AI Hallucinations Are Real. So Are the Ways to Prevent Them.

AI is remarkably capable, but it is not infallible. Under the wrong conditions, generative AI can confidently produce information that is simply untrue. These errors, known as AI hallucinations, are amusing when they involve pizza recipes. They're far more serious when they involve insurance policies, underwriting decisions or claims.

For insurers, hallucinations are a legitimate concern. Fortunately, they're also a manageable engineering problem.

How Have AI Hallucinations Caused Problems?

Soon after Google introduced AI Overviews, users began sharing screenshots of incorrect and sometimes bizarre responses. According to the BBC¹, one widely shared example advised users to use glue to help cheese stick to pizza. Another suggested eating one small rock each day.

Although those responses were easy to dismiss, AI hallucinations can have serious real-world consequences.

CNN covered one of the earliest high-profile examples when Google unveiled Bard.² Asked about discoveries from the James Webb Space Telescope, Bard incorrectly stated that the telescope captured the first images of a planet outside our solar system. Although the mistake appeared minor, investors reacted immediately. According to CNN, Alphabet shares fell 7.7%, wiping roughly $100 billion from the company's market value in a single day.³

Hallucinations have also created legal problems. In one widely publicized case⁴, a lawyer used ChatGPT to research a court filing. The AI generated fabricated judicial decisions and then falsely confirmed they were authentic when questioned. In another case, lawyers on both sides relied on hallucinated legal research, resulting in fines and a two-year ban from practicing law in that federal district.

Hallucinations have even led to litigation. According to The Verge⁵, a radio host sued OpenAI after ChatGPT falsely stated that he had been accused of embezzling funds.

What Do AI Hallucinations Mean for the Insurance Industry?

AI adoption continues to accelerate. According to the Federal Reserve, 78% of employees now work for organizations using AI, and 54% work for companies using large language models (LLMs).⁶ In insurance, adoption is even more advanced. Boston Consulting Group reports that insurance ranks second only to the technology, media and telecommunications sector in AI maturity.⁷

When high AI adoption meets an industry that depends on precision, hallucinations become a meaningful business risk. Even isolated inaccuracies can have significant financial, operational and regulatory consequences.

Consider two examples:

• Underwriting. An underwriter uses AI to summarize account information. The AI incorrectly introduces negative details that do not exist, leading to higher premiums or declined applications. The insurer loses profitable business. If a pattern later emerges showing those errors disproportionately affect certain applicants, the insurer could face allegations of discrimination.
• Policy servicing. A homeowners policyholder asks an AI assistant whether flood damage is covered. The AI incorrectly states that flood coverage already exists, so the customer decides not to purchase a separate flood policy. After a flood, the claim is denied, creating a coverage dispute and potential litigation.

These scenarios illustrate why insurers cannot simply deploy generic AI and hope for the best.

Can Insurers Mitigate the AI Hallucination Risk?

The encouraging reality is that hallucinations are not random. Researchers have identified many of the conditions that cause them, which means they can also be engineered out of production systems.

According to TechTarget, hallucinations often stem from poor-quality training data, bias introduced during training, ambiguous prompts or conflicting information.⁸  AI models may also fabricate answers when they are asked questions beyond the limits of the information available to them.

Researchers at the University of California San Diego found hallucination rates can rise dramatically when LLMs are asked questions outside the scope of their training data.⁹

Understanding these causes makes it possible to reduce the risk. An AI expert interviewed by TechCrunch explained that hallucinations can be minimized through careful training, high-quality knowledge bases and disciplined deployment practices.¹⁰ IBM similarly emphasizes the importance of high-quality training data, clearly defining the AI's purpose and limiting responses to appropriate use cases.¹¹

For insurers evaluating AI platforms, six elements are critical.

1. The AI is purpose-built for insurance. Generic AI is trained for breadth, not insurance-specific precision. Insurers need AI designed specifically for insurance workflows, terminology and decision-making.
2. The AI operates within defined guardrails. AI is more likely to hallucinate when asked questions outside its intended scope. Insurance AI should follow well-defined rules, recognize when confidence is low and seamlessly escalate conversations to a licensed or human representative when appropriate.
3. The deployment targets appropriate use cases. AI delivers the greatest value in structured, repeatable workflows such as payment collection, routine policy servicing requests and first notice of loss (FNOL). Organizations should begin with these well-defined processes before expanding into more complex tasks.
4. The platform provides complete visibility. Insurers should be able to review conversations, transcripts, summaries and performance reports. Without ongoing monitoring, problems may go undetected until they affect customers.
5. The AI has been rigorously tested. Every update should undergo regression testing before deployment to ensure previous capabilities remain intact and new issues have not been introduced.
6. Failures are detected automatically. If an AI agent hallucinates an incorrect claim number or policy detail, someone needs to know immediately. Automated evaluation frameworks can identify these issues quickly so they can be corrected before they become systemic problems.

How Does Liberate Reduce the Risk of Hallucinations?

Trust is not something AI vendors should ask customers to assume. It should be demonstrated through engineering discipline, testing and measurable performance.

Every Liberate AI agent is purpose-built for insurance and validated through Agent Arena, our automated testing environment. Agent Arena runs hundreds of synthetic insurance conversations that mirror real-world scenarios, allowing us to identify regressions before new capabilities ever reach production.

Once deployed, every conversation is automatically evaluated for quality, accuracy and potential failure conditions. Combined with full transcripts, conversation summaries and reporting dashboards, insurers maintain complete visibility into AI performance rather than treating it as a black box.

The results demonstrate what's possible when AI is purpose-built for insurance. Liberate has achieved a hallucination rate of less than 1%.

Hallucinations may never disappear entirely, but with the right architecture, rigorous testing and continuous operational oversight, they become an exception rather than a business risk.

Sources:

1. https://www.bbc.com/news/articles/cd11gzejgz4o
2. https://www.cnn.com/2023/02/08/tech/google-ai-bard-demo-error/index.html
3. https://www.cnn.com/2023/05/27/business/chat-gpt-avianca-mata-lawyers/index.html
4. https://www.msn.com/en-us/entertainment/gaming/both-lawyers-in-case-use-hallucinating-ai-causing-judge-to-call-the-whole-thing-off/ar-AA25i9xZ
5. https://www.theverge.com/2023/6/9/23755057/openai-chatgpt-false-information-defamation-lawsuit
6. https://www.federalreserve.gov/econres/notes/feds-notes/monitoring-ai-adoption-in-the-u-s-economy-20260403.html
7. https://www.bcg.com/publications/2025/insurance-leads-ai-adoption-now-time-to-scale
8. https://www.techtarget.com/whatis/definition/AI-hallucination
9. https://today.ucsd.edu/story/how-much-does-chatbot-bias-influence-users-a-lot-it-turns-out
10. https://techcrunch.com/2023/09/04/are-language-models-doomed-to-always-hallucinate/ 
11. https://www.ibm.com/topics/ai-hallucinations