Artificial Intelligence Changes 30 Years Process in Disaster Risk Modeling

While insurance and risk management sector faces the increased impact of climate change, artificial intelligence enters into action to solve the basic problem in this field. Since 1990s, it seems time to get rid of the changing manual map creation process.

New research shared on arXiv offers a solution called TAISE framework:

• Reuse of weather forecast models: Varolan deep learning technologies are stimulated to create consistent scenarios of extreme weather events
• ** Malaria drop**: It can produce complex scenarios with only one part of traditional methods -Uyum cost: Due to the intensiveization of climate attachments, it can quickly respond to increased pressure in the fuse and reasürans chain
• Tutarliness: Produced scenarios remain physically realistic and statistically valid

Traditional CAT risk models are based on expensive calculation and expert intervention to simulate large disaster scenarios. The rainy seasons, hurricane roads and extreme climate events are designed by hand, scaling is difficult, acceleration is impossible. TAISE recommends reverse this old process and implements artificial intelligence-supported forecast technology to risk modeling.

The study may be an important milestone for reasürans companies and independent risk analyzers. Fast scenario production, dynamic premium calculation and more flexible risk portfolio management is possible. As the risks of climate change are fastened, artificial intelligence-supported solutions become the key tools that the industry can keep up.