Over the better part of the past decade, the combination of climatological and anthropogenic factors has resulted in a significant increase in wildfire activity across the western United States. For portfolio managers, this has caused many headaches across the insurance risk value chain, from underwriting resilient properties all the way through to obtaining attractive reinsurance pricing. The key to success for any wildfire-exposed portfolio revolves around sound analytics at all levels of the business, particularly as part of an underwriting strategy aimed at finding resilient risks. In areas susceptible to significant wildfire activity, the identification of these “diamonds in the rough” often comes down to the physical characteristics of the property and any risk mitigation practices the property owner may be implementing. While primary risk characteristics, such as the construction and occupancy of the property, are regularly captured data points, it is the frequently less-captured distinguishing features that can differentiate the bad risks from the attractive ones. In the world of catastrophe risk management, these are referred to as secondary risk characteristics, or secondary modifiers, and are a key driver of risk differentiation at the point of sale.
In this context, Green Shield Risk Solutions and Lockton Re collaborated to conduct an in-depth case study aimed at exploring the critical importance of having a comprehensive and communicative analytical toolset in managing wildfire risks. The study highlighted a crucial finding: portfolio managers can realize substantial improvements in their modeled losses by incorporating secondary modifiers into their portfolio data analytics. Specifically, using a representative notional dataset based on real wildfire exposed risks in California and Colorado, the study showed that by integrating these secondary risk characteristics into the modeling process, portfolio managers could reduce their portfolio modeled losses by 20% or more. This reduction in modeled losses is far from a negligible impact; it has significant implications on portfolio profitability, leading directly to improved pricing and coverage from reinsurance markets, and ultimately achieving greater resiliency. The impact of producing an accurate risk profile at the portfolio level demonstrates the value of communicative analytics in managing the increasingly complex and volatile wildfire risk landscape facing the western United States.
In conclusion, the findings from this case study reinforce the importance of leveraging sophisticated analytical tools that communicate seamlessly with one another. For portfolio managers dealing with wildfire-exposed assets, the incorporation of secondary modifiers into their loss model views is not just a theoretical exercise – it is a practical strategy that can lead to meaningful reductions in loss estimates, more accurate reinsurance pricing, and a more resilient portfolio. As the wildfire risk landscape continues to evolve, adopting this type of analytical approach will be essential for achieving sustained resilience in this challenging and high-risk sector.
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