AML Test Data
Generate labeled transaction data with 14 money-laundering typologies, multi-party entity networks, and ground-truth labels for every record.
14 AML Typologies with Ground Truth
Each typology generates realistic transaction patterns calibrated against FinCEN enforcement actions
Built for Compliance Teams
Multi-Party Networks
Each typology generates realistic entity networks: beneficial owners, shell companies, correspondent banks, and intermediaries with proper jurisdiction assignments.
Velocity Features
Transaction velocity, amount distribution shifts, counterparty diversity, and time-of-day patterns are calibrated against real FinCEN enforcement actions.
Sanctions Screening
Generate entities that match OFAC SDN, EU sanctions, and UN lists at configurable fuzzy-match thresholds for testing screening system recall.
VynFi vs Alternatives
| Feature | Manual Cases | Prod Sampling | Generic Synth | VynFi |
|---|---|---|---|---|
| Ground-truth labels | ||||
| Typology coverage | ||||
| Network structure | ||||
| Scale (millions of txns) | ||||
| Privacy safe | ||||
| Cost |
Python SDK Example
import vynfi
client = vynfi.Client(api_key="vf_live_...")
job = client.generate(
sector="banking",
module="aml",
typologies=["structuring", "layering", "round_tripping"],
rows=500_000,
suspicion_rate=0.05, # 5% suspicious transactions
network_depth=3, # 3-hop entity networks
output_format="parquet",
)
# Download with ground-truth labels
df = job.to_pandas()
print(df[df["is_suspicious"]].groupby("typology_id").size())Stop waiting for production data samples
Generate labeled AML test data in minutes. 5,000 free credits to start.