Deep feature representation for anti-fraud system

Abstract Online payment is becoming popular due to the development of e-commerce. So payment safety is more important. Since there is a fraudulent situation, an anti-fraud system is indispensable. GMM was leveraged in many anti-fraud applications, but it only takes positive sample into account. Convolutional neural network is a strong strategy for learning deep representation of samples. So in this paper, we propose a CNNs architecture to deal with this problem. And the distance metric method can effectively identify whether candidates are the same person. Experimental results show the effectiveness of our method.