They need to prove who owns the model, how it was tested before deployment, how it is monitored after release, and how exceptions are escalated. Regulators are looking for evidence, not just policy language, so the governance programme must produce versioned records, reviewer attribution, and outcome logs that can survive examination.
What insurers must be able to demonstrate about AI decisions
For underwriting and claims, the core issue is not whether AI was used, but whether the insurer can show a controlled decision process. That means there must be a clear owner for the model, evidence of pre-deployment testing, a post-release monitoring process, and a defined path for exceptions. The governance record has to be specific enough to withstand regulatory review, not just describe intent.
In practice, regulators and examiners tend to look for traceability across the full decision lifecycle. If a model influenced pricing, acceptance, fraud handling, reserve movement, or claim settlement, the insurer should be able to explain what version ran, what data or rules shaped the output, who reviewed it, and what happened when the result was challenged or overridden.
Why ownership and versioned evidence matter more than policy language
A policy says what the organisation expects; evidence shows what actually happened. For AI in insurance workflows, that distinction matters because model behaviour can change as data, thresholds, prompts, or exception logic change. Versioned records tie a specific decision to a specific model state, reviewer, and outcome, which is what makes the control auditable rather than aspirational.
Ownership is equally important because AI decisions often cross actuarial, claims, underwriting, compliance, and technology boundaries. If nobody can name the accountable owner, then testing, monitoring, and remediation become fragmented. The practical question is whether the insurer can produce a named decision owner and the artefacts that prove that owner had control over the model’s use in production.
For governance programmes that need a reference point for evidence quality, NIST AI 600-1 GenAI Profile is a useful external benchmark for pre-deployment testing, monitoring, and lifecycle controls.
What examiners will expect to see in testing, monitoring, and exception handling
Pre-deployment testing should show that the insurer checked the model under conditions relevant to its business use, including accuracy, stability, bias, and failure modes that could affect adverse selection, unfair pricing, or incorrect claim outcomes. Post-release monitoring should then show that the insurer kept watching for drift, degradation, and unexpected outputs after the model was put into service.
Exception handling is the other half of the story. If an underwriter, adjuster, or supervisor overrode the AI result, the insurer should be able to show why the exception was permitted, who approved it, and whether the exception reveals a systematic issue. The exam question is often whether the organisation can prove that exceptions are governed, not improvised.
That is also why control evidence should include outcome logs, review notes, and escalation records rather than only a high-level governance deck. In an audit or supervisory review, the most persuasive material is the operational record that connects the decision to the control that governed it.
Risk and Threat Considerations
When AI is allowed to influence underwriting or claims, the main risk is silent decision failure, where the model keeps producing outputs that look plausible but are no longer reliable, explainable, or appropriate for the product line. The second risk is governance failure, where the organisation cannot prove who approved, reviewed, or escalated a disputed decision.
Failure mechanism: Version drift, weak monitoring, or poor exception logging breaks the evidence chain between the model, the decision, and the accountable reviewer. That leaves the insurer unable to reconstruct why a customer was priced, accepted, denied, or paid in a particular way.
Impact: The insurer can face regulatory findings, disputed decisions, remediation cost, and loss of confidence in the underwriting or claims function. If the model is used at scale, a single control gap can propagate across many decisions before it is detected.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST AI 600-1, NIST AI RMF and NIST SP 800-53 Rev 5 set the technical controls, while ISO/IEC 42001:2023 defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI 600-1 | GenAI Profile | Covers pre-deployment testing, monitoring, and governance evidence for AI use in decisions. |
| Recommendation — Adopt profile controls for testing, monitoring, and documented accountability around AI-assisted decisions. | ||
| NIST AI RMF | AI Risk Management Framework | Directly addresses trustworthy AI governance, traceability, and lifecycle risk management. |
| Recommendation — Implement govern, map, measure, and manage practices for AI decisions that affect underwriting and claims. | ||
| ISO/IEC 42001:2023 | AI Management System standard | Applies to organisational AI governance, accountability, and documented control over AI systems. |
| Recommendation — Establish an AI management system with defined ownership, evidence retention, and continual oversight. | ||
| NIST SP 800-53 Rev 5 | AU-2 — Event Logging | Decision and exception logs are central evidence for AI-governed insurance outcomes. |
| AU-12 — Audit Record Generation | Supports versioned records and review evidence needed to reconstruct AI-influenced decisions. | |
| CM-3 — Configuration Change Control | Model updates and release changes must be controlled so decision behaviour stays attributable. | |
| Recommendation — Log model use, reviewer actions, exceptions, and outcome events with sufficient detail for audit. Generate audit records that preserve model version, reviewer attribution, and decision outcomes. Control model changes through approved release and change-review processes before production use. | ||
Practitioner Guidance
What to prioritise: Build the evidence trail first, then refine the model. For insurance use cases, the strongest control is a decision record that links the model version, test evidence, reviewer identity, exception rationale, and final outcome.
What to verify: Confirm that every materially affected decision can be reconstructed from retained logs, not from memory or policy statements. If you cannot show who approved the release and who reviewed the exception, the control is not mature enough for supervisory scrutiny.
Practitioner takeaway: Treat AI governance in insurance as an evidentiary discipline, not a policy exercise, because the decisive test is whether the organisation can prove control over real decisions after the fact.
Related resources from NHI Mgmt Group
- How should insurers prepare unstructured data before deploying GenAI and AI agents in underwriting and claims workflows?
- What breaks when insurers try to use AI on claims and underwriting documents without governance?
- What makes agentic AI an NHI governance issue?
- Why is NHI governance critical in the age of AI attacks?