VynFi/aml-100k

---
license: apache-2.0
task_categories:
- tabular-classification
tags:
- synthetic
- financial-data
- vynfi
- aml
- banking
- fraud-detection
size_categories:
- 10K<n<100K
configs:
- config_name: transactions
data_files: "transactions/*.parquet"
- config_name: account_labels
data_files: "account_labels/*.parquet"
- config_name: customer_labels
data_files: "customer_labels/*.parquet"
- config_name: relationship_labels
data_files: "relationship_labels/*.parquet"
---

# VynFi aml-100k

Regenerated with **DataSynth 3.1.1** (2026-04-19). What's new vs. prior releases:

- **Behavioral fraud biases now fire** on every `is_fraud` path — weekend ×32, round-dollar ×170, post-close ×3,106 lift measured on fraud-labeled entries (vs ~1× pre-3.1.1).
- **Document→JE fraud propagation** correctly sets `is_fraud_propagated` + `fraud_source_document_id` (was broken in 3.1.0, now verified on every doc-flow JE).
- **AML typology coverage** reaches the 0.80 evaluator threshold (0.000 → 0.857).
- **OCEL timestamps** are now microsecond-precision — pandas `to_datetime(..., utc=True)` retains 100 % of events (was losing 95 %).
- **Audit artifacts** (`audit/audit_opinions.json`, `audit/key_audit_matters.json`) + `process_variant_summary.json` always ship in the archive.

## Configs

| Config | Records |
|--------|---------|
| `transactions` | 752,568 |
| `account_labels` | 1,434 |
| `customer_labels` | 620 |
| `relationship_labels` | 10,660 |

## Quick start

```python
from datasets import load_dataset

ds = load_dataset("VynFi/aml-100k", name="transactions", split="train")
print(ds.features)
print(ds[0])
```

Or via the VynFi Python SDK (v1.5.1):

```python
import os
from vynfi import VynFi

client = VynFi(api_key=os.environ["VYNFI_API_KEY"])
job = client.jobs.generate_config(config={...}) # see https://github.com/VynFi/VynFi-python/tree/main/examples
```

See the SDK cookbook for worked examples:
- `examples/document_level_fraud.py`
- `examples/behavioral_fraud_patterns.py`
- `examples/sector_dag_presets.py`
- `examples/audit_opinions_kam.py`

## License

Apache 2.0. Entirely synthetic — no real individuals, companies, or transactions.