By Gautam Naik, Bloomberg
Insurers are betting on a suite of new AI-driven techniques to better predict surging losses from climate-driven weather catastrophes ranging from unprecedented wildfires to hurricanes and floods.
Less than three months into the year, natural disasters are already causing major economic disruption around the world, including recent fires across Los Angeles, an economy-denting cyclone in Australia, floods in Jakarta and a giant storm that left dozens dead in the US. According to a recent report by broker Gallagher Re, annual insured catastrophe losses of $150 billion have become “the new normal.”
While traditional models apply complex physics and elaborate computer simulations to estimate the probability of future losses, the results often can fall short. Flood models designed to measure the same risk have yielded conflicting outcomes. Wildfire models can struggle to accommodate the dizzying number of variables in play—everything from the role of human intervention to the possible flight path of a wind-borne ember.
Some investors in catastrophe bonds expressly shun securities exposed to such perils because they don’t trust the modeling. Every model “is an imperfect representation of a very complex phenomena,” said Firas Saleh, director of product management at Moody’s Corp.
That’s where artificial intelligence comes in. Its proponents contend it can provide a more accurate estimate of property-level risk for weather calamities.
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