Online from: 1997
Subject Area: Accounting and Finance
|Title:||A framework for data mining-based anti-money laundering research|
|Author(s):||Zengan Gao, (School of Economics and Management, Southwest Jiaotong University, Chengdu, People's Republic of China), Mao Ye, (School of Economics and Management, Southwest Jiaotong University, Chengdu, People's Republic of China)|
|Citation:||Zengan Gao, Mao Ye, (2007) "A framework for data mining-based anti-money laundering research", Journal of Money Laundering Control, Vol. 10 Iss: 2, pp.170 - 179|
|Keywords:||Crimes, Data analysis, Money laundering|
|Article type:||Research paper|
|DOI:||10.1108/13685200710746875 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
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.
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