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Mathematical Modeling and Nonlinear Optimization in Determining the Minimum Risk of Legalization of Income from Criminal Activities in the Context of EU Member Countries

Author

Listed:
  • Alena Vagaská

    (Department of Natural Sciences and Humanities, Faculty of Manufacturing Technologies with a Seat in Prešov, The Technical University of Košice, 080 01 Presov, Slovakia)

  • Miroslav Gombár

    (Department of Management, Faculty of Management and Business, University of Prešov, 080 01 Presov, Slovakia)

  • Antonín Korauš

    (Department of Information Science and Management, Academy of the Police Force in Bratislava, 835 17 Bratislava, Slovakia)

Abstract

Legalization of the proceeds of crime represents a worldwide problem with serious economic and social consequences. Information technologies in conjunction with advanced computer techniques are important tools in the fight against money laundering (ML), financial crime (FC) and terrorism financing (TF). Nowadays, the applied literature on ML/FC/TF uses much more mathematical modelling as a solving strategy to estimate illicit money flows. However, we perceive that there is preference of linear models of economical dependences and sometimes lack of acceptance of nonlinearity of such investigated economic systems. To characterize the risk of legalization of crime proceeds in a certain country, the scientific researchers use the Basel anti-money laundering (AML) index. To better understand how the global indicators (WCI, CPI, EFI, GII, SEDA, DBI, GSCI, HDI, VAT GAP , GDP per capita) affect the level of risk of ML/TF in the countries of EU, the authors use a unique data set of 24 destination countries of EU over the period 2012–2019. The article deals with two main research goals: to develop a nonlinear model and optimize the ML/TF risk by implementation of nonlinear optimization methods. The authors contribute: (i) providing the cross-country statistical analysis; (ii) creating the new nonlinear mathematical-statistical computational model (MSCM); and (iii) describing the observed dependent variable (Basel AML index). This study deepens previous knowledge in this research field and, in addition to the panel regression analysis, also applies nonlinear regression analysis to model the behavior of the investigated system (with nonlinearity). Our results point out the differences between the estimates of the investigated system behavior when using panel and nonlinear regression analysis. Based on the developed MSC model, the optimization procedure is conducted by applying an interior point method and MATLAB toolboxes and the second goal is achieved: the statement that such values of input variables at which the risk of legalization of income from criminal activity will be minimal.

Suggested Citation

  • Alena Vagaská & Miroslav Gombár & Antonín Korauš, 2022. "Mathematical Modeling and Nonlinear Optimization in Determining the Minimum Risk of Legalization of Income from Criminal Activities in the Context of EU Member Countries," Mathematics, MDPI, vol. 10(24), pages 1-25, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4681-:d:999255
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    References listed on IDEAS

