Your clients won’t share their data for testing. You shouldn’t need them to.
RegTech companies face a unique chicken-and-egg problem: you need realistic financial data to build and demonstrate compliance solutions, but your prospective clients can’t share their customer data for testing — and shouldn’t.
Most RegTech platforms solve this by generating toy datasets internally or begging prospects for anonymized samples. The first approach produces models that fail against real-world complexity. The second creates legal exposure before the contract is even signed.
Born-synthetic data eliminates this dependency entirely. Realistic financial profiles with Pareto-distributed wealth, multi-jurisdictional structures, and culturally accurate identities — generated from mathematical models. No client data required. No NDAs needed. No re-identification risk.
Available Datasets for Regulatory Technology
Each dataset is available in three tiers: 1,000 records ($499–$999), 10,000 records ($2,499–$4,999), and 100,000 records ($12,500–$24,999). All datasets include a Certificate of Sovereign Origin documenting the generation methodology.
| Use Case | Description |
|---|---|
| AML Training Data | Synthetic transaction histories and customer profiles with embedded suspicious activity patterns. Train detection models on realistic scenarios without regulatory exposure. |
| KYC Testing | 29-field synthetic customer profiles for identity verification workflows. Test onboarding, document validation, and risk-tier assignment without exposing real PII. |
| Sanctions Screening | Profiles with culturally accurate naming conventions across 6 geographic niches. Test name-matching algorithms against realistic patterns without touching watchlist data. |
| Enhanced Due Diligence Simulation | Complex wealth structures, multi-jurisdictional holdings, and PEP-adjacent profiles. Stress-test EDD workflows on edge cases that rarely appear in production. |
| Transaction Monitoring | Synthetic financial flows with realistic volume patterns, cross-border transfers, and layering scenarios. Calibrate alert thresholds without production data leakage. |
| Model Validation | Statistically controlled datasets with known distributions for backtesting risk models. Validate under Pareto-distributed wealth and algebraically constrained fields. |
| Risk Scoring | Profiles with calibrated risk indicators across wealth tiers and geographies. Validate and tune risk scoring models with known-distribution inputs. |
| Onboarding Simulation | End-to-end customer lifecycle data from application through approval. Test digital onboarding pipelines, form validation, and conversion funnels. |
Why Born-Synthetic for Regulatory Technology?
RegTech companies building AI-powered compliance tools must demonstrate EU AI Act Article 10 compliance for their training data. Born-synthetic data provides the documented, governed datasets that Article 10 demands — without dependency on client data.
Born-synthetic data addresses all of these requirements simultaneously. Every profile is generated from mathematical models — no real data input, no anonymization that can be reversed, no data lineage that connects to production systems. The Certificate of Sovereign Origin documents exactly how each dataset was produced.
The Born-Synthetic Difference
| Approach | Real Data Risk | GDPR Status | Re-identification Risk | Audit Trail |
|---|---|---|---|---|
| Production data in test | 🔴 Full exposure | 🔴 Requires full DPIA | 🔴 100% | 🔴 Same as production |
| Anonymized/masked data | 🟡 Residual risk | 🟡 Contested | 🟡 3–87% reversible | 🟡 Lineage preserved |
| Born-Synthetic data | 🟢 Zero | 🟢 Not personal data | 🟢 Impossible | 🟢 Certificate of Origin |
Get Started
Free sample — no registration. Download 100 synthetic profiles from any of our 6 geographic niches. Run your own validation. Check the Balance Sheet Test. Then decide.
Download Free KYC Sample → | Check Your GDPR Risk Score →
Frequently Asked Questions
Why do RegTech companies need synthetic data?
RegTech platforms need realistic financial data to build, test, and demonstrate compliance solutions. But prospective clients cannot share production data for testing. Born-synthetic data provides realistic profiles without any client data dependency.
Can I use synthetic data for product demos?
Yes. Born-synthetic profiles are designed to be realistic enough for product demonstrations while carrying zero compliance risk. Show prospective clients exactly how your platform handles complex scenarios without needing their data first.
How does synthetic data help RegTech companies comply with the EU AI Act?
EU AI Act Article 10 requires documented, governed training datasets for high-risk AI systems. Born-synthetic data provides verifiable provenance, documented generation methodology, and zero personal data processing — satisfying Article 10 requirements by construction.
What volume of data is available?
Datasets range from 1,000 to 100,000 records per geographic niche, with 6 niches covering global financial markets. Start with 100 free profiles to evaluate data quality before purchasing.
Related Resources
- What Is Born-Synthetic Data? — The methodology behind zero-lineage data generation
- Compliance Testing Data — Full KYC/AML product overview with 29 enhanced fields
- GDPR Risk Assessment — Free tool to evaluate your current test data exposure
- Download Free UHNWI Sample — 100 profiles, 19 fields, no registration
- Download Free KYC Sample — 100 profiles, 29 fields, no registration
- Platform Comparison — How Sovereign Forger compares to Mostly AI, Tonic, Gretel, and others
- Glossary — 50 essential terms in synthetic data and financial compliance
- Regulatory Guides — EU AI Act, DORA, and data protection frameworks
