The literature on fraud analytics and fraud detection has seen a substantial
increase in output in the past decade. This has led to a wide range of research
topics and overall little organization of the many aspects of fraud analytical
research. The focus of academics ranges from identifying fraudulent credit card
payments to spotting illegitimate insurance claims. In addition, there is a
wide range of methods and research objectives. This paper aims to provide an
overview of fraud analytics in research and aims to more narrowly organize the
discipline and its many subfields. We analyze a sample of almost 300 records on
fraud analytics published between 2011 and 2020. In a systematic way, we
identify the most prominent domains of application, challenges faced,
performance metrics, and methods used. In addition, we build a framework for
fraud analytical methods and propose a keywording strategy for future research.
One of the key challenges in fraud analytics is access to public datasets. To
further aid the community, we provide eight requirements for suitable data sets
in research motivated by our research. We structure our sample of the
literature in an online database. The database is available online for fellow
researchers to investigate and potentially build upon.

本文系统分析了 2011 年至 2020 年间近 300 篇有关欺诈分析的研究文献，总结了应用领域、面临的挑战、方法和绩效度量，并提出了未来研究的关键词策略和数据集要求。此外，本文提供了一个在线数据库，以帮助其他研究者进行进一步的研究。