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This paper attempts to analysis the credit risk at the angle of industrial and macroeconomic factor using grey incidence analysis method.

Credit asset quality problem is one of the obstacles limiting the further development of commercial banks; the research on credit risk becomes an important part of the implementation of a commercial bank's risk management. Different industries may have different effects on the credit risk of commercial bank. This paper proposes finding out the different incidences between industries and credit risk, as well as macroeconomics. Incidence identification method is established to investigate whether the industry and macroeconomic factor could affect an impaired loan ratio of a bank using the grey incidence analysis method.

The results indicate that the impaired loan ratio differs with diverse industry's influence and the macroeconomics also affect it. From the angle of the industry, the result can also determine the risk deviation scope in the grey risk control process which offers new content and ideas within the grey risk control.

Under the guidance of the principle of “differential treatment, differential control”, this research will help to strengthen the implementation of differentiated credit policy, focus on guiding and promoting the optimization of credit structure, so as to maintain a reasonable size of credit facilities and build a steady currency credit system.

The paper succeeds in finding the top five influent industries compared with others by using one of the newest developed theories: grey systems theory.

The purpose of this paper is to identify any differences between the Islamic and non‐Islamic banks in the UAE on credit risk management.

The study uses survey based methodology for data collection. The sample for the study consists of six commercial banks from UAE with three non‐Islamic and three Islamic banks and with 148 credit risk managers as respondents for the survey. The study aims to investigate factors which distinguish between Islamic and non‐Islamic banks in UAE. This is achieved by fitting a binary logistic regression model.

The study shows that the managers in Islamic banks now do not rely only on personal experiences and simple credit risk analysis. The Islamic banks appear also to be developing and practising the newer and robust techniques, in addition to traditional methods, to manage their credit risk in UAE compared to non‐Islamic banks, which indicates a possibility of further improvement in their credit risk management.

The paper uses questionnaire‐based methodology, which has not been used previously in the UAE financial sector, as well as in studies of credit risk management. Therefore, this research could become the cornerstone of further academic research in other developing countries using this methodology.

The authors provide the reader with a simple introduction to credit derivatives. The article includes a broad overview of the market, estimates of the global market size, and a description of the most widely used products.

Proposes to investigate the current practices of credit risk management by the largest US‐based financial institutions. Owing to the increasing variety in the types of counterparties and the ever‐expanding variety in the forms of obligations, credit risk management has jumped to the forefront of risk management activities carried out by firms in the financial services industry. This study is designed to shed light on the current practices of these firms.

A short questionnaire, containing seven questions, was mailed to each of the top 100 banking firms headquartered in the USA.

It was found that identifying counterparty default risk is the single most‐important purpose served by the credit risk models utilized. Close to half of the responding institutions utilize models that are also capable of dealing with counterparty migration risk. Surprisingly, only a minority of banks currently utilize either a proprietary or a vendor‐marketed model for the management of their credit risk. Interestingly, those that utilize their own in‐house model also utilize a vendor‐marketed model. Not surprisingly, such models are more widely used for the management of non‐traded credit loan portfolios than they are for the management of traded bonds.

The results help one to understand the current practices of these firms. As such, they enable us to make inferences about the perceived importance of the risks. The paper is of particular value to the treasurers intending to better understand the current trends in credit risk management, and to academics intending to carry out research in the field.

This study investigates the relationship between credit booms and bank risk in Association of Southeast Asian Nations (ASEAN) countries, with credit information sharing acting as a moderator.

The authors use a two-step System Generalized Method of Moments (SGMM) estimator on a sample of 79 listed banks in 5 developing ASEAN countries: Indonesia, Philippines, Malaysia, Thailand and Vietnam in the period 2006–2019. In addition, the authors perform robustness tests with different proxies for credit booms and bank risk. The data are collected on an annual basis.

Bank risk is positively related to credit booms and is negatively associated with credit information sharing. Further, credit information sharing reduces the detrimental effect of credit booms on bank stability. The authors find that both public credit registries and private credit bureaus are effective in enhancing bank stability in ASEAN countries. These results are robust to regression models with alternative proxies for credit booms and bank risk.

