An audit-readiness playbook for Chief Risk Officers in regulated insurance markets.
Your audit trail isn't.
Most insurance carriers have AI in production.
Retrofitting an audit trail is more expensive than building one.
The good news is that the artifact set is finite and stable.
Every model decision must be traceable back to the data that informed it. The data lineage shows source system, transformation, feature engineering, model version, and decision output.
Model cards in regulated insurance go beyond the academic format. They include intended use, training data, evaluation results, known failure modes, monitoring plan, owner, and review cadence.
Every model-influenced decision (underwriting accept/decline, claim approve/deny, fraud flag, retention offer) must be logged with input features, model version, model output, human decision, and final outcome. The log is immutable, retained for the regulatory window, and queryable.
Every model decision must be traceable back to the data that informed it.
Model cards in regulated insurance go beyond the academic format.
Every model-influenced decision (underwriting accept/decline, claim approve/deny, fraud flag, retention offer) must be logged with input features, model version, model output, human decision, and final outcome.
If you have AI in production and an audit trail you cannot produce in under a day, the answer is not better archival.
12 to 16 weeks for the artifact pipelines and the first quarterly drill. Existing models in production take additional weeks each to onboard, depending on data lineage debt.
Per-state artifact templates with shared underlying data. The pipelines produce the data once; the formatting layer handles the variation.
Yes. The framework was designed for AI but the artifact discipline applies to any model-driven decision. Several carriers have extended it to actuarial models.
