Share:


Money laundering risk in developing and transitive economies: analysis of cyclic component of time series

    Valentyna Levchenko   Affiliation
    ; Anton Boyko   Affiliation
    ; Victoria Bozhenko   Affiliation
    ; Serhii Mynenko   Affiliation

Abstract

Money laundering has become a global threat to the international stability and security, leading both to economic and social upheavals, and to an increase in terrorist threats. Therefore, an objective necessity arises for a more detailed study of the money laundering within the scope of its developmental patterns and time-dependent behaviour. The study mission is the development of a theoretical framework and methodological support for modelling the cyclic component of the money laundering risk. The correlation and regression are used for isolating the cyclic component. In turn, the Fourier harmonic analysis allows specifying the cyclic component. Additionally, we carried out a decomposition of time series, analysis of its volatility and persistence using the Hurst exponent. We determined the peaks, downturns and duration of the money laundering cycles in the developed economies and economies in transition, and established the possibility of predicting this process in the medium term. We proved the internationalization of the money laundering and the similarity of behaviour of trends that characterize it both for developed economies among themselves and between groups of countries. The further scientific research is needed within the framework of the imposition of trends in the development of the money laundering processes of some countries on others and the formation of international medium-term anti-fraud strategies.

Keyword : anti money laundering, money laundering risk, time series analysis, trend analysis, cyclic component, Fourier analysis, volatility, persistence

How to Cite
Levchenko, V., Boyko, A., Bozhenko, V., & Mynenko, S. (2019). Money laundering risk in developing and transitive economies: analysis of cyclic component of time series. Business: Theory and Practice, 20, 492-508. https://doi.org/10.3846/btp.2019.46
Published in Issue
Dec 17, 2019
Abstract Views
2179
PDF Downloads
869
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Araujo RA (2010) An evolutionary game theory approach to combat money laundering. Journal of Money Laundering Control 13 (1): 70-78. https://doi.org/10.1108/13685201011010236

Arnone M, Padoan P (2008) Anti-money laundering by international institutions: a preliminary assessment. European Journal of Law and Economics 26 (3): 361-386. https://doi.org/10.1007/s10657-008-9069-3

Buriak A, Lyeonov S, Vasylieva T (2015) Systematically important domestic banks: an indicator-based measurement approach for the Ukrainian banking system. Prague Economic Papers 24 (6): 715-728. https://doi.org/10.18267/j.pep.531

Dmytrov S, Medvid Т (2017) An approach to the use of indices-based analysis subject to money laundering and terrorist financing national risk assessment. SocioEconomic Challenges 1 (1): 35-47. https://doi.org/10.21272/sec.2017.1-04

Dmytrov SO, Levchenko LH, Medvid TA, Kuzmenko OV (2014) Determining the bank risk of using its services to legalize criminal income or financing terrorism during inspections. Sumy, Ukraine.

Dmytrov SO, Medvid TA, Kuzmenko OV (2015) National risk assessment of legalizing crime, financing terrorism and the proliferation of weapons of mass destruction: emerging challenges. Cherkasy, Ukraine.

Dobrovic J, Koraus A, Rajnoha R (2018) Activity management of the action plan for a sustainable fight against tax fraud and tax evasion in Slovakia as compared with the EU. Marketing and Management of Innovations 3: 313-323. https://doi.org/10.21272/mmi.2018.3-28

United Nations Office on Drugs and Crime (2011) Estimating illicit financial flows resulting from drug trafficking and other transnational organized crimes research report https://www.unodc.org/documents/data-and-analysis/Studies/Illicit_financial_flows_2011_web.pdf

FATF (2019) FATF Recommendations http://www.fatf-gafi.org/

Federici FR (2007) Money laundering, terrorist financing and how to contrast them: data and text mining in business intelligence solutions. In: WIT Transactions on Information and Communication Technologies 38: 315-324. https://doi.org/10.2495/DATA070311

Ferwerda J, Kleemans ER (2019) Estimating money laundering risks: an application to business sectors in the Netherlands. European Journal on Criminal Policy and Research 25 (1): 45-62. https://doi.org/10.1007/s10610-018-9391-4

Filipkowski W (2008) Cyber laundering: an analysis of typology and techniques. International Journal of Criminal Justice Sciences 3 (1): 15-27.

Geiger H, Wuensch О (2010) The fight against money laundering: An economic analysis of a cost‐benefit paradoxon. Journal of Money Laundering Control 10 (1): 91-105. https://doi.org/10.1108/13685200710721881

Grant Jr, Reynolds Т (2006 Method and system to evaluate anti-money laundering risk. United States Patent https://patentimages.storage.googleapis.com/ee/09/67/541789af380ebb/ US8412601.pdf

Horne R (2014) BBA Report The cyber threat to banking – A global industry challenge https://www.bba.org.uk/wp-content/uploads/2014/06/BBAJ2110_Cyber_report_May_2014_WEB.pdf

International centre for asset recovery (2019) Methodological approach https://index.baselgovernance.org/methodology

Kordík M, Kurilovská L (2017) Protection of the national financial system from the money laundering and terrorism financing. Entrepreneurship and Sustainability 5 (2): 243-262. https://doi.org/10.9770/jesi.2017.5.2(7)

Kulish A, Petrushenko M, Reznik O, Kiselyova E (2018) The relations unshadowing in business activities: the economic and legal factors of security at the macroeconomic level. Problems and Perspectives in Management 16 (1): 428-436. https://doi.org/10.21511/ppm.16(1).2018.40

Kuzmenko OV, Levchenko LH, Medvid TA (2011) Practical application of Bayesian analysis in financial monitoring at banks. Sumy, Ukraine.

