A framework for data mining‐based anti‐money laundering research

Purpose – The purpose of this paper is to propose a framework for data mining (DM)‐based anti‐money laundering (AML) research. Design/methodology/approach – First, 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. Findings – The key of the framework lies in ML network analysis involving link analysis, community generation, and network destabilization. Originality/value – The paper offers insights into DM in the context of AML.