  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.
