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Using Large Language Models in Insurance to Eliminate Headaches

Written by
Amrish Singh
large language models in insurance

Insurance documents aren’t exactly a light read. The average insurance document is lengthy and packed with jargon and technical terms. Whether you’re a policyholder or a seasoned insurance professional, reading policy language to find the information you need can be a daunting task. Thanks to emerging AI tools and the use of large language models in insurance, there’s now an easier way to navigate dense insurance documents.

The Rise of Large Language Models 

Calling a large language model a chatbot is a bit like calling Buckingham Palace a house – technically accurate, perhaps, but a major understatement. 

Gartner 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 TechTarget, 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. 

How Large Language Models Can Help Insurance Pros

Even though insurance professionals are familiar with insurance industry terms, wading through long documents can still be a time-consuming and headache-inducing task. Large language models can help by analyzing long documents and pulling out key elements. 

The original documents are still available for insurance professionals to peruse and doublecheck, but the AI provides an instant overview. This enables a faster response and allows insurance professionals to prioritize tasks. It’s like having a personal assistant who reads everything for you.

Demand letters are a great example of how insurers can use large language models to analyze documents. Insurers may receive demand letters alleging liability for a loss. In certain lines, such as auto insurance, these demand letters may occur frequently. Before you can respond to a 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 documents, such as police reports, and this adds to the amount of reading required. To simplify the process, insurance professionals can feed documents into a large language model and then ask specific questions, such as the cause of the loss and the date of the loss. 

Large language models can also be used in underwriting to analyze applications and summarize FNOL claims statements. Anytime you have text-based information that you need to summarize or analyze, you can use AI to make it easier.

How Large Language Models Can Help Policyholders

Reading insurance jargon is hard enough for professionals; but for the average person, it might as well be a foreign language. According to Forbes, most drivers don’t understand their auto insurance policies. For example, 74% of young drivers between the ages of 18 and 24 think their auto insurance includes AD&D insurance and 69% think it covers additional living expenses – neither of which is true. Forbes also found that policyholders are similarly confused about their homeowners insurance policies, with 72% misunderstanding essential home insurance coverage terms.

Large language models can help. The average policyholder 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 huge. 

  • 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.

This Is Just the Tip of Iceberg

Large language models can save an immense amount of time when deciphering dense insurance texts, and this is just the beginning of what AI can do. 

To see more examples of how insurers can leverage AI and large language models in insurance, download our white paper, “How Generative AI Makes the Insurance Business Easier: Seven Use Cases."