Abstract
The big data of socio-economic systems is investigated in order to synthesize a generalized model of the typology of the risk of money laundering. The analysis of actual requirements to the typology model was carried out. A model for input, output and classification of objects, sets, categories in big data represented by topos has been developed. It is shown that classified objects can be entered into an artificial intelligence database and used to search and analyze typologies of money laundering risks. The concept is introduced and the category-theoretic formula of the event of the risk of money laundering is synthesized. In addition, a retrospective event was singled out and its theoretical justification was carried out. A method for fragmentation of chains of Markov occurrences is proposed and analyzed. Within the framework of the category-theoretic approach, chains are represented by category compositions. A new scheme developed instead of the well-known scheme of the normal Markov algorithm is analyzed. In the scheme, locally final occurrences were discovered and theoretically substantiated, which were not taken into account and were not used by Markov. Locally final occurrences in the scheme of the Markov algorithm are found in parallel chains of occurrences and voids. It has been established that locally final substitutions play an important role in the search and analysis of typologies of money laundering risks in big data of socio-economic systems. It has been determined that typologies of money laundering risks can be represented as categories of sets and n-categories. A formula for an incomplete risk typology has been developed. It is concluded that the synthesized model of the typology of money laundering risks provides for the detection and analysis of local typologies of individual risks.
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