This study evaluates the impact of digital systems and IT audit frameworks on tax performance and integrity within tax administrations. Using international data from organizations like the World Bank, OECD (Organisation for Economic Co-operation and Development), and IMF (International Monetary Fund), the research examines the relationship between tax revenue as a percentage of GDP, digital infrastructure, corruption perception, e-government development, and cybersecurity readiness. Quantitative analysis, including correlation, regression, and clustering methods, reveals a strong positive relationship between digital maturity, e-governance, and tax performance. Countries with advanced digital governance systems and robust IT audit frameworks, such as COBIT, tend to show higher tax revenues and lower corruption levels. The study finds that e-government development and anti-corruption measures explain over 40% of the variance in tax performance. Cluster analysis distinguishes between digitally advanced, high-compliance countries and those lagging in IT adoption. The findings suggest that digital transformation strengthens fiscal integrity by automating compliance and reducing human contact, which in turn mitigates bribery risks and enhances fraud detection. The study highlights the need for adopting international best practices to guide the digitalization of tax administrations, improving efficiency, transparency, and trust in public finance.

Currently, a more significant added value is created by intellectual rather than physical capital; yet, according to the current standards of accounting, only a minor segment of intellectual capital is presented in financial accounts of enterprises as it usually does not satisfy one of the criteria of property recognition in financial accounting, namely reliable valuation. As a result, there is an increasing demand for novel methods of valuation, enabling enterprises to establish reliably the value of intellectual capital or its specific segments of a given enterprise. A number of scholars have dealt with the methods of intellectual capital valuation. However, their works analyze and classify different methods of intellectual capital valuation, and different criteria of classification are employed; hence, there is no universally accepted opinion on the issue. That is why the aim of this research was to generalize a scheme of methods of intellectual capital valuation. The main methods applied in the present study are the synthesis and generalization of academic writings, including content analysis. The results of the research and its conclusions are based on the analysis of academic investigations conducted by various authors and the resulting publications. Results. The article generalizes the methods of intellectual capital valuation, suggested by a number of scholars, provides comparisons, reveals the multiplicity of the methods, and highlights the unlimited research of the academic field. Besides, the main classifications of methods of intellectual capital valuation are provided, and the applied criteria are defined. Conclusions. It was established that more than sixty different methods for the valuation of the intellectual capital of an enterprise are available. The results of the research show that these methods are classified according to the four following features. Correspondingly, a classification scheme of intellectual capital valuation methods has been developed. It has been established that most methods of intellectual capital valuation are based on scorecard, they assess specific components; of intellectual capital and in the process of valuation do not employ monetary units of measurement. Tis reveals a lack of the studies that focus on the financial aspect of intellectual capital valuation methods.Key words: intellectual capital, valuation, valuation methods, classification of methods

Risk disclosures contribute to financial stability by providing stakeholders with a better understanding of companies’ risk exposures and risk management practices. Presently, corporate risk has been accelerated by the COVID-19 pandemic, and the level of disclosure varies across industries, companies, and organizations. Due to the strategic importance of the energy industry, the paper aims to assess COVID-19-related risk disclosure in the biggest electricity companies in Central and Eastern European countries, and to identify the main determinants of the disclosure. For this purpose, risk disclosure was assessed based on publicly available data disclosed by the 10 biggest public electricity companies operating in this region. Our findings indicate that factors such as the company’s size, leverage, and profitability do not significantly affect COVID-19-related risk disclosure in financial reports; nevertheless, COVID-19 risk disclosure in non-financial reports is significantly correlated with the company’s assets and revenues. Moreover, there is a significantly strong positive relationship between the scope of COVID-19-related risk disclosure in the management reports and the number of women on the company’s management board. COVID-19-related risk disclosure in management board’s reports is significantly higher than disclosure in non-financial reports and explanatory notes of financial statements. Our results suggest that risk disclosure is needed to mitigate information asymmetry, especially in pandemic situations.

Financial accounting information presented in financial statements should keep to rigid requirements in order to present a ‘true and fair view’ of the company. This information must be qualitative both in private and public sectors and should fully answer the needs of users regarding the accounting information. A universal model of financial reporting quality assessment has been created and tested with the case of a Lithuanian public sector’s institution. After the public sector reform, the quality of financial reporting in the public sector became higher because the users of information can compare it, make horizontal and vertical analysis, or calculate a number of ratios. DOI: http://dx.doi.org/10.5755/j01.ss.82.4.6609

Finding not typical financial operations is a complicated task. The difficulties arise not only due to the sophisticated actions of fraudsters but also because of the large number of financial operations performed by business companies. This is especially true for large companies. It is highly desirable to have a tool to reduce the number of potentially irregular operations significantly. This paper presents an implementation of NLP-based algorithms to identify irregular financial operations using comments left by accountants. The comments are freely written and usually very short remarks used by accountants for personal information. Implementation of content analysis using cosine similarity showed that identification of the type of operation using the comments of accountants is very likely. Further comment content analysis and financial data analysis showed that it could be expected to reduce the number of potentially suspicious operations significantly: analysis of more than half a million financial records of Dutch companies enabled the identification of 0.3% operations that may be potentially suspicious. This could make human financial auditing easier and more robust task.