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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">finance</journal-id><journal-title-group><journal-title xml:lang="ru">Финансы: теория и практика/Finance: Theory and Practice</journal-title><trans-title-group xml:lang="en"><trans-title>Finance: Theory and Practice</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2587-5671</issn><issn pub-type="epub">2587-7089</issn><publisher><publisher-name>Financial University under The Government of Russian Federation</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26794/2587-5671-2020-24-4-6-17</article-id><article-id custom-type="elpub" pub-id-type="custom">finance-1039</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ФИНАНСОВЫЙ МОНИТОРИНГ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>FINANCIAL MONITORING</subject></subj-group></article-categories><title-group><article-title>Синтез социально-экономических карт и визуализация девиантной деятельности объектов финансового мониторинга</article-title><trans-title-group xml:lang="en"><trans-title>Synthesis of Socio-Economic Maps and Visualization of Deviant Activity Measures of Financial Monitoring of Entities</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-1005-6265</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Бекетнова</surname><given-names>Ю. М.</given-names></name><name name-style="western" xml:lang="en"><surname>Beketnova</surname><given-names>Yu. M.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Юлия Михайловна Бекетнова — кандидат технических наук, доцент факультета прикладной математики и информационных технологий.</p><p>Москва</p></bio><bio xml:lang="en"><p>Yuliya M. Beketnova — Cand. Sci. (Eng.), Assoc. Prof., Faculty of Applied Mathematics and Information Technology.</p><p>Moscow</p></bio><email xlink:type="simple">beketnova@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Финансовый университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Financial University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2020</year></pub-date><pub-date pub-type="epub"><day>17</day><month>08</month><year>2020</year></pub-date><volume>24</volume><issue>4</issue><fpage>6</fpage><lpage>17</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Бекетнова Ю.М., 2020</copyright-statement><copyright-year>2020</copyright-year><copyright-holder xml:lang="ru">Бекетнова Ю.М.</copyright-holder><copyright-holder xml:lang="en">Beketnova Y.M.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://financetp.fa.ru/jour/article/view/1039">https://financetp.fa.ru/jour/article/view/1039</self-uri><abstract><p>Анализ задач Росфинмониторинга по противодействию отмыванию доходов показал, что фактическая потребность в количестве объектов, подлежащих анализу, многократно превышает возможности аналитиков. Цель исследования состоит в повышении оперативности оценки обстановки лицами, принимающими решения, за счет визуализации данных финансового мониторинга. Методологическую основу исследования определил тот факт, что для картирования информации об объектах финансового мониторинга необходимо провести их ранжирование. Однако объекты финансового мониторинга - хозяйствующие субъекты, профессиональные участники рынка ценных бумаг - описывают наборами характеристик, т.е., по сути, являются объектами векторной природы. В математике же порядковые отношения для векторов, как известно, не определены. Для отыскания скалярных оценок объектов финансового мониторинга перспективным является метод главных компонент. Произведено моделирование предметной области финансового мониторинга и подобран математический и методологический инструментарий для решения задачи картирования девиантных объектов финансового мониторинга. Результатом моделирования является инфографика географической составляющей отмывания преступных доходов. На основе государственных данных из различных источников - картотеки арбитражных дел, единого государственного реестра юридических лиц, сведений о состоянии преступности МВД России - получены социально-экономические карты: бизнес-активности федеральных округов, федеральных округов по склонности предоставления теневых финансовых услуг, регионов по склонности к легализации денежных средств, состояния преступности. Автор делает вывод о том, что приведенные результаты исследования могут служить мощным инструментом поддержки принятия стратегических решений и макроанализа ситуации в сфере финансового мониторинга.</p></abstract><trans-abstract xml:lang="en"><p>The task analysis of the Federal Financial Monitoring Service has revealed that the money laundering risk assessment process is greatly limited by insufficient resources. The aim of the study is to increase the efficiency of decision-making processes by using visualization of financial monitoring data. The methodological basis of the study suggests to rank objects in order to map financial monitoring data. However, the objects of financial monitoring, such as business entities, professional securities market participants, have sets of characteristics, i.e. are of vector nature. As known, there is no mathematical definition of ordinal relations for vectors. The author used the method of principal component to estimate a scalar value of financial monitoring. The article provides a subject area modeling of financial monitoring, and the author used mathematical and methodological tools to map deviant objects of financial monitoring. The result of the study presents the geographical infographics of the money laundering process. The author refers to socio-economic regional maps obtained from various official sources (arbitration case files, the Unified State Register of Legal Entities, the crime rate in Russia from the Ministry of Internal Affairs). The maps include information about the business activity of the federal districts, regions with a propensity for illegal and legal financial activities, crime rate. The author concludes that the results of the study may serve as a powerful tool to support the strategic decision-making process and microanalysis of financial monitoring.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>поддержка принятия решений</kwd><kwd>картирование</kwd><kwd>интегральные оценки</kwd><kwd>меры девиантной деятельности</kwd><kwd>финансовый мониторинг</kwd><kwd>научная визуализация</kwd></kwd-group><kwd-group xml:lang="en"><kwd>decision making support</kwd><kwd>mapping</kwd><kwd>integrated assessments</kwd><kwd>deviant activity measures</kwd><kwd>financial monitoring</kwd><kwd>scientific visualization</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Beketnova Yu.M., Prikazchikova G. S., Prikazchikova A. S. The modification of the T. Saaty’s analytic hierarchy process in order to improve the risk management system of the Federal Customs Service. 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