    as
    1. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    2. Alena Vagaská & Miroslav Gombár & Ľuboslav Straka, 2022. "Selected Mathematical Optimization Methods for Solving Problems of Engineering Practice," Energies, MDPI, vol. 15(6), pages 1-22, March.
    3. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    4. Premti, Arjan & Jafarinejad, Mohammad & Balani, Henry, 2021. "The impact of the Fourth Anti-Money Laundering Directive on the valuation of EU banks," Research in International Business and Finance, Elsevier, vol. 57(C).
    5. Akartuna, Eray Arda & Johnson, Shane D. & Thornton, Amy, 2022. "Preventing the money laundering and terrorist financing risks of emerging technologies: An international policy Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    6. Mohammad Anamul Haque & Syed Mehmood Raza Shah & Muhammad Usman Arshad, 2022. "Sustainable Economic Growth and FDI Inflow: A Comparative Panel Econometric Analysis of Low-Income and Middle-Income Nations," Sustainability, MDPI, vol. 14(21), pages 1-14, November.
    7. Svajone Bekesiene & Igor Samoilenko & Anatolij Nikitin & Ieva Meidute-Kavaliauskiene, 2022. "The Complex Systems for Conflict Interaction Modelling to Describe a Non-Trivial Epidemiological Situation," Mathematics, MDPI, vol. 10(4), pages 1-24, February.
    8. Gandal, Neil & Hamrick, JT & Moore, Tyler & Oberman, Tali, 2018. "Price manipulation in the Bitcoin ecosystem," Journal of Monetary Economics, Elsevier, vol. 95(C), pages 86-96.
    9. Bartolozzi, D. & Gara, M. & Marchetti, D.J. & Masciandaro, D., 2022. "Designing the anti-money laundering supervisor: The governance of the financial intelligence units," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 1093-1109.
    10. Miroslav Gombár & Antonín Korauš & Alena Vagaská & Štefan Tóth, 2022. "Analytical View on the Sustainable Development of Tax and Customs Administration in the Context of Selected Groups of the Population of the Slovak Republic," Sustainability, MDPI, vol. 14(3), pages 1-22, February.
    11. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    12. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    13. Ardizzi, Guerino & De Franceschis, Pierpaolo & Giammatteo, Michele, 2018. "Cash payment anomalies and money laundering: An econometric analysis of Italian municipalities," International Review of Law and Economics, Elsevier, vol. 56(C), pages 105-121.
    14. Fayyaz Ahmad & Muhammad Umar Draz & Su-Chang Yang, 2018. "Causality nexus of exports, FDI and economic growth of the ASEAN5 economies: evidence from panel data analysis," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 27(6), pages 685-700, August.
    15. Sarka Hoskova-Mayerova & Jan Kalvoda & Miloslav Bauer & Pavlina Rackova, 2022. "Development of a Methodology for Assessing Workload within the Air Traffic Control Environment in the Czech Republic," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    16. Gullo, Valentina & Montalbano, Pierluigi, 2022. "Financial transparency and anomalous portfolio investment flows: A gravity analysis," Journal of International Money and Finance, Elsevier, vol. 128(C).
    17. Giovanni Cicceri & Giuseppe Inserra & Michele Limosani, 2020. "A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study," Mathematics, MDPI, vol. 8(2), pages 1-20, February.
    18. Antonín Korauš & Miroslav Gombár & Alena Vagaská & Radovan Bačík & Peter Korba & Filip Černák, 2021. "Bitcoin price as one of basic cryptocurrencies in relation to the basic stock market's indicators," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 9(2), pages 552-569, December.
    19. Maryam Mousavi & Grzegorz Zimon & Mahdi Salehi & Nina Stępnicka, 2022. "The Effect of Corporate Governance Structure on Fraud and Money Laundering," Risks, MDPI, vol. 10(9), pages 1-25, September.
    20. Stephen G. Donald & Kevin Lang, 2007. "Inference with Difference-in-Differences and Other Panel Data," The Review of Economics and Statistics, MIT Press, vol. 89(2), pages 221-233, May.
    21. Jushan Bai & Sung Hoon Choi & Yuan Liao, 2021. "Feasible generalized least squares for panel data with cross-sectional and serial correlations," Empirical Economics, Springer, vol. 60(1), pages 309-326, January.
    22. Chih-Wen Hsiao & Ya-Chuan Chan & Mei-Yu Lee & Hsi-Peng Lu, 2021. "Heteroscedasticity and Precise Estimation Model Approach for Complex Financial Time-Series Data: An Example of Taiwan Stock Index Futures before and during COVID-19," Mathematics, MDPI, vol. 9(21), pages 1-18, October.
    23. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    24. Antonín Korauš & Miroslav Gombár & Alena Vagaská & Stanislav Šišulák & Filip Černák, 2021. "Secondary Energy Sources and Their Optimization in the Context of the Tax Gap on Petrol and Diesel," Energies, MDPI, vol. 14(14), pages 1-22, July.
    25. Araujo, Luis & Camargo, Braz, 2015. "Limited monitoring and the essentiality of money," Journal of Mathematical Economics, Elsevier, vol. 58(C), pages 32-37.
    26. Jan Lánský & Jiří Mihola & Petr Wawrosz, 2022. "Mathematical Modelling of Qualitative System Development," Mathematics, MDPI, vol. 10(15), pages 1-23, August.
    27. Larissa Batrancea & Malar Mozhi Rathnaswamy & Ioan Batrancea, 2021. "A Panel Data Analysis of Economic Growth Determinants in 34 African Countries," JRFM, MDPI, vol. 14(6), pages 1-15, June.
    28. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
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