North American property and casualty (P&C) insurers that invested more resources into advanced analytics and AI generated higher profitability and stronger premium growth, according to a new survey from WTW.
WTW’s 2026 Advanced Analytics & AI Survey asked 59 P&C insurers in the US and Canada, drawing insights from senior executives across analytics, actuarial, and strategy functions.
The survey found that insurers using more sophisticated analytics achieved combined ratios six percentage points lower and premium growth three percentage points higher than slower adopters between 2022 and 2024.
Laura Doddington, Head of Personal and Commercial Lines, Insurance Consulting and Technology, North America at WTW, said, “Advanced analytics and AI are beginning to yield significant payoffs, as lead carriers report measurable returns on investment. With insurers planning to ramp up investment across personal and commercial lines, advanced analytics is shifting rapidly from competitive advantage to essential requirement to maintain market viability and drive sustainable growth.”
Almost 80% of insurers now rely on advanced rating and pricing models, with an additional 11% planning to implement them soon, making predictive rating models almost universal.
Adoption of claims advanced analytics has been slower, with only one-third or fewer carriers currently using these tools for fraud detection (33%) or severity assessment (29%). However, these figures are expected to rise to 65–70% within the next two years. An additional 36% plan to introduce straight-through processing in claims workflow automation, up from the current 14%.
Although large language models (LLMs) and generative AI are relatively new to the insurance industry, over half of respondents report already using them, with another 29% planning adoption within the next two years. While only 16% currently use AI to augment human underwriting, this figure is expected to rise sharply, with 60% of insurers planning to prioritise this by 2028.
Survey respondents identified data concerns and IT bottlenecks as the main challenges in adopting advanced analytics. 42% cited data-related issues—such as poor quality and limited accessibility—and inadequate IT support as significant barriers.
Building an analytics-driven culture also remains a work in progress. Only 20% report having a well-defined analytics strategy to guide daily activities, and just 12% regularly offer analytics training to employees.
Doddington added, “The ability to harness advanced analytics and AI will increasingly define market relevance, operational efficiency, and strategic agility. At the same time, using AI tools without a solid foundation may exacerbate existing issues rather than solve them.
“Data quality and robust governance, combined with the capability to deploy analytics without hitting IT bottlenecks, are crucial for successful AI and machine learning adoption. Insurers that master these fundamentals will be best positioned to leverage these advanced tools and techniques to gain a competitive edge in an increasingly data-driven market.”
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