A new survey finds that property and casualty (P&C) insurers in North America and Canada that invested more resources in advanced analytics and AI achieved greater profitability and premium growth, according to global broker WTW.

Insurers using more sophisticated analytics achieved combined ratios six percentage points lower and premium growth three percentage points higher compared to slower adopters between 2022 and 2024, according to the WTW 2026 Advanced Analytics and AI Survey.

Larger insurers, along with those focused on personal lines, demonstrated greater use of advanced analytics, while smaller and specialty-line insurers showed less progress in specific areas.

While specialized segments showed much slower adoption rates, all personal lines auto and nearly all homeowners’ insurers already employ advanced pricing analytics.

“Advanced analytics and AI are beginning to yield significant payoffs, as lead carriers report measurable returns on investment, said Laura Doddington, head of Personal and Commercial Lines, Insurance Consulting and Technology, North America. “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 all of the 59 insurers that took part in the WTW survey now use underwriting and pricing analytics for predictive rating models.

Close to 80% rely on advanced rating and pricing models, with an additional 11% planning to implement them soon.

The survey showed that claims functions have been slower to adopt, though more insurers are signaling aggressive plans to expand the use of advanced analytics.

Currently, one-third or less use claims advanced analytics for fraud detection (33%) and severity assessment (29%), but these figures are expected to reach 65-70% within the next two years.

An additional 36% plan to introduce straight-through processing in claims workflow automation, a significant increase from the current 14%.

A lag in analytics use in other areas of claims was identified, with 25% of insurers reporting its use in claim triage models to route complex claims, and 20% to identify subrogation.

In addition, less than 15% of survey respondents model claims for the probability of litigation or attorney engagement, and only 4% use advanced analytics and AI to personalize customer communications or service delivery.

Insurers have identified other areas where analytics could boost a return on investment, including reserving, expense management, marketing, and agency and broker management.

Currently, just 20 percent of insurers reported using analytics for case reserving.

While only 16% currently use AI to augment human underwriting, this is set to rise sharply, with 60% of insurers planning to prioritize this between now and 2028.

Gradient boosting machines are still the applications of choice for underwriting scoring and fraud detection due to their superior predictive power over generalized linear models, WTW found.

But large language models are gaining ground, with more than half of survey respondents reporting using LLMs and generative AI, and another 29% planning to adopt the technologies within the next two years.

Data concerns and IT bottlenecks are the main challenges experienced by survey respondents in the adoption of analytics, with 42% reporting 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, the survey found. Just 20% report having a well-defined analytics strategy to guide daily activities, and only 12% of insurers in the survey regularly offer analytics training to employees.

“The ability to harness advanced analytics and AI will increasingly define market relevance, operational efficiency, and strategic agility,” said Doddington. “At the same time, using AI tools without a solid foundation may exacerbate existing issues rather than solve them.

Harnessing quality data, deploying analytics without hitting IT bottlenecks, and maintaining robustness will be key for successful AI and machine learning adoption, Doddington added. “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.”

If survey respondents follow through with their intended AI and machine learning initiatives, adoption in underwriting, claims, and customer service is set to increase two or even threefold by 2028, WTW stated.