Exploring the drivers behind tax base erosion: evidence from Poland’s banking sector

This research delves into the intricate dynamics of financial risks—specifically credit, market, and operational risks—within the banking, investment, and corporate sectors, with a focus on both global and Indonesian contexts. By examining the key factors contributing to credit risk, the impact of global market volatility on financial stability, and the operational risks associated with the digital transformation of the financial sector, the study seeks to offer a comprehensive analysis that is both theoretically robust and practically relevant. This research employs a qualitative systematic literature review (SLR) to explore credit, market, and operational risks within the banking, investment, and corporate sectors, focusing on global and Indonesian contexts. The SLR process includes formulating research questions, identifying and screening relevant literature from databases like Scopus and Google Scholar, and synthesizing findings into key themes: credit risk dynamics, market volatility, operational risks in the digital age, and integrated risk management. This research provides a comprehensive analysis of financial risk management in the banking, investment, and corporate sectors, with a focus on Indonesia and global perspectives. The study reveals that digitalization has a significant impact on operational risk, enhancing efficiency but also increasing vulnerability to cybersecurity threats and disruptions. This underscores the need for robust risk management frameworks to address technology-driven challenges. The research also highlights the importance of improving risk disclosure transparency, which can positively influence credit risk management. Liquidity risk is identified as having a greater short-term impact on financial stability than credit risk, necessitating proactive liquidity management strategies. Technological innovations in finance are found to correlate with increased risks, including failures and cybersecurity threats, which must be carefully managed. The study examines the risks associated with platform-based financing models and the influence of global market volatility on investment strategies. In Indonesia, the banking sector faces distinct credit risk challenges due to high market concentration and systemic shocks, as well as operational risks from rapid digital transformation. The research emphasizes the necessity for Indonesian financial institutions to implement comprehensive cybersecurity measures, maintain resilient IT infrastructure, and utilize advanced monitoring tools to address these emerging risks. The study also stresses the importance of adopting integrated risk management frameworks that account for the interdependencies between credit, market, and operational risks in a globalized market.

PurposeThe purpose of this paper is to explore the effect of monetary policy on bank risk in the banking system in some MENA countries. It explores how some economic and credit indicators affect the level of risk in the banking sector. It combines many factors that could affect banks’ risk appetite such as macroeconomic conditions, banks’ credit size and lending growth. The authors use nonperforming loans as a proxy for banking sector risks. At first, the authors have analyzed the linear relationship between monetary policy and credit risk. As mentioned above, nonlinearity is expected in the underlying relationship, and, thus, they have investigated the nonlinear relationship to deeply analyse the relationship using the dynamic panel threshold model, as stimulated by Kremeret al.(2013). Threshold models have gained a great importance in economics and finance for modelling nonlinear behaviour. Threshold models are useful in showing the turning points in the behaviour of financial and economic indicators. This technique has been applied in this study to study the effect of monetary policy on credit risk.Design/methodology/approachThis paper is divided into the following sections: Section 2 which previews the recent literature; Section 3 which includes some stylized facts about the relationship between credit risk and monetary policy; Section 4 which deals with the model and methodology; Section 5 which handles the data sources and discusses the results, and finally Section 6 which is the conclusion. The paper adopts dynamic panel threshold model of Kremeret al.(2013).FindingsThe results show that the relationship between monetary policy and credit risk is positive and significant to a certain threshold, 6.3. If the lending interest rate is higher than 6.3, this increases the credit risk in the banking sector, because increasing the lending interest rate imposes huge burdens on the borrowers, and, therefore, the bad loans and nonperforming loans become more likely. Thus, the MENA countries need to decrease the lending interest rate to be less than 6.3 to reduce the effect of monetary policy on credit risk. Further, these results are qualitatively robust regarding the inclusion of additional control variables, using alternative threshold variables and further endogeneity checks of the credit risk, such as Risk premium and the squared term of the lending interest rate. The results of taking the risk premium and the squared term of the lending interest rate as a threshold served the analysis and confirmed the positive relationship between monetary policy and credit risk above a certain threshold. As for the risk premium, the relationship below the threshold was negative and significant. Other related research points might be a good avenue for the future research such as applying this approach to micro data of banks from different MENA countries. Also, more sophisticated approaches like time-varying panel approach to assess the relationship over the time can be applied.Originality/valueThe importance of this paper lies in the fact that it does not only study the effect of time, but it also focuses on the panel data about some economic and credit indicators in the MENA region for the first time. This is because central banks in the MENA region have common characteristics and congruous level of economic growth. Therefore, to study how the monetary policy affects those countries’ credit risks in their lending policies, this requires careful analysis of how the central banks in this region might behave to control default risks.

