Insurance Business ReviewAPRIL 20248IN MY OPINIONRecent technological breakthroughs in Generative AI have the potential to transform the insurance industry. At its core, insurance is the ultimate data-driven business. Every day, insurers and brokers receive and generate massive quantities of data via email, call centers, PDFs and spreadsheets. This data, often granular and proprietary to the insurer, has never been comprehensively analyzed. But the emergence of large-language-models (LLMs) presents an opportunity for the industry to finally change that. Widespread 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 basis. Up to now, training machine learning algorithms required enormous amounts of data to perform a specific task. With the recent breakthroughs in foundational LLMs, the additional training or 'fine-tuning' of LLMs to perform the same tasks can be accomplished with much less data, speeding up deployment. When considering how this might play out in the insurance industry, there are still more questions than answers: What are the killer use cases? What is the best operating model for insurers to take advantage of this technology? Will startups build verticalized LLM applications? Will established software companies like Guidewire succeed in embedding LLMs into existing products? Will carriers go direct to the source and build their own products on top of foundational LLMs? To form a view on these questions, we've been having conversations with leaders across the industry, including large carriers, established tech vendors, multi-line insurance brokers, and insurtech startups of all shapes and sizes. Here are some preliminary findings:Most promising use casesSelf-service tools ­ Conversational AI has been available for several years. However, deployment of virtual assistants (i.e., `chat bots') to-date has been limited to the simplest of servicing use cases. This has often caused significant frustration among consumers who find they give all the information to a chat bot and then must repeat it again to a human agent in a call center when the chat bot fails to provide a solution. Modern self-service tools harnessing the power of LLMs such as Parloa and Replicant can engage in more nuanced, empathic conversations with customers, as well as read and write to core systems and complete more complex servicing tasks without human supervision. For example, today's models can change policy effective dates, named insureds, and other minor coverages/endorsements. For low severity claims, customers can now complete the entire FNOL process by engaging exclusively with text and voice-based conversational AI assistants. Document understanding ­ Given the insurance industry's reliance on PDFs and spreadsheets, document automation has long been viewed as a `killer use case' for AI. But previous generations of this technology (e.g., OCR, NLP, and RPA) have primarily been useful in extracting unstructured data from documents that arrive in consistent formats. These solutions often struggled with documents where the formatting is inconsistent or the data is nested in complex tables. The latest product releases from companies like Instabase and Indico use cutting edge deep learning techniques and the latest LLMs to help insurers tackle long standing, complex problems - with more limited training data sets - such as commercial insurance submission intake and bodily injury/medical claims triage. Beyond extraction, these tools give users the ability to converse with documents (e.g., `what is the policy number on this page'?) and generate summaries (e.g., `summarize all of the notes related to this claim file'), enabling more complex workflow automation.Co-pilots ­ The holy grail of AI in insurance would be applications that create a sustainable competitive advantage in the core functions of the insurance value chain, namely underwriting and claims. Carriers have long adopted rules-based rating engines to automate underwriting and pricing in personal lines and small commercial business. Generative AI `co-pilots' like Sixfold and Capitola could enable carriers and brokers to bring this level of automation and discipline into more complex business lines such as mid/large commercial risks, specialty products, and high-value life insurance. While these lines of business will certainly still require human input and judgement, LLMs combined with third-party data can help dramatically GENERATIVE AI IN INSURANCE ­ WHERE WE ARE TODAYBy Nick Bendell, Venture Capital Investor, MTech Capital
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