Banks in ASEAN countries tend to have strong lending growth to support the economy, but this could be detrimental to stability of the sector. Credit information sharing schemes should be encouraged because these schemes might enable growth of credit without compromising bank stability. Therefore, policymakers could promote private credit bureaus (PCB) and public credit registries (PCR) to realize their benefits. The authors' research focuses on developing ASEAN countries, but future research could provide more evidence by expanding this study to other emerging economies. In-depth interviews and surveys with bankers and regulatory bodies about these concerns could provide additional insights in the future.

The study is the first to examine the role of PCB and PCR in alleviating the negative impact of credit booms on bank risk. Furthermore, the authors use both accounting-based and market-based risk measures to provide a fuller view of the impact. Finally, there is little evidence on the link between credit booms, credit information sharing and bank risk in ASEAN, so the authors aim to fill this gap.

Providing loans, particularly to small-scale farmers, is one of the roles of formal financial institutions. Lending to small farmers is risky. An institution's health is closely related to the institution's ability to manage credit and portfolio risk. Expanding smallholder farmers' access to finance while maintaining a sustainable financial system is essential; however, pandemics present additional challenges. Accordingly, as reported in the literature, the pandemic's high loan default rates and decreases in return on assets (ROAs) call for further credit risk management research. There have been limited studies on credit risk management during coronavirus disease 2019 (COVID-19), so this article aims to provide useful information on its influences.

Researchers used data from formal financial institutions in 2018 (before COVID-19) and in 2021 (during COVID-19) to accomplish the study's broad objective. Descriptive and inferential statistics were the main analytical tools. The credit risk management indicators were categorized into collateral management, loan management, loan recovery management, governance and Information and Communication Technology (ICT). Weights were assigned to each category based on the importance to credit risk management. A binary logit model was employed in assessing the factors influencing credit risk management as proxied to loan repayment, while Ordinary Least Square (OLS) was used to examine factors that influence ROAs.

One of the most noteworthy findings is that credit risk management is affected by different factors and magnitudes before and during the COVID-19 era. Loan recovery and ICT management indicators were most influential during the pandemic. In addition, the study noted that low agricultural productivity during the pandemic contributed to an additional challenge in loan default rates because of various COVID-19-containing measures. Additionally, there was a lack of governance and ICT management capacity to drive credit and portfolio risk management during the epidemic.

The paper presents new empirical findings on credit risk management during the COVID-19 era. The study used a methodology which has not been used previously in credit risk management in Nigerian financial institutions. Therefore, this research could become the cornerstone of further academic research in other developing countries using this methodology.

Using bank-level data over the 1994–2015 period, the authors aim to investigate the role of bank-specific factors on credit risk in Italy by considering two different groups of banks, namely, cooperative and non-cooperative (commercial and popular), in different local markets.

Relying on highly territorially disaggregated data at labour market areas’ level, the authors estimate the impact of the role of bank-specific factors on credit risk in Italy from the estimation of a fixed-effect estimator. Non-performing loans to total loans has been used as a proxy of credit risk; the bank-specific factors are as follows: growth of loans, reflecting credit policy; log of total assets, controlling for banks’ size; loans to total assets, reflecting the volume of credit market; equity to total assets, capturing the solvency of banks and reflecting their capital strength; return on assets, reflecting the profitability of banks; deposits to loans, reflecting the intermediation cost; cost of total assets, reflecting the banks’ efficiency or volume of intermediation cost.

The empirical findings suggest that regulatory credit policy, capitalisation, volume of credit and volume of intermediation costs are the main bank-specific factors affecting non-performing loans. Nevertheless, the present analysis suggests that the behaviour of cooperative banks’ behaviour seems to be in line with that of commercial rather than popular banks, casting doubts about the feasibility of their credit policies. It turns out that recent reforms involving popular and cooperative banks represent the first step toward the enhancement of the stability and efficiency of the Italian banking system. While the present study’s benchmark results are not particularly affected by the degree of competition in the banking sector and by banks’ size, it shows that both cooperative and non-cooperative banks have undertaken more prudent credit policies after the advent of the financial crisis and the introduction of the Basel regulation.

