基于图卷积神经网络的医保欺诈检测算法

针对医疗保险欺诈检测当中欺诈样本不足、数据标注昂贵和传统基于欧氏空间的模型准确率低的问题,提出了一种新的基于图卷积和变分自编码的单分类医保欺诈检测模型(OCGVAE)。首先,通过病人就诊记录建立社交网络,计算病人和医生之间的权重关系,并设计了一个2层的图卷积神经网络(GCN)作为社交网络数据的输入,用以降低社交网络的数据维度;然后,设计了一个变分自编码(VAE)用以实现只存在一类欺诈样本标签的情况下的模型训练;最后,设计了一个逻辑回归(LR)模型用以判别数据类别。实验结果表明,OCGVAE模型的检测准确率达到87.26%,相较于一类对抗神经网络(OCAN)、一类高斯过程(OCGP)、一类近邻(OCNN)、一类支持向量机(OCSVM)和半监督图卷积神经网络(Semi-GCN)算法,分别高出16.1%、70.2%、31.7%、36.5%和27.6%,说明所提模型有效提高了医保欺诈筛查精度。

Medical insurance fraud detection algorithm based on graph convolutional neural network

Aiming at the problems of insufficient fraud samples, expensive data labeling and low accuracy of traditional Euclidean space model, a new One-Class medical insurance fraud detection model based on Graph convolution and Variational Auto-Encoder (OCGVAE) was proposed. Firstly, a social network was established through patient visit records, the weight relationships between the patients and the doctors were calculated, and a 2-layer Graph Convolutional neural Network (GCN) was designed as the input of the social network data to reduce the data dimension of the social network. Secondly, a Variational Auto-Encoder (VAE) was designed to implement the model training under only one-class fraud sample label. Finally, a Logistic Regression (LR) model was designed to discriminate the data category. The experimental results show that the detection accuracy of the OCGVAE model reaches 87.26%, which is 16.1%,70.2%,31.7%,36.5%,and 27.6% higher than that of One-Class Adversarial Net (OCAN), One-Class Gaussian Process (OCGP), One-Class Nearest Neighbor (OCNN), One-Class Support Vector Machine (OCSVM) and Semi-supervised GCN (Semi-GCN) algorithm, demonstrating that the proposed model effectively improves the accuracy of medical insurance fraud screening.