Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field
Christopher Bockel-Rickermann, Tim Verdonck, Wouter Verbeke
TL;DR
This paper addresses the fragmentation and lack of cross-domain organization in fraud analytics literature by performing a systematic review of about 294 records from 2011–2020 and introducing a two-layer clustering framework for fraud-detection methods. It provides a domain map (five main domains among 19) and highlights key challenges such as class imbalance, concept drift, and data availability, along with common metrics and processing routines. The authors contribute an online database and a four-part keyword taxonomy to standardize indexing, propose eight data-set requirements, and outline four future research directions, including benchmarking, unstructured data integration, and data sharing. The work advances the field by offering an integrated, domain-spanning view aimed at bridging research and practice, improving reproducibility, and guiding targeted, cross-domain research efforts.
Abstract
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.