Commercial Health Insurance Demand And Forecast Analysis Based On BP Neural Network Model

In 2020,the China Banking and Insurance Regulatory Commission jointly issued a document "Opinions on Promoting the Development of Commercial Insurance in the Field of Social Services"(hereinafter referred to as the "Opinions"),which encourages insurance institutions to adapt to consumer needs and provide and improve comprehensive health insurance products and services.While providing policy support,the state has also put forward higher requirements for the development of commercial health insurance.The "Opinions" stated that it will strive to exceed 2 trillion yuan in the commercial health insurance market by 2025.Since the 13 th Five-Year Plan,my country’s commercial health insurance has achieved the greatest development since the reform and opening up.The premium income of commercial health insurance has increased from 404.2 billion in 2016 to817.3 billion in 2020,but it is far from the 2 trillion target required by the Opinions.There is still a long way to go.Maintaining the high-quality and sustainable development of my country’s commercial health insurance is the key to achieving the market size target,and it also indicates that my country’s commercial health insurance will enter a new stage in the next five years.It is based on this background that this article starts from the micro-individuals who make commercial health insurance needs,analyzes the influencing factors of commercial health insurance demand at the micro level,and establishes a commercial health insurance demand behavior prediction model and commercial health insurance demand degree prediction model based on the BP neural network model.Introducing machine learning into the commercial health insurance sales process,abandoning traditional extensive marketing methods,accurately positioning customer needs,and promoting insurance product design closer to consumer needs,thereby gradually increasing consumers’ insurance trust and promoting commercial health insurance high-quality sustainable development will escort the realization of the 2 trillion commercial health insurance market scale goal by 2025.The first chapter elaborates the background and significance of the topic,combing and commenting on the commercial health insurance demand and the relevant Chinese and foreign literature based on the BP neural network prediction model.The second chapter firstly defines the related concepts of commercial health insurance,and then introduces the theory and model of commercial health insurance consumption.Finally,it analyzes my country’s commercial health insurance market.The third chapter is a qualitative analysis of the demand factors affecting commercial health insurance.The content includes four aspects: consumer factors,socioeconomic factors,product supply factors,and emergent factors.Among them,consumer factors are the analysis of consumers’ personal characteristics and family factors.Socio-economic factors include indicators such as gross domestic product,interest rates,and inflation.Product supply factors include insurance agency factors and insurance product factors.The emergent factors are mainly considering the impact of the sudden new crown epidemic in 2020 on the demand for commercial health insurance.The fourth chapter is an empirical analysis of the factors affecting the demand for commercial health insurance.Based on the qualitative analysis of the factors affecting the demand for commercial health insurance in the previous chapters,the Probit and Tobit models are established.The analysis shows that gender,age,education level,income,family assets,the proportion of family children and the proportion of the family elderly population have a significant positive impact on the demand for commercial health insurance,and the degree of risk aversion and selfowned real estate have a significant impact on business health.Insurance demand has a significant negative impact.The fifth chapter is the commercial health insurance demand forecasting based on the BP neural network model.Based on the empirical analysis in the fourth chapter,this chapter establishes a commercial health insurance demand behavior prediction model and a commercial health insurance demand degree prediction model based on the BP neural network model.The results show that the two models have good prediction results.Among them,the AUC value of the commercial health insurance demand behavior prediction model is 0.8876,which is far better than the Probit model,which gives play to the advantages of the BP neural network model under unbalanced sample conditions.The MSE of the commercial health insurance demand prediction model is 0.905.It is found that the BP neural network refined by the Tobit model factor has better expressive power in the case of a small sample.Comprehensive data results confirmed the feasibility of the BP neural network model in commercial health insurance demand forecasting,and proposed a method for insurance institutions to tap customer needs,precise positioning,and precise marketing.The sixth chapter is the research conclusions and development suggestions.This chapter summarizes the results of the previous measurement model and the neural network model to draw the conclusions of this paper.At the same time,from the perspectives of relevant government departments and insurance institutions,it puts forward relevant suggestions to promote the development of commercial health insurance.The innovation of this paper is to apply the BP neural network model to the field of commercial health insurance demand,establish a commercial health insurance demand behavior prediction model and a commercial health insurance demand degree prediction model,and tentatively test the BP neural network model for commercial health The feasibility of insurance demand forecasting.At the same time,this article combines the measurement model with the neural network to give full play to the advantages of the measurement model’s strong explanatory and high prediction accuracy of the neural network model.