An Empirical Study on the Factors Affecting Life Insurance Charge Premiums

Predicting an individual's annual insurance premium can help insurance companies evaluate risk and pricing more accurately, thus calculating insurance costs more effectively. With this valuable information, analysts can have a better understanding of consumers' spending habits and the impact on their premium payments. In this paper, the linear regression model is used to construct the regression equation between the influencing factors to find the correlation between each factor and the insurance charge. Moreover, through factor analysis, the most important factor named ‘Physique health factors’ affecting insurance charges are found. Finally, through comparative analysis, the most critical influencing factors for large insurance charges and small insurance charges are found, which are the presence or absence of a bad appetite factor and Physical health factors. Understanding the patterns in this analysis is crucial for insurance companies as they need to ensure that their insurance fees cover potential claims and other expenses while also staying competitive and attracting more customers. Additionally, predicting an individual's annual insurance premium can help individuals understand their insurance needs and budget, making more informed decisions.