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NAIC’s AI Systems Evaluation Tool: What the Feb. 9 Public Meeting Signals for Insurance AI Governance

ByInnoVirtuoso

What if a one-hour Webex could reshape how underwriting models are built, deployed, and defended in your organization? On February 9, 2026, the National Association of Insurance Commissioners (NAIC) Big Data and Artificial Intelligence (H) Working Group did exactly that—advancing work on an AI Systems Evaluation Tool that could become the de facto playbook for trustworthy AI in insurance.

Whether you’re a carrier deploying machine learning in underwriting, claims, or customer engagement—or a vendor selling AI solutions into the sector—the signals are loud and clear: the bar for reliability, fairness, transparency, and risk management is rising. Fast.

In this post, we unpack what the Working Group’s public session means, how the emerging tool aligns with existing frameworks, and the practical steps insurers and insurtechs can take now to get ahead.

The Short Story: Why This Meeting Matters

  • Regulators, industry experts, and stakeholders reviewed and discussed the AI Systems Evaluation Tool, including Exhibits B–D, in a public forum.
  • The direction is unmistakable: standardized evaluation criteria for AI systems—spanning performance, fairness, transparency, and governance—are coming into tighter focus.
  • Expect more rigorous expectations around detecting data drift, surfacing discriminatory outcomes, and managing operational failures.
  • Momentum is building toward harmonized U.S. AI governance for financial services—aligning with global moves toward accountable AI.

You don’t need to wait for a final template to start preparing. Much of what the Working Group emphasized can be translated into concrete action today.

Quick Background: Who Is the NAIC Big Data & AI Working Group?

The NAIC coordinates state insurance regulation across the U.S. The Big Data and Artificial Intelligence (H) Working Group focuses on how advanced analytics and AI affect the market, consumers, and regulatory oversight. It has been steadily moving from high-level principles to operational expectations. If you followed the NAIC’s earlier work on AI principles and data governance, this is the next step: turning guardrails into testable criteria.

What Is the AI Systems Evaluation Tool?

Think of it as a structured way to evidence that an AI system used in insurance is: – Reliable across intended use cases and populations – Fair and non-discriminatory – Transparent and appropriately explainable – Governed by robust controls, monitoring, and accountability

While the details are still being refined, the intent is clear: a repeatable, regulator-ready toolkit that surfaces where an AI system is strong, where it’s fragile, and what risks and mitigations are in place. It’s not just a checklist—it points toward a living program that spans development, validation, deployment, and ongoing monitoring.

Highlights from the Feb. 9 Public Meeting

The Working Group advanced discussions on the evaluation tool’s structure and content, including Exhibits B–D. Without pre-judging final text, the dialogue reinforced several themes:

  • Standardized testing expectations across model lifecycle stages
  • Evidence of fairness analyses that go beyond superficial checks
  • Clear documentation linking business purpose, data, features, and outcomes
  • Monitoring that detects performance erosion and data drift early
  • Preparedness for operational failures, including incident response and rollback
  • Interoperability with established frameworks (e.g., NIST AI RMF, ISO guidance)

The takeaway: if your AI oversight relies on ad hoc documents or heroics from a few data scientists, it’s time to industrialize.

Reliability and Performance: Beyond a Single AUC

In regulated domains, “it works on my test set” won’t cut it. Expect the tool to push for richer, more scenario-based validation. Key components likely include:

  • Scenario and stress testing: Evaluate performance under edge cases—rare claim types, catastrophic event spikes, shifts in applicant demographics, or sudden channel mix changes.
  • Robustness checks: Perturb inputs (within realistic bounds) to assess sensitivity. Ensure minor, benign input fluctuations don’t cause major score swings.
  • Out-of-distribution detection: Identify when the model is asked to score cases outside its training domain, triggering special handling or fallbacks.
  • Population stability and data drift: Continuously monitor shifts in data distributions and feature relationships. Track PSI/CSI-type metrics, but pair them with practical impact analysis.
  • Calibration and stability: Ensure probability outputs (if used) are well-calibrated over time and across subgroups. Reassess after retraining or major environmental changes.
  • Human-in-the-loop thresholds: Define when automation should defer to human review, especially under uncertainty or flagged risk conditions.

