Combating credit card fraud with online behavioural targeting and device fingerprinting
Combating credit card fraud with online behavioural targeting and device fingerprinting
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DOI：
10.1504/ijesdf.2019.10016642
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发表时间：
2019-01-01
影响因子：
0.8
通讯作者：
Phefo, Othusitse S. D.
中科院分区：
文献类型：
--
作者：
Moalosi, Motlhaleemang;Hlomani, Hlomani;Phefo, Othusitse S. D.
全球发卡机构和研究人员采用了许多现有的欺诈检测技术。尽管有几种欺诈检测技术的发展，但每年仍有数十亿美元因信用卡/借记卡欺诈而损失。本文提出了一个欺诈检测框架，使用在线行为目标（OBT）数据和设备指纹（DF），以提高效率的融合方法，使用Dempster-Shafer理论和贝叶斯学习。OBT和DF为我们的在线行为提供了大量的见解，可以用来查明欺诈者以及了解信用卡用户的购物模式。这些技术能够跟踪和分析互联网用户，直到他们使用什么设备以及他们最有可能购买什么。本文还介绍了该框架的理论基础及其应用场景。
There are many existing fraud detection techniques employed by card issuers and researchers globally. Despite this evolution of several fraud detection techniques, billions of dollars are still lost due to credit/debit card fraud every year. This paper proposes a fraud detection framework that uses online behavioural targeting (OBT) data and device fingerprinting (DF) to improve the efficiency of the fusion approach using Dempster-Shafer theory and Bayesian learning. OBT and DF provide massive insights into our online behaviour and can be used to pinpoint fraudsters as well as know shopping patterns of credit card users. These technologies are able to track and profile Internet users up to the level of what device they are using and what they are most likely to purchase. The paper also presents the theoretical underpinnings of the framework and its application scenarios.