Purpose The purpose of the research is to study conceptual foundations and methodology of evaluation of effectiveness of state tax policy and to determine effectiveness of state tax policy that is implemented in modern Russia. Methodology For complex evaluation of effectiveness of state tax policy, the proprietary methods, which allows combining advantages of both existing conceptual approaches and overcoming their drawbacks, is used. This method envisages evaluation of effectiveness of state tax policy through separate calculation of the value of financial indicator and values of non-financial indicators with the following unification and treatment of the received results. Results It is showed that the modern Russian state tax policy is peculiar for low effectiveness. The main reason for that is insufficiently successful implementation of the most important function of the taxation system—provision of collection of taxes for state budgets of all levels of the budget system—due to deficit of the consolidated state budget of the Russian Federation and critically large volume of tax evasion (shadow economy). Costs of tax policy exceed its positive results by more than two times, even without consideration of expenditures for state tax administration and control. In addition to this, state tax policy in Russia does not fully conform to the declared principles of stability, transparency, justice, and stimulation of national interests. Recommendations it is concluded that low effectiveness of tax policy of the state could be one of the reasons of non-optimality of modern Russia’s taxation system. That’s why increase of effectiveness of state tax policy is recommended for optimization of this system.

Comment

This study examines the nexus between liquidity and credit risks and their impact on bank stability in Nigeria. In order to achieve the research objectives, this study utilizes secondary data, which covers 12 Nigerian banks from 2010 to 2021. The Generalized Method of Moment (GMM) was estimated using the Arellano and Bond estimation technique. The results revealed that credit and liquidity risks negatively and significantly impacted bank stability individually and jointly in Nigeria. Furthermore, we deduced a positive correlation between credit and liquidity risks, with the correlation result statistically significant. Further investigation using the Pairwise Dumitrescu-Hurlin Panel Causality Tests indicated a one-way (uni-directional) causality from liquidity to credit risk. Further analysis showed that other internal bank-related indicators significantly impact bank performance. Bank Size, Equity, and Capital Adequacy positively and significantly impact bank performance. Likewise, macroeconomic indicators such as economic growth positively and significantly impact bank performance. In contrast, the inflation rate has a negative but insignificant impact on bank stability in Nigeria. Based on the findings, the study recommends joint management of credit and liquidity risks since a rise in liquidity risk will increase credit risk, resulting in bank instability. Thus, the results support bank regulation emphasizing the reduction of credit and liquidity risks in the banking sector since credit and liquidity risks have an attendant adverse effect on bank stability. The study will help bank managers balance liquidity, profit maximization, and risk minimization.

<p>Credit risk refers to the probability of financial loss resulting from a borrower’s failure to repay a loan. Essentially, it encompasses the risk that a lender may not receive the owed principal and interest, leading to disrupted cash flows and increased collection costs. Lenders can mitigate credit risk by analyzing factors related to a borrower’s creditworthiness, such as their current debt load and income. In the last decade, many banks have started to make use of models to assess the risks of lending credit. The credit risk models are very complex and include algorithm-based methods of assessing credit risk. Such a model aims to help banks quantify, aggregate, and manage credit risk. Despite the method, the focus of credit risk assessment stays on credit quality and risk exposure. Strategies to reduce losses and manage risks are pertinent in credit risk management. However, banks have to organize and manage the lending function professionally and proactively and use advanced techniques to measure and manage risks. Credit risk management has become a hot topic due to the ongoing global economic crises, the rapid digital transformation, the recent technological innovations, and the growing use of artificial intelligence in banking. Regulators expect banks to have a clear and comprehensive understanding of their customers and their credit risk and to be transparent and capable in this area. As the Basel regulations change, banks will face more regulatory pressure. To meet the changing regulatory demands and to manage risk better, many banks are changing their credit risk practices. However, banks that see this as only a compliance issue are missing the point. The research findings indicate that the bank’s credit management practices have strongly influenced its profitability. In brief, manoeuvring through the complex risk terrain poses a significant hurdle for financial organizations. This requires ongoing adjustments and strategic choices to ensure both stability and profitability. Consequently, persistent academic research is essential to guide management, governments, and regulators.</p><p><strong> </strong></p><p><strong>JEL</strong>: G21, G32, G33, C53, E58, D81</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0771/a.php" alt="Hit counter" /></p>

