A graph-based contrastive learning framework for medicare insurance fraud detection  

作　　者：Song XIAO Ting BAI Xiangchong CUI Bin WU Xinkai MENG Bai WANG 

机构地区：[1]School of Computer Science,Beijing University of Posts and Telecommunications,Beijing 100876,China

出　　处：《Frontiers of Computer Science》2023年第2期245-247,共3页中国计算机科学前沿（英文版）

基　　金：supported by the National Key Research and Development Program of China(No.2018YFC0831500);the National Natural Science Foundation of China(Grant No.61972047).

摘　　要：1 Introduction With the improvement of people's living standards,medical insurance has gradually moved towards universal coverage in recent years.Nevertheless,problems such asmedical insurance fraud,resource waste and drug abuse emerge successively,which cause a colossal waste of public resources.Therefore,reducing or eliminating medical insurance fraud can safeguard the medical insurance fund,which is essential for promoting economic development,improving public health,and maintaining social stability[1].The specialized challenges for medical insurance fraud detection are summarized as follows.

关 键 词：specialized INSURANCE summarized 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] F842[自动化与计算机技术—控制科学与工程]