README.md

---
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}
}
```