Journal
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
Volume 8, Issue 3, Pages 643-654Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSS.2021.3052950
Keywords
Online banking; Anomaly detection; Measurement; Credit cards; Collaboration; Radio frequency; Data models; Anomaly detection; embedding; multi-contextual behavior profiling; online banking fraud detection
Funding
- National Key Research and Development Program of China [2018YFB2100801]
- National Natural Science Foundation of China (NSFC) [61972287]
- Major Project of the Ministry of Industry and Information Technology of China [TC200H01J]
- Postgraduate Education Reform and Research Project of Tongji University [ZD1903031]
- Municipal Human Resources Development Program for Outstanding Young Talents in Shanghai
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Anomaly detection in online banking fraud detection faces challenges such as limited historical behavior data, heterogeneous attribute values, and underutilized label information. Our proposed ReMEMBeR model addresses these challenges by treating fraud detection as a pseudo-recommender system problem, utilizing collaborative filtering and embedding methods to improve performance.
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.
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