A framework for data mining‐based anti‐money laundering research
Abstract
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.
Keywords
Citation
Gao, Z. and Ye, M. (2007), "A framework for data mining‐based anti‐money laundering research", Journal of Money Laundering Control, Vol. 10 No. 2, pp. 170-179. https://doi.org/10.1108/13685200710746875
Publisher
:Emerald Group Publishing Limited
Copyright © 2007, Emerald Group Publishing Limited