Smart Economy, Entrepreneurship and Security

ACCOUNTING METHODOLOGY FOR STABLECOIN TRANSACTIONS IN INTERNATIONAL TRADE: RECOGNITION AND VALUATION ISSUES

2026-03-19T22:06:24+02:00

The growing use of stablecoins as a settlement instrument in cross-border trade creates a gap between the rapid evolution of digital financial instruments and the existing accounting framework, which lacks unified methodological approaches to their recognition, measurement, and disclosure. This study aims to develop theoretical foundations and practical recommendations for the accounting of stablecoin transactions in international trade, addressing the issues of identification, classification, valuation, and financial reporting presentation. The research employs a combination of general scientific and special methods: dialectical and abstraction methods to formulate the conceptual apparatus, systemic and structural analysis to construct a classification model, comparative legal analysis of IFRS and Ukrainian national standards, economic and statistical analysis of market dynamics including correlation analysis, and modelling to develop accounting procedures and chart of accounts correspondence. The study reveals that stablecoins cannot be unambiguously classified under existing IAS 38 or IAS 2 frameworks due to their hybrid nature as both a means of payment and a store of value. A classification algorithm is proposed based on the purpose of holding and the existence of an active market, which for leading stablecoins is confirmed by statistical data showing a 0.94 correlation between transaction volumes and capitalisation and a bid-ask spread of 0.08–0.12%. The comparative analysis of valuation models demonstrates that the cost model defers the recognition of impairment and precludes reflecting value increases, whereas the revaluation model provides more relevant information for users. A comprehensive chart of accounts correspondence for recording stablecoin acquisition, settlement, exchange differences, and impairment in international trade transactions is developed and substantiated. The study concludes that the current regulatory framework in Ukraine requires amendments at the by-law level, specifically to the Regulation on the Inventory of Assets and Liabilities and the Methodological Recommendations for the Accounting of Intangible Assets, to introduce provisions for virtual asset accounting and mandatory disclosure of information on accounting policies, fair value, and risk concentration. The proposed methodological approaches ensure faithful presentation of stablecoin transactions, minimise tax risks, and enhance the informational value of financial statements for stakeholders in the digital economy.

ANALYSIS OF APPROACHES TO THE INTEGRATION OF ARTIFICIAL INTELLIGENCE METHODS INTO INTELLECTUAL CAPITAL MANAGEMENT

2026-03-19T21:52:23+02:00

The article examines approaches to integrating artificial intelligence methods into intellectual capital management systems of higher education institutions in the context of forced migration and organizational transformation. A comparative analysis of the US market-oriented approach and the EU human-centric approach is conducted, revealing fundamental differences in the philosophical foundations of artificial intelligence implementation in human resources management. The research proposes a hybrid model that combines technological flexibility of American People Analytics with European ethical standards, adapted to the specific challenges faced by Ukrainian higher education institutions. The study analyzes key artificial intelligence technologies: Organizational Network Analysis for diagnosing integration and identifying isolated groups, Natural Language Processing for monitoring psychological well-being and early detection of burnout, and Learning Experience Platforms for personalized professional development. Machine learning models including classification, clustering, regression, and optimization algorithms are proposed for workload redistribution and performance prediction. The developed approach integrates exploratory data analysis, predictive modeling, and optimization techniques (convex optimization and genetic algorithms) with fuzzy logic to ensure transparency and interpretability of managerial decisions. The proposed model enables higher education institutions management to proactively forecast risks, optimize resource allocation, segment staff for targeted support, and fairly evaluate performance considering the forced displacement context.

