By Jeffrey Simser
Money laundering has generally been defined as a process under which "dirty money" produced by criminal activity is turned into "clean money" and moved into the economy in places less likely to attract the attention of criminal authorities. The following Emerging Issues Analysis by Jeffrey Simser, an asset forfeiture specialist, explores the crimes involved in money laundering, steps taken to effectuate the conversion of dirty money to clean money, and Canadian efforts to combat these endeavors.
"The acquisition of, and control over, wealth is the motivation for most serious crimes involving premeditation. Money laundering or ML is a technique used to disguise the origin of tainted property, shielding that property from law enforcement, victims and criminal predators. Estimates suggest that global ML involves between $500 billion and $1 trillion a year. One commentator has suggested that the most authoritative estimate is a decade old and has a $1 trillion deviation between the high and low end of the scale. In my country, Canada, between $5 billion and $15 billion (Cdn) is estimated to be laundered annually. We clearly lack an accurately quantified understanding of ML. This isn't surprising. A truly successful money launderer is a covert figure hoping never to be discovered. Laundered money is not meant to be counted by financial institutions, authorities or statisticians. We know, through typologies, prosecutions and financial intelligence unit analysis, that ML is a serious problem which governments and international bodies have grappled with for the past twenty-five years. An industry devoted to anti-money laundering or AML efforts has resulted. AML efforts are targeted at conventional laundered money (drug proceeds, for example) as well as countering the financing of terrorism (CFT) efforts. This paper focuses primarily in two areas: the predicate crimes that lead to ML and the techniques that launderers use. In concluding, the paper takes a brief look at AML systems and the consequent enforcement mechanisms to deal with ML."
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