbench
+cd canfraudbench
+python -m canfraudbench.synthid.generate --n 20000 --seed 23 --out data/synthid/
+```
+
+The seed makes generation deterministic — the regenerated file matches this one.
+
+---
+
+## Submitting to the leaderboard
+
+CanFraudBench is **submission-by-protocol**: you run the evaluation harness on
+your own model and submit the produced evidence pack (metrics, never raw data).
+Rankings sort by **E-23 status first**, then mean per-typology recall, then AUC —
+a high-AUC model that fails a governance dimension does not outrank a governable
+one. See the [code repo](https://github.com/CrillyPienaah/canfraudbench) for the
+submission protocol.
+
+---
+
+## Privacy & Ethics
+
+- **Fully synthetic.** Names are sampled from generic token lists, not
+  registries. Addresses use real province/city *labels* with fictitious civic
+  numbers and documentation-style postal codes.
+- **No real Social Insurance Numbers.** Most records carry numbers that
+  deliberately *fail* the Luhn checksum so they can never collide with an issued
+  SIN; the `blended` typology uses a Luhn-valid-but-fictitious, overwhelmingly
+  unassigned number to exercise checksum-aware detectors. These are test
+  fixtures, not PII.
+- **Honest scope.** This v0.1 generator is a *typology-grounded simulator*, not
+  a differential-privacy mechanism trained on real data — because it never
+  touches real data, a DP guarantee would be vacuous. This is stated plainly
+  rather than overclaimed. See `docs/DATA_ETHICS.md` in the code repo.
+- **Not affiliated with or endorsed by OSFI.** The benchmark *maps to* the
+  public E-23 guideline; it is not approved by any regulator and is
+  decision-support, not regulatory advice.
+
+## License
+
+CC BY 4.0 for this generated dataset. Benchmark code is Apache-2.0.
+
+## Citation
+
+```bibtex
+@misc{canfraudbench2026,
+  title  = {CanFraudBench: A Canadian Identity-Fraud Benchmark with OSFI E-23 Governance Mapping},
+  author = {Pienaah, Christopher},
+  year   = {2026},
+  url    = {https://github.com/CrillyPienaah/canfraudbench}
+}
+```

canfraudbench_synthid_n20000_seed23.jsonl

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