Pro tip: Incorporate performance thresholds and alerting in your MLOps stack before regulators ask to see it. You’ll learn faster, and audits become simpler.

Fairness and Non-Discrimination: Moving from Intent to Evidence

AI can inadvertently reproduce or amplify historical inequities. Insurance, with its complex proxies and correlations, is especially vulnerable. Expect the evaluation tool to emphasize:

  • Clear fairness objectives tied to legal standards: Spell out which unfair discrimination risks you are testing and why, aligned to applicable insurance laws and guidance.
  • Protected class and proxy analysis: Even if protected attributes aren’t used, test whether features act as proxies (e.g., geography, certain behavioral markers).
  • Multiple fairness metrics and trade-offs: Use more than one measure (e.g., error rate parity, calibration within groups, adverse impact ratios) and document rationales for choices and trade-offs.
  • Use-case segmentation: Different fairness risks arise in underwriting vs. claims fraud vs. marketing. Tailor analyses and mitigations accordingly.
  • Remediation playbook: Bias mitigation techniques (re-weighting, post-processing, constrained optimization) should be pre-defined, tested for business impact, and documented.
  • Governance for continuous improvement: Fairness monitoring must not be a one-time prelaunch task. It’s part of operational oversight with owners, thresholds, and escalation paths.

Regulators aren’t looking for perfection; they’re looking for accountable processes, traceable decisions, and demonstrable improvement over time.

Resources to explore: – FTC blog guidance: Aiming for truth, fairness, and equity in your company’s use of AI – Colorado’s legislative groundwork: SB21-169 | Protect Consumers from Unfair Discrimination in Insurance Practices

Transparency and Explainability: Show Your Work

Transparency doesn’t mean open-sourcing your models. It means the right people can understand the system at the right level of detail:

  • For regulators: Clear purpose statements, data lineage, feature governance, validation summaries, fairness findings, and monitoring results.
  • For internal governance: Model cards or equivalent documentation, with known limitations, intended use, and deprecation criteria. See: Model Cards concept
  • For consumers (where appropriate): Plain-language explanations of important factors that influenced individual decisions, plus accessible recourse options.
  • For auditors and risk teams: Technical explainability (e.g., feature importance, counterfactuals) with known caveats. Tools like SHAP can help, but document limitations and validation. See: SHAP documentation

The golden rule: If a competent, independent reviewer can’t reconstruct what you did and why, your documentation isn’t done.

Risk Management and Governance: Make It Real, Not Ritual

An AI system is only as trustworthy as the controls around it. Expect emphasis on:

  • Defined roles and accountability: Business owners, model developers, validators, compliance, and internal audit each play distinct parts. Document the “three lines” structure if you use it.
  • Policies and standards: Codify minimum requirements for data, modeling, validation, deployment, monitoring, change control, and decommissioning.
  • Independent validation: Separate from development, with authority to challenge, require remediation, or block go-live.
  • Model inventory and criticality tiers: Central register with risk-tiering to scale controls proportionally.
  • Change management: Versioning, approval workflows, canary releases, rollback plans, and post-change monitoring.
  • Incident and issue management: What triggers an incident? Who gets notified? How do you contain, analyze root cause, and prevent recurrence?
  • Periodic effectiveness reviews: Check that controls still work as intended as tech and business needs evolve.

For alignment, see: NIST AI RMF functions (Map, Measure, Manage, Govern)

Data Management: Trustworthy Inputs, Traceable Outputs

Bad data makes good models look bad—and creates consumer harm. Strengthen:

  • Data lineage and provenance: Where did data originate? How was it transformed? Who approved it?
  • Data quality controls: Completeness, accuracy, timeliness, consistency, and outlier handling, with logged exceptions.
  • Sensitive data handling: Minimize collection, apply access controls, and document any use of inferred or synthetic data.
  • Feature governance: Business justification for each feature; tests for proxy risk; guardrails for derived variables.
  • Retention and deletion: Align data retention with business need and legal requirements. Be able to prove it.

