NAIC AI Compliance for Insurance Carriers Ahead of 2026

How carriers can get ahead of bias, transparency, and governance requirements with a human‑in‑the‑loop approach

Why NAIC’s 2026 AI Pilot Matters

Artificial intelligence is no longer experimental in insurance. From automated insurance claims handling to medical record review and fraud detection, AI systems are now embedded in daily carrier operations. Recognizing both the opportunity and the risk, the National Association of Insurance Commissioners (NAIC) is launching AI Evaluation Pilot Programs in early 2026.

These pilots are designed to help regulators assess how insurers are using AI, with a particular focus on bias, transparency, governance, and consumer impact. For carriers, this is a clear signal: AI oversight is moving from principles and guidance into practical examination territory.

This blog breaks down:

• What NAIC is expected to evaluate in the 2026 pilots

• Why human‑in‑the‑loop (HITL) AI approaches are increasingly favored by regulators

• A practical checklist carriers can use now to demonstrate compliance readiness

  
  

What NAIC Is Evaluating in the 2026 AI Pilot Programs

While the pilots are not formal enforcement actions, they closely mirror how future market conduct exams and model law expectations may look. Based on NAIC guidance, working groups, and recent regulatory themes, insurers should expect evaluations in four core areas:

1. Bias and Fairness Controls

Regulators want assurance that AI systems used in automated claims, underwriting, utilization review, and fraud detection do not result in unfair discrimination.

Key questions include:

• How are training datasets selected and reviewed for bias?

• Are outcomes tested across protected classes and proxy variables?

• How often are bias audits performed, and by whom?
  

2. Transparency and Explainability

NAIC has consistently emphasized that insurers must be able to explain AI‑assisted decisions—both internally and, when necessary, to regulators or consumers.

Expect scrutiny on:

• Whether AI outputs can be interpreted by non‑technical staff

• Documentation explaining how models influence decisions

• The ability to trace how inputs lead to outcomes
  

3. Governance and Oversight

AI governance is no longer optional. Regulators want to see formal structures, not ad‑hoc controls.

Evaluators will look for:

• Defined ownership of AI systems

• Policies governing model changes and retraining

• Clear escalation paths when AI recommendations are challenged
  

4. Human Accountability in Decision‑Making

Perhaps most importantly, NAIC wants confirmation that people—not algorithms—remain accountable for insurance decisions that impact consumers.

This is where human‑in‑the‑loop insurance automation becomes a differentiator.

Why Human‑in‑the‑Loop AI Is Favored Under New Standards

Fully autonomous AI may promise speed, but regulators are signaling strong preference for AI‑assisted, not AI‑replaced, decision‑making.

Human‑in‑the‑Loop Defined

A human‑in‑the‑loop approach means:

• AI accelerates analysis, document review, or prioritization

• Skilled insurance professionals validate, override, or approve outcomes

• Final accountability remains with licensed or designated personnel
  

Why Regulators Prefer HITL Models

Human‑in‑the‑loop systems address several regulatory concerns at once:

• Bias Mitigation: Humans can identify contextual or edge‑case issues AI may miss

• Explainability: Adjusters and reviewers can articulate decisions in plain language

• Governance: Oversight is embedded into workflows, not bolted on afterward

• Consumer Protection: Decisions are defensible, reviewable, and appeal‑ready
  

For insurers using automated insurance solutions like intelligent document processing or AI‑driven medical review, HITL design aligns operational efficiency with regulatory confidence.

Preparing for NAIC’s 2026 Pilot: A Practical Compliance Checklist

Carriers don’t need to wait for formal guidance to begin preparing. Below is a practical readiness checklist aligned with likely NAIC expectations.

✅ AI Inventory and Use‑Case Mapping

• Document all AI and machine‑learning tools in use

• Identify which processes impact consumers directly (claims, underwriting, SIU, utilization review)

• Classify risk levels for each use case
  

✅ Bias Testing and Monitoring

• Establish routine bias testing schedules

• Document datasets, assumptions, and limitations

• Retain results and remediation actions for audit review
  

✅ Explainability Documentation

• Maintain plain‑language descriptions of model behavior

• Ensure business users can explain AI‑assisted decisions

• Prepare sample explanations for common claim scenarios
  

✅ Human‑in‑the‑Loop Controls

• Define where human review is required

• Document override authority and escalation paths

• Track human intervention rates and outcomes
  

✅ Governance and Policies

• Assign executive and operational AI ownership

• Maintain AI usage, change‑management, and risk policies

• Align AI governance with existing compliance frameworks
  

✅ Vendor and Technology Due Diligence

• Require transparency from AI vendors

• Validate that third‑party tools support HITL workflows

• Ensure contractual language supports regulatory review
  

What NAIC AI Compliance for Insurance Carriers Means Beyond the 2026 Pilot

NAIC’s 2026 AI Evaluation Pilot Program is not just a regulatory hurdle—it’s an opportunity.

Insurers that can demonstrate:

• Responsible AI governance

• Bias‑aware automated claims processes

• Transparent, explainable outcomes

• Strong human‑in‑the‑loop oversight

... will be better positioned not only for regulatory exams, but also for consumer trust, operational resilience, and sustainable insurance automation.

As regulatory scrutiny increases, NAIC AI compliance for insurance carriers will depend on clear governance structures, explainable decision-making, and documented human-in-the-loop controls across all AI-assisted workflows.