Algorithms of network data analysis in the disclosure of a tax crime scheme

The paper considers the possibility of using network (graph) analysis tools in the disclosure of a tax crime scheme and the formation of a strategy for its investigation. A data model is proposed that allows building a network topology of a crime based both on direct material about a crime event and...

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Bibliographic Details
Main Authors: Sergey V. Duga, Viktoriya V. Efimova, Andrey I. Trufanov
Format: Article
Language:English
Published: Joint Stock Company "Experimental Scientific and Production Association SPELS 2022-03-01
Series:Безопасность информационных технологий
Subjects:
Online Access:https://bit.mephi.ru/index.php/bit/article/view/1409
Description
Summary:The paper considers the possibility of using network (graph) analysis tools in the disclosure of a tax crime scheme and the formation of a strategy for its investigation. A data model is proposed that allows building a network topology of a crime based both on direct material about a crime event and on additional information obtained by accessing the databases of law enforcement and control agencies. The algorithms of network analysis that contribute to the identification of new information about the criminal scheme are also considered. These network algorithms make it possible to detect hidden and non-obvious connections between the defendants in the case, to identify the hierarchical structure of their relationships, which helps to identify the key participants in the criminal scheme. Using the examples of various criminal cases of tax crimes, it is shown that the use of the considered variants of network data analysis allows forming a crime scheme, providing the correct criminalistic characterization of the crime, determining the range of its subjects, offering the investigator a forensic methodology for investigating this type of crime (investigation algorithm) and standard investigative versions, that all together contribute to the proper organization of the investigation.
ISSN:2074-7128
2074-7136