.avif)
Key Takeaways
- Dense insurance documents take time to read, slowing down insurance processes.
- Policyholders often don’t bother reading insurance documents, and they have a lot of misconceptions about coverage as a result.
- Large language models can pinpoint critical details in dense documents, speeding up communications.
- Policyholders can use large language models to learn about their coverage.
- Large language models can also cross-check multiple documents, such as demand letters, police reports and policies.
Most people don't enjoy reading insurance policies, and many never get around to reviewing them. Unfortunately, that can lead to costly coverage misunderstandings. For example, a homeowner might believe they have replacement cost coverage for their roof, only to learn after a storm that their policy pays only the roof's actual cash value, resulting in a settlement that’s thousands less than they need.
Now large language models are providing an easier way for both insurance professionals and policyholders to navigate dense insurance documents and avoid these types of oversights.
What Are Large Language Models?
Gartner1 defines a large language model as “a specialized type of artificial intelligence (AI) that has been trained on vast amounts of text to understand existing content and generate original content.” You provide a question or instructions, and the large language model provides a response. Importantly, you’re not limited to short prompts. If you provide the AI with content – such as an insurance document – the AI can answer questions about the content.
According to TechTarget2, large language models have been around since about 2014. However, these AI-based tools took a giant leap forward when ChatGPT was released in 2022, attracting more than 100 million users in just two months.
These days, large language models are embedded into search engines, email service providers, online retailers, and a growing number of other sites. Large language models are everywhere because they’re helpful. They pinpoint information and answer questions, and they do so quickly and effectively. That’s exactly what you need when you’re reviewing insurance documents.
How Can Large Language Models Help Insurance Pros?
Anytime you have text-based information that you need to summarize or analyze, you can use large language models to make it easier.
AI can point out key considerations or respond to specific questions. The original documents are still available to review and double-check, but the AI provides an instant overview. This enables a faster response and allows insurance professionals to prioritize tasks.
Deciphering demand letters is one way insurers can use large language models to analyze documents. The demand letter alleges liability for a loss and demands a payment.
Before you can respond to the letter, you need to determine what the loss entails and whether the insurer is responsible. The information in the demand letter may need to be corroborated with additional information, such as weather reports, police reports, DMV records, and policy language. A large language model can simplify the task.
The real power emerges when large language models connect information across documents. Instead of reviewing a demand letter, a police report and an insurance policy separately, claims professionals can ask AI to identify inconsistencies, highlight coverage issues and surface key facts across the entire claim file. That capability can significantly reduce review times while helping adjusters focus on judgment rather than document retrieval.
How Can Large Language Models Help Policyholders?
Reading insurance jargon is hard enough for professionals. For the average person, it might as well be a foreign language.
And that’s a problem. A Trusted Choice3 survey found that the average policyholder has a lot of misconceptions about their coverage. For example, 56% of Americans think a standard homeowners insurance policy covers flood damage, and 55% think a standard auto policy covers business use of a vehicle.
Large language models can help. Most policyholders may not be able to parse an insurance policy to determine exactly what’s covered and what’s not, but AI can. With AI-powered tools, policyholders can ask questions about their policy and receive clear, concise answers in plain language.
The potential is immense.
- When policyholders understand their coverage, they’re in a better position to manage their risks. For example, policyholders who don’t understand deductibles may not set aside funds to cover a deductible, but policyholders who know how deductibles work can prepare for this expense.
- When policyholders know what’s covered and what’s not, many disputes over claims can be avoided. For example, a policyholder who thinks their auto insurance includes personal items stolen from their vehicle may become angry and think they’re being cheated when the claim is denied. If policyholders understand their coverage, claim handlers can save time because they won’t have to explain the terms or deal with angry claimants. It may also prevent bad reviews and policyholder churn.
- Policyholders may even be motivated to buy additional insurance policies when they realize they don’t have coverage. For example, a policyholder who learns their car insurance doesn’t cover stolen personal items might decide to buy renters insurance to secure this coverage.
Insurance has always depended on information, but too much of that information remains trapped inside lengthy documents. Large language models are helping unlock it. By turning dense insurance text into actionable insights, AI enables professionals to spend less time searching for answers and more time making decisions.
