Econometric Modeling of the Influence of Relevant Indicators of Gender Policy on the Efficiency of a Banking System

The wide variety of schemes to use companies for money laundering, such as oil smuggling, illegal gas sales, misappropriation of the Central Bank refinancing, misappropriation of bank funds and state-owned enterprises, form the research issues. The sample under study includes 102 countries around the world, which are closely monitored by the Financial Action Task Force (FATF) and have different levels of sociopolitical and economic development. The scientific and methodological approach to assess the financial monitoring risk in terms of the use of financial institutions for money laundering is based on the methods of multidimensional static analysis, descriptive, cluster and dispersive data analysis, gravity theory, nonlinear econometric modeling, differential and bifurcation analysis of dynamic nonlinear systems. The result of the study is a developed model of comprehensive risk assessment for the countries' financial institutions for money laundering, which considers grouping of countries by the

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“…The evaluation methodology will be presented in four stages (Zarutska et al, 2020;Kuzmenko et al, 2018).…”

Section: Risk Assessment By Gravity Modeling Methodsmentioning

confidence: 99%

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

Lyeonov

Żurakowska-Sawa

Kuzmenko

et al. 2020

Journal of International Studies

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“…Saving funds outside the centralized financial sector of the economy in the form of cash savings in foreign or national currency shows erosion of trust at the institutional (to the national currency, the central bank (the National Bank of Ukraine), individual financial corporations (bank, insurance company, non-state pension fund, etc.)) and system levels (Djalilov et al, 2015;Halbusi & Tehseen, 2018;Baburina et al, 2017;Buriak et al, 2015;Merenkova & Boyko, 2009;Kuzmenko & Koibichuk, 2018, Kuzmenko & Kyrkach, 2014Onyshchenko & Chen, 2017;Luchko et al 2019;Simionescu, 2019).…”

Section: Methodsmentioning

confidence: 99%

Trust cycle of the finance sector and its determinants: The case of Ukraine

Brychko¹,

Polách²,

Kuzmenko³

et al. 2019

Journal of International Studies

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The paper presents the results of exploring the characteristics of trust cycle in the financial sector of the economy in general and across its main components (trust in banking and non-bank financial and credit systems) illustrated by the economy in transition-Ukraine-for the period 1st quarter 2009-1st quarter 2018. Determinants of nonlinear dynamics of public trust in the financial sector of the economy were also identified. For this purpose, an algorithm has been developed that enables studying the cyclical nature of public trust in the financial sector and testing the hypotheses about the determinants of its nonlinear dynamics. To highlight the cyclical component of the time series, harmonic analysis via Fourier transformations was used. In order to analyze public trust in the financial sector and its main components the following indicators were used: absolute volatility, relative volatility (in relation to financial and business cycles), the persistence of actual values and cyclic components, correlation with financial and business cycles. Our main

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“…Gender is one of the least studied factors in human development. The influence of gender is due to differences in the socialization of men and women, as well as economic factors related to structural market changes [1][2]. The gender dimension of human potential is largely due to legislation regulating gender segregation [3][4][5].…”

Section: қазақстан республикасы ұлттық ғылым академиясы «қр ұға хабаршысы» ғылыми журналының Web Of Science-тің жаңаланған нұсқасы Emergimentioning

confidence: 99%

Is Gender Inequality a Factor in Female's Brain Drain? Possible Measures of Public Regulation

et al. 2021

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Econometric Modeling of the Influence of Relevant Indicators of Gender Policy on the Efficiency of a Banking System

Cited by 14 publications

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

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

Trust cycle of the finance sector and its determinants: The case of Ukraine

Is Gender Inequality a Factor in Female's Brain Drain? Possible Measures of Public Regulation

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