MODEL OF ILLEGAL ECONOMIC ACTIVITY IN ANTI-MONEY LAUNDERING PROCEEDS FROM CRIME

Abstract

In the interests of developing the theoretical provisions of the methodology for classifying the typologies of the risks of laundering proceeds from crime and financing of terrorism in big data from a variety of data sources of organizational systems, a study is being made of the signs of illegal economic activity. Theoretically substantiates and synthesizes a reference model of illegal economic activity of organizational systems. This model considers four stages of possible illegal economic activity of organizational systems. The stages have symbols: “institutions, organizations, structuring and actions”. The structure and composition of the elements of these stages has been analyzed. For the stage of establishment, an analysis of the sources of data necessary for counteraction was carried out, signs of illegal economic activity characteristic of this stage were identified. For the organization stage, it has been established that it is necessary to use an additional data source containing data on the competence of the head of the organizational system. For the organization stage, it was found that it is the most secretive, so it is necessary to further analyze the activity in managing systems in their telecommunications environment. The alphabet of signs of income laundering risks , presented in a linguistic, categorical form is considered. Synthesized and scientifically substantiated events in fragments of interactions of organizational systems. The categorical alphabet containing letters similar to the letters of the alphabet is considered, its formalization is carried out. A scientific substantiation and synthesis of models of individual typologies that form a generalized mathematical model of the typology of the risk of laundering proceeds from crime and the financing of terrorism has been carried out. Among these models, two have been identified that provide automatic classification of typologies of individual risks and selection of words in the Markov alphabet A±2, denoting objects. It is concluded that the categorical alphabets  or provide a classification of typologies of individual risks of laundering proceeds from crime and financing of terrorism in big data of organizational systems, in an automatic mode. The classification of typologies is possible at all stages of illegal activity; for this, it is necessary to use several sources of big data.