Artificial intelligence is moving fast in the P&C insurance industry, and for good reason. Carriers and MGAs are under real pressure to reduce expense ratios, accelerate policy processing, and compete on service speed. AI promises to help on all three fronts. But there is a problem that does not get enough serious treatment in the conversations happening inside carrier conference rooms: AI hallucination. Not as a theoretical concern, and not only in the context of chatbots. As a practical risk embedded in any AI deployment that touches insurance workflows, including underwriting support, policy administration, document extraction, and customer-facing tools.
This post is a grounded look at what hallucinations actually are, how they manifest in a P&C context, and what governance frameworks help carriers deploy AI responsibly.
What Does “AI Hallucination” Actually Mean
Large language models (LLMs) generate outputs by predicting statistically likely sequences of text based on patterns in their training data. They do not reason from facts. They do not retrieve verified information from a database unless they are specifically architected to do so. They produce text that is plausible given what they have seen before.
When the model generates content that is factually wrong, invented, or misleading, that output is called a hallucination. The term is borrowed loosely from neuroscience and is somewhat imprecise, but it has stuck. What matters operationally is this: a hallucinated output looks like a confident, well-formed answer. There is no error flag. No asterisk. The system does not know it is wrong.
This is what makes hallucination categorically different from a calculation error or a broken process. A broken process fails visibly. A hallucinating AI succeeds invisibly.
The scale of the problem is real. A 2024 Deloitte survey found that 47% of enterprise AI users made at least one major business decision based on hallucinated content. (Deloitte Global AI Survey, 2024)
On domain-specific tasks, the risk is meaningfully higher than general benchmarks suggest. In controlled summarization tests, leading models show hallucination rates below 1-3%. But in specialized domains, including legal, scientific, and technical analysis, independent evaluations have documented rates of 10-20% or higher. (Stanford HAI, 2024; Vectara, 2025)
Why Insurance is a High-Consequence Environment for AI Hallucination
Most industries can absorb a percentage of AI errors as part of an acceptable margin of variance. Insurance cannot, at least not in the same way. Here is why.
Regulatory accountability is document-level. State regulators, the NAIC, and increasingly the courts expect carriers to demonstrate that adverse actions, rating decisions, and coverage determinations are traceable to documented facts. An AI output that fabricates a policy condition or misrepresents a form filing creates a paper trail that contradicts the source record. That gap is a regulatory and litigation exposure.
Policy language is highly specific. ISO forms, manuscript endorsements, and state-mandated provisions are precise by design. A general-purpose LLM trained on broad internet data may have seen fragments of standard forms, but it has not been trained to distinguish between a BOP and a CPP, or to correctly interpret how a specific state’s anti-stacking statute applies to a given loss scenario. When it answers as though it has, that is a hallucination.
The downstream decisions are consequential. A hallucinated summary of a commercial application that understates prior losses, or an AI-extracted coverage limit that misreads a declaration page, does not result in a bad blog post. It can result in a miscalculated premium, a coverage dispute, or a bad-faith allegation.
The regulatory environment is actively tightening. As of early 2026, 24 states have adopted the NAIC Model Bulletin on the Use of AI Systems by Insurers, and the NAIC’s new AI Systems Evaluation Tool pilot is live across 12 states, running through September 2026, with formal adoption expected at the fall meeting in November. (NAIC, 2026; Holland & Knight, 2025) For carriers writing across a national footprint, the majority of their exposure already sits in bulletin-adoption states, where the examination apparatus is real and now actively developing.
How does AI Hallucination Show Up in P&C Insurance Operations
These are not hypothetical scenarios. They reflect the categories of error that emerge when general-purpose AI tools are applied to structured insurance workflows without sufficient domain-specific guardrails.
Document extraction errors. AI tools used to extract data from applications, loss runs, or supplemental questionnaires may misread field labels, transpose values, or fill in fields based on what typically appears in that position rather than what is actually written. A prior loss count of zero and a prior loss count from a different policy period are not the same thing. An LLM may not reliably distinguish them.
Coverage interpretation errors. When underwriters use AI to summarize coverage forms or compare manuscript language against a standard, hallucinations can produce plausible-sounding interpretations that are simply wrong. The model pattern-matches to familiar language and generates a conclusion that sounds authoritative, even when the specific form interaction at issue has no clear precedent in its training data.
Fabricated citations. A 2024 Stanford University study found that LLMs asked about legal precedents collectively invented over 120 non-existent court cases, complete with realistic names and fabricated legal reasoning. (Stanford RegLab, 2024) The same failure mode applies to regulatory citations in an insurance context. A model asked to identify the applicable statute or bulletin governing a coverage issue may generate a citation that looks real but does not exist.
Cross-document inconsistency. Complex insurance transactions involve multiple documents: applications, loss runs, inspection reports, prior policy declarations. When AI processes these separately and then synthesizes conclusions, it may produce outputs that contradict the source documents or draw connections between information that is not actually connected.
Temporal confusion. Loss histories, rate filings, and coverage terms are time-bound. AI tools that do not rigorously maintain temporal context may misattribute policy periods, conflate current and historical information, or apply rates that were valid at a different point in time.
What is the Root Cause of AI Hallucination in Insurance
Understanding why hallucinations happen is more useful than cataloging them.
General-purpose LLMs are trained on enormous corpora of text. That breadth is their strength for many tasks. It is also a structural limitation for specialized domains. Insurance has its own vocabulary, logic, regulatory framework, and document conventions. A model that has not been trained specifically on insurance data, and constrained by domain-specific rules, will apply general patterns to insurance-specific problems.
