Identifying online credit card fraud using Artificial Immune Systems
Identifying online credit card fraud using Artificial Immune Systems
复制标题
使用人工免疫系统识别在线信用卡欺诈
DOI：
10.1109/cec.2010.5586154
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
D. Walsh
中科院分区：
文献类型：
--
作者：
A. Brabazon;J. Cahill;P. Keenan;D. Walsh
现在，大量的支付流程都是在网上进行的，因此需要高效有效的系统来检测信用卡欺诈。这个问题的一个特殊方面是，它是高度动态的，因为欺诈者不断调整他们的策略，以应对日益复杂的检测系统。因此，通过接触以前的欺诈性交易的例子来进行系统训练，可以使欺诈检测系统容易受到新的欺诈性交易模式的影响。问题的性质表明，人工免疫系统（AIS）可能在欺诈检测系统中具有特殊的效用，因为可以构建AIS，它可以标记“非标准”交易，而无需在算法训练期间看到所有可能的此类交易的示例。在本文中，我们研究了人工免疫系统（AIS）在信用卡欺诈检测中的有效性，使用从在线零售商获得的大型数据集。实现了三种AIS算法，并根据逻辑回归模型对其性能进行了基准测试。结果表明，AIS算法有可能包含在欺诈检测系统中，但需要进一步的工作来实现其在该领域的全部潜力。
Significant payment flows now take place on-line, giving rise to a requirement for efficient and effective systems for the detection of credit card fraud. A particular aspect of this problem is that it is highly dynamic, as fraudsters continually adapt their strategies in response to the increasing sophistication of detection systems. Hence, system training by exposure to examples of previous examples of fraudulent transactions can lead to fraud detection systems which are susceptible to new patterns of fraudulent transactions. The nature of the problem suggests that Artificial Immune Systems (AIS) may have particular utility for inclusion in fraud detection systems as AIS can be constructed which can flag ‘non standard’ transactions without having seen examples of all possible such transactions during training of the algorithm. In this paper, we investigate the effectiveness of Artificial Immune Systems (AIS) for credit card fraud detection using a large dataset obtained from an on-line retailer. Three AIS algorithms were implemented and their performance was benchmarked against a logistic regression model. The results suggest that AIS algorithms have potential for inclusion in fraud detection systems but that further work is required to realize their full potential in this domain.