Wildfire risk has become an increasing challenge for insurers, particularly in North America and Australia, with the last few years recording some of the biggest wildfire losses in history.

A lack of understanding of wildfire risk has led to some insurers withdrawing coverage and heightened political focus on the issue. And to make matters worse, insurers have been warned that climate change is likely to exacerbate wildfire risk, particularly in forested areas.

Aon wins the climate risk modelling solution of the year because it has responded to these obstacles and developed a suite of analytics and modelling solutions that help insurers to better understand, quantify, mitigate and transfer wildfire risk.

The Aon approach quantifies the likelihood that a location will be impacted by wildfires, models the potential third-party losses from an event and determines the potential that the events were caused by electric utilities. It also projects future volatility and trends in wildfire frequency caused by climate change out to the year 2100.

The results of this analysis are also core inputs in the underwriting process to help insurers make better decisions and more accurately price wildfire-related risks.

Aon's entry was praised by the judging panel "for giving a better understanding of potential tail scenarios". Another judge said it was a very strong submission that tackles an area of modelling "that has typically lagged other perils".

A spokesperson for Aon told InsuranceERM it plans to enhance its wildfire liability modelling tool over the next year with expanded geographic coverage, the ability to project risk forward in time considering climate change and by adding risk mitigation advisory for areas identified to be at most risk.

To deal with the increasing challenges from wildfires, the Aon spokesperson says: "Insurers need to understand the importance of differentiating wildfire at a high level of granularity – wildfire is a very location-sensitive peril and even within a postal code, risk can differ significantly."