Credit risk represents a threat not only to financial stability, but also to the slow recovery from the Great Recession in most countries of the world, and especially in Serbia. Previous studies of this problem are usually focused on banking sector, seeking solutions in the area of credit risk management, new prudential measures, more effective monitoring, high Capital Adequacy and multiplied collateral. In this paper we made a step forward from the standard framework, analyzing the correlational and causal relationship of credit risk and business performance of private enterprises, the dominant debtor of domestic banks. We particularly investigate the sectoral dispersion of credit risk, which reflects the business performance and the resistance of each sector to the impacts of the recession. We evaluated the business performance of sectors with the highest share in non-performing loans (NPL) and the highest credit risk indicator (NPL ratio). Shifting the focus of research to relationship between corporate sector and banking sector, allows us to shed light on the fundamental causes of the increasing credit risk. As a result, a real range of the system of credit risk management, that was standardized by pre-crisis Basel II document, and now by the new Basel III Capital Accord, can be evaluated. Finally, we explained the relationship between credit and systemic risk, that shows what kind of danger a high credit risk constitutes in the economy of any country.

To what extent do car buyers undervalue future fuel costs, and what does this imply for the effectiveness of alternative tax policies? To address both questions, we show it is crucial to account for consumer heterogeneity in mileage and other dimensions. We use detailed product-level data for a long panel of European countries, and exploit variation in fuel prices by engine type. We find there is only modest undervaluation of fuel costs. As a consequence, fuel taxes are unambiguously more effective in reducing fuel usage than product taxes based on fuel economy, because fuel taxes better target high mileage consumers.

The banking sector is essential to the global economy, functioning as a central financial hub responsible for allocating funds, providing essential services, and assessing economic health. However, this pivotal role exposes banks to various risks, including geopolitical, credit, and financial risks. These risks arise from the banking sector’s role as a financial facilitator, its global reach, and its handling of complex financial instruments. A quantitative approach is employed in this research, utilizing multiple regression analysis to analyze the impact of geopolitical, credit and financial risks on bank performance in the ASEAN region. The data analyzed from 2013 to 2022, using a purposive sample yielding 690 samples, reveals that geopolitical risk, credit risk, and financial risk have a negative impact on bank performance. These findings offer valuable insights for policymakers and regulators, informing the development of targeted regulations to address the specific risk landscape faced by banks and potentially enhancing financial stability.

This study aims to fill the gap in the literature by specifically investigating the impact of country risk on the credit risk of the banking sectors operating in Brazil, Russia, India, China, and South Africa (BRICS), emerging countries. More specifically, we explore whether the country-specific risks, namely financial, economic, and political risks significantly impact the BRICS banking sectors’ non-performing loans and also probe which risk has the most outstanding effect on credit risk. To do so, we perform panel data analysis using the quantile estimation approach covering the period 2004–2020. The empirical results reveal that the country risk significantly leads to increasing the banking sector’s credit risk and this effect is prominent in the banking sector of countries with a higher degree of non-performing loans (Q.25 = − 0.105, Q.50 = − 0.131, Q.75 = − 0.153, Q.95 = − 0.175). Furthermore, the results underscore that an emerging country’s political, economic, and financial instabilities are strongly associated with increasing the banking sector’s credit risk and a rise in political risk in particular has the most positive prominent impact on the banking sector of countries with a higher degree of non-performing loans (Q.25 = − 0.122, Q.50 = − 0.141, Q.75 = − 0.163, Q.95 = − 0.172). Moreover, the results suggest that, in addition to the banking sector-specific determinants, credit risk is significantly impacted by the financial market development, lending interest rate, and global risk. The results are robust and have significant policy suggestions for many policymakers, bank executives, researchers, and analysts.

