TECHNICAL NOTE · AI GOVERNANCE
The governed AI control plane
An application of the Pricing Actuary’s Lens to evidence, control and customer outcomes in insurance AI.
Collect evidence, validate inputs and classify the task.
Apply deterministic rules, permissions and risk thresholds.
Use the model only for the bounded work it is authorised to perform.
Escalate exceptions and keep human authority where judgement matters.
Record inputs, checks, outputs, overrides and the final decision.
Why separate assessment from generation?
A language model is good at interpreting and drafting. It should not silently become the source of truth for identity, cover, pricing authority or payment. Those decisions need explicit evidence and rules that can be inspected later.
Where this helps
- Quote and onboarding validation
- Underwriting referrals
- Policy and document integrity checks
- Claims triage and evidence review
- Customer-service drafting
- Compliance monitoring
The design test
Can you show which evidence was used, which deterministic controls ran, what the model produced, who had authority and why the final action was allowed? If not, the system is difficult to govern even when its answer happens to be correct.
This technical note explains the operating pattern at a high level. It is an application of the Pricing Actuary’s Lens, not a separate signature framework.
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