Institute of Service Engineering, Hangzhou Normal University, Hangzhou 311121, China; College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China.
College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China.
Neural Netw. 2018 Jun;102:78-86. doi: 10.1016/j.neunet.2018.02.015. Epub 2018 Mar 5.
Recently telecom fraud has become a serious problem especially in developing countries such as China. At present, it can be very difficult to coordinate different agencies to prevent fraud completely. In this paper we study how to detect large transfers that are sent from victims deceived by fraudsters at the receiving bank. We propose a new generative adversarial network (GAN) based model to calculate for each large transfer a probability that it is fraudulent, such that the bank can take appropriate measures to prevent potential fraudsters to take the money if the probability exceeds a threshold. The inference model uses a deep denoising autoencoder to effectively learn the complex probabilistic relationship among the input features, and employs adversarial training that establishes a minimax game between a discriminator and a generator to accurately discriminate between positive samples and negative samples in the data distribution. We show that the model outperforms a set of well-known classification methods in experiments, and its applications in two commercial banks have reduced losses of about 10 million RMB in twelve weeks and significantly improved their business reputation.
最近，电信诈骗在中国等发展中国家已成为一个严重的问题。目前，协调不同机构来彻底防范诈骗可能非常困难。在本文中，我们研究如何检测从欺诈者处受骗的受害者在收款行发出的大额转账。我们提出了一种新的基于生成式对抗网络（GAN）的模型，以便为每笔大额转账计算出一个欺诈概率，以便银行在概率超过阈值时可以采取适当措施防止潜在的欺诈者取走钱款。推理模型使用深度去噪自编码器来有效学习输入特征之间的复杂概率关系，并采用对抗训练，在判别器和生成器之间建立最小最大博弈，以准确区分数据分布中的正样本和负样本。我们的实验表明，该模型的性能优于一组著名的分类方法，并且它在两家商业银行的应用在十二周内减少了约 1000 万元的损失，并显著提高了它们的商业声誉。
