Use Case — Finance
Synthetic data for financial AI — certified and audit-ready
Financial AI models need large, labeled datasets. Certified synthetic data gives you the training volume you need without exposing real customer records — and with cryptographic proof of synthetic origin for every regulatory review.
What this means for your data strategy
Financial institutions are under increasing pressure to document the provenance of AI training data. Whether you are building fraud detection, credit scoring, AML monitoring, or risk models, the training data must be traceable, non-PII, and available for audit. Certified synthetic financial data satisfies all three requirements while remaining statistically realistic enough to train production-grade models.
How CertifiedData helps
- →Generate labeled synthetic transaction data for fraud and AML model training without using real customer records
- →Produce certified datasets for credit scoring backtests that satisfy SR 11-7 model documentation requirements
- →Create stress-test scenarios with rare fraud typologies that are difficult to sample from real data
- →Support EU AI Act Article 10 and 12 data governance documentation with a cryptographically signed provenance record
- →Share datasets across business units or with third parties without data sharing agreements for real PII
Regulatory context
Financial AI systems face scrutiny under DORA (Digital Operational Resilience Act), SR 11-7 (Federal Reserve model risk management), EU AI Act Articles 10 and 12 (training data requirements), and fair lending laws (ECOA, FHA). Certified synthetic training data provides the provenance documentation these frameworks require — proof that training data is synthetic, dated, and unmodified since generation.
Why cryptographic certification matters
A fraud detection model trained on certified synthetic data can demonstrate in an audit exactly what data was used, when it was generated, and that it contains no real customer records. The Ed25519 certificate is independently verifiable: a regulator or counterparty can hash the dataset and verify the signature without contacting CertifiedData. This is the difference between 'we used synthetic data' and 'here is the cryptographic proof.'
Each certificate records: dataset SHA-256 fingerprint, generation algorithm, timestamp, and an Ed25519 signature from CertifiedData's signing infrastructure.
Verification is public: any third party can verify the certificate without a CertifiedData account.
Frequently asked questions
Is synthetic financial data realistic enough for fraud model training?
Yes. CertifiedData uses CTGAN (Conditional Tabular GAN) which learns statistical relationships in your input data and generates new records that preserve those distributions. The result is statistically realistic synthetic data suitable for model training, without any real customer records.
Does certified synthetic data satisfy SR 11-7 documentation requirements?
SR 11-7 requires model risk documentation including training data provenance. A certified dataset provides a timestamped, cryptographically signed record of what data was used — meeting the documentation requirement without exposing real customer data.
Can I use certified synthetic data for EU AI Act compliance?
EU AI Act Articles 10 and 12 require training data governance and documentation for high-risk AI systems. A CertifiedData certificate provides a machine-verifiable provenance record that can be included in technical documentation submitted to conformity assessment bodies.
Related resources
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