ReMEMBeR: Ranking Metric Embedding-Based Multicontextual Behavior Profiling for Online Banking Fraud Detection
ReMEMBeR: Ranking Metric Embedding-Based Multicontextual Behavior Profiling for Online Banking Fraud Detection
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DOI：
10.1109/tcss.2021.3052950
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发表时间：
2021-06-01
影响因子：
5
通讯作者：
Wang, Cheng
中科院分区：
文献类型：
--
作者：
Cui, Jipeng;Yan, Chungang;Wang, Cheng
异常检测依赖于个人的行为特征，并通过检测任何偏离正常的情况来工作。然而，当用于网上银行欺诈检测时，它主要受到三个缺点的影响。首先，对于个人来说，历史行为数据往往过于有限，无法描述他/她的行为模式。其次，由于事务数据的异构性，缺乏对不同类型属性值的统一处理，这成为模型开发和进一步使用的潜在障碍。第三，交易数据高度倾斜，如何有效利用标签信息成为一个挑战。针对这三个缺点导致的异常检测泛化能力差和误检率高的问题，提出了一种基于分级度量嵌入的多上下文行为描述模型来有效地解决这三个问题。我们将原来的欺诈检测问题转化为一个伪推荐系统问题，其中一个人被视为一个伪用户，他/她的行为被看作一个伪项目，标签被视为相应的伪评级。有了协作过滤的想法，对于个人来说，来自其他类似个人的信息可以用来建立他/她的行为简档。为了获得对异类属性的统一处理，我们转向了一种基于嵌入的方法，在共同的潜在空间中同时学习属性嵌入和个体的行为特征。为了更好地利用标签信息，我们的模型被设计成适合伪用户对伪条目的正确偏好排序。通过这样做，它明确地学会了区分欺诈性和合法性。最后，我们提出在不同情境下识别和区分个体，并进一步将行为剖析模型推广为多情境模型。因此，提出的模型可以集成多上下文行为模式，并允许在不同的上下文中检查交易。在真实的网上银行交易数据集上的大量实验表明，我们的模型不仅在所有指标上都优于基准，而且可以结合它们来获得更好的性能。
Anomaly detection relies on individuals' behavior profiling and works by detecting any deviation from the norm. When used for online banking fraud detection, however, it mainly suffers from three disadvantages. First, for an individual, the historical behavior data are often too limited to profile his/her behavior pattern. Second, due to the heterogeneous nature of transaction data, there lacks a uniform treatment of different kinds of attribute values, which becomes a potential barrier for model development and further usage. Third, the transaction data are highly skewed, and it becomes a challenge to utilize the label information effectively. The three disadvantages result in both poor generalization and high false positive rate of anomaly detection, and we propose a ranking metric embedding based multi-contextual behavior profiling (ReMEMBeR) model to battle them effectively. We solve the original fraud detection problem as a pseudo-recommender system problem, where an individual is treated as a pseudo-user, his/her behavior as a pseudo-item, and the label as the corresponding pseudo-rating. With the idea of collaborative filtering, for an individual, information from other similar individuals can be used to establish his/her behavior profile. In order to obtain a uniform treatment of heterogeneous attributes, we turn to an embedding based method to learn both attribute embedding and individuals' behavior profiles within a common latent space simultaneously. To utilize the label information better, our model is designed to fit pseudo-users' correct preference ranking for pseudo-items. By doing so, it explicitly learns to tell the fraudulent from the legitimate. Last but not least, we propose to identify and distinguish individuals under different contexts and further generalize the behavior profiling model to be a multi-contextual one. The proposed model can, thus, integrate the multi-contextual behavior patterns and allow transactions to be examined under the different contexts. Extensive experiments on a real-world online banking transaction dataset demonstrate that our model not only outperforms benchmarks on all metrics but also can be combined with them to achieve even better performance.