Research on insurance policy formulation based on ARIMA-KMEANS algorithm

Authors

  • Zhufangyuan Ji
  • Yukai Wang
  • Jin Su
  • Jie Wu

DOI:

https://doi.org/10.54097/491e2j22

Keywords:

K-means Algorithm, Arima Model, Extreme Weather Insurance.

Abstract

This paper is about the property insurance industry facing the challenge of extreme weather events. The research focuses on how to adapt and mitigate these challenges in order to maintain the sustainability of the insurance market and the financial security of property owners. This study focuses on the impact of extreme weather events on the property and casualty insurance industry, the risk assessment framework, and the use of open source risk assessment software and climate prediction models to analyze the impact of climate change on exposure to extreme climate events. This research fills a gap in the existing literature by developing more robust risk assessment methodologies, integrating climate change projections into insurance policies, and considering the role of socioeconomic factors in insurance decisions. A scientific disaster risk quantification framework is developed, which provides theoretical basis and methodological support for the formulation of disaster management strategies. The risk region is divided by K-means algorithm, and the Arima model is used to predict the potential future economic loss of the insurance industry.

Downloads

Download data is not yet available.

References

CHENG Qiyun, SUN Caixin, ZHANG Xiaoxing, et al. Short-term load forecasting model and method for power system based on complementation of neural network and fuzzy logic [J]. Transactions of China Electrotechnical Society, 2023, 19(10): 53-58.

FANG Fang. Research on power load forecasting based on improved BP neural network [D]. Harbin Institute of Technology, 2021.

AZNAR-SIGUAN G, BRESCH D.N. CLIMADA v1: a global weather and climate risk assessment platform [J]. Geosci. Model Develop., 2019, 12: 3085-3097.

OASIS LMF. Oasis loss modelling framework [EB/OL]. https://oasislmf.org/, 2022. Accessed: 2022-05-24.

SALMANIDOU D.M., EHARA A., HIMAZ R., HEIDARZADEH M., GUILLAS S. Impact of future tsunamis from the Java trench on household welfare: merging geophysics and economics through catastrophe modelling [J]. Int. J. Disaster Risk Reduct., 2021, 61: 102291.

KAM P.M., et al. Global warming and population change both heighten future risk of human displacement due to river floods [J]. Environ. Res. Lett., 2021, 16: 044026.

LANGE S., et al. Projecting exposure to extreme climate impact events across six event categories and three spatial scales [J]. Earth's Future, 2020.

STALHANDSKE Z., et al. Projected impact of heat on mortality and labour productivity under climate change in Switzerland [J]. Natural Hazards Earth Syst. Sci., 2022, 22: 2531–2541.

NATIONAL CENTRE FOR CLIMATE SERVICES, Zurich. NCCS: CH2018 – climate scenarios for Switzerland, technical report [EB/OL]. https://www.nccs.admin.ch/nccs/en/home/climate-change-and-impacts/swiss-climate-change-scenarios/technical-report.html, 2018. Accessed: 15-08-2022.

JACOB D., et al. EURO-CORDEX: new high-resolution climate change projections for European impact research [J]. Regional Environ. Change, 2014, 14(2): 563–578.

Downloads

Published

17-07-2024