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This study aims to explore and analyse the credit risk assessment procedure conducted by the Indonesian Islamic banks to address the issue of asymmetric information among their micro-, small- and medium-sized enterprise (MSME) clients. This study also investigates the gaps in credit risk assessment procedures by comparing Islamic banks’ practices and presenting several recommendations to reinforce the credit risk evaluation procedures and eventually promote more inclusion of the MSME segment into the Islamic financial services.

This paper adopts a qualitative method by implementing a multi-case study research strategy. The data were gathered primarily through an interview approach by incorporating purposive uncontrolled quota sampling.

The result of this study implies that the Islamic banks in Indonesia have their own unique approaches and strategies in assessing the credit risk and have several similarities in performing their evaluation procedures for the MSME. Despite seemingly adequate approaches and measures taken by the Islamic banks to eliminate the asymmetric information problem, the study identifies several gaps that occur within the Islamic banks’ methods of credit risk assessment.

Since this study focuses on Indonesia and emphasises the two segments of Islamic banks, which consist of Islamic commercial and rural banks, in performing the MSME credit risk assessment; therefore, the findings of this study were limited around the observed Islamic banks within the MSME segment purview.

By referring to the recommendations as proposed by this paper, four implications could be expected from adopting these respective recommendations, among others: more effective evaluation procedures for the MSME, provision of a clear path and more efficient approach to assess the MSME units, lower financing cost and increase the confidence of Islamic banking industry in disbursing more financing to the MSME sector. This mechanism will potentially improve Islamic financial inclusion for the MSME due to the greater access to financial services; hence, the sector could contribute even more to Indonesia’s growing economy.

By incorporating a multi-case study among Indonesian Islamic banks pertaining to their methods in evaluating MSME customers, this study identifies several gaps affecting the effectiveness of MSME credit risk assessment. Furthermore, this study also presents a proposed framework to address these gaps accordingly by suggesting the salient strategies to minimise the issues of information asymmetry and enhance the MSME credit risk assessment procedure.

This research aims to model the relationship between the credit risk signals in the credit default swap (CDS) market and agency credit ratings, and determines the factors that help explain the variation in such signals.

A comprehensive analysis of the differences in the relative credit risk assessments of CDS-based risk signals and agency ratings is provided. It is shown that the divergence between credit risk signals in the CDS market and agency ratings is explained by factors which the rating agencies may consider differently than credit market participants.

The results suggest that agency credit ratings of relative riskiness of a reference entity do not always correspond with assessments by CDS spreads, as the price of risk is a function of additional macro and micro factors that can be explained using statistical analysis.

This research is unique in modeling the relationship between the credit risk assessments of the CDS market and the agency ratings, which to the best of the authors' knowledge has not been analyzed before in terms of their agreement and the level of discrepancy between them. This model can be used by investors in debt instruments that are not explicitly CDSs or which have illiquid CDS contracts, to replicate market-based, point-in-time credit risk signals. Based on both market-based and firm-specific factors in this model, the results can be used to augment through-the-cycle credit risk assessments, analyze issues surrounding the pricing of CDSs and examine the policies of credit rating agencies.

Why do lenders shrink back from full risk pricing in certain credit markets, even when a sophisticated system of credit scoring is already in place? Fear of bad publicity is the usual reason cited but this paper offers a complementary explanation which suggests that there may be an underlying financial process driving such behaviour. The key proposition of the paper is that risk pricing can cause adverse selection which has the potential to mitigate any positive benefits such a pricing strategy may bring to the lender. This explanation is developed by introducing risk pricing into the seminal Stiglitz and Weiss model and in so doing offers the first substantial link between the risk assessment and credit rationing literatures.

This study aims to investigate whether Islamic morality is considered by Islamic rural banks in credit application assessments.

To achieve the objective, the authors conduct an exploratory analysis of data gathered through document analysis, focus group discussions and interviews with Islamic rural banks stakeholders. Additionally, the authors conduct a survey to validate the findings.

The authors find that while Islamic rural banks conduct conventional credit analysis using 5Cs (character, capacity, collateral, capital and conditions), the banks also consider Islamic morality in their credit decisions. They emphasise several indicators such as reputation for keeping promises to customers, to suppliers, to neighbours and the Islamic character (akhlaq) of the credit applicants. Overall, the authors conclude that Islamic rural banks consider Islamic morality in their credit assessments.

To the best of the authors knowledge, this is the first study using a mixed method of quantitative and qualitative approach to investigate how Islamic rural banks assess the credit applications. In particular, this study examines whether Islamic rural banks consider Islamic morality in their credit decisions.