The relationship between bank-specific factors and credit risk has been analysed using a rich sample of cooperative, commercial and popular banks in Italy over the 1994–2015 period. The authors rely on labour market areas being sub-regional geographical areas where the bulk of the labour force lives and works. The contribution is motivated by the financial distress experienced after the 2008 financial crisis, which has significantly hit the Italian banking system and cooperative banks in particular.

This paper aims to assess the impact of credit risk on the market values of private banks during the corona pandemic.

This study is identifying critical issues of credit risk at six great private banks. A conceptual framework is designed based on the Tobin Q model for investigating study hypotheses. Quantitative financial analysis methods have been used for processing data, such as financial ratios, arithmetic mean and multiple linear regression.

The most important result of this study is the lack of influence of credit risk on the market value of selected banks. Because the dimensions of credit risk have critical importance in increasing or decreasing the market value, these banks must continue to adopt quantitative financial analysis to measure credit risks to avoid their risk.

This study elaborates the need for financial indicators to help assess the market value of banks during the economic crises caused by the closure of commercial institutions during the corona pandemic. There is continued increase in bank credit to support these institutions, borrowers and cash withdrawals, which may affect their market reputation.

This paper aims to analyse the recent changes to the Pillar 2 regulatory-prescribed methodologies to classify and calculate credit concentration risk. Focussing on the Prudential Regulation Authority’s (PRA) methodologies, the paper tests the susceptibility to bias of the Herfindahl–Hirscham Index (HHI). The empirical tests serve to assess the assumption that the regulatory classification of exposures within the geographical concentration is subject to potential misuse that would undermine the PRA’s objective of obtaining risk sensitivity and improved banking competition.

Using the credit exposure data from three global banks, the HHI methodology is applied to the portfolio of geographically classified exposures, replicating the regulatory exercise of reporting credit concentration risk under Pillar 2. In doing so, the validity of the aforementioned assumption is tested by simulating the PRA’s Pillar 2 regulatory submission exercise with different scenarios, under which the credit exposures are assigned to different geographical regions.

The paper empirically shows that changing the geographical mapping of the Eastern European EU member states can result in a substantial reduction of the Pillar 2 credit concentration risk capital add-on. These empirical findings hold only for the banks with large exposures to Eastern Europe and Central Asia. The paper reports no material impact for the well-diversified credit portfolios of global banks.

This paper reviews the PRA-prescribed methodologies and the Pillar 2 regulatory guidance for calculating the capital add-on for the single name, sector and geographical credit concentration risk. In doing so, this paper becomes the first to test the assumptions that the regulatory guidance around the geographical breakdown of credit exposures is subject to potential abuse because of the ambiguity of the regulations.

The purpose of this paper is to shed light on the factors that affect microfinance institutions’ (MFI) credit risk. These factors include MFIs’ characteristics and country-level indicators.

This empirical study uses an unbalanced panel data of 638 MFIs from 87 countries observed over a period ranging from 2005 to 2015. Random-effects models are used to estimate the models.

The results reveal that group-lending methodology, percent of loan granted to women and diversification activities reduce credit risk; credit quality is enhanced by the relevance of the information published by public or private bureaus and law enforcement cost increases credit risk. Finally, credit risk tends to be limited in a good institutional environment.

Several implications can be drawn in light of these findings. For MFIs’ managers, using group lending or granting more credit to women and diversifying their activities enhance their credit quality. Furthermore, authorities need to strength debt repayment institutions and reinforce institutional environment to help MFIs to limit their credit risk.

Previous studies focus on specific MFIs’ practices that enhance repayment rate or on country-level indicators. One of the contributions of this paper is the use of both types of indicators.