Insurers are fundamentally transforming catastrophe (CAT) modeling approaches with artificial intelligence (AI), machine learning (ML) and advanced climate data analytics, as traditional models—without significant enhancements—no longer capture the evolving nature of climate risks.
Executive Summary
As climate risks intensify and traditional CAT models fall short, insurers must embrace AI and machine learning to stay ahead. AI enables real-time, data-rich modeling that enhances risk assessment and portfolio optimization. This article outlines a practical framework for integrating AI into CAT modeling—covering data preparation, enrichment and analysis.AI-powered tools enable insurers to handle complex, data-led tasks more efficiently, ultimately improving their ability to perceive and manage risk.
This article presents a practical framework for modernizing CAT models, leveraging AI-based solutions, cutting-edge technology and data sources, while accounting for emerging risk considerations.
A Paradigm Shift in Risk Modeling
AI in CAT modeling is not just an enhancement to traditional methodologies; it represents a paradigm shift, redefining how insurers and reinsurers can understand, prepare for and mitigate catastrophic risks. By transcending the limitations of legacy systems, AI is unlocking opportunities to refine risk assessments, optimize resource allocation and reshape the overall CAT modeling process.