Third-Party and Vendor Models: Shared Risk, Shared Accountability

If you buy or license models, you still own the risk. The evaluation tool will likely expect:

  • Due diligence: Security, privacy, development practices, validation evidence, data sources, and fairness testing.
  • Contractual hooks: Audit rights, documentation deliverables, incident notification, model update transparency, and right to suspend/deactivate.
  • Integration controls: Sandbox testing, performance baselines, guardrails (e.g., confidence thresholds), and staged rollout.
  • Ongoing oversight: SLAs for monitoring data, model drift notifications, and periodic re-validation.

Pro tip: Create a standardized vendor AI questionnaire aligned to your internal evaluation criteria. It speeds procurement and sets clear expectations.

Operational Resilience: Prepare for When Things Go Sideways

Even strong models can fail in production. Build resilience:

  • Kill switches and rollbacks: Technical ability to disable or revert models without disrupting core operations.
  • Degraded modes: Predefined manual or rules-based fallbacks to maintain service continuity.
  • Alerts and observability: Health checks for data pipelines, inference latency, error rates, and out-of-bounds predictions.
  • Post-incident reviews: Blameless root-cause analysis with tracked remediation.

Operational readiness turns a regulatory requirement into a business advantage.

How This Aligns with Broader Frameworks

  • NIST AI RMF: The NAIC effort appears directionally consistent with NIST’s Map-Measure-Manage-Govern functions. If you’ve mapped risks, measured harms and performance, and put governance around it, you’re on the right track. Learn more
  • ISO guidance: ISO/IEC 23894:2023 offers risk management guidance for AI—useful for formalizing your control set. ISO/IEC 23894 overview
  • EU AI developments: The EU’s AI rulemaking underscores risk-based oversight, documentation, and transparency—principles echoing in U.S. insurance regulation. EU AI policy hub

Harmonization doesn’t mean identical requirements, but the core building blocks (governance, testing, monitoring, documentation) are converging globally.

Implications for Carriers: What To Do Now

You don’t need to wait. Start with a pragmatic program that’s regulator-ready:

  • Build your model inventory and risk-tiering: Catalog systems used in underwriting, rating, claims, SIU, marketing, and customer service. Tier by potential consumer impact.
  • Standardize model documentation: Adopt a model card-like template with business purpose, data sources, features, training/validation methods, fairness tests, limitations, and monitoring plan.
  • Stand up independent validation: Even a small team with clear charters can materially reduce risk and improve credibility.
  • Implement production monitoring: Drift metrics, calibration checks, fairness indicators, and alert thresholds. Instrument logs for traceability.
  • Formalize change control: Versioning, approvals, and auditable releases. Require post-change performance reviews.
  • Run a fairness “deep dive” on one high-impact model: Pilot methods, document trade-offs, and socialize learnings across the enterprise.
  • Train stakeholders: Product owners, actuaries, data scientists, compliance, and claims leaders need a common language for AI risk.

These steps create immediate value and reduce remediation pain later.

Guidance for Insurtechs and Vendors

If you sell AI into carriers, your best sales enablement is trust:

  • Provide a comprehensive developer pack: Model documentation, validation summaries, known limitations, and integration guidance.
  • Offer fairness testing options: Show subgroup performance and mitigation approaches. Be candid about trade-offs.
  • Build regulator-friendly features: Configurable thresholds, human-in-the-loop options, robust logging, and administrative controls.
  • Prepare for due diligence: Security, privacy, SOC/ISO attestations where applicable, and clear statements on data usage and model update cadence.

When customers ask for evidence, respond with artifacts—not aspirations.

