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This paper aims to investigate the correlation between banking sector non-performing loans (NPLs) and the level of sustainable development.

Pearson correlation test statistic was used to assess the correlation between bank NPLs and sustainable development.

There is a significant positive correlation between banking sector NPLs and the level of sustainable development measured by the sustainable development index (SDI). The significant positive correlation is evident in European countries and in countries in the region of the Americas. There is a significant negative correlation between banking sector NPLs and achieving SDG3 and SDG7 in African countries and European countries. There is also a significant negative correlation between NPLs and achieving SDG10 in European countries. There is a significant positive correlation between banking sector NPLs and achieving SDG4 and SDG7 in the region of the Americas. There is also a significant positive correlation between NPLs and achieving SDG10 in African countries and in countries in the region of the Americas.

The present study is unique and different from other studies because it used a unique SDI to capture the level of sustainable development. The analysis is also unique because it covers several regions, which have not been covered in previous studies.

This paper examines the correlation of economic policy uncertainty (EPU) with nonperforming loans and loan loss provisions for 22 major developed countries over the 2008–2017 period.

The study used the Pearson correlation methodology to assess the correlation between EPU, bank nonperforming loans and loan loss provisions.

The findings reveal that EPU is negatively correlated with nonperforming loans and loan loss provisions in the banking sector of EU countries but not for non-EU countries. Also, EPU is negatively correlated with nonperforming loans in the banking sector of the most advanced economies – the G7 countries, while loan loss provisions are more responsive to changes in EPU than NPLs in EU countries.

The implication of the findings is that the correlation of EPU with loan loss provisions and nonperforming loans is influenced by regional characteristics.

This study is the first to analyze the association of EPU with bank nonperforming loans and loan loss provisions under regional classifications such as the EU, non-EU and the G7 countries. This study provides insights on how regional differences might explain the co-movement of EPU with bank nonperforming loans and loan loss provisions.

The purpose of this paper is to empirically assess the significant indicators of macroeconomic environment that influence credit risk in high-income countries.

The study employs the system generalized method of moments estimator to avoid the dynamic panel bias and endogeneity issues. Different indices of economic growth are used in each model in order to find the most significant proxy of the economic cycle that influences problem loans. The analysis is carried out using a sample of 49 developed countries covering a 16-year period (2000–2015).

The overall empirical results highlight that the development of industrial sectors and exports are the main drivers of loan performance in high-income countries. The findings specifically recommend adopting an expansionary fiscal policy to boost per capita income and potential productivity for the safety of the banking system.

The findings have direct practical applicability for stabilizing the financial system. The study recommends the government to increase the productivity of export-oriented industries in order to boost employment and increase the payment obligations of individuals and business firms. More importantly, it highlights the essentiality of perfect economic policy to control default risks.

To the best of the authors’ knowledge, this is the first empirical study that compares the relative effect of three alternative proxies of the economic cycle on credit risk and identifies the most significant proxy. The current study also empirically shows that industrial development could be one of the crucial factors to improve financial health in developed countries.

The 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 Kremer et 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.

This 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 Kremer et al. (2013).

The 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.

The 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.

This study aims to examine and compare the credit risk management (CRM) scenario of Islamic banks (IBs) and conventional banks (CBs) in Pakistan, keeping in view the phenomenal growth of Islamic banking and its future implications.

A sample of five CBs and four IBs was chosen out of the whole banking industry for the study. Secondary data obtained from the banks’ annual financial reports for 13 years, starting from 2004 to 2016, were analyzed. Multiple regression, correlation and descriptive analysis were used in the examination of the data.

The results show that loan quality (LQ) has a positive and significant impact on CRM for both IBs and CBs. Asset quality (AQ), on the other hand, has a negative impact on CRM in the case of IBs, but has a significantly positive relation with CRM in the case of CBs. The impact of 16 ratios measuring LQ and AQ have also been individually checked on CRM, by making use of a regression model using a dummy variable of financial crises for robust comparison among CBs and IBs. The model proved significant, and CRM performance of IBs was observed to be better than that of CBs. Moreover, the mean average value of financial ratios used as a measuring tool for these variables shows that the CRM performance of IBs operating in Pakistan was better than that of CBs over the period of the study.

The research findings are expected to facilitate bankers, investors, academics and policy makers to build a better understanding of CRM practices as adopted by CBs and IBs. The findings would be useful in formulating policy measures for the progress of the banking industry in Pakistan.

This research is unique in terms of its approach toward analyzing and comparing CRM performance of CBs and IBs. Such work has not been carried out before in the Pakistani banking industry.

This study examines the causal relationship between intellectual capital and asset quality of banks in Ghana.

Using annual data extracted from audited financial statements of 24 banks from 2006 to 2015, a ratio of non-performing loans to gross loans and advances is employed to estimate asset quality growths while the value-added intellectual coefficient by Pulic (2008, 2004) measures intellectual capital. The panel-corrected standard errors estimation technique is used to estimate panel regressions with asset quality as the dependent variable.

Asset quality of banks in Ghana is generally not affected by intellectual capital. However, when intellectual capital is divided into its components, the study indicates that there are significant positive relationships between asset quality and two components of intellectual capital. Thus, structural capital and human capital efficiencies positively affect the asset quality of banks.

The findings of the study implore managements of banks to increase structural and human capital investments and efficiencies to improve asset quality. Furthermore, the results have direct implications on developments in financial markets in emerging economies.

The study analyses the link between typical intellectual capital and asset quality of banks which is yet to be empirically examined in an emerging banking market.

The study examines the dynamic association between competition, risk-taking, performance and income diversification of frontier and emerging economy (FEE) banks. It additionally, explores the effect of banking sector depth and economic performance on the level of competition, performance and risk-taking behavior of banks in these economies.

