Manipulating the banking system for money-laundering purposes is a relatively new phenomenon. The problem was first identified in 1986 in the United States, when it was classified as a criminal offence. In Russia, anti-money laundering measures are regulated by Federal Law On Counteracting Money Laundering and the Financing of Terrorism of August 7, 2001 ¹ 115-FZ (as amended). Given the high-speed, digitalized nature of banking transactions, the technology for countering money laundering should be no less sophisticated; and the speed of detecting suspicious transactions should be commensurate with that of the underlying bank transactions. The Central Bank of Russia (CBR) is now highly experienced in identifying mechanisms and schemes for money laundering. Reviewing the CBR's typology for suspicious transactions, we have categorized them as follows: multi-parameter, complex, dynamic and multivariate. To develop an algorithm for detecting suspicious transactions, it is important to adhere to a correct sequencing (hierarchy) of operations. We also propose to supplement the principle of algorithmic hierarchy with a taxonomical approach. Adapting our proposed taxonomic approach to formulating a typology of parameters characterizing suspicious transactions, in combination with the principle of hierarchical analysis, will create an effective and efficient algorithm for identifying suspicious money laundering transactions.
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