Synthesis of Socio-Economic Maps and Visualization of Deviant Activity Measures of Financial Monitoring of Entities
https://doi.org/10.26794/2587-5671-2020-24-4-6-17
Abstract
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.
Keywords
JEL: E58, G21, C53
About the Author
Yu. M. BeketnovaRussian Federation
Yuliya M. Beketnova — Cand. Sci. (Eng.), Assoc. Prof., Faculty of Applied Mathematics and Information Technology.
Moscow
Competing Interests: not
References
1. 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. Vestnik Rossiiskoi tamozhennoi akademii = The Russian Customs Academy Messenger. 2016;(3):128-136. (In Russ.).
2. Beketnova Yu. M. Expert assessments of financial activity subjects. Diskussiya = Discussion. 2013;(8):52-54. (In Russ.).
3. Huang M. L., Liang J., Nguyen O. V. A visualization approach for frauds detection in financial market. In: 13th Int. conf. information visualisation (Barcelona, 15-17 July, 2009). New York: IEEE; 2009:197-202. DOI: 10.1109/IV.2009.23
4. Cardenas A. A., Manadhata P. K., Rajan S. P. Big data analytics for security. IEEE Security & Privacy. 2013;11(6):74-76. URL: https://mlsec.info/pdf/sp13.pdf
5. Khine M. S. Spatial cognition: Key to STEM success. In: Khine M. S., ed. Visual-spatial ability in STEM education. Cham: Springer-Verlag; 2017:3-8. DOI: 10.1007/978-3-319-44385-0_1
6. Beketnova Yu.M., Krylov G. O., Larionova S. L. Models and methods for solving analytical problems of financial monitoring. Moscow: Prometei; 2018. 274 p. (In Russ.).
7. Evteev O. A. Design and compilation of socio-economic maps. Moscow: MSU; 1999. 219 p. (In Russ.).
8. Volodchenko A. The e_glossary “Cartosemiotics”. Dresden. 2009. URL: https://docplayer.ru/77039023-A-vo-lodchenko-kartosemiotika.html (In Russ.).
9. Vajjhala Sh.P. “Ground truthing” policy: Using participatory map-making to connect citizens and decision makers. 2006. URL: https://media.rff.org/archive/files/sharepoint/WorkImages/Download/RFF-Resources-162_ GroundTruthing.pdf
10. Sutcliffe A. G. A design framework for mapping social relationships. PsychNology Journal. 2008;6(3):225-246.
11. Davies G., Burgess J., Eames M., Mayer S., Staley K., Stirling A., Williamson S. Deliberative mapping: Appraising options for addressing ‘the kidney gap’. Final report. 2003. URL: https://www.researchgate.net/publica-tion/228530224_Deliberative_mapping_appraising_options_for_addressing_the_kidney_gap’
12. Teichler U., Ferencz I., Wachter B., eds. Mapping mobility in European higher education (in 2 vols.). Vol. 1: Overview and trends. Brussels: the European Union; 2011. 271 p. URL: http://www.acup.cat/sites/default/files/teichlerferenczwaechtermappingmobilityineuropeanhighereducation_0.pdf
13. Kuzmina E. S. Social mapping used to study international academic mobility. Obshchestvo: sotsiologiya, psik-hologiya, pedagogika = Society: Sociology, Psychology, Pedagogics. 2018;(12):115—119. (In Russ.). DOI: 10.24158/spp.2018.12.19
14. Skalaban I. A. Social mapping as a method for analyzing socio-territorial space. Zhurnal issledovanii sotsial’noi politiki = The Journal of Social Policy Studies. 2012;10(1):61-78. (In Russ.).
15. Goloukhova D. V. The research methodology of the socio-territorial structure of a Russian city (on the example of Moscow). Cand. sociol. sci. diss. Synopsis. Moscow: MGIMO University; 2017. 25 p. (In Russ.).
16. Geiger B., Kubin G. Relative information loss in the PCA. In: Proc. IEEE information theory workshop (Lausanne, 3-7 Sept. 2012). New York: IEEE; 2012:562-566. DOI: 10.1109/ITW.2012.6404738
17. Howard M. C. A review of exploratory factor analysis decisions and overview of current practices: What we are doing and how can we improve? International Journal of Human-Computer Interaction. 2016;32(1):51-62. DOI: 10.1080/10447318.2015.1087664
18. Amerioun A., Alidadi A., Zaboli R., Sepandi M. The data on exploratory factor analysis of factors influencing employees effectiveness for responding to crisis in Iran military hospitals. Data in Brief. 2018;19:1522-1529. DOI: 10.1016/j.dib.2018.05.117
19. Eskindarov M. A., Solov’ev V.I., eds. Paradigms of the digital economy: Artificial intelligence technologies in finance and fintech. Moscow: Kogito-Center; 2019. 325 p. URL: http://www.fa.ru/org/div/uoonir/Documents/%D1%82%D0%BE%D0%BC_4_print2.pdf (In Russ.).
20. Krylov G. O., Beketnova Yu.M., Prikazchikova A. S. Factor analysis and clustering theory application in financial monitoring tasks. Vestnik Irkutskogo gosudarstvennogo tekhnicheskogo universiteta = Proceedings of Irkutsk State Technical University. 2016;20(10):102-110. (In Russ.). DOI: 10.21285/1814-3520-2016-10-102-110
Review
For citations:
Beketnova Yu.M. Synthesis of Socio-Economic Maps and Visualization of Deviant Activity Measures of Financial Monitoring of Entities. Finance: Theory and Practice. 2020;24(4):6-17. https://doi.org/10.26794/2587-5671-2020-24-4-6-17