The Empirical Study Of Urban And Rural Residents’ Basic Medical Insurance Fraud

From a global perspective,with the continuous development of the health insurance industry,insurance fraud has become a major obstacle to the smooth development of the health insurance industry.On the one hand,the increasing scale of social medical insurance expenses has become a heavy burden on China’s public expenditure.Among them,insurance fraud is one of the reasons for the high medical expenses;on the other hand,for commercial insurance companies,although the underwriting business is growing rapidly,it is accompanied by high loss claims.When determining the rate,insurance companies have to consider fraud and increase the rate,which ultimately harms the interests of honest policyholders.Health insurance fraud seriously disrupts market order and threatens the potential interests of the public.Therefore,it is of great theoretical and practical significance to study and reveal the inherent characteristics and laws of China’s health insurance fraud,and to improve the ability of health insurance organizations to identify fraud risks and control fraud risks.Taking the basic medical insurance for urban and rural residents as the research object,this study firstly introduces the relevant concepts,types and forms of insurance fraud,and In-depth discussion the insurance fraud theory from the causes and harms.Then,taking the medical insurance big data audit as an example,a "top-down" fraud identification audit path is proposed.For the problem of measuring the fraud risk of urban and rural residents medical insurance with the characteristics of "high frequency and low frequency" and "high frequency and high loss",the TVaR value of fraud risk loss was calculated by using the two-stage monte carlo simulation model based on bayes MCMC under the framework of loss distribution method.Where,the lognormal distribution is used to fit the "high frequency and low loss" part,and the GPD distribution is used to fit the "low frequency and high loss" part.Based on this,Taking 2006-2019 national urban and rural residents’ medical insurance fraud loss data as a sample,based on the Gibbs sampling of Bayesian MCMC for parameter estimation to solve the problem of increasing errors caused by missing data,and then through the empirical analysis calculation the urban and rural residents medical insurance fraud risk reserve amount.According to the above method,the loss risk value derived from each fraud subject is measured separately which provides a decision-making basis for scientific calculation of financing standards and fraud risk pricing and fraud risk warning.Finally,it analyzes the anti-fraud methods of medical insurance including big data,and proposes countermeasures suitable for anti-fraud in medical insurance in China by learning from the experience of medical insurance anti-fraud in the United States and other countries.