The result is a model that sounds fluent in insurance language because it has encountered insurance text during training, but that does not actually understand how insurance works. It knows that declarations pages contain limits. It does not necessarily know which limit applies to a specific loss scenario under a specific form edition in a specific state.
This gap between fluency and knowledge is the core of the hallucination problem.
What Responsible AI Deployment Looks Like for P&C Insurers
There is no AI architecture that eliminates hallucination entirely. That is an honest statement that any vendor claiming otherwise should be pressed on. But there are architectural and governance choices that meaningfully reduce the risk and, critically, make errors detectable before they cause harm.
Retrieval-augmented generation (RAG) over closed document sets. Rather than asking a model to answer from memory, RAG architectures retrieve relevant passages from a defined document corpus and ask the model to synthesize only from what was retrieved. For insurance applications, that means grounding answers in the actual policy form, the actual application, the actual loss run, rather than the model’s probabilistic sense of what those documents usually say. Research has consistently shown RAG to be the most effective available technique for hallucination reduction, with measured reductions ranging from 40-71% depending on implementation. (AllAboutAI, 2025; MEGA-RAG, Frontiers in Public Health, 2025)
Citations as an output requirement. AI tools that generate outputs without citations are structurally more vulnerable to hallucination going undetected. When every claim in an AI output is required to cite the specific document and location it came from, reviewers can spot misattributions. Unsupported claims become visible.
Human-in-the-loop at consequential decision points. AI should accelerate human decisions, not replace them where the stakes are high. For coverage determinations, adverse underwriting actions, and anything that touches regulatory filings, the workflow architecture should require human review before any AI output becomes a record of decision. Across industries, 76% of enterprises now include human-in-the-loop processes specifically to catch hallucinations before they affect outcomes. (Drainpipe.io, 2025)
Domain-specific fine-tuning and rules. AI systems used for insurance workflows benefit significantly from training and configuration that reflects insurance logic specifically. This includes form edition awareness, state-level regulatory variation, line-of-business specific document conventions, and underwriting guideline integration. Generic models applied to specialized problems without this grounding will hallucinate more.
Audit trails that pre-date the decision. The NAIC Model Bulletin, adopted in December 2023 and now in force across 24 states, explicitly requires insurers to maintain documented AI governance programs with auditing processes, transparent governance frameworks, and robust risk management controls. (NAIC Model Bulletin, December 2023) The new AI Systems Evaluation Tool currently being piloted goes further, giving examiners a structured four-exhibit framework covering AI usage scope, governance risk assessment, high-risk system details, and data inputs. (NAIC AI Systems Evaluation Tool, March 2026) Audit capability is not a post-deployment addition. It needs to be built into the system architecture from the start.
What This Means for Carriers Evaluating AI Tools
If you are currently evaluating AI tools for policy administration, underwriting support, or document processing, the hallucination question deserves a specific line of inquiry beyond the demo.
Ask how the system handles documents it has not seen before. Ask what happens when the model encounters ambiguous or conflicting information. Ask where citations come from and whether they can be verified against source documents. Ask how the system logs its reasoning and whether that log is sufficient to satisfy a regulatory inquiry under the NAIC’s evaluation framework.
Vendors who answer these questions with specificity, and who acknowledge the residual risks rather than claiming to have eliminated them, are more credible than those who promise hallucination-free AI. The former have thought carefully about the problem. The latter may not have.
A Note on the Pace of Change
AI capabilities in this space are improving quickly. Some leading models have reported up to a 64% drop in hallucination rates over the course of 2025 on controlled benchmarks. (AllAboutAI, 2025)
That is a reason for optimism, and also a reason for humility. Benchmark improvements on summarization tasks do not automatically translate to the kind of complex, multi-document, regulatory-context reasoning that insurance workflows require. No carrier should assume that a tool evaluated and deployed today will perform the same way as the underlying models are updated, the training data changes, or the vendor’s architecture evolves.
Ongoing monitoring, periodic audit, and clear vendor accountability provisions in contracts are not bureaucratic overhead. They are the operational infrastructure that makes AI deployment sustainable in a regulated industry.
The carriers and MGAs that will use AI most effectively are not the ones who move fastest. They are the ones who move with enough structure to catch and correct errors before those errors compound. Hallucination is a manageable risk. But only if it is treated as a real one.
About WaterStreet
WaterStreet Company is a cloud-based policy administration platform purpose-built for small to mid-size property and casualty carriers and managing general agents. Designed to support the full policy lifecycle, including quoting, binding, endorsements, renewals, billing, and regulatory reporting, WaterStreet helps carriers go to market faster without the implementation overhead of enterprise legacy systems. WaterStreet’s Back Office Support Services (BOSS) division extends that value with outsourced policy processing, document management, and operational support for carriers who need scalable staffing alongside scalable technology.
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Sources and Further Reading:
1. NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers (December 2023)
4. Fenwick: NAIC Expands AI Systems Evaluation Tool Pilot Program to 12 States (March 2026)
5. Crowell & Moring: NAIC Intensifies AI Regulatory Focus (March 2026)
6. AllAboutAI: AI Hallucination Statistics 2025 (includes Deloitte survey, RAG reduction data)
7. Stanford HAI / RegLab: Legal AI Hallucination Study (fabricated citations research, 2024)
8. Vectara Hughes Hallucination Evaluation Model (HHEM) Leaderboard
10. WaterStreet Company: What the NAIC Model Bulletin Means for Insurance AI (April 2026)
