Research On Social Medical Insurance Fraud Detection Model And Empirical Study

In recent years, social medical insurance fraud cases have occurred repeatedly, which has resulted in loss of medical healthcare insurance fund. Fraud not only hurts the interest of other insured but also poses a threat to the fairness and social functions of social medical insurance. In this background, it is necessary to do intensive research on the issue of social medical insurance fraud in order to prevent fraud. Many researchers have studied on the issue of medical insurance fraud. From the qualitative view, these researches mainly focus on the construction of legal and regulatory system. Some foreign scholars tried to prevent medical insurance fraud from the quantitative view. However, there are still relative fewer studies like this in China. Therefore, this paper gives study on the issue of social medical insurance fraud with technological means.This research mainly aims at the detection of social medical insurance fraud. Based on the summary of social medical insurance fraud forms and prevention measures, current existing fraud behaviors have been deep analyzed. Then, the features of these fraud behaviors have been concluded and the performance characteristics have been abstracted at the data level, as the basis of fraud detection. After that, a model of social medical insurance fraud detection based on BP neural network has been proposed. With reference to the data items related to fraud behavior characteristics, actual data has been extracted in order to train the neural network model and test its validity. Finally, several suggestions have been thrown out according to the results. The results prove that the model can be an effective method to detect social medical insurance fraud under certain conditions. In this study, the characteristics and patterns of social medical insurance fraud have been discussed and technological method to prevent social medical insurance fraud has been tried. In consequence, this study has practical significance and provides a new insight and a new perspective for the research concerned.