Leonov S, Yarovenko H, Boiko A, Dotsenko T (2019) Information system for monitoring banking transactions related to money laundering. Proceedings of the Selected Papers of the 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-EEMLPEED 2019), Odessa, Ukraine, May 22-24, 2019. https://doi.org/10.1051/shsconf/20196504013

Lilley P (2006) Dirty dealing. The untold truth about global money laundering, international crime and terrorism (3rd ed). UK and USA: Kogan Page.

Lyeonov SV, Vasylieva TA, Lyulyov OV, Kyrychenko KI (2018) Macroeconomic stability and its impact on the economic growth of the country. Montenegrin Journal of Economics 1: 159-170. https://doi.org/10.14254/1800-5845/2018.14-1.12

Mabunda S (2018) Cryptocurrency: the new face of cyber money laundering. Proceedings International Conference on Advances in Big Data, Computing and Data Communication Systems (IcABCD), Durban, South Africa, 17 Sep 2018. https://doi.org/10.1109/ICABCD.2018.8465467

Masciandaro D (2017) Global financial crime: terrorism, money laundering and offshore centres. Routledge. https://doi.org/10.4324/9781315254241

Melnikov VN, Movsesyan AG (2007) Anti-money laundering. Moscow, Russia.

Mirea V, Ionescu L, Blăjan A (2011) Fraud, corruption and cyber crime in a global digital network. Economics, Management and Financial Markets 6 (2): 373-380.

More MM, Jadhav, MP, Nalawade KM (2015) Online banking and cyber attacks: the current scenario. International Journal of Advanced Research in Computer Science and Software Engineering 5 (12): 743-749.

Naheem MA (2018) Is tackling Trade Based Money Laundering (TBML) through stricter reporting regulation the most effective response? Journal of Money Laundering Control 21 (3): 345-357. https://doi.org/10.1108/JMLC-08-2015-0034

Nguyen CL (2018) Preventing the use of financial institutions for money laundering and the implications for financial privacy. Journal of Money Laundering Control 21 (1): 47-58. https://doi.org/10.1108/JMLC-01-2017-0004

Piller G, Zaccariotto E (2009) Cyber-laundering: the union between new electronic payment systems and criminal organizations. Transition Studies Review 16 (1): 62-76. https://doi.org/10.1007/s11300-009-0048-3

Plaksiy K, Nikiforov A, Miloslavskaya N (2018) Applying big data technologies to detect cases of money laundering and counter financing of terrorism. In Proceedings – 2018 IEEE 6th International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2018 (pp. 70-77). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/W-FiCloud.2018.00017

PwC (2019) Global annual review https://www.pwc.com/gx/en/about/global-annual-review-2019.html#1

Reuter P, Edwin M Truman (2004) Chasing dirty money: the fight against money laundering. institute for international economics, Peterson institute press https://ideas.repec.org/b/iie/ppress/381.html

Savage D, Wang Q, Chou P, Zhang X, Yu X (2016) Detection of money laundering groups using supervised learning in networks https://arxiv.org/pdf/1608.00708.pdf

Şcheau MC, Pop Zaharie S (2017) Methods of laundering money resulted from cyber-crime. Economic Computation and Economic Cybernetics Studies and Research 51 (3): 299-314.

Sekgwathe V, Talib M (2012) Cyber forensics: computer security and incident response. International Journal on New Computer Architectures and Their Applications 2 (1): 127-137.

Sharman JС, Chaikin D (2009) Corruption and anti‐money‐laundering systems: putting a luxury good to work. In Governance: An International Journal of Policy, Administration, and Institutions 22 (1): 27-45. https://doi.org/10.1111/j.1468-0491.2008.01420.x

Sittlington S, Harvey J (2018) Prevention of money laundering and the role of asset recovery. Crime, Law and Social Change 70 (4): 421-441. https://doi.org/10.1007/s10611-018-9773-z

Stiawan D, Idris MY, Abdullah AH, Aljaber F, Budiarto R (2017) Cyber-attack penetration test and vulnerability analysis. International Journal of Online Engineering 3 (1): 125-132. https://doi.org/10.3991/ijoe.v13i01.6407

Stokes R (2012) Virtual money laundering: the case of bitcoin and the Linden dollar. Information and Communications Technology Law 21 (1): 221-236. https://doi.org/10.1080/13600834.2012.744225

Subeh M, Boiko А (2017) Modeling efficiency of the state financial monitoring service in the context of counteraction to money laundering and terrorism financing. SocioEconomic Challenges 1 (2): 39-51. https://doi.org/10.21272/sec.1(2).39-51.2017

Šimonová J, Čentéš J, Beleš A (2019) Financial analysis of innovative forms of money, Entrepreneurship and Sustainability 7 (1): 69-80. https://doi.org/10.9770/jesi.2019.7.1(6)

Tax justice network (2019) https://www.taxjustice.net/

The World Bank (2019) https://www.worldbank.org

The World Economic Forum (2019) https://www.weforum.org/

Tjalling C (2011) Gravity models of trade-based money laundering. Koopmans Research Institute. Utrecht School of Economics. Utrecht University https://www.uu.nl/files/rebousedp201111-16pdf

Transparency International (2019) https://www.transparency.org/

Trautman LJ (2016) E-commerce, cyber, and electronic payment system risks: lessons from PayPal. 16 U.C. Davis Business Law Journal 16 (1): 261-292 https://doi.org/10.2139/ssrn.2314119

Tropina T (2014) Fighting money laundering in the age of online banking, virtual currencies and internet gambling. ERA Forum 5 (1): 69-84. https://doi.org/10.1007/s12027-014-0335-2

Zhubrin RV (2011) The fight against money laundering: theoretical and practical aspects. Moscow, Russia.