AI Governance Documents to Prepare for a Market Conduct Exam
The insurance AI exam documentation to have ready for a market conduct exam: an insurer-built readiness file of eight evidence categories, and how to record a gap.
Simon Li edits InsureAI Wire and follows how insurance regulators are dealing with AI.
Much of his time goes into reading NAIC meeting papers, state bulletins, bills, court filings, and public comments. He also keeps the site's 51-jurisdiction tracker up to date.
Some regulatory questions do not have a settled answer. When the available record is unclear, Simon explains what is known, what remains uncertain, and why.
Simon is an independent researcher and editor. He is not a regulator, attorney, actuary, or NAIC representative. InsureAI Wire does not provide legal advice.
Long-form guides and analysis are personally bylined. Short news dispatches are attributed to InsureAI Wire as the publication of record.
The insurance AI exam documentation to have ready for a market conduct exam: an insurer-built readiness file of eight evidence categories, and how to record a gap.
A playbook for insurers on AI model monitoring, validation, drift detection, and retesting records that satisfy NAIC Model Bulletin and Exhibit C expectations.
Five questions that flag when the AI regulations for insurance companies may reach your business, and what to review before treating the answer as settled.
NAIC Model Law 668 binds where a state enacts it: a written security program, third-party oversight, breach investigation, and notice to the insurance commissioner.
A market conduct exam is how state regulators inspect how insurers treat consumers: what triggers one, what examiners ask for, and how it differs from a financial exam.
The NAIC is the nonprofit standard-setting organization run by state insurance commissioners. States supply the regulatory power and decide whether its documents bind.
U.S. insurance is regulated state by state, not federally. Why that is, what regulators control across the policy lifecycle, and how the pieces fit together.
A playbook for insurers preparing NAIC Exhibit D responses: document the 25 data elements, show internal and third-party sources, and close the data gap before the exam.
A practical guide to implementing an AI governance framework for NAIC Exhibit B, from AIS programs to vendor oversight and consumer evidence.
How proxy variables and outcome differences appear in insurance AI pricing, what testing can establish, and where state-specific procedures belong.
Shadow AI corrupts the counts reported in NAIC Exhibit A. What ungoverned AI use means for insurers, and a practical plan to close the gap.
Trump's AI executive order tells agencies to challenge state AI laws. Why McCarran-Ferguson still shields state insurance rules, and how far the federal push has got.
A practical NAIC-aligned checklist for AI vendor risk assessment: due-diligence questions, contract clauses, and the ongoing monitoring that stays with the insurer.
Most of Colorado's SB 26-189 waits for January 1, 2027, but one section gave the insurance Commissioner AI disclosure rulemaking power on signing. Where the duties land.
What NYDFS Circular Letter No. 7 expects for AI and external data in underwriting and pricing: scope, the proxy test, consumer notice, and vendor audits.
A plain-language map of AI governance in insurance: the regulatory stack, the program it expects, and where to go for rules, implementation, and evidence.
A sourced UnitedHealth AI governance case study on the unresolved nH Predict dispute, prior authorization data, and the limits of the public record.
What the NAIC AI Systems Evaluation Tool is, what Exhibits A through D ask, how regulators select them, and where each evidence task belongs.
What the NAIC AI Model Bulletin is, how adoption works, what belongs in a written AIS Program, and which implementation guide to use next.
Will AI replace insurance agents? What current employment projections can show, which tasks are changing, and where agency AI use creates compliance exposure.
Insurance AI decision documentation for reconstructing one underwriting or claims outcome, including human review, notice, appeal, and model version.
What conversational AI and chatbots actually do across insurance, from quotes to claims, and the point where a customer-facing bot becomes a compliance question.
AI in insurance claims, step by step from intake to appeal: what each system decides, where it can go wrong, and what record makes the step reviewable.
What generative AI is, how it differs from the predictive models insurers already use, where it is being applied, and why regulators treat its risks differently.
How insurers use AI to detect fraud, how generative AI changes the fight, and what governance is needed to avoid false positives and bias.
How health insurers use AI in prior authorization, claims adjudication, and risk adjustment. What the NAIC, CMS, and recent litigation mean for governance.
How homeowners insurers use AI, aerial imagery, and catastrophe models to price property risk, and what state regulators now require for transparency.
Producer services AI sits in NAIC Exhibit A. Learn how insurers use AI for lead scoring, producer onboarding, licensing, and product recommendations.
How life insurers use AI in accelerated underwriting and what regulators now require for proxy testing, fairness, and documentation.
How reinsurers use AI in treaty pricing, catastrophe modeling, and contract analysis. What model transparency and capital governance mean for risk carriers.
Map your insurance AI systems by line of business for NAIC Exhibit A. Use this template to capture underwriting, pricing, claims, fraud, and customer service AI.
Insurance AI pilots often fail at scale because organizations focus on the model rather than outcomes, workflow integration, and oversight. A readiness checklist.
A map of AI in health insurance: plan and jurisdiction differences, high-stakes workflows, and the right guide for prior authorization, claims, and risk adjustment.
How AI in underwriting works across P&C, life, and health lines, what the NAIC evaluation tool expects, and how to prove your models are fair and traceable.
A business-line map of insurance AI: the decision each system shapes, the risk that makes the line distinct, and the guide to read next.
How agentic AI in insurance changes claims decisions, where Exhibit C records the risk, and how carriers can test whether human review is meaningful.