Imagine having an "insurance genie" to tell you about your portfolio's hidden risks. Not a character from Aladdin but an AI-driven chatbot — a large language model (LLM) — poised to reveal the hidden truths within vast amounts of data. For instance, you could ask, "Which region in my portfolio is most susceptible to wildfire damage?" and receive a comprehensive answer promptly, highlighting specific regions, underlying conditions, trends, and potential risk mitigations. It would completely change how insurers leverage tools to manage risk. And with more data available than ever around properties, LLMs are set to push portfolio-wide analysis from a wish into reality.

How LLMs work

For any LLM to be effective, it requires the right input. Two critical components at the heart of its functionality are aerial imagery and geospatial data. Having consistent, reliable, and high-resolution imagery gives you multiple points of time by which to analyze a given property or region. Comprehensive geospatial data allows you to learn as much as possible about a property's attributes. Computer vision and machine learning models then help the LLM give the insurers actionable information. As you add more and more policies into the mix, you create an LLM working on an aggregate level to bring this all to life. And that is where the fun begins.