The insurance industry is increasingly leveraging AI-powered weather models to achieve granular, community-level forecasting. These models promise to predict events like flooding with unprecedented accuracy, enabling timely warnings and proactive risk management. Traditional weather forecasting, which relies on supercomputers and complex equations, struggles with hyperlocal predictions. AI offers a cost-effective solution by analysing vast datasets to identify patterns and make precise forecasts. This advancement allows for better adjustments to wind turbine operations, optimised traffic flow, and informed agricultural planting schedules.
Insurers are adopting AI for risk assessment, with a recent survey revealing that one in four property and casualty firms now use AI models for convective storm risk assessment and 18% for wildfire threat evaluation. AI's ability to improve claim accuracy has led to significant savings for some insurers, with one company estimating a 20% reduction in claims after implementing an AI-enabled network of rooftop sensors. As extreme weather events become more frequent, AI's role in predicting and mitigating climate-related losses is expected to grow, offering benefits to businesses, communities, and the insurance industry alike.