AI-Ready Insurance Regulation: Lessons from NAIC Leadership

A state insurance regulator just received the NAIC’s top service honor, and the real story isn’t the plaque—it’s what that kind of leadership looks like in an AI-driven insurance market.

On December 18, 2025, the National Association of Insurance Commissioners (NAIC) recognized Texas Department of Insurance (TDI) Deputy Commissioner Jamie Walker with the Robert Dineen Award, the highest honor NAIC gives a state regulator. The citation emphasized something that matters a lot more than ceremony: Walker built a career around the unglamorous work of financial regulation—examinations, accounting practices, reinsurance oversight, capital adequacy, and group capital.

That’s also where AI is about to have its most immediate impact. Not in flashy chatbots. In the hard, technical guardrails that decide whether insurers stay solvent, whether reinsurance is real, and whether consumers are protected when the market turns.

This post is part of our “AI in Government & Public Sector” series, and it’s aimed at a specific audience: insurance executives, compliance leaders, and public-sector teams who need a practical way to connect AI governance to day-to-day insurance regulation.

What the Dineen Award signals about AI and regulatory leadership

The award spotlights a leadership style that AI adoption in insurance can’t succeed without: disciplined, data-fluent, and process-driven regulation. If your AI roadmap doesn’t include that mindset, you’ll end up with pilots that look good in demos and fail in audits.

Walker’s career path—starting as an examiner trainee in 2000 and rising through TDI’s financial regulation ranks—highlights a core truth: in insurance, “innovation” only sticks when it fits into existing accountability systems.

NAIC leadership noted Walker’s growth into a nationally and internationally respected authority in financial regulation. That kind of credibility increasingly hinges on one question regulators, legislators, and the public are asking: Can you explain your decisions—especially when software is involved?

Why financial regulation is the real proving ground for insurance AI

AI affects the parts of insurance that regulators care about most:

• Reserving and capital: Are automated models pushing overly optimistic assumptions?
• Reinsurance structures: Are risk transfers transparent, or are they hiding fragility?
• Market conduct and consumer outcomes: Are AI-driven decisions fair, consistent, and defensible?
• Group supervision: Are multi-entity groups using models differently across subsidiaries?

When a regulator is deeply engaged in accounting practices, reinsurance, and capital adequacy task forces (as Walker is), that’s a signal that AI oversight will be judged through financial truth, not marketing narratives.

How AI can strengthen insurance compliance (without creating new risk)

AI improves regulatory compliance when it’s treated as a control system, not a product feature. The fastest path to credible AI in insurance is using it to reduce errors, surface anomalies, and document rationale.

Below are practical ways insurers and regulators can apply AI in compliance-heavy workflows.

AI for examinations and financial oversight: anomaly detection that auditors trust

Financial exam teams review large volumes of structured and semi-structured data. AI can help by:

• Flagging outlier trends in premium growth, loss ratios, expense drift, or reserve changes
• Detecting inconsistent coding across lines of business or entities
• Identifying unusual journal entries and late-quarter adjustments
• Prioritizing file review by risk scoring (what looks “most exam-worthy”)

The catch: anomaly detection only helps if you can show why something was flagged.

A practical standard I like: if an AI model can’t produce a plain-language reason an examiner could repeat in a workpaper, it’s not ready for regulated use.

AI for statutory accounting: faster review, better documentation

Walker chairs the NAIC Accounting Practices and Procedures Task Force and participates in statutory accounting groups—exactly where AI will be pressured to perform responsibly.

Statutory accounting workflows are documentation-heavy, and AI can help in ways that are less controversial than underwriting decisions:

• Drafting internal accounting memos that explain treatment of new products
• Summarizing policy forms and endorsements to map accounting implications
• Comparing draft entries to historical accounting positions for consistency
• Producing “change narratives” for quarter-close packages

This isn’t about replacing accountants. It’s about reducing the time spent on repetitive write-ups so humans can focus on judgment and sign-off.

AI for reinsurance oversight: transparency beats complexity

Reinsurance is an area where complexity can be used to confuse. AI helps when it increases transparency:

• Extract key terms from treaties (limits, attachment points, exclusions, commutations)
• Compare treaty language to bordereaux and claims movement
• Flag mismatches between ceded premium patterns and expected risk transfer

But there’s a line you shouldn’t cross: using black-box AI to justify risk transfer positions.

A snippet-worthy rule: If you can’t explain the model, you can’t rely on the model—especially in reinsurance and capital.

The AI governance checklist regulators will expect in 2026

Regulators don’t need you to be perfect; they need you to be accountable. The companies that win trust will be the ones that can answer “what, why, who approved it, and how it’s monitored” without scrambling.

Here’s a field-tested checklist for AI governance in insurance that aligns with public-sector expectations.

