Impact Forecasting, Aon's catastrophe model development centre of excellence, wins catastrophe risk modelling solution of the year for its customisable models, real-time insights, and broad peril coverage.
The judges commended the usefulness of the platform as a tool to quantify the effects of climate change for diverse emission scenarios with complex adaption models. According to one judge, Impact Forecasting enables both primary insurers and reinsurers to talk about the effect of climate change on their portfolios.
Another factor that scored highly with the judges is the platform successfully incorporates climate science into business models, as it is supported by academic inputs and functionality to include adaption measures.
Aon's Impact Forecasting continues to expand its global reach and depth of analysis. The platform now offers over 135 probabilistic and scenario models, covering 12 perils across almost 90 territories.
Overall, the models enable users to quantify the effects of climate change for different emission scenarios as defined by the Intergovernmental Panel for Climate Change future climate projection perspectives.
Impact Forecasting also wins the award for considering the "totality of hazard risk", which combines aspects of hazard, such as frequency, behaviour, location and readiness to understand and quantify the catastrophe risks of tomorrow.
Himavant Mulugu, senior director at Impact Forecasting, tells InsuranceERM developing catastrophe models for the Asia Pacific region is particularly challenging due to its exposure to a wide range of natural hazards, including typhoons, earthquakes, floods, and tsunamis. And despite a significant protection gap, insurance penetration remains low especially in emerging markets, which affects the availability and peril coverage of catastrophe models.
Commenting on Impact Forecasting's strengths, Mulugu cites "the transparency and explainability of our models, which run on the ELEMENTS platform and in Oasis-based environments". Mulugu adds this allows insurers to seamlessly integrate these models into their own catastrophe modelling frameworks.
