Study on the Development of Property Insurance Industry Based on ARIMA and CGDAM-WRIR Models

Authors

  • Yibo Huang
  • Guoli Sun
  • Xiaoyin Huang
  • Cen Lin
  • Hang Li

DOI:

https://doi.org/10.54097/bty3m559

Keywords:

ARIMA Model, CGDAM-WRIR Model, Risk Assessment

Abstract

This study aims to address the climate change challenges faced by the property-casualty insurance industry by using the ARIMA model to assess the potential impacts of extreme weather events on the insurance industry and the CGDAM-WRIR model to analyze the regional impacts of tornadoes and the impacts of climate change on communities. With these models, we provide accurate risk management and underwriting strategy adjustment recommendations. Future research will further explore complex models, overcome limitations, integrate multi-source data, and develop real-time predictive models to help the insurance industry better manage climate change uncertainty, safeguard property owner interests, and ensure market sustainability and resilience.

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References

The property and casualty insurance industry are facing unprecedented challenges as the frequency and intensity of extreme weather events triggered by climate change increases.

The increased frequency and intensity of extreme weather events such as floods, hurricanes, and droughts have led to significant property damage that has a direct impact on insurers' ability to pay and risk management strategies.

Kumar S V, Vanajakshi L. Short-term traffic flow prediction using seasonal ARIMA model with limited input data[J]. European Transport Research Review, 2015, 7: 1-9.

Arumugam V, Natarajan V. Time Series Modeling and Forecasting Using Autoregressive Integrated Moving Average and Seasonal Autoregressive Integrated Moving Average Models[J]. Instrumentation, Mesures, M├ętrologies, 2023, 22(4).

Boyce M S, Pitt J, Northrup J M, et al. Temporal autocorrelation functions for movement rates from global positioning system radiotelemetry data[J]. Philosophical Transactions of the Royal Society B: Biological Sciences, 2010, 365 (1550): 2213-2219.

Nouri N, Devineni N, Were V, et al. Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices[J]. Scientific reports, 2021, 11(1): 1-14.

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Published

27-06-2024

Issue

Section

Articles

How to Cite

Huang, Y., Sun, G., Huang, X., Lin, C., & Li, H. (2024). Study on the Development of Property Insurance Industry Based on ARIMA and CGDAM-WRIR Models. Frontiers in Computing and Intelligent Systems, 8(3), 59-64. https://doi.org/10.54097/bty3m559