关键词: 生成对抗网络, 不平衡类, 自编码特征表示, 医保欺诈检测, 集成学习

Abstract:

Medical insurance fraud detection has urgent practical significance. The current work is mainly concentrated on machine learning methods and confronted with two important issues：（1）The problem of imbalanced data is prominent and the proportion of fraud data among medical insurance data is extremely small, which affects the identification effect; （2）The selection and construction of data features depend on domain business knowledge, and it is difficult to guarantee the validity of features. Aiming at these problems, this paper proposes a fraud detection method for imbalanced healthcare data—WKAG：The Wasserstein Generative Adversarial Network-Kernel Density Estimation（WGAN-KDE） method is used to improve the imbalance of medical insurance data. The Auto-Encoder is used to extract the deep hidden features of data. The Gradient Boosted Decision Tree（GBDT） is used to detect medical insurance fraud. The validity of the method has been verified on multiplepublic data sets as well as the real medical insurance business data set. The results show that WKAG can be used as an effective detection method for medical insurance fraud.

Key words: generative adversarial network, imbalance dataset, auto-encoder feature representation, medical insurance fraud detection, ensemble learning