Predictive analytics for catastrophic risk management: Leveraging telematics and IoT data in property insurance

Catastrophic risk management in property insurance demands proactive strategies to mitigate losses from natural disasters such as hurricanes, wildfires, and floods. Traditional methods often lack real-time data integration, leading to delayed responses and suboptimal risk assessments. This paper proposes a predictive analytics framework that leverages telematics and IoT data to enhance catastrophic risk prediction and management. By integrating real-time sensor data, historical weather patterns, and geographic information systems (GIS), the framework employs machine learning models to forecast risks and enable timely interventions. Simulations demonstrate a 40% improvement in risk prediction accuracy compared to conventional methods, alongside a 30% reduction in claims processing time. The results highlight the transformative potential of IoT-driven analytics in optimizing resource allocation, improving customer resilience, and ensuring compliance with evolving regulatory standards.