论文标题
混乱的变量自动编码器基于一个班级分类器用于保险欺诈检测
Chaotic Variational Auto Encoder based One Class Classifier for Insurance Fraud Detection
论文作者
论文摘要
最近，由于巨大的财务和声誉损失的欺诈行为以及欺诈检测技术的惊人成功，保险欺诈检测具有巨大的意义。保险主要分为两类：（i）生活和（ii）非生命。反过来，非寿险保险包括健康保险和汽车保险。在任何一个类别中，欺诈检测技术都应以它们捕获尽可能多的欺诈交易的方式进行设计。由于欺诈性交易的稀有性，在本文中，我们提出了一个混乱的变异自动编码器（C-VAE在真实交易上执行一级分类（OCC）。在这里，我们采用了逻辑混乱的地图，以在c-vae的空间中产生随机噪声，从而在c-vae中产生了c-vae的有效性。c-vae的有效性是对保险保险的范围。 （VAE）作为基线，C-VAE在两个数据集中的表现都超过了VAE。
Of late, insurance fraud detection has assumed immense significance owing to the huge financial & reputational losses fraud entails and the phenomenal success of the fraud detection techniques. Insurance is majorly divided into two categories: (i) Life and (ii) Non-life. Non-life insurance in turn includes health insurance and auto insurance among other things. In either of the categories, the fraud detection techniques should be designed in such a way that they capture as many fraudulent transactions as possible. Owing to the rarity of fraudulent transactions, in this paper, we propose a chaotic variational autoencoder (C-VAE to perform one-class classification (OCC) on genuine transactions. Here, we employed the logistic chaotic map to generate random noise in the latent space. The effectiveness of C-VAE is demonstrated on the health insurance fraud and auto insurance datasets. We considered vanilla Variational Auto Encoder (VAE) as the baseline. It is observed that C-VAE outperformed VAE in both datasets. C-VAE achieved a classification rate of 77.9% and 87.25% in health and automobile insurance datasets respectively. Further, the t-test conducted at 1% level of significance and 18 degrees of freedom infers that C-VAE is statistically significant than the VAE.
