In short

• Technologies like artificial intelligence, machine learning and drones are improving the accuracy of bushfire risk modelling.
• This is helping reinsurers better understand and price their risks.
• But experts warn modelling alone won’t mitigate the risks. Others need to act on the data to develop better building and planning policies and codes, control fuel loads and tackle climate change.

Insurers can’t predict the next climate crisis, but we are getting close.’ That’s a tagline from Kettle Re, a San Francisco-based start-up reinsurer that uses artificial intelligence and machine learning (ML) to predict the likelihood of a wildfire in any given area. 

Kettle Re’s catastrophe (cat) modelling software ingests data from 47 different sources, including NASA satellites, weather satellites and laser-based lidar mapping sensors.

Circumstances that make fires more likely, such as dry undergrowth, high winds, little rainfall and hot temperatures, are also picked up. For every analysis, models run 42 million simulations using a statistical approach called swarm neural networks. 

By providing what it believes is a more accurate view of the fire risk, Kettle Re hopes to make the risk more understandable and more insurable.

So far, the expected loss ratio for 2020 wildfires for most major primary carriers that underwrite for wildfire damage is well above an unsustainable 100 per cent. However, Kettle Re says a simple and direct application of its risk estimate of wildfire probabilities improves pricing and reduces the loss ratio by significantly more than half. 

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