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Predictive analytics for catastrophic risk management: Leveraging telematics and IoT data in property insurance
IEEE Sr. Member, American National, Springfield, MO, USA.
Research Article
International Journal of Science and Research Archive, 2022, 05(02), 387-391.
Article DOI: 10.30574/ijsra.2022.5.2.0076
Publication history:
Received on 20 February 2022; revised on 27 March 2022; accepted on 29 March 2022
Abstract:
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.
Keywords:
Predictive analytics; Catastrophic risk management; Telematics; IoT; Property insurance; Machine learning
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
