基于概率图的银行电信诈骗检测方法  

Probabilistic Graphical Model Based Approach for Bank Telecommunication Fraud Detection

作　　者：刘枭 王晓国[1] LIU Xiao;WANG Xiao-guo(Department of Computer Science and Technology,College of Electronics and Information Engineering,Tongji University,Shanghai 201800,China)

机构地区：[1]同济大学电子与信息工程学院计算机科学与技术系,上海201800

出　　处：《计算机科学》2018年第7期122-128,134,共8页Computer Science

摘　　要：近几年,经由电信网络实施的诈骗频发,给银行用户带来了巨大的经济损失。现有的银行欺诈检测方法通常先提取账户交易的RFM(Recency,Frequency,Monetary Value)特征,然后采用有监督的方法训练分类器来识别诈骗交易。但是,这类方法没有考虑交易网络的结构特征。电信诈骗具有明显的集团特性,在交易网络中会呈现出特定的结构特征,使用交易网络的结构特征有助于识别电信诈骗。针对电信诈骗的集团特性,设计相应的马尔可夫网络用于识别电信诈骗中的欺诈账户。给出了该马尔可夫网络的线性迭代优化式,并证明了其理论收敛条件。最后在模拟数据和真实数据上测试了所提方法的性能,并将其与CIA和SybilRank进行比较。实验结果表明,所提方法具有更低的假阳性和更好的抗噪性。在真实数据上,将基于账户交易特征的方法与所提方法结合,可以取得比单独使用两种方法更好的识别性能。Over the past few years,telecommunication fraud has caused enormous economic losses for bank users.Existing detection methods firstly extract statistical features,such as RFM（Recency,Frequency,Monetary Value）of user transactions,and then use supervised learning algorithms to detect fraud transactions or fraud accounts through training classifiers.However,the RFM features don＇t make use of the network structure of the transaction network.This paper designed a pairwise markov random field to capture the characteristics of the network structure in telecommunication fraud.Then,it exploited a linear loopy belief propagation algorithm to estimate the posterior probability distribution and predict the label of an account.Finally,it compared the proposed method with CIA and SybilRank on both synthetic dataset and real-world dataset.The results show that the proposed method outperforms other methods and can improve the F1-score of the RFM features based method.

关 键 词：欺诈检测 半监督学习 数据挖掘 电信诈骗 马尔可夫网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]