"Scientific research" Almanac

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  EFFECTS OF GOVERNMENT BUDGET BALANCE ON ECONOMIC GROWTH

  The necessity of adequate use of public budget as a fiscal instrument for economic regulation is even more tangible in conditions of slow economic growth rate. The debate on whether the public budget can maintain a balance between economic stability and social equity has a long history. Another question is whether budget deficit or budget surplus ... The necessity of adequate use of public budget as a fiscal instrument for economic regulation is even more tangible in conditions of slow economic growth rate. The debate on whether the public budget can maintain a balance between economic stability and social equity has a long history. Another question is whether budget deficit or budget surplus would trigger higher economic growth. The main objective of the empiric analysis is to evaluate the budget deficit/ surplus impact on real economic growth rate by using econometric analysis and comparative analysis between Bulgaria and the other Member States of the European Union as well as the Balkan States (Cyprus, The Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, Slovenia, Romania, Croatia, Greece, Serbia, Turkey and North Macedonia). The specific task is to justify the need to balance the public budget and reduce the budget deficit in order to achieve sustainable real economic growth. The thesis of the research is that balancing public budget and reducing distributive and redistributive government activities contribute to higher real economic growth rate in the analyzed Bulgarian and European economies. The main results of the research prove that reduction of budget deficit and balanced government budget have a positive effect on realized economic growth rate in the analyzed European countries over the period 2007 – 2018.

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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.

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  THE ROLE OF INTERNAL AND EXTERNAL AUDITORS FOR IDENTIFYING FRAUDS IN ORGANIZATIONS

  The variety of ways and approaches to fraud necessitates the updating of measures and mechanisms to identify and limit them. By properly implementing these mechanisms and approaches, the risk assessment and the display of indicators signalling misconduct contribute to reducing them and improving the effectiveness of the control impact. The ... The variety of ways and approaches to fraud necessitates the updating of measures and mechanisms to identify and limit them. By properly implementing these mechanisms and approaches, the risk assessment and the display of indicators signalling misconduct contribute to reducing them and improving the effectiveness of the control impact. The relevance of the study is due to the growing threat of spreading fraud and the need for adequate and timely measures to limit them. The purpose of the research study is to outline the possible actions to curb fraud, applied by internal and external auditors, as well as to examine the methodology used in the preparation of fraud risk assessments. The tasks are as follows: to clarify the role of internal audit in combating fraud; examining methodological guidelines for fraud risk assessment; implementation of anti-fraud management measures and adequate audit procedures. The thesis being defended is that fraud is a complex phenomenon and the role of internal and external auditors is essential for detecting and limiting it by applying innovative and adequate methods. The study substantiates the relationship between management actions and the ability to prevent and detect fraud, as well as the conditionality of audit procedures, performed by external auditors on the type and nature of possible fraudulent financial reporting schemes.