Generative AI keeps speeding up the metabolic rate of the insurance industry, and underwriting shows how the gains are accelerating. When GenAI made its debut in late 2022, it quickly introduced efficiencies into the process. The AI could go off and gather information that underwriters would previously have had to assemble themselves. The AI could also triage submissions to help underwriters focus on the most important ones first and could do some analysis, such as seeing what had changed when a policy came up for renewal. The efficiencies have continued to pile up now that AI agents can be used to take certain actions on behalf of underwriters. A whole other stream of GenAI work, related to “continuous underwriting,” has stepped up the pace of improvement by letting underwriters learn in near real time about changes in circumstances even before a policy comes up for removal. If a restaurant changes its hours, adds a deep fryer or starts selling alcohol, an AI can spot the change online and notify the underwriter. If a homeowner adds an outdoor trampoline or a pool, AI can likewise alert an underwriter by monitoring aerial imagery. (Bobby Touran and Tom Bobrowski have written about continuous underwriting at length, and the three of us discussed the topic on a webinar that, in my humble opinion, was exceptional.) In this month’s ITL Focus interview, Katie Klutts Wysor, a partner at PwC, takes us to a whole new level. While efficiencies and real-time notifications on individual policies already promise exceptional gains, Klutts Wysor describes how carriers can use AI to better manage their whole portfolios, quickly pivoting toward categories of risk that have become desirable and away from those that are looking problematic. She says: “AI can help inform research on the front end, and [you can] then use something like a GenAI-enabled underwriting platform to begin systematically embedding strategic capital decisions into appetite, process and guidelines at scale, so underwriters evaluating risks are working from more relevant information. Then you can use AI to respond to new business decisions more quickly, respond to renewal decisions more effectively, and potentially take certain actions during a policy’s term to support risk mitigation conversations. If you can start mastering that link—how you’re deploying capital and setting appetite, all the way down to those micro process decisions—that represents a new level of maturity.” She adds: “The fundamental approach is to look at your underwriting returns against the capital you’re deploying to the business, map that out, compare outcomes, and decide where you want to grow and where you may need to pull back. Improvements in technology may allow carriers to do that analysis more frequently. Instead of doing it once a year as part of strategic planning,… many carriers may be able to move… toward monthly or weekly review cycles, depending on how they make decisions. They may also be able to do the analysis in a more automated way and make decisions more intentionally on micro-segments of the business (by geography, class, line, etc.) that would have been too time-consuming to identify and react to previously.” A whole lot of business processes will need to be changed to take advantage of the new insights—getting the word out that the carrier’s risk appetite has changed, providing incentives to encourage brokers to submit the newly desirable risks, removing internal obstacles so the new business can be underwritten quickly, and so on. So the change will be a journey, not a one-off effort—and I suspect the pace will keep accelerating. Cheers, Paul | 
