Fusing multi-model climate risk assessment and insurance profitability prediction: a machine learning-based cross-country comparative analysis
DOI:
https://doi.org/10.54097/zwpzb031Keywords:
Climate Risk, Insurance Profitability, Machine Learning, LightGBM, XGBoost, Cross-Country Comparison, Entropy Weighting Method, Fuzzy Assessment.Abstract
Climate change-induced increases in the frequency and intensity of extreme weather events year after year pose a significant challenge to the profitability of the global insurance industry. Traditional risk assessment models have limitations in predicting insurance profitability due to the difficulty in coping with the nonlinearity and complexity of climate risk. To this end, this study proposes a multi-model fusion approach that combines fuzzy assessment models, entropy weighting, linear regression, and machine learning models (LightGBM & XGBoost) to assess the impact of climate risk on the profitability of the insurance industry. By analyzing cross-country empirical data from the U.S. and U.K. insurance markets, this study reveals the differences and challenges in coping with climate risk in different countries. The findings show that climate risk significantly affects the profitability of insurance companies and that machine learning models exhibit higher accuracy and reliability in risk prediction compared to traditional methods. This paper provides an empirical basis for insurers and policymakers to address the economic impacts of climate change and makes recommendations for optimizing insurance risk management.
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