Synthetic AML Data for Transaction Monitoring Teams
Your Transaction Monitoring System Has Never Seen a Real Threat
Here’s what happens at every financial institution: the compliance team builds transaction monitoring rules, tests them against sanitized production data or randomly generated records, declares the system operational, and moves on.
Then a real money laundering pattern arrives — layered transactions across multiple jurisdictions, structured deposits below reporting thresholds, rapid movement through shell company networks — and the monitoring system misses it. Because it was never tested against anything that looked like an actual threat.
The regulators notice:
| Institution | Failure | Fine | Year |
|---|---|---|---|
| Block Inc (Square) | Transaction monitoring gaps | $120M | 2024 |
| Starling Bank | AML control failures | £29M | 2024 |
| OKX | AML/KYC deficiencies | $504M | 2025 |
| Monzo | AML compliance gaps | £21M | 2024 |
| Revolut | AML remediation orders | €3.5M | 2024 |
Every one of these institutions had transaction monitoring in place. Every one passed internal testing. The systems failed against the complexity they were never tested for.
Born-Synthetic AML data changes this equation.
What Born-Synthetic AML Data Delivers
Our KYC/AML Enhanced datasets provide 29 interlocked fields per profile — designed specifically for the scenarios that transaction monitoring teams need to test but can never source safely.
Realistic risk distributions
Not every profile is clean. Not every profile is suspicious. Our datasets include the full spectrum — from low-risk private banking clients to Very High risk profiles with PEP-adjacent connections, multi-jurisdictional beneficial ownership, and elevated due diligence triggers. The distribution reflects real-world compliance environments.
Correlated compliance fields
A “Very High” risk profile doesn’t exist in isolation. It comes with PEP-Adjacent status, Enhanced customer due diligence, a specific EDD trigger reason, a complex beneficial ownership structure, and a source-of-wealth narrative that matches the archetype. Your monitoring system gets profiles that behave like real ones — because the fields are mathematically correlated, not randomly assigned.
Six geographic patterns
AML patterns differ by region. Suspicious activity in Zurich looks nothing like suspicious activity in Dubai, São Paulo, or Singapore. Our 6 geographic niches provide region-specific transaction baselines:
| Niche | Relevant AML Testing Scenarios |
|---|---|
| Silicon Valley | Crypto liquidity events, stock option exercises, concentrated equity |
| Old Money Europe | Cross-border trust distributions, private banking complexity |
| Middle East | Sovereign wealth adjacency, multi-currency commodity flows |
| LatAm Barons | Agribusiness cash concentration, infrastructure contract payments |
| Pacific Rim | Supply chain financing, shipping industry cash flows |
| Swiss-Singapore | Multi-family office structures, dual-jurisdiction arbitrage |
How AML Teams Use This Data
Transaction monitoring rule testing
Feed synthetic profiles into your TM system. Verify that detection rules trigger on the right patterns — and don’t trigger on legitimate high-value transactions from complex but compliant clients. Reduce false positives without the risk of tuning against real customer data.
SAR training and analyst development
New compliance analysts need practice identifying suspicious patterns. Born-Synthetic profiles provide realistic training scenarios without accessing actual SAR filings or production client records. Build analyst expertise without compliance exposure.
Alert threshold calibration
Too many false positives burn analyst time. Too few miss real threats. Use statistically valid synthetic data to find the right threshold — without the regulatory risk of experimenting with production data.
Vendor evaluation and platform comparison
Comparing AML platform vendors? Feed identical Born-Synthetic datasets through each system and benchmark detection rates, false positive ratios, and processing speed. Standardized test data eliminates vendor excuses.
AI/ML model training for anomaly detection
Train machine learning models on diverse, realistic profiles with documented provenance. Born-Synthetic data provides the volume, variety, and documentation that EU AI Act Article 10 requires for high-risk AI systems in financial services.
Regulatory demonstration
Show regulators the depth of your testing methodology — without exposing a single real customer record. The Certificate of Sovereign Origin documents exactly how every profile was generated.
Why Not Anonymized Production Data?
Most institutions attempt to use anonymized production data for AML testing. This creates three problems:
Legal friction. Extracting production data — even for anonymization — requires privacy impact assessments, legal review, and often months of negotiation with your DPO. Every quarter. For every project.
Re-identification risk. Academic research has demonstrated successful re-identification attacks on anonymized financial data. A “Very High” risk client with a specific jurisdiction and industry sector may be identifiable even after anonymization. Your legal liability doesn’t disappear with the name.
Correlation destruction. Anonymization techniques that effectively prevent re-identification also destroy the field correlations that make data useful for testing. You get privacy OR realism — not both.
Born-Synthetic data eliminates all three problems. No production data is involved. No real person exists to re-identify. Field correlations are mathematically preserved because the data was generated with constraints, not stripped of identifying details.
Regulatory Compliance — Built In
Born-Synthetic AML data is not personal data. It was never personal data. Every profile is generated from mathematical distributions and cultural models with zero real data input.
- GDPR Article 25 — Data protection by design, achieved by construction
- EU AI Act Article 10 — Documented data governance for AI training (enforcement August 2026)
- DORA Articles 24-25 — Resilience testing with realistic but safe data (in force)
- PCI DSS 4.0 Req 6.5 — No real financial data in test environments (mandatory)
Pricing
| Tier | Records | Price | Per Record |
|---|---|---|---|
| Compliance Starter | 1,000 | $999 | $1.00 |
| Compliance Pro | 10,000 | $4,999 | $0.50 |
| Enterprise | 100,000 | $24,999 | $0.25 |
All tiers include: CSV delivery, Certificate of Sovereign Origin, all 29 KYC/AML fields, your choice of geographic niche or mixed dataset. JSON and Parquet formats available for enterprise packages.
No subscription. No recurring fees. One-time purchase, immediate delivery.
Try Before You Buy
Download a free 100-record KYC/AML sample — all 29 fields, full Certificate of Sovereign Origin, no registration required.
Not sure if your current testing data creates compliance risk?
Q: Is synthetic AML data accepted by regulators for compliance testing?
A: Synthetic data is increasingly recognized as a valid testing methodology. DORA explicitly requires resilience testing with realistic data, and the EU AI Act mandates documented data governance for AI training. Born-Synthetic data satisfies both with full provenance documentation via the Certificate of Sovereign Origin.
Q: How realistic are the transaction patterns?
A: Profiles are generated using verified statistical distributions — Pareto curves for wealth, sector-appropriate ranges for income and transaction volumes. Every field correlates with every other field across 29 dimensions, creating profiles that behave like real compliance data without being derived from real people.
Q: Can I customize the risk distribution in my dataset?
A: Enterprise packages include customization for risk score distributions, geographic focus, industry sector weighting, and PEP/sanctions ratios. Contact us to discuss your requirements.
Q: How does this differ from randomly generated test data?
A: Random generators like Mockaroo and Faker produce fields independently — a 25-year-old with $50M net worth and “Standard” due diligence. Born-Synthetic data enforces algebraic constraints across all 29 fields, producing coherent entities where every field is consistent with every other field.
Q: What format is the data delivered in?
A: Standard delivery is CSV with full field documentation and Certificate of Sovereign Origin. JSON and Parquet formats available on request for enterprise packages.
