Aon's Impact Forecasting team wins the award for its offerings designed to enable insurance firms to analyse the financial implications of catastrophic events and achieve a greater understanding of their risks.

The firm says it has evolved traditional catastrophe modelling with a focus on improved product development and automated accumulation control to help insurers navigate volatility, build resilience and adopt a holistic approach.

"Our key differentiator is the transparency with which we develop our models – every step is well documented and our clients have full access to model IP and underlying data," says Jakub Aska, business development manager at Aon, Impact Forecasting.

"Moreover, with our open catastrophe models we give re/insurers the possibility to incorporate their view of risk into modelled results."

Highlights in the past 12 months include the expansion of its severe convective storm (SCS) model for Europe to now include Slovakia, Hungary, Italy and Switzerland. Aon says the model now offers a comprehensive view of risk for 13 countries in Europe and incorporates the latest climate insight, as well as event sets for 19 storms. 

In addition, the firm launched new probabilistic earthquake models for Singapore, Switzerland and South Korea and enhanced its Automated Event Response (AER) service.

The main challenge of 2023 was undoubtedly the impact of the Turkey-Syria earthquakes that damaged more than 340,000 buildings and was the costliest natural catastrophe of the year.

"Leveraging Impact Forecasting's earthquake model for the region, our team provided event response, including loss estimates to support re/insurers (and others) to take the most effective action," Aska says.

On the regulatory front, Aska says its models were tested in the past 12 months with clients moving from the Solvency II standard formula to a partial internal model using Impact Forecasting's catastrophe models, including flood and windstorm.

"Going forward we believe that the focus will be (among other things) on robust cross-peril correlation of cat models for a given region – we already build our atmospheric peril models (flood, SCS, windstorm) on global climate model data," Aska says.

"This is essential for accurate holistic assessment of future climate risk, and its impacts on natural catastrophe losses across perils."