5 things to know about large language models in claims management

Claims management in property and casualty insurance is often a complex process. Yet the emergence and ongoing advancement of large language models (LLMs) significantly improves the efficiency and accuracy of these processes, marking a substantial shift in the industry's approach to handling claims.

1. LLMs are a powerful form of AI

LLMs possess the ability to understand, generate, and interpret text. This domain of artificial intelligence (AI) is broadly known as natural language processing (NLP), which bridges the gap between human communication and computer understanding.

LLMs can be thought of as digital linguists. They read enormous amounts of text, learning from the structure, context, and use of words and phrases. After this learning phase, these models can create their own text that can closely resemble human writing.

2. LLMs have non-generative uses too

While much of the buzz about LLMs is to generate human-like text (a la ChatGPT), this is not the only application of LLMs. The ability of LLMs to understand and interpret language makes them useful for a variety of non-generative tasks.

For instance, LLMs can be used for information extraction, where the model is tasked with identifying and understanding specific information from a large corpus of text. They can also be used in sentiment analysis to determine the sentiment (positive, negative, neutral) expressed in a section of text.

Moreover, LLMs can be deployed for tasks like text classification, machine translation, and question-answering systems where the primary task is not generating new text but understanding and responding to existing text in specific ways.

In short, while the ability of LLMs to generate coherent, context-appropriate text is often highlighted, their capacity to comprehend and interpret language is equally significant and forms the basis for a wide range of both generative and non-generative applications.

3. LLMs are more accessible than ever, but it takes effort to go from ”cool” to ”effective”

The potential of LLMs is undoubtedly compelling, but how can this be effectively transformed into practical application within your claims organization? Here are some key considerations:

1. Start on a modest scale: Prior to a full-scale rollout, consider a smaller pilot project to assess the functionality and benefits of LLMs.
2. Prioritize quality data: The efficacy of LLMs is largely dependent on the quality of the data, both in training and in production. Hence, it’s crucial to ensure your data is robust and systems are in place for a constant supply of quality data to the LLMs.
3. Explore collaborations: Unless you have access to an in-house team of AI specialists, it may be advantageous to collaborate with an experienced AI solution provider. They can offer guidance throughout the process and help tailor an LLM to your specific requirements.
4. Invest in training and support: While LLMs can augment workflows, they do not render human input redundant. Therefore, it’s crucial to invest in training and support for your team, helping them to adapt and collaborate effectively with this new technology.
5. Monitor results: Establish a concrete methodology to track the impact of LLMs for your use case. This evaluation should be a continuous process, tracking performance from testing to production.

4. Take steps to protect your data

Data is the fuel for LLMs, but it can also be a point of vulnerability. While these models require massive data sets to learn and function, the sensitive nature of claims data calls for caution. Confidential and high-risk information should not be shared with ChatGPT, for instance.

Deploying LLMs should follow stringent security protocols. To ensure you’re leveraging LLMs safely, it’s vital to engage your legal and information security teams. Their expertise will help guide you toward best practices and the most secure way to harness LLMs.

5. LLMs are game-changing for claims management

Picture a seasoned claims adjuster, who's handed a stack of claims files. Each file is filled with lengthy, dense text such as accident reports, medical records, and other unstructured text data. The adjuster sifts through the information, identifying key details, spotting inconsistencies, and determining a claim's validity—now picture all of these tasks being done by an LLM at a speed and scale far beyond human capacity, at a fraction of the cost.

The transformative potential of LLMs in claims management cannot be overstated, given the vast amount of text data generated during the claims process. By taking on significant portions of the processing workload, these AI models can expedite the claims process, increase accuracy, and substantially reduce the operational burden.

Claims systems often hold structured data that, while useful, may be peppered with missing pieces or inconsistencies, presenting an incomplete picture. LLMs can unlock the value of this text data and provide a more comprehensive narrative about a claim, in turn driving better outcomes. LLMs capture the nuances, context, and details that structured data might miss. This includes observations, descriptions, and other insights, painting a more holistic picture of each claim. Hence, the fusion of structured data and unstructured text data can significantly enhance the understanding and handling of claims.

By leveraging the strengths of LLMs to improve claims-handling processes to achieve lower costs and better outcomes, companies will gain a competitive advantage in the challenging markets they face today. LLMs stand to revolutionize claims management, adding an unprecedented level of efficiency, accuracy, and speed to traditionally labor-intensive processes. The ability of LLMs to analyze and interpret complex texts can drastically reduce turnaround times, ensure consistency, and even aid in the early detection of high-cost claims.