Overview
Insurance claims handling in the United States has long been governed by a state-based licensing system designed for human adjusters.
These requirements typically include:
- state-specific licensing
- continuing education (CE)
- adherence to jurisdictional claim handling standards
As artificial intelligence becomes more integrated into claims operations, a fundamental question emerges:
How do existing adjuster licensing frameworks apply when AI systems are performing core claims functions?
This creates a growing area of regulatory uncertainty and potential compliance risk.
The Structural Conflict
Traditional licensing frameworks assume that:
- a human adjuster is responsible for claim decisions
- that individual is licensed in the applicable state
- the adjuster maintains competency through CE requirements
AI systems, however:
- are not licensed
- do not complete CE
- can operate across multiple jurisdictions simultaneously
- may perform functions traditionally associated with licensed adjusters
This creates a structural mismatch between existing regulatory frameworks and emerging claims technology.
Who Is the Adjuster?
One of the central regulatory questions is:
Who is the licensed adjuster of record when AI is used?
If an AI system:
- evaluates coverage
- analyzes damages
- determines payment amounts
- communicates claim decisions
then regulators may ask whether:
- the AI is functioning as an adjuster
- the system is operating under a licensed individual’s authority
- or the carrier is relying on an undefined compliance model
Without clear answers, accountability becomes blurred.
State-by-State Complexity
Licensing requirements vary significantly across states.
- Some states require adjusters to be individually licensed
- Others allow staff adjusters to operate under a company license
- Reciprocity exists, but is not uniform
- Certain states maintain stricter or non-reciprocal standards
AI systems, however, do not operate within a single jurisdiction.
They may:
- process claims across multiple states simultaneously
- apply generalized logic across differing regulatory environments
- lack mechanisms to adapt to state-specific requirements in real time
This creates cross-jurisdictional compliance risk.
The Continuing Education Gap
Human adjusters are required to maintain competency through continuing education.
AI systems:
- do not complete CE
- are updated through data and model changes
- may evolve without formal regulatory oversight
This raises an important question:
What is the equivalent of continuing education for AI systems?
Without a defined standard, regulators may question whether AI-driven decisions meet the same expectations of competency as licensed professionals.
Emerging Compliance Models
Carriers deploying AI in claims handling may attempt to address licensing requirements through several approaches:
1. Human-in-the-Loop Oversight
A licensed adjuster reviews and approves AI-generated decisions.
2. Supervisory Model
A licensed adjuster oversees a portfolio of AI-handled claims.
3. AI-as-a-Tool Positioning
The carrier asserts that AI is not making decisions, but assisting licensed adjusters.
Each of these models presents its own compliance challenges, particularly if human involvement is limited or not clearly documented.
Regulatory Risk Indicators
Carriers should monitor for:
- Lack of clearly identified licensed adjuster of record
- Minimal or undocumented human involvement in AI-handled claims
- AI systems operating across multiple states without jurisdictional controls
- Absence of governance over model updates and changes
- Inability to demonstrate compliance with state-specific licensing requirements
These indicators may signal exposure to regulatory scrutiny.
Why Regulators Will Care
Departments of Insurance are likely to focus on:
- accountability for claim decisions
- compliance with state licensing laws
- protection of policyholders
- assurance of adjuster competency
If AI systems are effectively performing adjuster functions without clear licensing alignment, regulators may view this as a gap in compliance.
ClaimSurance Insight
AI does not eliminate licensing requirements — it complicates them.
A system that performs adjuster-level functions without a clearly defined, licensed point of accountability introduces uncertainty into the claims process.
Regulators will not only ask whether the claim was handled correctly, but:
Who was responsible for handling it — and were they properly authorized to do so?
Bottom Line
As AI continues to expand within claims operations, carriers must reconcile advanced automation with legacy regulatory frameworks.
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