A new whitepaper from Lockton Re and catastrophe risk mitigation firm Green Shield Risk Solutions, advocates for the integration of disparate analytics tools used in underwriting, with those used for catastrophe modelling and broader portfolio management, as well.
Entitled Aligning Underwriting and Portfolio Analytics: The Key to Resiliency in an Ever-Changing Landscape, the paper notes that wildfires are a growing concern for portfolio managers. “As the risk of wildfires continues to intensify, portfolio managers need advanced analytics that not only identify property-specific risk but also seamlessly integrate these insights across the insurance value chain, ultimately building greater resiliency,” they write.
Matt Cohen, head of global CAT modelling for Lockton Re, also adds that “sound analytics, at all levels of the business are becoming table stakes for any wildfire portfolio manager. The key to a resilient business (is) making sure these analytics communicate with each other.”
The paper itself says the tools being used to measure wildfire risk are becoming more crucial. “It is vital that these processes don’t work in isolation from one another,” it adds.
Secondary modifiers
It goes on to advocate for the inclusion of secondary modifiers, as well – roof coverings, roof vents, decks, defensible space and skylights are all discussed – saying it is frequently these less captured and distinguishing features that can differentiate risks. It also discusses scaling these secondary modifiers across an entire portfolio or book of business using a notional portfolio to demonstrate how secondary modifiers can impact a book of business.
“Over the past 10 years, the analytics space surrounding wildfire risk has exploded with different products and toolsets to help portfolio managers find resilient risks and manage their portfolios appropriately. However, each tool is fit for a single purpose, and therefore is difficult, if not impossible to find a single solution that helps navigate all wildfire-related challenges, from risk selection, all the way downstream to portfolio analytics,” the paper states. The disconnect, they add, can directly result in a mischaracterization of the risk profile of any wildfire-exposed portfolio.
“It is possible for a location to be assigned low risk by an underwriting tool due to one set of property assumptions and then be subsequently viewed as a high risk by a catastrophe model using a different set of property assumptions. This divergence is ultimately what can cause portfolio-modelled losses to be overstated, resulting in an overspend on reinsurance,” the paper states. “The ultimate solution is to have an underwriting tool that speaks a similar language to the downstream catastrophe models.”
