Fraud detection and prevention play an important part in ensuring the sustained operation of any e-commerce business. Machine learning (ML) often plays an important role in these anti-fraud operations, but the organizational context in which these ML models operate cannot be ignored. In this paper, we take an organization-centric view on the topic of fraud detection by formulating an operational model of the anti-fraud departments in e-commerce organizations. We derive 6 research topics and 12 practical challenges for fraud detection from this operational model. We summarize the state of the literature for each research topic, discuss potential solutions to the practical challenges, and identify 22 open research challenges.
翻译:发现和预防欺诈在确保任何电子商务业务持续运作方面起着重要作用,机器学习在这些反欺诈业务中往往发挥重要作用,但不能忽视这些ML模式运作的组织背景,在本文件中,我们通过在电子商务组织中制定反欺诈部门的业务模式,对欺诈发现问题采取以组织为中心的观点,我们从这一业务模式中得出6个研究专题和12个欺诈发现实际挑战。我们总结了每个研究专题的文献状况,讨论了解决实际挑战的可能办法,并确定了22个公开研究挑战。