Study shows AI may mitigate increasing NAT CAT insured losses
AI can enhance predictions for floods, earthquakes and hurricanes for better disaster response and recovery.
Natural catastrophe insured losses reached a minimum of $20 billion in the first quarter of 2024, according to Gallagher Re. Of the $20 billion estimate, $11 billion was due to severe convective storms, and $10 billion occurred in the U.S. With a record number of storms forecasted in the U.S. in 2024, mitigating these astronomical NAT CAT losses is more imperative than ever. Researchers are investigating if artificial intelligence (AI) can help people in disaster-prone areas and their insurers reduce property damage and fatalities.
“Gen AI helps P&C insurers do the next to impossible — go through eons of data, across the world, and in a flash — and therefore predict far more accurately through pre- and post-catastrophe processes,” said Suhas Sethi, Global SVP of insurance at Genpact. “Before a catastrophe can strike, leaning on gen AI can enhance exposure management and catastrophe modeling to increase predictive capabilities for insurers. This enables insurers to choose risks and price claims more accurately, saving headaches and unexpected risks/prices when it’s crunch time.”
A mere 27% of insurers use advanced predictive modeling, according to the World Property and Casualty Insurance Report 2024. Many insurers still rely on outdated models that don’t consider the increased frequency and severity of natural disasters.
AI use in disaster risk management
Natural disasters require timely response and a coordinated effort to move people out of the affected region and mitigate damage. In a 2023 study published in Environmental Health Insights, Bari et al. poured through research on Google Scholar, PubMed and Scopus to examine the use of AI in disaster risk management and emergency health management. They found that AI can optimize recovery planning and improve resource distribution by finding the areas most affected that require immediate or long-term help for disaster resiliency.
“It [AI] can be used as an indicator for future disasters, and it may also be used for mitigation of any disaster situation as well,” wrote researchers. “AI can be used in disaster situations in four ways such as disaster mitigation, preparedness, response, and recovery.”
Bari et al. noted that AI can “enhance and predict” floods accurately when combined with global luminescence. Researchers have also predicted tsunami amplitudes by combining AI with global navigation satellite system data, and it can increase early warnings of earthquakes. Machine learning models and generative AI can forecast hurricanes and improve government and insurer response by making timely decisions and dictating proper resource allocation.
AI insurtechs improve disaster response
AI has helped insurers overcome many obstacles in recent years, from customer service to claims management. These technologies reduce the cost of claims, assess weather-related risks and enhance customer satisfaction, driving new revenue streams and mitigating losses for insurers amid uncertain times.
“[AI] can help insurers get ahead of planning before a catastrophe,” said Sethi. “[The] ability to parse through unimaginable amount of data across geographies inevitably leads to a substantial increase in predictability. If and when customers are impacted, insurers can respond far more rapidly, which is integral for customer experience and ensures they feel supported in emergency situations.”
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