Property and casualty insurers in North America that invested heavily in advanced analytics and AI delivered stronger financial results.
A new survey from WTW shows clear performance gaps between early adopters and slower movers. The data covers results between 2022 and 2024.
Carriers using more advanced analytics reported combined ratios six percentage points lower than peers. They also achieved premium growth three percentage points higher over the same period. The gap reflects operational gains rather than short-term market effects.
Laura Doddington from WTW said insurers are now seeing measurable returns from analytics investments.
Firms that moved early are translating data into pricing, underwriting, and portfolio decisions more effectively.
According to Beinsure analysts, this shift moves analytics from optional capability into baseline requirement across competitive markets.
Adoption in underwriting and pricing has already reached high levels. Nearly all surveyed insurers use analytics in these functions.
80% rely on advanced rating and pricing models. Another 11% plan to deploy them soon. Predictive pricing models are becoming standard across the industry.
Claims operations still lag but momentum is building. About 33% of insurers use analytics for fraud detection. Around 29% apply it to severity assessment.
Those figures are expected to reach 65% to 70% within two years. Automation is also expanding. About 36% plan to introduce straight-through processing in claims workflows, up from 14% today.
Generative AI adoption is accelerating. More than half of insurers already use large language models in some capacity. Another 29% expect to adopt them within two years.
Usage in underwriting remains limited for now. Only 16% currently apply AI to support underwriting decisions. That share could reach 60% by 2028.
If current plans hold, AI adoption across underwriting, claims, and customer service could double or triple within the next few years. Growth depends less on intent and more on execution. Many firms still struggle with foundational issues.
Data quality remains the main constraint. Around 42% of respondents cite poor data quality and limited access as major barriers. IT bottlenecks add further friction, slowing deployment and integration across systems.
Cultural readiness also trails technology investment. Only 20% of insurers report having a defined analytics strategy guiding daily operations. Just 12% provide regular analytics training to employees. Skills gaps remain visible.
Doddington pointed to the need for stronger data governance and infrastructure. Without that foundation, AI deployment can amplify existing inefficiencies rather than fix them.
Firms that resolve data and system constraints move faster from experimentation into production use.
The survey includes responses from 59 insurers across the United States and Canada. Participants represent senior leaders in analytics, actuarial, and strategy roles.
Results show a widening divide between firms that operationalize analytics and those still building core capabilities.
