AI Hallucinations in Insurance: Understanding Risks and Mitigation Strategies

As artificial intelligence becomes increasingly integrated into insurance operations—from underwriting and claims processing to customer service and risk assessment—insurance executives must understand a critical AI limitation: hallucinations. These aren’t science fiction scenarios, but real technical challenges that can have significant business and legal implications for carriers, agencies, and wholesalers.

What Are AI Hallucinations?

AI hallucinations occur when artificial intelligence systems, particularly large language models (LLMs), generate information that appears credible and well-structured but is factually incorrect, misleading, or entirely fabricated. These occur when GenAI, despite its impressive pattern recognition capability, stumbles upon patterns that are statistically plausible but factually incorrect.

Think of it this way: an AI system trained to recognize patterns in data might confidently state that a specific insurance regulation exists when it doesn’t, or provide incorrect coverage details that sound perfectly reasonable. The AI isn’t intentionally deceiving—it’s following learned patterns that led to an incorrect conclusion.

Recent research indicates that factual inaccuracies show up in 27% of chatbot responses, highlighting the scope of this challenge across AI applications.

Why Insurance Companies Should Be Concerned

The insurance industry’s reliance on accurate data and precise risk assessment makes AI hallucinations particularly dangerous. Consider these potential scenarios:

Underwriting and Risk Assessment

The risk of AI “hallucinations”, for example, could lead to incorrect risk assessments, mispricing of policies, or inappropriate claims decisions. This can subsequently undermine the reliability of underwriting processes, distort financial forecasts, and cause reputational damage.

An AI system might incorrectly assess a commercial property’s flood risk based on fabricated historical data, leading to significant underpricing and potential losses during actual flood events.

Customer Service and Claims Processing

AI-powered chatbots and customer service systems can provide incorrect policy information, claim procedures, or coverage details. Take Virgin Money, for example. A chatbot reprimanded one unsuspecting customer for using the word ‘virgin’ in a customer service query—demonstrating how AI systems can misinterpret context and provide inappropriate responses.

Regulatory Compliance

Insurance is a heavily regulated industry. AI systems that hallucinate regulatory requirements or compliance procedures could lead companies to inadvertently violate state insurance laws or reporting requirements.

Financial Impact and Legal Exposure

In 2025, as startups shift from experimentation to commercialization, hallucinations are no longer treated as harmless model quirks. They’re treated as potential liabilities. When users begin to rely on AI-generated content to make decisions, the legal landscape changes.

The industry has responded to these risks—Lloyd’s of London has debuted an insurance product through a startup called Armilla for companies dealing with AI-related malfunctions, indicating the growing recognition of AI-related business risks.

Practical Strategies to Minimize AI Hallucinations

1. Implement Robust Data Governance

In order to prevent hallucinations, ensure that AI models are trained on diverse, balanced and well-structured data. This will help your model minimize output bias, better understand its tasks and yield more effective outputs.

For insurance companies, this means:

• Using verified, industry-specific datasets for training
• Regularly auditing training data for accuracy and completeness
• Ensuring data represents diverse risk profiles and geographic regions

2. Deploy Retrieval-Augmented Generation (RAG)

One of the best approaches to reduce hallucinations is to use RAG, which gives the AI access to reliable databases. It guarantees that answers are supported by actual data.

Insurance companies should connect AI systems to authoritative sources such as:

• Current policy databases
• Up-to-date regulatory guidelines
• Verified claims histories
• Actuarial tables and risk models

3. Establish Human Oversight Protocols

Users must approach AI outputs with a critical mindset. Implement workflows where:

• Critical decisions require human verification
• AI recommendations include confidence scores
• Subject matter experts review AI outputs in high-risk scenarios

4. Choose Trusted AI Platforms

Make every effort to ensure your generative AI platforms are built on a trusted LLM. In other words, your LLM needs to provide an environment for data that’s as free of bias and toxicity as possible.

Evaluate AI vendors based on:

• Transparency about model limitations
• Track record in regulated industries
• Availability of audit trails and explainable AI features

5. Implement Comprehensive Testing

Preventing AI hallucinations just needs a complete approach that combines smart prompt engineering, resilient guardrails, and thorough testing protocols.

Develop testing frameworks that:

• Validate AI outputs against known correct answers
• Test edge cases and unusual scenarios
• Monitor for drift in AI performance over time

6. Create Clear Usage Guidelines

To prevent AI hallucinations, users can double-check the answers and should ask more straightforward questions.

Train employees to:

• Verify AI-generated information through independent sources
• Use specific, unambiguous prompts
• Understand the limitations of your AI systems

The Road Ahead

The insurance industry’s adoption of AI continues to accelerate, bringing significant benefits in efficiency, customer experience, and risk management. However, The increasing use of artificial intelligence (AI) could trigger claims across many lines of business. Insurers will need to develop an understanding of intended and unintended effects, and design products that mitigate the risks.

Success requires balancing innovation with risk management. Insurance executives should view AI hallucinations not as a reason to avoid AI technology, but as a manageable risk that requires appropriate controls, similar to how the industry approaches cyber risks or catastrophic events.

By implementing robust governance frameworks, maintaining human oversight, and choosing trusted AI partners, insurance companies can harness AI’s benefits while minimizing the risks associated with hallucinations. The key is approaching AI deployment with the same disciplined risk assessment that has made the insurance industry successful for centuries.

About the Author: James W. Moore brings over 40 years of insurance industry experience, including work with carriers, agencies, and wholesalers. He holds a bachelor’s degree in finance with a specialization in insurance and is the founder of insuranceindustry.ai.

Sources:

• Kennedy’s Law: “The current and future impacts of AI in the insurance sector” (December 2024)
• Swiss Re Institute: “AI – unintended insurance impacts and lessons from ‘silent cyber'” (September 2024)
• IBM Think: “What Are AI Hallucinations?” (June 2025)
• Cloud Security Alliance: “AI Hallucinations: Generative AI’s Costly Blunders” (2024)
• MIT Sloan Teaching & Learning Technologies: “When AI Gets It Wrong: Addressing AI Hallucinations and Bias” (June 2025)
• PYMNTS: “Insurers Begin Covering AI Mishap-Related Losses” (May 2025)

AI Disclaimer: This content was created with assistance from artificial intelligence technology. While content is based on factual information from the source material, readers should verify all details directly with the respective sources before making business decisions.