This study investigates the effects of liquidity and credit risks on the stability of banks with empirical evidence from the Afghanistan banking sector over the period 2014–2020. The stability of a bank is measured through the dependent variable of its capital adequacy ratio. Credit risk (calculated by the ratio of impaired loans) is included as an independent variable along with liquidity risk. The bank specific factors, namely bank net interest margin, size of the bank, return on assets, loan growth rate, liquidity gap, return on equity, loan to asset and macro-economic factors, inflation and GDP growth rate are included as control variables. This study includes all 10 operationalized banks in Afghanistan, excluding the two branches of foreign banks. The panel dataset was collected from banks’ websites and the macroeconomic data was derived from World Bank reports. This study employed the simultaneous equation approach of a two-stage least square and a fixed effect panel regression model to investigate the affiliation between liquidity and credit risks and their effects on the stability of banks. The results of this study indicate that liquidity and credit risks don’t have a mutual relationship, while the interaction of both types of risks jointly impacts bank stability. It shows that NIM, loan assets, ROA, liquidity gap, loan growth rate, and ROE have positive impacts on bank stability, whereas the size of the bank has negative effects on bank stability. Among the macroeconomic variables, only the growth rate of GDP signifies a negative effect on the stability of banks. The finding under this paper recommends that the governance body of the banking sector drafts policies aimed at strengthening bank capital and taking liquidity measurement according to the best standards introduced by the Basel committee. Also, to create frameworks for measuring liquidity and capital standards.

The global COVID-19 pandemic has greatly affected people, especially in the economic and banking sectors. The Indonesian Financial Services Authority (Otoritas Jasa Keuangan, OJK) issued a credit restructuring policy, effective from March 2020 to March 2022, to reduce credit and bank capital risk. This study proposes the bank risk scenario after the credit restructuring policy of the OJK moratorium in March 2022 and proposes the internal bank policy simulation to mitigate credit and capital risks in terms of Non-Performing Loan (NPL) and Capital Adequacy Ratio (CAR). The difficulty of this study is how to develop the risk scenario and to simulate the bank risk mitigation policy after the policy moratorium, while the COVID-19 pandemic is still ongoing and the economy is not yet normal. To that purpose, this study uses a system dynamics methodology with Powersim Studio 10© software that is able to make scenarios on the level of credit risk (NPL) and bank capital (CAR) and able to simulate internal bank policy to overcome the risk by considering the environmental and policy changes. Based on the policy simulation, it is recommended that bank can implement the restructuring policy to control the credit risk and strengthening the NPL monitoring activity in order to manage and decrease the loan impairment expenses. To increase CAR, the result shows that the combined policy consists of the NPL monitoring program, the interest rate and the operating cost management program is able to produce a significant increase in bank’s capital (CAR). The original contribution of this study is to provide new model of credit and capital risk scenario and risk mitigation simulation during the COVID-19 pandemic. The advantage of this study is that the model can be tested and implemented to other banks.

