Use Case — Insurance
Certified synthetic data for insurance AI — actuarial to claims
Insurance AI spans underwriting, claims, fraud detection, and actuarial modeling. Each requires large, labeled datasets with sensitive policyholder records. Certified synthetic insurance data gives you the volume and labels without the privacy exposure.
What this means for your data strategy
Insurance companies sit on some of the most sensitive personal data: health history, driving records, property details, financial information. Training AI models on this data requires navigating state insurance regulations, CCPA, GDPR, and NAIC model laws around data use. Certified synthetic insurance data resolves this tension: realistic training data with cryptographic proof that no real policyholder records were used — satisfying regulatory and legal requirements while enabling model development at scale.
How CertifiedData helps
- →Generate synthetic claims datasets with realistic loss distributions for claims triage and fraud detection models
- →Produce actuarial training data with realistic mortality, morbidity, and property loss patterns
- →Create synthetic policyholder cohorts for underwriting model training without real customer records
- →Certify that AI training data contains no real policyholder PII — documented for state insurance department inquiries
- →Share certified datasets with reinsurers and AI vendors without triggering data sharing restrictions
Regulatory context
Insurance AI models face scrutiny from state insurance departments, the NAIC (National Association of Insurance Commissioners), and increasingly from the EU AI Act for products sold in Europe. The NAIC Model Bulletin on the Use of AI Systems (2023) requires governance of AI decision models including training data documentation. CCPA and state privacy laws restrict use of real policyholder data. Certified synthetic training data addresses all of these requirements.
Why cryptographic certification matters
State insurance regulators are beginning to ask: 'What data trained this underwriting or claims AI?' A CertifiedData certificate provides a documented, verifiable answer — the dataset fingerprint, generation date, and algorithm — without disclosing real policyholder records. This supports model risk management documentation and regulatory examination readiness.
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
Can synthetic data capture rare insurance events like catastrophe claims?
Yes. One advantage of synthetic data is the ability to oversample rare events. CertifiedData can generate synthetic catastrophe claim scenarios, rare fraud patterns, or extreme loss events at a frequency that enables model training on scenarios that are underrepresented in real historical data.
Does this support NAIC Model Bulletin requirements?
The 2023 NAIC Model Bulletin on AI Systems requires insurers to maintain governance over AI models including training data documentation. A CertifiedData certificate provides the provenance record that supports this documentation requirement.
Related resources
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