Insurance
Insurance AI Governance for Claims,
Underwriting, and Service Agents
An agent that drafts a settlement or answers a policyholder is part of a decision a regulator may ask about. Control each agent’s authority before it acts, and keep the evidence your compliance and model risk teams need.
What Is AI Governance for Insurance Companies?
As the use of AI in insurance grows, from underwriting and claims to customer service, carriers need a way to control it. A strong AI governance program covers:
- Clear Accountability
A named owner for each AI system, with board-level oversight of the program. - Vendor Oversight
The same scrutiny for AI bought from third parties as for AI built in-house. - Ongoing Monitoring
Checks for drift and unfair outcomes after launch, not only before it. - Consumer Transparency
Clear information for policyholders when AI contributes to an adverse decision.
That matters more as artificial intelligence in insurance shifts to agents that take actions, and as more insurance AI solutions come from vendors. Lumenova AI governs those agents with real-time controls, continuous evaluations, and records built for market conduct exams.
Expectations for AI in the insurance industry are already in force. The NAIC Model Bulletin, adopted by roughly half of US states, expects a written AI program, Colorado and New York add their own rules, and OSFI Guideline E-23 reaches Canadian insurers from May 1, 2027.
Where AI Agents Work in Insurance
Insurance agents can shape coverage, settlement, and service outcomes.
Clear limits and traceable actions help teams oversee their work and explain decisions when questions arise.
Underwriting and Pricing
Agents gather applicant data and summarize risk for underwriters. A decline or a rate may later need explaining to the applicant or a regulator.
Claims
Agents triage first notice of loss, request documents, and draft settlements. Amounts above an agent's authority should never go out alone.
Policyholder Service
Assistants answer coverage questions and process changes. A wrong answer about coverage becomes a complaint, or a market conduct finding.
Capabilities
How Lumenova AI Supports Insurance AI Compliance
Turn compliance requirements into enforceable controls, documented approvals, and evidence your teams can review and defend.
Pre-Built Insurance Controls
Start from pre-configured controls for insurance compliance and AI model risk management, then adapt them to your own AI program.
Agent Authority Limits
Cap the claim amounts an agent can act on, restrict what it can say to policyholders, and block anything outside its authority before it happens.
Policyholder Data Protection
Mask or block personal data, policy numbers, and claim numbers before a model sees them, keeping sensitive details out of prompts.
Fairness & Accuracy Testing
Test AI for bias and accuracy before launch and in production, benchmarked against your adjusters and underwriters.
Exam and Filing Evidence
Decision records and evaluation results give your team the documentation behind each agent for market conduct exams and regulatory filings.
Vendor AI Oversight
Register third-party AI tools with owners and risk tiers, and test vendor agents from the outside before they reach a policyholder.
An Example: A Settlement Recommendation
A claims agent drafts a settlement
The amount is above what the agent may send on its own.
Send context
The SDK sends the proposed settlement to the policy engine.
Evaluate
The policy checks the amount against the agent's authority.
Next step
Recommendations within the agent's authority go ahead.
Routed to an adjuster
The action is blocked, and your application sends the file to an adjuster.
Decision record
The adjuster, and later an examiner, can see which rule stopped the action and the recommendation exactly as the agent wrote it.
Frequently Asked Questions
It expects insurers that use AI systems to maintain a written program covering governance, risk management, internal controls, and oversight of third-party AI and data. Examiners can ask for evidence that practice matches the program. Lumenova AI keeps that evidence as agents run: controls, test results, and decision records.
A predictive model scores a risk. An agent acts on it: it requests documents, drafts a settlement, or answers a policyholder. Insurers need limits on each action and a record of why it was allowed, as well as testing of the model underneath. Lumenova AI covers both.
Lumenova AI evaluates AI systems for bias and fairness before launch and in production, and keeps the results with each use case. Your actuarial and compliance teams can draw on those results and the decision records for testing under rules such as Colorado’s SB21-169 regulations.
Yes. The same controls apply wherever an agent runs: claims triage, policyholder service, or underwriting support. Each use case carries its owner, approver, and risk tier, and its approval status decides what its agents can reach.
No. Lumenova AI sits beside the policy, claims, and underwriting systems and the insurtech tools you already run. It governs the AI agents that work across them, controlling actions, masking data, and recording decisions, without replacing any of those systems.