9Insurance Business ReviewAPRIL 2024Widespread commercial access to foundational LLMs like OpenAI's GPT-4 is still very new and innovation is accelerating, with breakthroughs appearing on an almost weekly basisimprove the analytics processes that inform these decisions, enabling dynamic optimization of a carrier's book of business -- based on, for example, target loss ratio, retention, and growth goals. Buy vs. build?Our initial hypothesis was that most carriers would need the help of third parties to scale production-grade LLMs across their businesses. That said, we have been pleasantly surprised to learn about several insurers in our network already experimenting directly with foundational LLMs ­ and indeed several carriers are mentioning LLMs in earnings calls. Some are achieving positive early results on use cases such as call center and claims note summarization / triage. We anticipate there will be stratification in the market between insurance companies that have the resources to `build' LLM use cases internally, namely the largest and most sophisticated carriers with hundreds of data scientists and engineers in house, and those that don't. However, we believe the playing field for the application of Generative AI across the insurance industry will be relatively flat given how readily available these tools are - even if SMEs need third-party help to deploy them. Therefore, we believe there will be a significant opportunity for all players in the insurance technology ecosystem to create and capture value from LLMs ­ from existing software vendors and system integrators/IT consultancies to insurtech startups with LLMs embedded into their products from day one. Based on what we have seen so far, we view LLMs as tools or features that can help create exceptional products rather than being `the product' itself. Healthy skepticismThe excitement about the potential impact of Generative AI in insurance should be balanced with a healthy dose of skepticism and practicality. There are several considerations carriers (and investors) should take into account when exploring LLMs in insurance: i)Safety and data security ­ especially with personally identifiable information (PII) and claims data, ii)misplaced confidence - LLMs are very capable of providing authoritative sounding but inaccurate answers. These hallucinations can be especially dangerous when answering policy and coverage related questions that have a definitive answer (and expose carrier/broker to liability if answered incorrectly), iii)the pace of change - do organizations have the ability to continuously maintain and upgrade to the latest tech,iv)compute cost ­ cost and availability of GPUs will remain a gating factor for adoption across industries, and v)data interoperability (or lack thereof) across legacy systems and between different stakeholders in the value chain will ultimately limit the most grandiose use cases from coming to fruition until more modern underlying infrastructure is in place.To paraphrase Hemingway, we see the impact of Generative AI on the insurance industry to be slow at first, then all of a sudden.
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