The use of artificial intelligence (AI) across the global insurance sector is not new. It has been deployed by marketing, pricing, underwriting, and claims teams for many years, with an increasing breadth of application.
As new generative AI capabilities become more mainstream, insurers have been quick to experiment with, refine, and deploy them to improve their operations, reduce costs, or deliver tailored customer services.
Laura Doddington, North American head of personal and commercial lines, Insurance Consulting and Technology, at WTW, has been impressed by how the industry is embracing the latest AI technologies.
“We are no longer the laggards of the financial services sector,” she says. “Insurers have increasingly dedicated AI R&D resources focused on identifying where the best opportunities are for its use across the insurance value chain, and, frankly, they have little choice!”
AI is rapidly transforming industries worldwide, and insurance is no exception. Insurers are on track to benefit from AI’s evolution, but more effort is needed to industrialize and future‑proof investments.
For decades, insurers’ pricing teams have utilized advanced statistical modeling techniques, such as generalized linear models (GLMs), to predict claim frequencies, claim severities, retention, and conversion rates. Analytics platforms like Radar have helped insurers rapidly build, refine, test, and deploy highly sophisticated GLM models for pricing and underwriting.
While these techniques will continue to be at the center of insurers’ predictive modeling strategy—not least because of their transparency and explainability—AI has offered a new tool in the analytics armory that insurers have been quick to pick up.
“Whether it’s the use of machine learning models in predicting claim amounts or liberating unstructured data to provide a significantly greater pool of information to derive insights from, AI in insurance is now mainstream,” says Laura. “We’ve helped insurers use AI to provide decision support at the point of underwriting, triage claims, identify underwriting and claims fraud, and pinpoint claims that might unexpectedly jump without early intervention. It’s very exciting!”
While insurers are making headway, it’s not all smooth sailing. More effort is needed to industrialize promising results coming from proof‑of‑concept investments. Appropriate governance controls must be in place to manage implementations and prevent the deployment of untested or unethical models into day‑to‑day operations.
“A key requirement”, Laura warns, “will be the need to monitor the performance of models once they are implemented, an activity that will add an increasing burden on analytics teams as the size of the model estate grows.”
Smart, carefully planned, and well‑monitored model deployments can deliver substantial benefits.
Laura believes the area that offers the most immediate value is generative AI, particularly large language models (LLMs)—a capability that insurers are already embracing. “These models can parse unstructured underwriting and claims data, or call center transcripts, and translate, summarize, and analyze text, speech, and video data.”
This enables access to previously untapped data, improving automation, speed, and customer satisfaction.
Laura also points to practical use cases: “A great example of this is how insurers are using machines to analyze photo and video footage of property damage—for example, following an auto collision—to instantly decide whether a write‑off offer can be made at the point of first notice of loss (FNOL).”
“Other insurers,” Laura says, “use machines to read external reports to provide underwriters with useful insights they may have missed, often in the form of scores.” The opportunities are limitless.
Today, most insurance AI applications support experts in claims, pricing, and underwriting rather than replacing their judgment.
A key question is whether this will remain true as AI advances—especially with agentic AI, which can act autonomously, interact with its environment, and pursue goals without human intervention.
In a world with AI agents, what is the role for humans?
“This is a significant difference from generative AI and other previous AI classes insurers have readily adopted,” says Laura. “I struggle to see agentic AI fully replacing jobs in what is a highly regulated industry where advice, accuracy, and human judgment are critical, but the nature of work will certainly change.”
Laura readily accepts that agentic AI is going to make waves in the sector. “I’m already hearing from insurers who are beginning to experiment with it in various low‑risk applications. But the need for guardrails is essential. Humans must review and validate agentic AI output and ensure it is used ethically and responsibly.”
WTW’s Radar technology has long been the benchmark for pricing and underwriting analytics. With the launch of Radar 5, the most advanced version yet—combining decades of expertise with cutting-edge generative AI capabilities to transform insurance pricing and portfolio management.
Radar 5 enables insurers to interact with data in new ways, using generative AI to automate experience monitoring and support natural language interaction through Radar Vision. These enhancements allow faster model development, scenario simulation, and insight generation, reducing time-to-market while maintaining transparency and governance.
Built for scale and speed, Radar 5 integrates statistical models, machine learning, and generative AI in a single SaaS-enabled platform. This evolution enables insurers to adopt AI responsibly and effectively, establishing a new standard for innovation in a highly regulated industry.
