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This paper aims to improve decision making in credit portfolio management through analytical data-mining methods, which should be used as data availability and data quality of credit portfolios increase due to (semi-)automated credit decisions, improved data warehouses and heightened information needs of portfolio management.

To contribute to this fact, this paper elaborates credit portfolio analysis based on cluster analysis. This statistical method, so far mainly used in other disciplines, is able to determine “hidden” patterns within a data set by examining data similarities.

Based on several real-world credit portfolio data sets provided by a financial institution, the authors find that cluster analysis is a suitable method to determine numerous multivariate contract specifications implying high or, respectively, low profit potential.

Nevertheless, cluster analysis is a statistical method with multiple possible settings that have to be adjusted manually. Thus, various different results are possible, and as cluster analysis is an application of unsupervised learning, a validation of the results is hardly possible.

By applying this approach in credit portfolio management, companies are able to utilize the information gained when making future credit portfolio decisions and, consequently, increase their profit.

The paper at hand provides a unique structured approach on how to perform a multivariate cluster analysis of a credit portfolio by considering risk and return simultaneously. In this context, this procedure serves as a guidance on how to conduct a cluster analysis of a credit portfolio including advices for the settings of the analysis.

A credit risk model suitable for agricultural lenders is identified. The model incorporates sector correlations and is applied to the loan portfolio of an agricultural credit association to create a distribution of loan losses. The distribution is used to derive the lender’s expected and unexpected losses. Results of the analysis indicate that the association is more than adequately capitalized based on 1997S2002 data. Since the capital position of the association is lower than that of most other associations in the Farm Credit System, this raises the issue of overcapitalization in the System.

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.

This paper illustrates that natural disasters can significantly threaten financial institutions serving the poor. The authors test the case of a microfinance institution (MFI) in Northern Peru, where severe El Niño events create catastrophic flooding.

Portfolio‐level, monthly data from January 1994 to October 2008 were examined using an intervention analysis. The paper tested whether the 1997‐1998 El Niño increased problem loans and estimated the magnitude of the effect.

The results indicate El Niño significantly increased problem loans, specifically the level of restructured loans. While restructured loans averaged 0.5 percent of the total loan portfolio before the El Niño, the estimated cumulative effect of El Niño indicates that an additional 3.6 percent of the portfolio value was restructured due to this event.

Future research could build on these results by modeling insurance‐type mechanisms for the MFI. Additional research that replicates these analyses in another context would be highly valuable for comparison across natural disasters and financial institutions.

The findings demonstrate that the correlated risk exposure of many small borrowers can significantly affect the lender and the importance of considering bank management in assessing disaster risk of a financial institution.

Lender strategies to minimize losses may require long‐term restructuring that perpetuates the effects of the disaster in the community.

This paper may be of particular value to researchers and practitioners hoping to improve the effectiveness and efficiency of MFIs concentrated in regions exposed to natural disaster risk.

The credit derivatives market is both fast‐growing and increasingly complex. This brings problems for banks and the infrastructure needed to support such products. There are also sophisticated questions of regulation. The authors bring a wealth of experience to bear on the topic and suggest a variety of ways in which the problems and opportunities can be dealt with satisfactorily.

The purpose of this paper is to study the correlation structure of the credit spreads.

The minimal spanning tree is used to find the risk center node and the basic correlation structure of the credit spreads. The dynamic copula and pair copula models are applied to capture the dynamic and non-linear correlation structure.

The authors take the enterprise bond with trading data from January 2013 to December 2013 as the research sample. The empirical study of minimum spanning tree shows that the credit risk of corporate bonds forms a network structure with a center node. Meanwhile, the correlation between credit spreads shows dynamic characteristics. Under the framework of dynamic copula, the lower tail dependence is less than the upper tail dependence, thus, in economic boom period, the dynamic correlation is more significant than in recession period. The authors also find that the centrality of credit risk network is not significant according to the pair copula and Granger causality test. The empirical study shows that the goodness-of-fit of D vine is superior to Canonical vine, and the Granger causality test additionally proves that the center node has influence on few other nodes in the risk network, thus the center node captured by the minimum spanning tree is a weak center node, and this characteristic of credit risk network indicates that the risk network of credit spreads is generated mostly by the external shocks rather than the internal risk contagion.

This paper provides new ideas for investors and researchers to analyze the credit risk correlation or contagion.

This paper aims to identify factors that affect agricultural mortgage default and prepayment.

Using a sample of farm credit system loans, prepayment and default are modeled as competing risks with potentially non‐stationary covariates using a statistical/econometric technique called survival snalysis (SA).