ARTIFICIAL INTELLIGENCE IN AUTOMATED FINANCIAL RISK MANAGEMENT SYSTEMS

2026-04-28T14:46:55+03:00

The article is aimed at studying the use of artificial intelligence in automated financial risk management systems to improve the accuracy, efficiency and efficiency of managerial decision- making in the financial sector. The study consists in a comprehensive study of the theoretical and methodological foundations of the use of artificial intelligence in management systems, analysis of modern approaches to the classification and assessment of financial risks using machine learning algorithms, formation of the architecture of the decision support system based on forecasting models, as well as assessment of the effectiveness of the built models based on the results of simulation modeling. Within the framework of the study, a model for forecasting the credit risk of bank customers has been developed, which allows assessing solvency based on historical data and modern machine learning methods. The research method is modeling using machine learning tools, including neural networks and ensemble learning methods (Random Forest), as well as data analysis using platforms to visualize results and evaluate the effectiveness of models. Particular attention is paid to data preparation, selection of relevant features, evaluation of model accuracy, and construction of interpreted visualizations such as SHAP graphs, ROC curves, etc. The result of the study was the creation of an effective model for predicting credit risk, which demonstrates a sufficiently high level of classification accuracy and the ability to adapt to changes in incoming conditions. The practical significance of the study lies in the possibility of implementing the developed model into the existing automated financial risk management systems of banking institutions, which will reduce the level of credit losses, increase financial stability and provide more accurate risk management. This approach contributes to the development of intelligent financial systems, increasing the level of automation of managerial decisions and strengthening the competitiveness of financial institutions in modern market conditions.

THE ESG (ENVIRONMENTAL, SOCIAL, AND GOVERNANCE) CONCEPT IN MODERN ECONOMIC SCIENCE AND ITS ROLE IN SHAPING SUSTAINABLE DEVELOPMENT

2026-04-28T23:23:47+03:00

The assessment of the role of the ESG (Environmental, Social, and Governance) concept is an important task for ensuring sustainable socio- economic development and strengthening the resilience of modern economic systems. At the same time, within contemporary economic science, there remains a lack of unified methodological approaches that allow for a comprehensive and objective evaluation of the impact of ESG factors on sustainable development outcomes. In this context, it is particularly relevant to consider the interaction of environmental, social, and governance components, as well as the system of external and internal factors that either stimulate or constrain sustainable transformation processes. An important scientific challenge is the improvement of analytical tools, methods, and stages for assessing ESG implementation, taking into account current global economic, environmental, and institutional changes.

The study employs a set of theoretical and empirical methods that enable the achievement of the research objectives, including bibliometric analysis, statistical analysis, abstraction, synthesis, induction, and deduction. Bibliometric mapping using the VOSviewer tool is applied to identify key research trends and conceptual linkages in the scientific discourse on ESG and sustainable development. The empirical basis of the study relies on official statistical data from Eurostat, which reflect environmental and social dimensions of sustainable development, including greenhouse gas emissions, labour market indicators, and social inclusion metrics. The results of the study confirm that ESG principles play a significant role in enhancing the sustainability of economic systems, improving corporate resilience, and supporting long-term socio- economic stability. The integration of ESG factors contributes to environmental responsibility, social inclusion, and governance quality, which together form the foundation for sustainable development. Consequently, ESG performance requires systematic monitoring and quantitative assessment at both corporate and macroeconomic levels. The findings indicate an increasing need to develop and implement a new generation of public and corporate policies based on ESG principles, aimed at ensuring balanced economic growth, environmental protection, and social well-being. The proposed analytical approach may be useful for policymakers, investors, and corporate stakeholders, as it enables timely assessment of ESG impacts and supports the formulation of effective strategies for sustainable development in the future.

DEVELOPING A DRONE ECONOMY: A METHODOLOGICAL FRAMEWORK

2026-04-28T23:33:56+03:00

The emergence of the drone economy represents a trans- formative element of modern digitalized national economies, integrating unmanned aerial vehicles (UAVs) into strategic industries such as energy, agriculture, logistics, infrastructure, and defense. This study proposes a comprehensive methodological framework for assessing the development of the drone economy, addressing its technological, economic, regulatory, industrial, and sustainable development dimensions. The research employs a systems approach, combining literature review, system- structural analysis, comparative assessment, statistical normalization, and expert judgment to construct an integral index of drone economy development (DEI). In addition, a sector- specific efficiency indicator is introduced to evaluate the impact of UAV implementation on productivity, cost reduction, safety, and environmental, social, and governance (ESG) outcomes. The proposed indicators enable a holistic assessment of UAV integration, capturing both direct and indirect effects, as well as structural and institutional constraints. The framework also facilitates cross- country and cross- sectoral comparisons and provides guidance for policymakers, industry stakeholders, and investors in identifying priority areas for fostering the drone economy. The findings highlight the importance of combining technological adoption with ESG principles to achieve sustainable, efficient, and socially responsible industrial transformation. By integrating drone technologies into strategic sectors, countries can enhance competitiveness, optimize resource utilization, and promote digital and green growth. The study contributes to the literature by offering a systematic tool for evaluating the multi- dimensional impact of UAV adoption and establishing a foundation for further research on the role of drones in economic development and sustainable industrial ecosystems.