TRACKING DIRTY PROCEEDS: EXPLORING DATA MINING TECHNOLOGIES AS TOOLS TO INVESTIGATE MONEY LAUNDERING
Authors: WATKINS R.C.1; REYNOLDS K.M.1; DEMARA R.1; GEORGIOPOULOS M.1; GONZALEZ A.1; EAGLIN R.1
Source: Police Practice and Research, Volume 4, Number 2, January 2003 , pp. 163-178(16)
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Abstract:
Money laundering enforcement operations in the USA and abroad have developed in the past decade from the simple use of informant information to the sophisticated analysis of voluminous, complex financial transaction arrays. Traditional investigative techniques aimed at uncovering patterns consume numerous man-hours. The volume of these records and the complexity of the relationships call for innovative techniques that can aid financial investigators in generating timely, accurate leads. Data mining techniques are well suited for identifying trends and patterns in large data sets often comprised of hundreds or even thousands of complex hidden relationships. This paper explores the use of innovative data mining methodologies that could enhance law enforcement's ability to detect, reduce, and prevent money laundering activities. This paper provides an overview of the money laundering problem in the USA and overseas and describes the nature and scope of the money laundering problems. It reviews traditional approaches to financial crime investigation and discusses various innovative data mining and artificial-intelligence-based solutions that can assist financial investigators.Keywords: Money laundering; Financial crime investigation; Artificial intelligence; Neural networks; Information technology; ??
Document Type: Research article
Affiliations: 1: Department of Criminal Justice, University of Central Florida, 4000 Central Florida Blvd, PO Box 161600, Orlando, FL 32816-1600, USA
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