Climate Risk Modeling: Utilizing AI for Informed Decision-Making

Artificial intelligence (AI) is set to revolutionize climate risk underwriting and modeling, according to Bob Quane, chief underwriting officer at Beazley. In an interview with AM Best TV at RIMS Riskworld 2024 in San Diego, Quane emphasized the potential for AI to transform how the insurance industry approaches climate-related risks. Quane highlighted the need to adapt catastrophe (cat) models to address the challenges posed by escalating climate change, such as wildfires and floods.

He pointed towards a shift towards forward-looking cat models that prioritize recent trends over historical data to provide clients with tailored advice to enhance their resilience to climate risks. "We are concentrating on the most recent trends and updating our cat models for climate risk, particularly for US wind, to be forward-thinking. This is to assist our clients by offering them specialized guidance on tackling the challenges brought about by climate change," said Quane.

Quane also noted a growing awareness among companies regarding the necessity for insurance coverage against climate risks. Beazley's risk and resilience paper revealed that 30% of their clients feel unprepared to handle climate-related threats, leading to an increased demand for insurance protection covering not only natural disasters but also litigation risks related to greenwashing. Quane observed that reinsurers have adjusted their pricing, terms, and attachment points in response to heightened risk perception.

He highlighted their significant investments in research and analytics to better comprehend climate risks and their impact on portfolios, strengthening resilience across the insurance value chain. Acknowledging the pivotal role of technology, especially AI, in addressing climate risks, Quane outlined Beazley's efforts to leverage AI for operational efficiencies, underwriting, claims processing, and scenario testing, enhancing insurers' capacity to swiftly and effectively respond to climate-related changes. He also emphasized the use of machine learning algorithms to detect climate signals and validate models, ensuring more precise risk assessment and event response.

"We are very optimistic about the potential benefits that AI can provide for various emerging needs," Quane added.