1) Model inventory that includes generative AI

If you’re using AI for anything that touches customers, pricing, claims, or financial reporting, maintain an inventory with:

• Owner (business + technical)
• Purpose and scope
• Data inputs and data retention
• Controls (monitoring, drift checks, access)
• Validation approach (pre-deployment and ongoing)

And yes—include internal generative AI tools used for drafting letters, summaries, or call-center assistance. Those can still create compliance exposure.

2) Controls that match the risk level

Not all models deserve the same scrutiny. A practical tiering approach:

1. High risk: underwriting eligibility, claim denial recommendations, fraud decisions
2. Medium risk: claim triage, subrogation identification, premium audit support
3. Lower risk: document summarization, internal search, policyholder FAQs

Your governance should scale accordingly: independent validation and strict monitoring for high-risk uses; lighter controls for low-risk support tools.

3) Explainability as a deliverable, not an afterthought

Explainability isn’t a philosophical debate in insurance. It’s an operational artifact.

Build requirements like:

• “Reason codes” a claims handler can review
• Audit trails showing which data influenced a decision
• Versioning of models, prompts, and key rules
• Adverse action and complaint-ready documentation

If you’re a carrier, assume that anything you can’t explain will eventually become a consumer complaint, a DOI question, or a discovery request.

4) Third-party and vendor accountability

A lot of insurance AI is vendor-supplied. Regulators will still treat the insurer as responsible.

Ask vendors for:

• Data lineage and model training disclosures (as far as they can legally share)
• Validation results and bias testing approach
• Security controls and incident response commitments
• Clear boundaries: what the tool does not do

If a vendor can’t explain their model in a way your compliance team can defend, it’s not “advanced”—it’s ungovernable.

Where regulators can use AI to serve the public better

AI in government works when it reduces backlog, improves consistency, and increases transparency. Insurance regulation is a strong fit because the work is document-heavy and deadline-driven.

In practical terms, public-sector insurance teams can apply AI to:

Speed up complaint triage and pattern detection

When consumer complaint volumes spike (often after storms, rate changes, or carrier exits), AI can:

• Categorize complaints by type (delay, denial, cancellation, misrepresentation)
• Identify carriers or regions with abnormal trends
• Surface repeat issues tied to specific adjuster notes or vendor partners

The public benefit is simple: faster escalation of the cases that are likely to indicate systemic harm.

Improve market conduct targeting

Instead of broad-based audits, AI can help prioritize market conduct exams where risk is highest:

• Disparities in outcomes by geography or product type
• Unusual claim closure rates or reopen rates
• Patterns of late payments or inconsistent documentation

This is where “smart regulation” becomes real: fewer random checks, more precision.

Clarify guidance for industry

Regulators produce bulletins, advisories, and procedural guidance. AI can support:

• Internal knowledge bases so staff apply guidance consistently
• Drafting plain-language summaries for consumer-facing pages
• Better cross-referencing of past guidance to new issues

Consistency is a consumer protection tool. It also reduces friction for carriers trying to comply.

What insurers should do next: a practical 30–60–90 day plan

If you want AI initiatives that survive regulatory scrutiny, start with governance and measurable outcomes, not a pilot. Here’s a simple plan that works for carriers, MGAs, and large agencies.

In 30 days: pick one compliance-forward AI use case

Good first choices:

• Complaint summarization for compliance teams
• Document classification for underwriting files
• Claims note summarization with strict human review

Define success metrics like:

• Time saved per file
• Reduction in rework
• Fewer missed documentation elements

In 60 days: implement controls and produce an “exam-ready” packet

Create a lightweight packet you could hand to a regulator:

• Purpose statement
• Data sources
• Human oversight steps
• Testing results (accuracy, error types, edge cases)
• Monitoring plan and escalation path

This is also a great forcing function internally—teams quickly see where the gaps are.

In 90 days: expand to a second use case and standardize the playbook

Once you have one governed success, reuse the template:

• Model/prompt review process
• Sign-off workflow
• Monitoring dashboard
• Vendor intake checklist

The goal isn’t to move fast at all costs. It’s to move steadily while building regulatory trust.

Why this moment matters for AI in insurance regulation

Walker’s Dineen Award recognition is a reminder that insurance regulation is built on service: protecting policyholders and keeping markets stable. AI should be judged by that standard.

My view is blunt: the insurance organizations that treat AI governance like “paperwork” will lose time, credibility, and eventually market flexibility. The ones that build explainable, controlled AI into compliance and financial oversight will earn something far more valuable than speed—permission.

If you’re leading AI, compliance, underwriting, or claims going into 2026, here’s the question to sit with: Could you explain your most important AI-driven decision to a regulator, a judge, and a policyholder—using the same facts?