Prediction of Risk Evaluation Prediction Model for the Insurance Industry under Extreme Weather

Authors

  • Shuaichen Ge

DOI:

https://doi.org/10.54097/emhpyn53

Keywords:

Risk Evaluation, Prediction Model, Insurance Industry, Extreme Weather.

Abstract

The insurance industry is one of the indispensable economic sectors of modern society. In recent years, the frequent occurrence and the unpredictable nature of extreme weather have brought economic losses to society reflected in the insurance industry, which prompts people to seek strategies to realize the sustainability of the property insurance industry. This paper addresses the problem by developing a Risk Evaluation Model (REM) for the insurance industry under extreme weather conditions. The model incorporates the Analytic Hierarchy Process (AHP), Entropy Weight Method (EWM), and game theory combination weights to create a comprehensive secondary assessment system. Through fuzzy clustering analysis, risk levels are classified into four categories. The model was validated by applying it to two different regions globally—Florida, USA, and Karachi, Pakistan—determining their insurance risk levels. The results demonstrate that the REM model significantly enhances the accuracy and reliability of risk evaluation, helping to mitigate financial losses and ensure sustainable operations within the insurance industry. The study's significance lies in its contribution to robust risk management and underwriting decisions, with potential applications in urban planning and real estate development, highlighting the model's broader applicability and value.

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References

[1] Lin Y H, Wang L J, Shi X Y, et al. Evolution of research on climate risk insurance: A bibliometric analysis from 1975 to 2022[J]. Advances in Climate Change Research, 2023, 14(4): 592-604.

[2] Newman R, Noy I. The global costs of extreme weather that are attributable to climate change[J]. Nature Communications, 2023, 14(1): 6103.

[3] Lyubchich V, Newlands N K, Ghahari A, et al. Insurance risk assessment in the face of climate change: Integrating data science and statistics[J]. Wiley Interdisciplinary Reviews: Computational Statistics, 2019, 11(4): e1462.

[4] Hudson P, De Ruig L T, De Ruiter M C, et al. An assessment of best practices of extreme weather insurance and directions for a more resilient society[J]. Environmental Hazards, 2020, 19(3): 301-321.

[5] Cremades R, Surminski S, Máñez Costa M, et al. Using the adaptive cycle in climate-risk insurance to design resilient futures[J]. Nature Climate Change, 2018, 8(1): 4-7.

[6] Collier S J, Elliott R, Lehtonen T K. Climate change and insurance[J]. Economy and Society, 2021, 50(2): 158-172.

[7] Gupta A, Venkataraman S. Insurance and climate change[J]. Current Opinion in Environmental Sustainability, 2024, 67: 101412.

[8] Agrawal A, Gans J S, Goldfarb A. Prediction machines, insurance, and protection: An alternative perspective on AI’s role in production[J]. Journal of the Japanese and International Economies, 2024, 72: 101307.

[9] Behari M. The Future of Foundation Models in Predicting Climate-Related Risks in the Insurance Sector: A Case Study in Louisiana[C]//Fragile Earth: Generative and Foundational Models for Sustainable Development. 2024.

[10] Liu H, Meng J, Li W, et al. Extreme Weather Risk Assessment and Insurance Decision Making Based on ARIMA Model and Visual Analysis[J]. Highlights in Science, Engineering and Technology, 2024, 101: 305-313.

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Published

25-11-2024

How to Cite

Ge, S. (2024). Prediction of Risk Evaluation Prediction Model for the Insurance Industry under Extreme Weather. Highlights in Business, Economics and Management, 44, 30-37. https://doi.org/10.54097/emhpyn53