Financial fraud detection: the use of visualization techniques in credit card fraud and money laundering domains

• Published in: Journal of money laundering control Vol. 26; no. 3; pp. 436 - 444
• Main Author: Lokanan, Mark E.
• Format: Journal Article
• Language: English
• Published: London Emerald Publishing Limited 18.04.2023; Emerald Group Publishing Limited
• Subjects: Algorithms; Business intelligence; Credit card fraud; Criminal investigations; Data mining; Datasets; Feature selection; Fraud prevention; Machine learning; Money laundering; Trends; Visualization; Visualization; Knowledge discovery; Financial crimes; Money laundering
• ISSN: 1368-5201; 1758-7808; 1368-5201
• DOI: 10.1108/JMLC-04-2022-0058

Purpose This paper aims to reviews the literature on applying visualization techniques to detect credit card fraud (CCF) and suspicious money laundering transactions. Design/methodology/approach In surveying the literature on visual fraud detection in these two domains, this paper reviews: the current use of visualization techniques, the variations of visual analytics used and the challenges of these techniques. Findings The findings reveal how visual analytics is used to detect outliers in CCF detection and identify links to criminal networks in money laundering transactions. Graph methodology and unsupervised clustering analyses are the most dominant types of visual analytics used for CCF detection. In contrast, network and graph analytics are heavily used in identifying criminal relationships in money laundering transactions. Originality/value Some common challenges in using visualization techniques to identify fraudulent transactions in both domains relate to data complexity and fraudsters’ ability to evade monitoring mechanisms.