The analysis suggests that the primary drivers of prepayment and default are the rate of interest charged by the lender at origination and the borrower's current ratio at origination. Tests of the existence of a geographic effect indicate that despite bank management belief to the contrary, branches may not be homogeneous.

This analysis would be improved if more data were available in an easily obtainable manner to control for unobserved heterogeneity. Unobserved heterogeneity or incomplete specification within a model can be problematic. Inferences among regression coefficients can be problematic in that the estimates have inflated variances and unreliable test statistics. In addition, more frequent measures of the time‐varying covariates could be obtained to improve upon the SA models presented above. Future analyses could also incorporate other sections of the agricultural credit association portfolio, as well as a comparison to variable rate notes. One other logical next step would be to obtain loan collateral values to obtain estimates of the exposure at default, and the loss given default, or the estimates needed for the advanced internal ratings based approach described in the Basel Accords.

This paper provides a method for lenders to measure and model mortgage termination, an important consideration for risk managers when determining capital adequacy described in the Basel Accords.

The main purpose of this paper is to reflect on the impact of the financial crisis of 2007 on commercial mortgage backed securities (CMBS) and to consider how market confidence in this form of financing can be re‐established.

The paper uses a two‐stage approach involving a questionnaire and structured interviews. The questionnaire was distributed to market participants in Europe with the goal of identifying their views on the future of CMBS. Structured interviews were held with the three largest credit rating agencies again with the purpose to illicit their opinions on steps necessary to create the confidence in this innovative financing tool.

The empirical results show that market revival will depend on a simplification of deal structures and transparency by all market players. It is clear that regulation of the CMBS instrument is not a top priority for the main market players, but rather, better and more open risk assessment, greater disclosure of information and more due diligence on behalf of investors are seen as crucial.

The paper finds that there are several pragmatic changes that have to be introduced into the way CMBS are designed if they are to be, once again, significant tools for the securitising loans underlying real estate assets.

As well as the practical applications of this analysis it makes an academic contribution in relation to the perceptions of the main market players in creating the environment for a resurgence of CMBS.

Credit ratings serve as an important input in several applications in risk management of the financial firms. The level of credit rating changes from time to time because of random credit risk and, thus, can be modeled by an appropriate stochastic process. Markov chain models have been widely used in the literature to generate credit migration matrices; however, emergent empirical evidences suggest that the Markov property is not appropriate for credit rating dynamics. The purpose of this article is to address the non-Markov behavior of the rating dynamics.

This paper proposes a model based on Markov regenerative process (MRGP) with subordinated semi-Markov process (SMP) to obtain the estimates of rating migration probability matrices and default probabilities. Numerical example is given to illustrate the applicability of the proposed model with the help of historical Standard & Poor’s (S&P) credit rating data.

The proposed model implies that rating of a firm in the future not only depends on its present rating, but also on its previous ratings. If a firm gets a rating lower than its previous ratings, there are higher chances of further downgrades, and the issue is called the rating momentum. The model also addresses the ageing problem of credit rating evolution.

The contribution of this paper is a more general approach to study the rating dynamics and overcome the issues of inappropriateness of Markov process applied in rating dynamics.

This paper aims to draw on the bank rent approach to evaluate the existing pattern of financing of Islamic banks and to propose a fairly new conceptualization of Islamic bank rent.

The bank rent theory is adopted to generate the theoretical underpinnings of the issue. After that, empirical evidence from the banking sector of Bangladesh is used to support the arguments.

Repeated transactions under murabaha are observed in the Islamic banking sector of Bangladesh. The asset-based financing gives the Bangladeshi Islamic banks relatively higher Islamic bank rent opportunity for protecting their “franchise value” as Shari’ah-compliant lenders, while responding to the periodic volatility in transaction costs of profit-and-loss sharing.

The bank rent approach suggests that the murabaha syndrome can be ironically justifiable. On the other hand, the current profit-and-loss sharing risk provides an idea of the difficulty in assuming the participatory financing with higher credit risk in practice. Islamic scholars and the regulatory authority need to design an appropriate financial architecture which can create different levels of rent opportunities for Islamic banks to avail the benefit from the variety of Islamic financing as declared by Islamic Shari’ah.

This paper introduces a fairly new concept of “Islamic bank rent” to make sense of the murabaha syndrome. This approach also contributes to clarifying the unique risk and cost to be compensated with the spreads that Islamic banks are expected to earn. To draw empirical evidence, as far as it could be ascertained, the data of both Islamic banks and conventional banks with Islamic banking windows/branches are used for the first time.