Consumer and Market Impacts

Stronger AI evaluation can: – Reduce unfair outcomes and opaque decisioning – Improve consistency and resilience of insurance processes – Build public trust where AI is used to price, underwrite, or handle claims

Transparency and recourse pathways matter. Carriers that communicate clearly and act proactively will be better positioned if and when consumer disclosures become standardized.

A Practical Readiness Checklist

Use this as a starting point and tailor to your risk profile:

  • Governance
  • Assign accountable owners for each AI system
  • Establish independent validation with authority to challenge
  • Maintain a current model inventory with risk tiers
  • Documentation
  • Model purpose, scope, and intended populations
  • Data lineage, feature governance, and proxy risk assessment
  • Validation and fairness testing results with decisions/trade-offs
  • Monitoring plans and incident response steps
  • Technical Controls
  • Scenario/stress testing and robustness checks
  • Drift detection (data and performance), calibration monitoring
  • Explainability tooling with documented limitations
  • Kill switch, rollback, and degraded mode procedures
  • Lifecycle Management
  • Change control workflow with approvals and audit trails
  • Periodic re-validation and effectiveness reviews
  • Decommissioning criteria and data retention/deletion alignment
  • Third Parties
  • Vendor due diligence and contractual safeguards
  • Integration tests and phased rollouts
  • Ongoing performance and fairness oversight
  • People & Process
  • Training for product, risk, compliance, and technical teams
  • Escalation paths for issues and consumer complaints
  • Regular program reporting to senior leadership

What’s Next from the NAIC?

While the Feb. 9 session focused on advancing the AI Systems Evaluation Tool and exhibits, additional iterations and stakeholder feedback are likely. As the tool converges, expect clearer expectations around testing depth, documentation artifacts, and ongoing monitoring.

Staying engaged through public sessions and industry associations will help you calibrate your own program and avoid surprises.

FAQs

Q: What exactly is the NAIC AI Systems Evaluation Tool? A: It’s an emerging, structured approach to evaluate AI systems used in insurance. It aims to standardize how insurers demonstrate reliability, fairness, transparency, and risk management across the AI lifecycle.

Q: Does this mean the NAIC will require specific algorithms or metrics? A: Not necessarily. The focus is on controls and evidence. Different use cases may warrant different metrics or techniques, but the expectation is that choices are justified, tested, monitored, and documented.

Q: How does this relate to NIST’s AI Risk Management Framework? A: The direction appears aligned. If you’re mapping risks, measuring impacts and performance, managing with controls, and governing with accountability, you’re building the right muscles for NAIC-aligned expectations.

Q: We don’t use protected class attributes. Are we safe from bias concerns? A: Not by default. Proxy variables and complex interactions can still create discriminatory outcomes. You’ll need subgroup performance analysis and mitigation strategies, with decisions and trade-offs documented.

Q: What about vendor-provided models? Isn’t the vendor responsible? A: You share responsibility. Regulators will expect carriers to perform due diligence, demand documentation, and maintain monitoring and controls in production—even for black-box vendor solutions.

Q: Will there be consumer disclosure requirements? A: The evaluation tool focuses on system assessment, but broader policy trends are pushing toward greater transparency. Preparing plain-language explanations and recourse processes is a prudent move.

Q: How soon should we act? A: Now. Many of the expected elements—model inventory, documentation, validation, monitoring—take time to build. Early movers will reduce compliance risk and improve model performance.

The Clear Takeaway

The NAIC’s Big Data & AI Working Group is sharpening the industry’s blueprint for accountable AI. Don’t wait for a final form to start acting. Build a living evaluation and governance program that proves your models are reliable, fair, transparent, and well-controlled. The same capabilities that satisfy regulators will also make your AI safer, more resilient, and more valuable to the business.

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InnoVirtuoso

Hello there! I'm passionate content writer navigating the digital landscape. With a deep love for words and an insatiable curiosity about technology.

With 10 years of experience in IT field, I write mostly about emerging tech, sharing news, informational articles and covering other topics I find interesting.

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