The article reveals the relevance of public financial resources management. It is noted that such management is based on general and special principles that must be taken into account when developing and making management decisions. The object, subjects and subject of public financial resources management are considered. The approaches to the interpretation of financial resources, which are ambiguous, and the classification of the financial resources of society are clarified. Analyzing various statements of scientists and conducting our own research, the differences between financial resources and capital and monetary resources are revealed. The definition of public financial resources as a component of the financial resources of society is given. The dynamics of the sources of public financial resources of Ukraine and their composition, structure, results of the distribution and use of public financial resources are reflected, and the dynamics of the ratio of Ukrainian budget expenditures and revenues of budgetary institutions during 2018-2024 are analyzed. The main problems that caused the change in approaches to reforming the public financial resources management system are revealed. The focus is on the strategic guidelines of such reform, in particular, changes in the state’s approaches to strengthening requirements for compliance with budget and tax discipline in the medium term, identification of specific areas for increasing the efficiency of the distribution of public financial resources at the level of state policy formation, development of effective management decisions and improvement of financial legislation to ensure proper implementation of state and local budgets.
The article is devoted to the study of adaptive marketing as a key element of strategic management of an enterprise in the conditions of global instability and a rapidly changing market environment. It analyzes the conceptual and practical aspects of integrating adaptive marketing strategies into the management system to ensure competitiveness and sustainable development of companies. The purpose of the study is to theoretically substantiate the role of adaptive marketing in the strategic management system of an enterprise, as well as to develop practical mechanisms for its implementation to increase the flexibility and efficiency of companies in a dynamic economic environment. The study uses a systematic analysis of scientific literature, methods of comparative analysis, business process modeling, as well as strategic management tools, in particular hypergraphic models and responsibility allocation matrices (RACI). To assess the effectiveness of adaptive strategies, three-dimensional TQC models are used, which allow for analyzing the relationship between the cost, duration and quality of marketing projects. The empirical basis was provided by examples of business processes of enterprises in various industries, as well as a generalization of modern marketing practices. The scientific results include the development of a conceptual model of adaptive marketing, which integrates the principles of adaptive management and marketing activities, as well as the formalization of business processes using directed hypergraphs that reflect the relationships between target segments, value propositions, resources and sales channels. A methodology for evaluating adaptation strategies through time-cost matrices and TQC diagrams is proposed, which allow for identifying critical stages of projects and optimizing management decisions. Prospects for further research are related to the deepening of the analysis of the impact of digital technologies, in particular artificial intelligence, on adaptive marketing strategies, as well as the development of universal algorithms for automating adaptation processes in conditions of high market turbulence.
With the rapid development of digital technology, the limitations of traditional credit supervision methods have become increasingly apparent. It is difficult to cope with the challenges of accelerating information flow and complicating social behavior. Digital credit supervision was introduced to provide a comprehensive credit portrait of stakeholders’ behavior, improving supervision efficiency and enhancing social equity and resource allocation. However, the practice of digital credit regulation faces many problems, including how to balance the relationship between regulatory efficiency and privacy protection. Moreover, the misuse of technology may result in risks such as algorithmic bias and social injustice. Hence, it is essential to study the logic and application path of digital credit regulation and clarify its development. Based on theoretical logic and application path, this study discusses digital credit regulation and its internal logic at the economic, technological, and social levels and analyzes specific cases to identify the key links and mechanisms of digital credit regulation in practice. Through theoretical analysis and case studies, the specific practice of digital credit supervision in data collection, classification management, and risk early warning is clarified. This study argues that as an important innovation in credit governance, digital credit regulation integrates technical, economic, and social logic, providing a new path for realizing trust mechanisms in modern society. Meanwhile, the intervention of intelligent technology has made digital credit supervision more accurate and forward- looking and opened up a broader space for credit management. Findings show that digital credit supervision has significant advantages for social development. It improves the efficiency of resource allocation and promotes the standardization of economic activities, playing an irreplaceable role in the reconstruction of the trust system. However, it faces multiple challenges, such as technology, systems, and internationalization. This study argues that in the future, it is necessary to promote the sustainable development of digital credit supervision through technological optimization as well as legal protection to better serve social governance and economic operations. Only through multiparty cooperation and continuous optimization of regulatory models and data governance can digital credit supervision become a key force in promoting social progress.
