PurposeThe purpose of this paper is to propose a framework for data mining (DM)‐based anti‐money laundering (AML) research.Design/methodology/approachFirst, suspicion data are prepared by using DM techniques. Also, DM methods are compared with traditional investigation techniques. Next, rare transactional patterns are further categorized as unusual/abnormal/anomalous and suspicious patterns whose recognition also includes fraud/outlier detection. Then, in summarizing the reporting of money laundering (ML) crimes, an analysis is made on ML network generation, which involves link analysis, community generation, and network destabilization. Future research directions are derived from a review of literature.FindingsThe key of the framework lies in ML network analysis involving link analysis, community generation, and network destabilization.Originality/valueThe paper offers insights into DM in the context of AML.
Based on the information visualization technology and the Web of Science database, which includes English research articles on anti-money laundering and counter-terrorism financing from 1993 to 2013, this paper explores the law and development status and research focus of international anti-money laundering research by analyzing national distribution, authors' distribution, organization distribution, cited journals, keywords, and situation to be cited by literatures (1993-2013) on money laundering with CiteSpace II application.
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