"Scientific research" Almanac

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  CHARACTERISTICS OF FRAUD AND PERPETRATORS OF FRAUD

  The development of economic processes and activities raises the need for adequate knowledge and skills related to fraud detection. The relevance of the study is related to the continuous change and development of the means and approaches used by the control bodies, the emerging phenomena and processes requiring innovative thinking and the ... The development of economic processes and activities raises the need for adequate knowledge and skills related to fraud detection. The relevance of the study is related to the continuous change and development of the means and approaches used by the control bodies, the emerging phenomena and processes requiring innovative thinking and the developing technologies and software tools for financial fraud. The purpose of the research study is to identify the inherent characteristics of financial fraud and their perpetrators by analysing the regulations governing its manifestations and systematizing the types of financial fraud. The goal is achieved by performing the following tasks: defining the concept of "fraud"; outlining the main features of financial fraud; analysis of the legal framework; systematizing the types of financial fraud by certain indicators; examining the profile of the perpetrator of financial fraud. The thesis being defended is that financial fraud is a dynamic and complex phenomenon that requires adequate measures by the control and judicial authorities, taking into account the changes in the profile of the perpetrators and the methods of committing misconduct. As a result of the study, the legal regulation of financial fraud was systematized, criteria for their differentiation were drawn up and the characteristics and profile of the perpetrators of financial fraud were drawn up.

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  EVALUATION OF CONVERGENCE IN SOCIAL PROTECTION EXPENDITURE IN THE EUROPEAN UNION

  The purpose of this paper is to evaluate the degree of convergence across the EU Member States in social protection expenditure for the period 2008-2016 by using statistical and econometric approaches for measuring convergence. This is achieved by accomplishing several tasks: presentation of theoretical concepts and statistical and econometric ... The purpose of this paper is to evaluate the degree of convergence across the EU Member States in social protection expenditure for the period 2008-2016 by using statistical and econometric approaches for measuring convergence. This is achieved by accomplishing several tasks: presentation of theoretical concepts and statistical and econometric approaches for convergence evaluation; exploring the convergence process through the club convergence approach; exploring the convergence process through the beta-convergence approach across European Union Member States as a whole, and between countries in individual convergence clubs; analysis of the impact of structural variables on social protection expenditure and the cohesion process. The thesis defended by the author is that by using the applied approaches for the study of the club convergence and beta-convergence, an adequate evaluation of the degree of convergence of the EU Member States is achieved. This makes it possible to establish the rate of convergence to the general equilibrium both within the European Union and within separate convergence clubs. As a result of the study of club convergence, convergence clubs, clubs in a process of transitional divergence, as well as countries that are not involved in that process are identified. The study of beta-convergence shows a process of reducing disparities in convergence clubs and the European Union as a whole. The findings of the study indicate that the additional variables in the models of conditional beta-convergence have a significant impact on the increase in the social protection expenditure and the cohesion process. EU countries converge at a faster pace into the general equilibrium level of social protection expenditure when it is presented as a percentage of Gross Domestic Product.

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  DEEP NEURAL NETWORKS FOR DETECTION OF CREDIT CARD FRAUD

  This publication presents research on the possibility of using Deep Neural Net-works (DNNs) for detection of credit card fraud. The research is based on an open da-taset and aims to determine how the varying number of hidden layers and other archi-tectural parameters (width of each layer, activation functions) affects training and predictive ... This publication presents research on the possibility of using Deep Neural Net-works (DNNs) for detection of credit card fraud. The research is based on an open da-taset and aims to determine how the varying number of hidden layers and other archi-tectural parameters (width of each layer, activation functions) affects training and predictive potential of the network. It also aims to determine how to use generative-adversarial approach efficiently in order to overcome the problem with the imbalance between legitimate and fraudulent transactions, which is crucial characteristic of the datasets related to detection of credit card fraud. The publication presents the results of various experiments, conducted to achieve the stated goals. A number of experiments have been carried out combining network training of various depth of the network and with different parameters of the learning process, for example, activation function and dropout values. The experiments focus on using generative-adversarial approach as a method for oversampling, in par-ticular. The results indicate that deep neural networks with moderate hidden layers, combined with methods for oversampling are performing much better in identifying fraudulent transactions than the classical neural networks, consisting of only one hid-den layer. The results also indicate that using generative-adversarial approach as a method for oversampling is comparable in efficiency to other widely used techniques.