The paper adopts a panel vector auto-regressive technique and collects data across ninety (90) FEEs.

The paper finds that competition increases with improvement in the depth of the banking sector, a surge in risk-taking behavior and the adoption of focused strategy by banks. Similarly, income diversification activities are driven by competition, banking sector depth, the state of the economy and bank performance. Additionally, risk-taking behavior, banking sector depth and the state of the economy are relevant in describing bank performance. Also, risk-taking behavior is influenced by bank performance, banking sector depth and economic growth.

The evidence indicates that although competition improves banking sector health, excessive competition and non-competitive banking environment constrain banks’ performance and stability.

This paper empirically investigates whether trade openness (TO) in Brazil, Russia, India, China and South Africa (BRICS) countries affects how banks might employ loan loss provisions (LLPs) to smooth out their earnings and how adopting the International Financial Reporting Standards (IFRS) can mitigate it.

The analysis includes 78 commercial banks from five BRICS nations and spans 2014 through 2020. To test these hypotheses, the authors utilized a fixed-effect and two-step system panel generalized methods of moments (GMM) estimator.

TO positively affects income smoothing (earnings management) across BRICS commercial banks. The effect is clearer in banks that make financial reports under the IFRS. Path analysis reveals that the effect of TO is driven by nonperforming loans (NPLs). Additionally, the IFRS restricts earnings management in the BRICS banking sector when a better institutional environment is present. The authors found that accounting rules (IFRS) and enforcement (better institutional settings) interact to enhance earnings’ quality.

The relationship between TO and bank earnings management practices is important for understanding the complex interplay between trade and finance and ensuring financial stability, investor confidence and regulatory compliance. This study recommends better regulations and governance mechanisms for financial reports in emerging nations like BRICS. Additionally, macro-prudential regulators and banking supervisors should work closely to ensure transparent TO decisions with improved discipline, institutional quality and regulatory support to enhance bank stability.

The study finds evidence of bank income smoothing in the BRICS and introduces TO as a determinant. It also identifies the evolving role of IFRS in the presence of higher institutional quality and TO, thereby expanding the financial reporting literature.

The contribution of the banking industry to the financial crisis of 2007/8 has raised public concerns about the financial soundness of banks around the world with many countries still suffering the backlogs of this crisis. The continuous emergence of such crises at both national and international levels increases governments', bank regulators' and financial market participants' need for reliable tools to assess the financial soundness of banks. In this context, this study investigates the financial soundness of the Kazakh banking sector, which is ranked by the World Bank as the first in the world in terms of the percentage of nonperforming loans (NPL) to total gross loans in 2012.

Using data about all Kazakh banks over the period January 01, 2008 to January 01, 2014, the study identifies a number of accounting indicators that influence the financial soundness of banks using principal component analysis (PCA). Then, it uses the outcomes of the PCA in a cluster analysis and groups the Kazakh banks into sound, risky and unsound banks at two points in time: January 01, 2008 and January 01, 2014. This methodology was further tested against a ranking system of banks and proved to be more reliable in detecting risky banks.

Fifteen financial ratios were initially selected as accounting indicators for the assessment of bank financial soundness. Using PCA, twelve indicators were isolated, which explain five principal components of capital adequacy, return on assets, profitability, asset quality, liquidity and leverage. Then using the “k-means” method, the results suggest a structure of the Kazakh banking sector on January 01, 2008 that includes two groups of banks: sound and risky banks. On January 01, 2014, this structure of the banking system has changed to include three groups of banks: sound, risky and unsound banks. Thus, in 2014 a new group of banks has emerged, i.e. financially unsound banks.

The proposed cluster-based methodology has proven to be a reliable tool to detect the financial soundness of Kazakh banks, which makes us advocate its employability for bank monitoring and supervision purposes.

This study is the first to employ a cluster-based methodology to assess the financial soundness of a banking sector. This methodology can be used at a micro-level to determine the structure of a banking sector. Also, it can be used to monitor any changes in the structure of a banking sector and provide early warning signals about the financial health of banks.

To enhance the loan repayment performance of microfinance institutions (MFIs) in Pakistan, this study aims to analyze the direct impact of social capital and loan credit terms on loan repayment performance and microenterprises’ business performance while considering the mediating role of microenterprises’ business performance on the relationship between social capital, loan credit terms and loan repayment performance.

The analysis was conducted based on the data gathered via a questionnaire distributed to 316 microenterprises owners. The respondents were selected using the stratified sampling technique by dividing the target population into three influential groups of manufacturing, trading and services microenterprises. The reliability and validity of the constructs were established using (1) factor loading, (2) Cronbach’s alpha, (3) composite reliability, (4) average variance extracted, (5) the variance inflation factor, (6) the Fornell–Larcker criterion and (7) the heterotrait–monotrait ratio. The structural equation modeling technique was then applied, and the hypotheses were tested based on the structure model generated through bootstrapping by using partial least squares structural equation modeling.

The results confirm the direct impact of social capital and loan credit terms on microenterprises’ business performance and loan repayment performance. It also supports the mediating role of microenterprises’ business performance toward the relationship between social capital, loan credit terms and loan repayment performance while considering the direct impact of microenterprises’ business performance on loan repayment performance.

To date, the direct impact of social capital and loan credit terms on microenterprises’ business performance and loan repayment performance has been hardly investigated in the context of Pakistan. This study also examines the mediating role of microenterprises’ business performance toward social capital, loan credit terms and loan repayment performance. The findings will enable both MFIs and microenterprises to improve their business performance and loan repayment performance through enhanced social ties and the development of more flexible credit products that protect the borrowers’ interests and the interest of lenders.