I’m thrilled to sit down with Simon Glairy, a renowned expert in insurance and Insurtech, whose deep knowledge of risk management and AI-driven risk assessment has shaped innovative approaches in the industry. Today, we’re diving into the transformative power of technology in storm-risk underwriting, the role of cutting-edge models in enhancing accuracy, and how these advancements are driving sustainable growth for insurers in a rapidly changing landscape. Our conversation explores the intersection of AI, property-level insights, and the future of risk mitigation in regions hit hardest by severe weather.
How did the idea of integrating AI-driven tools for storm-risk underwriting come about, and what makes this approach stand out for insurers?
The push for AI-driven tools in storm-risk underwriting really came from the growing frequency and intensity of severe weather events across the U.S. Insurers needed a way to move beyond traditional models that often generalized risk across broad areas. What makes AI stand out is its ability to drill down to the property level, analyzing specific factors like roof condition or material type alongside local weather patterns. This creates a much clearer picture of risk, allowing for more precise underwriting and pricing decisions that reflect the true vulnerability of a property.
What are some of the key goals insurers aim to achieve by adopting advanced risk assessment models like those focused on storm vulnerability?
The primary goal is accuracy—getting a handle on the real risk for each property rather than relying on outdated, broad-brush approaches. This leads to better pricing that matches the actual exposure, which is critical for profitability. Beyond that, insurers are looking for sustainable growth, especially in high-risk regions. These models also help with regulatory compliance, ensuring that pricing and underwriting decisions align with evolving standards. Ultimately, it’s about building confidence in decision-making and expanding responsibly.
Can you walk us through how AI models assess storm risks at such a detailed, property-specific level?
Absolutely. These models use a combination of data points to create a detailed risk profile. They look at things like the condition and material of a roof—whether it’s old and worn or made of impact-resistant materials. Then they factor in the property’s surroundings, like nearby trees or structures that could amplify damage during a storm. On top of that, they integrate local weather data to predict the likelihood and severity of events like hail or high winds. By blending all this together, the model offers a tailored assessment that’s far more accurate than older methods.
In what ways do these advanced tools speed up and improve decision-making for underwriters?
These tools are game-changers for efficiency. Underwriters can access instant insights on a property’s risk without wading through piles of data or relying on slow, manual processes. The AI crunches the numbers and delivers actionable information, so decisions on whether to underwrite a policy or how to price it can be made much faster. More importantly, the decisions are grounded in precise data, which reduces guesswork and boosts confidence in the outcomes, especially in volatile markets.
How do features like mitigation-aware scoring change the game for both insurers and property owners?
Mitigation-aware scoring is a fantastic innovation because it recognizes and rewards proactive steps taken by property owners. For instance, if someone replaces an old roof with a sturdier material, the model adjusts the risk score to reflect that lower vulnerability. For insurers, this means more accurate pricing that accounts for real improvements. For property owners, it offers transparency—they can see how their actions directly impact their risk profile and potentially their premiums. It’s a win-win that encourages risk reduction and builds trust.
Could you share some insights on the regions where these storm-risk models are making the biggest impact right now?
Right now, these models are fully operational in areas most prone to severe convective storms, like the Great Plains, Midwest, and parts of the southern U.S. These are regions where hail and wind events can cause massive damage, so having precise tools to assess risk is critical. Insurers in these areas are seeing the value of property-level insights, as it helps them manage exposure in places where traditional models often fell short. The reception has been strong, with many carriers adopting these tools for both rating and underwriting.
What unique aspects of storm vulnerability do these AI models uncover that older approaches might have missed?
Older models often treated large geographic areas as having uniform risk, which ignored the nuances of individual properties. AI models reveal differences that matter—like how two houses on the same street can have vastly different vulnerabilities based on their construction or maintenance history. They also account for hyper-local weather patterns that can influence storm severity. This granular understanding helps insurers spot risks that would’ve been overlooked, allowing for more tailored and fair pricing.
What is your forecast for the future of AI in storm-risk management and its broader impact on the insurance industry?
I see AI becoming the backbone of storm-risk management in the coming years. As weather events grow more unpredictable, the demand for precise, data-driven tools will only increase. These models will likely evolve to incorporate even more variables, like real-time climate shifts or community-level mitigation efforts. For the broader insurance industry, this means a shift toward hyper-personalized policies and pricing, stronger resilience in high-risk areas, and a deeper focus on prevention. It’s an exciting time, as technology is not just reacting to risks but helping us get ahead of them.
