论文标题
使用基于数据挖掘的方法对在线卡付款欺诈检测检测的调查
A Survey of Online Card Payment Fraud Detection using Data Mining-based Methods
论文作者
论文摘要
卡支付欺诈是一个严重的问题，对于最佳运行的数字经济，卡片（借方和信用）是全球最受欢迎的数字支付方法。尽管发生欺诈行为可能相对罕见，但欺诈的影响可能很大，尤其是对持卡人。在这项研究中，已经有许多尝试开发基于数据挖掘技术来检测潜在欺诈交易的方法，主要利用了过去十年来机器学习空间中的发展。这项调查提出了基于对现有研究尝试和实验的综述的分类法，该分类法主要阐述了研究人员采取的方法将欺诈（和欺诈检测）的业务影响（i）纳入其工作中，（ii）专注于持卡人行为的特征工程技术，以将欺诈性活动与同一card的欺诈行为分开，并（III III）努力，并努力地努力地努力，并努力地努力进行适当的努力。此外，将对所使用的分类算法进行比较性能评估以及解决阶级不平衡问题的工作。对2009年至2020年之间的卡欺诈检测领域发表的四十五个经过同行评审的论文进行了深入的审查，以开发本文。
Card payment fraud is a serious problem, and a roadblock for an optimally functioning digital economy, with cards (Debits and Credit) being the most popular digital payment method across the globe. Despite the occurrence of fraud could be relatively rare, the impact of fraud could be significant, especially on the cardholder. In the research, there have been many attempts to develop methods of detecting potentially fraudulent transactions based on data mining techniques, predominantly exploiting the developments in the space of machine learning over the last decade. This survey proposes a taxonomy based on a review of existing research attempts and experiments, which mainly elaborates the approaches taken by researchers to incorporate the (i) business impact of fraud (and fraud detection) into their work , (ii) the feature engineering techniques that focus on cardholder behavioural profiling to separate fraudulent activities happening with the same card, and (iii) the adaptive efforts taken to address the changing nature of fraud. Further, there will be a comparative performance evaluation of classification algorithms used and efforts of addressing class imbalance problem. Forty-five peer-reviewed papers published in the domain of card fraud detection between 2009 and 2020 were intensively reviewed to develop this paper.