This study provides a comparative analysis of the accounting systems of Ukraine and China, focusing on their alignment with the International Financial Reporting Standards (IFRS). The paper examines the historical evolution, regulatory frameworks, and methodological approaches to accounting in both countries. Ukraine follows a continental accounting model adapted to European standards, while China employs a mixed system that integrates IFRS-based practices with centralized state regulation. Special attention is given to the level of digitalization and automation of accounting processes, highlighting China’s implementation of the Golden Tax System and the use of artificial intelligence technologies. The study identifies key similarities between the two accounting systems, such as integration with tax authorities, while emphasizing differences in regulatory approaches, the degree of centralization, and flexibility in responding to economic changes. The research also explores the challenges and prospects for improving Ukraine’s accounting system by incorporating best practices from China, including the development of a unified digital platform for accounting and taxation, the automation of accounting processes, and the adoption of blockchain and Big Data technologies to minimize financial fraud. The findings provide practical recommendations for enhancing financial transparency and reducing administrative burdens on businesses. This research is relevant for academics, professional accountants, financial analysts, and regulatory bodies interested in optimizing national accounting systems and fostering international harmonization.
The intensive development of artificial intelligence (AI) technologies and their implementation in the business environment pose serious challenges and uncertainty regarding the further application of innovations in many areas of life. The highest level of generative artificial intelligence adoption in the areas of marketing, software engineering, and analytics has been achieved by the technology sector, thanks to the possibilities of personalization, automation, and optimization of operational processes. However, along with this, the implementation of AI has become more complicated due to the growth of cyber threats, as well as ethical and legal restrictions, particularly in traditional sectors such as manufacturing and supply chain management. Accordingly, there is a need to outline the advantages, disadvantages, and consequences of integrating artificial intelligence into real economic processes based on accumulated experience and to adjust algorithm training to optimize the development of enterprises. The article examines key problems of AI implementation in business and marketing, identifying negative consequences and potential threats associated with the incorrect use of innovative technologies. Indeed, the use of generative artificial intelligence in business processes for the purpose of creating differentiated content is related to many challenges (deep fakes, disinformation, manipulation, etc.), and there is also a serious need to regulate the system of regulatory acts at the national and global levels, which will help protect the rights and interests of all stakeholders. The results of the analysis have shown the evolution of tools and policies for regulating the use of artificial intelligence in several countries, including the EU, the USA, and China. The main problems of implementing artificial intelligence in the business environment have been identified, which are classified according to the following criteria: data problems, integration of AI with existing information systems, insufficient computing power and infrastructure, an acute shortage of qualified AI specialists, security problems, and ensuring data privacy. Their consideration requires focusing close attention on the use of artificial intelligence as a tool for implementing cyberattacks. Based on the analysis, priority strategic development vectors for companies operating in the field of AI have been identified: investing in data processing infrastructure, implementing transfer and federated learning, developing tamper-resistant models, strengthening cybersecurity, and cooperating with regulatory authorities to comply with regulations and protect personal data, such as the General Data Protection Regulation (GDPR) - an EU regulatory act that regulates the protection of individuals personal data, giving them control over their data and establishing rules for companies to collect, process, and store it, as well as the Artificial Intelligence Act (AI Act) – a new EU regulation that establishes security rules and compliance with citizens’ rights when using artificial intelligence technologies. The choice of strategies is justified depending on the size of the company, time horizon, and market opportunities, which ensures the implementation of a proactive strategy based on maximizing the use of the AI potential and minimizing the risks of its implementation.
