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Abstract As people's health awareness continues to grow， the demand for medical insurance is characterized by diversification. To further promote the healthy and sustainable development of commercial medical insurance， it is necessary to study the accurate prediction of commercial medical insurance payouts. Based on empirical data including individual behavioral preferences， innovative predictive machine learning models for commercial medical insurance are constructed and compared to analyze the important factors associated with the risk of medical insurance payouts. It is found that behavioral factors such as individual's attention to health information are highly correlated with claim risk， which can provide a good complement to traditional factors of claim for empirical analysis such as age， gender， education level， marital status， and region. Age is the most associated with claim risk， and the times of claims is the most associated with claim amount， which are much more influential than other factors. When someone claims insurance for five times or more， the predicted average medical claims per case presents a dispersed distribution. Claims are more likely to occur when the insured is a woman or has a lower level of education. The disease burden is characterized by regional imbalance. Accordingly， recommendations are proposed to improve the risk management of commercial medical insurance.
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Received: 24 July 2023
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2023, Vol. 62 
