Data mining and bifurcation analysis of the risk of money laundering with the involvement of financial institutions

Abstract

The current trends of globalization, the integration of banks and insurance companies worldwide into a single financial conglomerate, as well as the emergence of new electronic payment instruments, force governments of different countries to search for new approaches to analyse the risks of involvement of financial institutions in money laundering. The research explains how to use the data mining and bifurcation analysis based on the limited information on general indices of a country's characteristics to evaluate the state's resilience to the involvement of its financial institutions in money laundering. The purpose of the article is to develop a scientific and methodological approach to assessing the risk of using financial institutions in money laundering. It is based on the study of the dynamic stability of this risk on the basis of bifurcation theory. Empirical calculations show that for a group of countries, to which Ukraine belongs, the dynamic system is in a non-equilibrium state and is described as a phase portrait "saddle". Therefore, the risk of using financial institutions for money laundering is high in Ukraine, although it is under certain control by the state. However, the calculations show that under conditions of the partial reform of the anti-money laundering system in Ukraine, the system will lose its conditional stability and the corresponding risk will increase even more

Gravitational and intellectual data analysis to assess the money laundering risk of financial institutions

“…According to the bifurcation theory and the variety of phase picture of two-dimensional space, we describe the money laundering risk of the Ukrainian financial institutions (Kuzmenko et al, 2020 The obtained trajectories of phase picture have the type of bifurcation "unstable focus" (Fig. 1) and "unstable node" (Fig.…”

Structural modelling for assessing the effectiveness of system for countering legalization of illicit money

“…( 2015) assessed the riskiness of the bank s client regarding involvement in money laundering using panel data regression modelling and log-linear modelling,. The Data Mining methods, namely the bifurcation analysis, determine the participation of financial institutions in the money laundering process (Kuzmenko O. et al, 2020;Papík M., andPapíková L., 2021). Subeh M. A. andBoiko A.…”

The impact of digital transformation on macroeconomic stability: Evidence from EU countries

“…In general, digital transformation of the economy influence the level of GDP (Chou and Chin, 2011;Vasylieva et al, 2020;Obeid et al, 2020;Melnyk et al, 2018;Tiutiunyk et al, 2021), the competitive advantage of business (Bondarenko et al, 2020;Petroye et al, 2020;Chigrin and Pimonenko, 2014), its investment potential (Kliestik et al, 2020;Zolkover and Georgiev;2020;Kotenko & Bohnhardt;Kuzmenko et al, 2020), indicators of its financial (Kuek et al, 2021;Leonov et al, 2019) and labor (Smiianov et al, 2020;Didenko et al, 2021) markets, ecology security (Vasylieva et al, 2019;Lyeonov et al, 2019) etc.…”