Fraud analytics: A decade of research

• Published in: Expert systems with applications Vol. 232; p. 120605
• Main Authors: Bockel-Rickermann, Christopher, Verdonck, Tim, Verbeke, Wouter
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-12-2023
• Subjects: Analytical challenges; Decision making; Fraud analytics; Fraud detection; Machine learning applications; Machine learning applications; Fraud analytics; Analytical challenges; Decision making; Fraud detection
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.120605

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 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.
•We analyze close to 300 manuscripts related to fraud analytics.•23 unique fraud analytical challenges are detected and described.•We provide a wholistic overview of fraud analytical domains and methods.•We map out 4 key areas for future research in fraud analytics.•We present a novel taxonomy for fraud analytical literature based on our research.