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A Record-Breaking Year
2023 marks the worst on record for severe convective storms, with an estimated $50 billion in insured losses. This is easily one of the biggest drivers of property and casualty losses, constituting 60 percent of global insured losses. Hail and wind have significantly contributed to homeowners and commercial property claims, with a 50 percent increase in severity over the past five years.
But severe convective storms are not new, so the question is, what is causing this surge in severe convective storm losses, and can it be solely attributed to climate change? Research shows that exposure changes, rather than climate change, are the primary drivers. Factors like population growth in high-hazard areas, property inflation, and GDP growth all substantially increase industry losses. But this is good news for insurers. What this means is severe convective storms are not an out-of-control problem. It is an exposure problem that can be solved with the right risk management solution.
Source: Aon
The Advantage of Property-Specific Models
Stochastic models have set a high standard for portfolio-level risk analysis, especially for reinsurance. They have been and will continue to be a mainstay for analyzing tail risk. These tools provide the powerful macro lens needed to understand portfolio-level risk.
AI models offer the ability to deepen our understanding of the interaction effects of the science discussed by IBHS and our evolving climate. Apply it to rating and underwriting decisions for each property, allowing us to operationalize for the portfolio we want to build going forward. They are complimentary tools that, in 2023, are necessary to get the job done when it comes to navigating hail risk.
ZestyAI’s has three innovative AI-powered climate risk models to help mitigate severe convective storm losses. These are Z-HAIL, Z-WIND, and Z-STORM, which combine the elements of both Z-HAIL and Z-WIND into a single model. Each of these models uses AI and machine learning to create a digital twin of every property in the United States, considering over 200 billion data points, such as roof quality and materials. The models provide property-specific insights for accurate risk assessment, focusing on both claim frequency and severity.
Case Study: Navigating Severe Storm Risks with ZestyAI's Z-STORM
The effectiveness of Z-STORM can be seen in the following case study.
In a Texas-based portfolio review, ZestyAI’s Z-STORM model demonstrated that there are good risks, even within high-risk areas. Using the data from Z-STORM, this ZestyAI customer was able to make strategic decisions based on property-specific information and identify the profitable and problem properties within a specific zip code. Because ZestyAI’s models offer granular risk assessment, insurers can identify low-risk properties for premium capture, manage moderate risks strategically, and take decisive actions on high-risk properties to protect their portfolios from losses.
By leveraging property-specific information and cutting-edge technology, insurers can make informed decisions to mitigate risks effectively. As the industry faces unprecedented challenges, ZestyAI demonstrates that the impact of severe convective storms can be navigated successfully with better risk management.
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