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Over the past seven years, we’ve had the opportunity to work with some of the biggest insurance carriers on using machine learning to improve their businesses. All now see the potential of large-language models (LLMs) and are developing strategies for leveraging them. There remains a lot of confusion about where and how LLMs will make a big impact, but this blog post from Andreessen Horowitz totally nails it:
While current machine learning technology allows for improved decisioning on simple products like auto and home insurance, more complex underwriting processes like commercial and life insurance remain challenging. This has less to do with the process of decisioning relevant data and more to do with collecting and synthesizing the relevant data.
Boom!
Despite massive investments over the past decade, automating complex processes like insurance underwriting hasn’t been realized except for simpler products like auto and home insurance. Traditional supervised machine learning approaches haven’t worked well for commercial or life insurance because much the relevant information isn’t available at the outset.
Underwriters go through a process of collecting and synthesizing the relevant data from multiple sources and formats, and this process varies by policy. For example, in life insurance underwriting, a vast array of information about the applicant is collected and synthesized from many sources such as applications, blood tests, or attending physicians statements. This can include personal health history, family health history, lifestyle habits, occupational hazards, financial status, and more. Each of these factors plays a crucial role in determining the risk associated with insuring the individual and therefore, the premium they should be charged. The underwriter usually doesn’t have all of this information at the outset, but instead must often gather it during the process of underwriting.
LLMs have the potential to automate these processes by gathering and synthesizing information from multiple sources, a use case that I’ve described previously: Routine Task Automation. Routine Task Automation leverages the reasoning power and task orchestration capabilities of LLMs to automate routine, repeated tasks currently performed by people. These tasks often involve gathering and analyzing bits of information from various sources, reasoning across them, and making quick decisions.
This is the transformative potential of LLMs in the insurance industry. By deploying them within a framework like LangChain or Hugging Face’s Transformer Agents, LLMs can automate these routine tasks and free up people to work on more complex decisions.
Well done, Andreessen Horowitz.
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Prolego is an elite consulting team of AI engineers, strategists, and creative professionals guiding the world’s largest companies through the AI transformation. Founded in 2017 by technology veterans Kevin Dewalt and Russ Rands, Prolego has helped dozens of Fortune 1000 companies develop AI strategies, transform their workforce, and build state-of-the-art AI solutions.
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