Research On Customized Pricing Of Private Medical Insurance Under The Background Of Big Data

In recent years,with the continuous increase of the incidence of diseases and the emergence of new diseases,medical expenses continue to rise,and the phenomenon of poverty caused by diseases in ordinary families is increasing."Expensive medical treatment" has become a common problem faced by ordinary people.This has brought tremendous opportunities for the development of health insurance.Judging from the current development of China,China has formed a medical security system that includes various forms of protection.Among them,basic medical insurance is guided by “low level and wide coverage”,which provides a huge space for commercial medical insurance.Judging from the history and current situation of China's commercial health insurance development,it is not difficult to find that at present,there are still many problems in the business health insurance management process that need to be solved urgently.For example,for cost-compensated health insurance products,the risk is not accurately reflected by differential pricing.With the continuous expansion of the insurance company's independent pricing,the China Insurance Regulatory Commission has also strengthened the supervision of the rate.Therefore,a scientific medical insurance product pricing mechanism is established to protect the insurance company's own operating efficiency,and the risk is accurately reflected through further differential pricing.Not only can it expand the scope of protection,but it can also attract more people to insure and reduce adverse selection.At the same time,the insurance industry is generating a lot of data all the time.From the perspective of information volume,the big data era of the insurance industry has arrived in advance.Therefore,the existing commercial health insurance pricing method is more and more difficult to deal with these ever-increasing and diverse massive data,and it is increasingly inconsistent with the current new demands and is urgently required to change.At present,with the advancement of science and technology,big data has entered all aspects of social production and people's lives,and governments around the world have raised big data to the height of national strategy.At present,in the insurance field,big data brings great opportunities for all aspects of the insurance industry,but at the same time it puts forward higher requirements on the data foundation and personnel training in the business process.At present,China's insurance industry's research in the field of big data is still in its infancy,and it faces challenges in terms of big data foundation and big data talents.Pricing as an important part of insurance companies,exploring the combination of big data and commercial medical insurance pricing has become a hot and difficult point of research.From the existing literature,there are many studies on the application of big data,big data technology,medical big data and big data in insurance,but there are few studies on the integration of big data and commercial medical insurance pricing.Rarely,there are studies using quantitative analysis.Therefore,this paper attempts to explore the impact of big data on commercial medical insurance pricing based on existing literature,analyze whether big data can be combined with commercial medical insurance pricing,whether it is necessary to combine with commercial insurance pricing,and finally try to find new ones.In the context of the era,the specific method of combining big data with commercial medical insurance pricing.And use the data obtained to empirically explain the necessity of differential pricing through empirical analysis,and the traditional methods are difficult to achieve personalized pricing,and then use big data technology to predict the individual disease incidence rate and individual treatment costs,and then Explain the feasibility of using big data technology to achieve personalized pricing,and also show that as the dimension increases,the reliability of prediction can be improved.Finally,the comparison between traditional pricing and random forest pricing results shows that random forest pricing can measure risk more accurately,and can achieve personalized and precise pricing.In addition,commercial medical insurance pricing based on medical big data processing is also a solution for big data and commercial medical insurance pricing,and medical big data is the premise and basis of pricing,so explore data by clearly defining the concept and characteristics of medical big data.The preparation and processing methods provide some reference for future research based on medical big data.The thesis uses literature review methods,empirical analysis methods and interdisciplinary methods to study the basis of accurate pricing,methods and processing of medical big data.The main structure of the paper is as follows: The structure of the paper is mainly divided into six parts:The first part is an introduction.Firstly,the background and significance of this paper are expounded.Then,through the combing of related literatures,the concepts of big data,big data technology,medical big data and “customized pricing” are clearly defined in this paper,and the pricing methods of commercial medical insurance and the application of big data in insurance are understood.The current research situation paves the way for the next study.Then the purpose of this paper and the main research contents are explained.Finally,the research ideas,methods and innovations and shortcomings of this paper are introduced.The second part is an overview of traditional commercial medical insurance pricing.Firstly,the principle of traditional commercial medical insurance pricing,that is,the law of large numbers and the principle of balance of payments is introduced.Then it analyzes the traditional commercial medical insurance pricing methods and existing problems.The third part is the feasibility analysis of the customized pricing of commercial medical insurance under the background of big data.Firstly,it analyzes the impact of big data on traditional actuarial pricing,including the effects of actuarial principles and commercial medical insurance pricing,and then analyzes the combination of big data and commercial medical insurance pricing.Finally,it explores the specific methods of customized pricing,paving the way for later empirical research.The fourth part is the preparation and integration of medical big data.Medical big data is the basis and premise of pricing,and commercial medical insurance pricing based on medical big data processing is also a combination of improving pricing.Therefore,this part first analyzes the data requirements,collection methods and pre-processing methods of the customized pricing of commercial medical insurance.Then it explains the current situation and problems of data integration and proposes solutions.The fifth part is the empirical analysis of the customized pricing of commercial medical insurance under the background of big data.Firstly,the data source,preprocessing and research objects are described.Then,the traditional hospitalization insurance-based hospitalization rate and hospitalization expenses are measured by the traditional loss-based pricing method.A simple product is taken as an example,indicating the necessity of differential pricing and traditional methods are difficult to achieve customized pricing,and then use random forest methods to measure the incidence of individual diseases and individual treatment costs,indicating that the use of big data technology can achieve customized pricing.Finally,taking a simple product as an example,it shows that big data technology can make more accurate risk measurement for individuals and achieve more customized pricing.The sixth part is the conclusion suggestion and research prospects.Mainly combined with the analysis of this article,provide advice and countermeasures for insurance companies.At the same time,the future research direction is proposed.The innovation of this paper is: based on a deep understanding of "commercial medical insurance pricing" and "big data",it analyzes the trend of commercial medical insurance pricing in the context of big data will be personalized and customized pricing,and " customized pricing" is a more precise and appropriate defined.Secondly,the paper analyzes the impact of big data on commercial medical insurance pricing,and explains the shortcomings of commercial medical insurance traditional pricing through theory and empirical analysis,and explores the method of customized pricing of commercial medical insurance under the background of big data,and through empirical explanation.The feasibility of using big data technology to achieve customized pricing.(1)The quantitative research on the pricing of commercial medical insurance using big data technology is weak.At present,the scholars' integration of big data technology and commercial medical insurance pricing stays at the conceptual level.No empirical research has been found in the literature currently referenced.This paper selects the foothold of using big data technology to achieve customized pricing,applies big data technology to the customized pricing of commercial medical insurance,and puts forward the idea of using big data technology to price commercial medical insurance,and conducts empirical research.(2)This paper comprehensively analyzes the traditional medical insurance pricing method.Combined with the current big data technology,this paper proposes a random forest method to calculate the incidence rate of each person's disease for different diseases and the treatment cost of individual diseases.Thereby achieving customized pricing for individuals.And through empirical analysis,the feasibility of the method is explained.(3)Combining the relevant definitions of “customized pricing” in the existing literature,its concept and connotation are clearly defined,which provides reference for the research of customized pricing in the future,and has certain innovative significance.The shortcomings of this paper are: due to the difficulty of obtaining data and limited research ability,the empirical research of the paper is not detailed and deep,and the author will continue to explore.