Credit risk constitutes a pivotal concern within the purview of commercial banking operations, commanding profound significance for regulators, the general public, and investors alike. In pursuit of a comprehensive comprehension of the intricacies of credit risk within the banking sector, this study selects a diverse cohort of 18 publicly traded commercial banks in China as its focal subjects. Employing financial and stock data from the year 2019, this research leverages the advanced KMV model to evaluate these banks’ default risks and subsequently calculate their respective default distances. The innovation of this paper lies in that we will use KMV model to quantify the credit risk level of commercial banks, and empirically study the credit risk of Chinese commercial banks through the case study of 18 listed commercial banks with different nature. The KMV model, a well-established approach for the assessment of default risk, computes the default distance predicated upon the market value’s volatility and the balance sheet’s structure, encompassing considerations of the debtor’s probability of default and default loss rate. Default distance denotes the discernible disparity between the debtor’s prevailing market value and the precipice of default, serving as a reliable metric to gauge the probability of a debtor’s default occurrence. This study meticulously curates financial and stock data pertaining to the 18 selected banks, subsequently subjecting them to rigorous evaluation through the KMV model. Through a meticulous analysis of the default distances thus derived, this research unveils the divergent spectra of default risks across banks of varying profiles, further elucidating potential risk factors. These analysis results are of important reference value for regulators and investors. Regulators can identify the potential high-risk institutions more accurately by analyzing the default distance of banks, so as to strengthen targeted regulatory measures and ensure the sound operation of the entire banking industry. At the same time, investors can also refer to these analysis results when making investment decisions to understand the credit status and risk levels of different banks, so as to better diversify risks and optimize the investment portfolio. In short, through the analysis of the default distance of banks, we can have a deeper understanding of the differences and potential risk factors in the default risk of banks of different properties, so as to provide strong decision support for regulators and investors.

The article is devoted to the assessment of financial risks of the banking sector, their impact on the activity of banks and their minimization in conditions of economic instability. The banking sector is a key link that ensures the development of the financial system, but banking activity is risky. Therefore, it is important to study banking risks and ways to minimize them in order to increase the efficiency and stability of the banking system. The article provides a classification of banking risks in accordance with domestic literature. Factors due to which banking institutions are exposed to risks are reflected. Financial risks of the banking sector affect the net profit, capital of the bank. Therefore, the impact of risks on the financial stability and efficiency of Ukrainian banks was analyzed. The bank's regulatory capital is one of the most important indicators of its activity, the main purpose of which is to cover the negative consequences of various risks. During the entire studied period, there is a tendency to increase this indicator, which is positive. Due to external risks, namely the difficult military and economic situation, the profit of Ukrainian banks decreased. The credit risk, which is one of the most significant in banking activity, is characterized. The causes of credit risk in banking are given. The dynamics of the assets of Ukrainian banks, the volumes of loans granted, were studied. It was concluded that in modern conditions of banking activity, there is a slight slowdown in lending to the population and an increase in the volume of loans granted to business entities. To reduce credit risks, commercial banks use various methods to protect against credit risk: diversification, limiting, insurance, risk distribution. The banking sector cannot exist without risk, so it is necessary to look for ways to minimize them. Minimizing the level of financial risks in the banking sector involves the implementation of risk management measures and methods. Measures to minimize banking risks include preventive and protective measures. Banking risk management methods include: methods of avoiding and accepting banking risks. The article reflects the peculiarities of these measures and methods. Keywords: financial risks, banking sector, bank profit, credit risk, bank assets, bank risk minimization.

Credit risk is an integral element of banking activities, the identification and effective management of which is essential in today’s environment. The financial stability of both individual banks and the banking sector as a whole depends on the feasibility and effectiveness of risk management. The purpose of the study is to systematise the approaches approved at the state level to assessing the credit risk of banks, analyse the current state of the loan portfolio of the Ukrainian banking sector, and identify the most promising methods of optimising potential risks to its quality.The study examines the essential features of the bank credit risk as well as the most common methods for its assessment. On the basis of a deep analysis of the Resolution of the National Bank of Ukraine No. 351, a detailed algorithm for calculating the amount of bank credit risk is proposed, and its main principles, which are important for the activities of domestic banks, are described. A statistical study of the quality of the loan portfolio of the banking sector of Ukraine for the time horizon of 2018 – 2024 is carried out and a gradual adaptation of the sector’s entities to the unstable conditions of the operating environment is revealed. The results of the analysis also pointed to a deterioration in the level of credit risk coverage of non-performing customer debt in the period 2022 – 2024, which may have negative consequences for the banking sector’s resilience. Based on the results of the study, the possible methods of optimising credit risk have been proposed, which application will have a significant positive effect on the financial stability of the sector and, as a result, the development of the national economy.A promising area for further research is the study of the possibilities of introducing digital tools for monitoring, controlling and preventing the growth of credit risk in banks.