Let’s make one thing clear from the start: AI hallucinations are not artificial intelligence “running amok” like HAL-9000, acting with “intent,” or trying to deceive anyone. They are a predictable outcome of how modern AI systems work, by finding probable answers – especially when given ambiguous information and insufficient constraints.
In insurance, understanding this distinction is critical. Accuracy isn’t optional. A confident but incorrect response about coverage, exclusions, or regulations isn’t just a technical mistake; it introduces significant operational, compliance, and customer risk. As insurers increasingly turn to AI to support claims handling, underwriting, and policy servicing, hallucinations become less of an abstract AI concern and more of a workflow or model design issue.
To be clear: hallucinations are neither mysterious nor inevitable. Facts and figures can be traced to their origins. Hallucinations cannot be. Things that aren’t verifiable need human review to provide critical understanding. Through verification, hallucinations can be detected, corrected, understood, and significantly reduced – especially when AI systems are designed with insurance workflows and domain expertise in mind.
What Are Examples of AI Hallucinations?
An AI hallucination occurs when a system produces information that appears coherent and confident, but is factually incorrect, unsupported by source material, or entirely fabricated.
Crucially, hallucinations don’t usually look like errors. They look like plausible answers.
In insurance workflows, that can take several familiar forms:
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A claims assistant referencing a policy exclusion that does not exist
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An AI-generated claim summary assigning a cause of loss that isn’t documented
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A customer-facing chatbot inventing a coverage limit when the policy language is unclear
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An underwriting tool citing outdated or misapplied state regulations
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A core system filling in missing information instead of clearly flagging uncertainty
What makes hallucinations especially risky in insurance is that they often resemble competence. The language is fluent. The tone is confident. To a non-expert, the answer may sound entirely reasonable. But insurance professionals know that “reasonable” is not the same as “correct.”
This is also why hallucinations are sometimes perceived as deception or intent. In reality, the system is doing exactly what it was designed to do: generate a coherent response when presented with incomplete or ambiguous inputs.
Why Does AI Hallucinate?
Generative AI systems are optimized to continue patterns. When given an input, they generate the most statistically likely continuation based on training data, available context, and the constraints placed around them.
Hallucinations tend to emerge when three conditions overlap:
1. Ambiguous or incomplete information
Insurance data is rarely neat. Insurance policies include endorsements, exceptions, and jurisdiction-specific language. Claims files contain unstructured adjuster notes, scanned documents, emails, and attachments – many of which contain hand-written information. Gaps and ambiguity are common.
2. Diffuse or underspecified instructions
When an AI system isn’t clearly constrained – by a clear task definition or prompt, through source material, like an ACORD form, or from user-set confidence thresholds – it has no way to distinguish between “answerable” and “should not answer.”
3. No explicit stopping rules
If uncertainty isn’t an allowed outcome, the system will continue generating what appears to be a coherent response, because that’s how it was trained. This is where concern about hallucinations often gets misplaced. The system isn’t “deciding” to guess or exercising judgment; it’s responding to uncertainty by continuing forward, because nothing in its design tells it where to stop.
Hallucinations are more likely when systems encounter ambiguous or insufficient context without clear instructions for when to stop generating – or when verification is required.
In insurance, those conditions often arise in edge cases – non-standard endorsements, jurisdiction-specific requirements, incomplete claim files, or other situations when AI producing any answer is riskier than producing none.
What this phenomenon reflects most is systems operating beyond the boundaries they were designed – or allowed – to respect.
How Can You Control or Prevent AI Hallucinations in Insurance Workflows?
Preventing hallucinations doesn’t require abandoning AI. It requires designing systems that recognize the cost of being wrong.
In insurance environments, that starts with a few practical principles.
1. Constrain the role of the system
AI performs best when its scope is explicit and supports human decision-making. Extracting data, summarizing known content, or classifying documents are very different tasks from interpreting coverage or making judgment calls.
Clear task boundaries reduce the likelihood that a system will generate unsupported conclusions.
2. Ground responses in authoritative sources
AI systems should rely on policy documents, endorsements, claims files, and jurisdiction-specific regulatory sources – not generalized knowledge.
In practice, this means grounding outputs in verifiable material rather than allowing free-form generation, and having robust exception-handling capabilities (e.g., human-in-the-loop (HITL)).
Put simply, the most reliable insurance AI doesn’t “know” everything. It’s a system that rigorously checks outcomes.
3. Treat uncertainty in AI answers as a valid outcome
“I don’t know” is often the correct answer in insurance. “I’ll find out” is equally often the best course of action. Insurance AI systems should be designed to surface ambiguity, flag missing information, and escalate edge cases to human experts, rather than trying to “fill in the blanks.” Paradoxical as it might sound, when uncertainty is permitted, hallucinations decrease.
4. Embed AI into workflows, not around them
Hallucinations are most dangerous when AI operates in isolation. HITL review, exception handling, auditability, and traceability – the ability for users to track a value back to the original source for verification to ensure an unbroken chain of evidence from origin to final outcome – to enable compliance and ensure AI supports insurance professionals rather than substituting their judgment.
Why Insurance Expertise Matters in Insurance AI
Domain context matters. Insurance is full of language and logic that can sound coherent without being correct. Systems built without deep insurance knowledge may produce answers that appear plausible to a general audience but immediately raise red flags for experienced human experts.
Limiting hallucinations isn’t just about better models – it’s about accurate data and discrete instructions by deliberately limiting the range of answers that merely seem coherent in the first place.
There’s an old expression that the smartest people are the ones who are unafraid to say, “I don’t know.” AI hallucinations are not an indication that the technology is untrustworthy. They are a sign that systems have been given too much latitude in environments where precision matters.
In insurance, trust doesn’t come from fluency or confidence. It comes from constraint, grounding, defined exception-handling practices, and workflow-aware design.
When AI systems are built with clear limits – and a clear understanding of insurance – they stop when they should, escalate when they must, and support experts instead of misleading them. And in insurance, it’s the difference that makes all the difference.
