School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai, Shandong, China.
PLoS One. 2019 Aug 19;14(8):e0220631. doi: 10.1371/journal.pone.0220631. eCollection 2019.
Classifying bank accounts by using transaction data is encouraging in cracking down on illegal financial activities. However, few research simultaneously use heterogenous features, which are embedded in the time series data. In this paper, a two route convolution neural network TRHD-CNN model, fed with two types of heterogeneous feature matrices, is proposed for classifying the bank accounts. TRHD-CNN adopts divide and conquer strategy to extract characteristics from two types of data source independently. The strategy is proved able in mining complementary classification characteristics. We firstly transfer the original log data into a directed and dynamic transaction network. On the basis of that, two feature generation methods are devised for extracting information from local topological structure and time series transaction respectively. A DirectedWalk method is developed in this paper to learning the network vector of vertices used for embedding the neighbor relationship of bank account. The extensive experimental results, conducted on a real bank transaction dataset that contains illegal pyramid selling accounts, show the significant advantage of TRHD-CNN over the existing methods. TRHD-CNN can provide recall scores up to 5.15% higher than competing methods. In addition, the two-route architecture of TRHD-CNN is easy to extend to multi-route scenarios and other fields.
利用交易数据对银行账户进行分类有助于打击非法金融活动。然而，很少有研究同时使用异构特征，这些特征嵌入在时间序列数据中。本文提出了一种双通道卷积神经网络 TRHD-CNN 模型，该模型使用两种异构特征矩阵进行银行账户分类。TRHD-CNN 采用分而治之的策略，从两种数据源独立提取特征。该策略证明能够挖掘互补的分类特征。我们首先将原始日志数据转换为有向动态交易网络。在此基础上，设计了两种特征生成方法，分别从局部拓扑结构和时间序列交易中提取信息。本文提出了一种 DirectedWalk 方法来学习顶点的网络向量，用于嵌入银行账户的邻居关系。在包含非法传销账户的真实银行交易数据集上进行的广泛实验结果表明，TRHD-CNN 优于现有方法。TRHD-CNN 可以提供高达 5.15%的召回分数提升，优于竞争方法。此外，TRHD-CNN 的双通道架构易于扩展到多通道场景和其他领域。