This paper aims to investigate bank credit policies and uncover yacht building finance assessment factors from bank credit policies toward the yacht industry.

This study’s questionnaire attempts to identify survey respondents’ degrees of awareness through difference analysis, and then uses entropy weighting and gray relational analysis to discover priority ranking order of bank credit assessment considerations from the perspective of Taiwan’s banking sector.

The research findings show that yacht builders have to review their ship financing application methods and improve shortcomings to meet banks’ credit granting requirements.

Banks emphasize yacht builders’ repayment ability to protect their depositors and shareholders.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

Lenders, regulatory agencies, and investors have increased their demand for credit risk exposure information to appropriately price risk and evaluate risk migration patterns that affect institution safety and soundness. This review provides a synthesis of the advances in credit risk assessment made through journal articles and other professional reports. Contributions in three primary areas are considered: (a) how the credit risk assessment problem has been defined and redefined over time in response to the changing information needs of lenders and regulators, (b) how methodological innovations have improved credit assessment procedures, and (c) how the efficiency of financial markets has changed due to the evolution of credit risk assessment. The paper concludes with a discussion of how transactional and relationship lending approaches are expected to evolve in the future and whether measures can be developed to more accurately assess factors such as management capacity and commitment to repay.

This account aims to introduce contrasting perspectives on teaching and learning methods, and to detail the growth of new forms and vocabularies of access to learning. As we move towards the new millennium, the development of national, yet diversified, credit frameworks and systems will provide an essential underpinning for the organisational culture that will be needed to sustain the wellbeing and growth of the educational system. These new systems are already being incorporated into the practice of ‘virtual’ education. Lifelong learning has widespread support across the social and political spectrum and its importance can hardly be over‐stated as we seek to maintain competitiveness in a changing world. Increasing knowledge and understanding to serve both the needs of the economy and of individuals to play a major role in democratic life has become an agenda of necessity as well as desire. An open society requires open systems of knowledge. A prognosis for the future is submitted where the significance of part‐time modular and open flexible learning is evaluated in terms of a curriculum rooted in useful knowledge and competences, acquired at different sites of learning, including the workplace. It is argued that modular structures, using the potential offered by credit accumulation and transfer to different institutions with different missions, can transcend and transform the learning opportunities for students in a mass system of higher education which is rapidly becoming part of a global market economy and society. Continuous lifelong learning involving its key features of open access, recognition of learning wherever it takes place and the growth of new learning networks and partnerships, is at the conceptual heart of the development of the virtual university.

Data mining for predicting credit risk is a beneficial tool for financial institutions to evaluate the financial health of companies. However, the ubiquity of selecting parameters and the presence of unbalanced data sets is a very typical problem of this technique. This study aims to provide a new method for evaluating credit risk, taking into account not only financial and non-financial variables, but also the class imbalance.

The most significant financial and non-financial variables were determined to build a credit scoring model and identify the creditworthiness of companies. Moreover, the Synthetic Minority Oversampling Technique was used to solve the problem of class imbalance and improve the performance of the classifier. The artificial neural networks and decision trees were designed to predict default risk.

Results showed that profitability ratios, repayment capacity, solvency, duration of a credit report, guarantees, size of the company, loan number, ownership structure and the corporate banking relationship duration turned out to be the key factors in predicting default. Also, both algorithms were found to be highly sensitive to class imbalance. However, with balanced data, the decision trees displayed higher predictive accuracy for the assessment of credit risk than artificial neural networks.

Classification results depend on the appropriateness of data characteristics and the appropriate analysis algorithm for data sets. The selection of financial and non-financial variables, as well as the resolution of class imbalance allows companies to assess their credit risk successfully.

This paper attempts to identify the areas for further research related to regulating credit‐rating agencies (CRAs), in order to assess whether the prerequisite for a “complete change” is present so to achieve a genuine paradigm shift on the matter.

An overview of the unregulated background of CRAs is presented followed by the European Union's and USA's regulatory initiatives together with a critical assessment of the former and an identification of the substantive areas for further thinking.

The adequacy of the recent CRAs regulation is questioned in the light of the need to take account of crucial elements such as scope, use of methodologies, due diligence and the regulatory reliance on ratings. A definition of competition is also warranted as well as a questioning of the “issuer pays” model and an assessment of the impact of ratings on systemic risk. An alternative regulatory response could take a more general view of regulating the credit‐rating activity as a whole and on a world wide scale.

This paper identifies areas for further research needed for an assessment of the most suitable regulation for the credit‐rating activity. Also, the paper focuses on the need to better understand the complicated nature, functioning and impact of CRAs in the financial system in order to map the different challenges for regulators, politicians, practitioners and academics.