Welcome to Francis Academic Press

Academic Journal of Business & Management, 2025, 7(1); doi: 10.25236/AJBM.2025.070102.

Extreme weather insurance underwriting decision model based on break-even theory and Monte Carlo algorithm

Author(s)

Yongmao Li1, Shuchang Wang2, Xiaowen Liang3, Guorui Zhao2

Corresponding Author:
Guorui Zhao
Affiliation(s)

1School of Materials Science and Engineering, Guangdong Ocean University (Yangjiang Campus), Yangjiang City, China

2School of Computer Science and Engineering, Guangdong Ocean University (Yangjiang Campus), Yangjiang City, China

3Business School, Guangdong Ocean University (Yangjiang Campus), Yangjiang City, China

Abstract

Extreme weather events such as floods, hurricanes, and wildfires are increasingly impacting the world, thereby driving reforms in the property insurance industry. This paper addresses the challenge of making insurance underwriting decisions in the context of extreme weather. To this end, the Monte Carlo algorithm was employed to optimize the risk assessment method for individual natural disasters, while the Loss-Cost Ratio (LCR) method was integrated to construct a comprehensive risk assessment model (ARA). An insurance underwriting strategy tailored to varying risk levels was developed by incorporating the break-even model. A representative sample of 722 regions worldwide was analyzed. The Monte Carlo algorithm was applied to optimize the risk index for each locality under extreme weather conditions. Likewise, using the LCR method and break-even theory, the long-term profitability of insurance companies was evaluated. The findings indicate that when the number of policies increases or the claim rate decreases, profitability remains favorable, suggesting that underwriting remains feasible despite the rising risks posed by extreme weather events. Finally, the decision model was applied to Henan Province and New York City for validation, demonstrating results that align closely with real-world data.

Keywords

Property insurance, extreme weather, risk assessment, Monte Carlo algorithm

Cite This Paper

Yongmao Li, Shuchang Wang, Xiaowen Liang, Guorui Zhao. Extreme weather insurance underwriting decision model based on break-even theory and Monte Carlo algorithm. Academic Journal of Business & Management (2025) Vol. 7, Issue 1: 13-21. https://doi.org/10.25236/AJBM.2025.070102.

References

[1] BCG Global. An insurance risk framework for climate adaptation [EB/OL]. (2023-11-29) [2024-06-03].Availablefrom:https://www.bcg.com/publications/2023/an-insurance-risk-framewor-for-climate-adaptation.

[2] Truong C, Malavasi M, Goldstein M A. Timing is (almost) everything: Real options, extreme value theory, climate adaptation, and flood risk management[J]. Journal of Environmental Management, 2024, 370: 122621. DOI: 10.1016/j.jenvman.2024.122621.

[3] Han S N. Analysis of agricultural catastrophe risk and determination of insurance premium rate: A case study of daily precipitation in corn producing areas of Jilin Province [D]. Beijing: Chinese Academy of Agricultural Sciences, 2008. DOI: 10.7666/d.Y1422089.

[4] Wen K, Li Z, Zhu J. Research on profit maximization of insurance companies under extreme weather events based on optimized model[J]. Highlights of Science, Engineering and Technology, 2024, 107: 469-476.

[5] Deng H, Huang X. Evaluation of new insurance models for extreme weather[J]. Highlights of Business, Economics and Management, 2024, 38: 95-100. DOI: 10.54097/5zdfbb51.

[6] 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.

[7] Chen Dihong, Dai Zhiliang, Wang Min. Analysis on underwriting decision of property insurance companies from the perspective of capital allocation[J]. Journal of Economic Mathematics, 2009, 26(2): 35-40. (in Chinese) DOI: 10.3969/j.issn.1007-1660.2009.02.006.

[8] Zhou Yunzhi, Huang Xiubi. Research and Case Study on Insurance Underwriting Decision Making Based on LSTM, SVM and Random Forest Modeling[J]. Transactions on Computer Science and Intelligent Systems Research, 2024, 5(AIDML 2024): 1226 - 1233.

[9] Ji Z, Wang Y, Su J, Wu J. Research on policy formulation based on ARMI-Kmeans algorithm[J]. Highlights of Business, Economics and Management, 2024, 36: 52-58. DOI: 10.54097/491e2j22.

[10] Guo M, Zhao S. Extreme Weather Risk Assessment and Underwriting Decision Making based on PCA-AHP algorithm and ARIMA modeling[J]. Journal of Computing and Electronic Information Management, 2024, 13(2): 17-21. DOI: 10.54097/455nfdzh.

[11] Nie Gaozhong, Gao Jianguo, Ma Zongjin, et al. Risk assessment of seismic hazards in China in the next 10-15 years[J]. Journal of Natural Hazards, 2002(01): 68-73.

[12] Xu Wei, Wang Jingai, Shi Peijun, et al. Risk assessment of earthquake disasters in Chinese cities[J]. Journal of Natural Disasters, 2004(01): 9-15.

[13] Jaquet O, Löw S, Martinelli B, Dietrich V, Gilby D. Estimation of volcanic hazards based on Cox stochastic processes [J]. Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy, 2000, 25(6-7): 571-579.

[14] Leng Huiqing. Research on Forest Fire Risk Assessment and Insurance Rate Setting in China [D]. Tsinghua University, 2011.

[15] Gambonini A P, Hadrich J C, Roberts A R. Estimation and analysis of cow-level cumulative lifetime break-even on financial resiliency[J]. Journal of Dairy Science, 2022, 105(5): 4653-4668. DOI: 10.3168/jds.2021-20644.