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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.

The purpose of this research is to investigate whether inclusion of risk assessment variables in the multiple discriminant analysis (MDA) model improved the banks ability in making correct customer classification, predict firm's performance and credit risk assessment.

The paper reviews literature on the application of financial distress and credit scoring methods, and the use of risk assessment variables in classification models. The study used a sample of 56 performing and non‐performing assets (NPA) of a privatized commercial bank in Tanzania. Financial ratios were used as independent variables for building the MDA model with a variation of five MDA models. Different statistical tests for normality, equality of covariance, goodness of fit and multi‐colinearity were performed. Using the estimation and validation samples, test results showed that the MDA base model had a higher level of predictability hence classifying correctly the performing and NPA with a correctness of 92.9 and 96.4 percent, respectively. Lagging the classification two years, the results showed that the model could predict correctly two years in advance. When MDA was used as a risk assessment model, it showed improved correct customer classification and credit risk assessment.

The findings confirmed financial ratios as good classification and predictor variables of firm's performance. If the bank had used the MDA for classifying and evaluating its customers, the probability of failure could have been known two years before actual failure, and the misclassification costs could have been calculated objectively. In this way, the bank could have reduced its non‐performing loans and its credit risk exposure.

The valiadation sample used in the study was smaller compared to the estimation sample. MDA works better as a credit scoring method in the banking environment two years before and after failure. The study was done on the current financial crisis of 2009.

Use of MDA helps banks to determine objectively the misclassification costs and its expected misclassification errors plus determining the provisions for bad debts. Banks could have reduced the non‐performing loans and their credit risks exposure if they had used the MDA method in the loan‐evaluation and classification process. The study has proved that quantitative credit scoring models improve management decision making as compared to subjective assessment methods. For improved credit and risk assessment, a combination of both qualitative and quantitave methods should be considered.

The findings have shown that using the MDA, commercial banks could have improved their objective decision making by correctly classifying the credit worthiness of a customer, predicting firm's future performance as well as assessing their credit risk. It has also shown that other than financial variables, inclusion of stability measures improves management decision making and objective provisioning of bad debts. The recent financial crisis emphasizes the need for developing objective credit scoring methods and instituting prudent risk assessment culture to limit the extent and potential of failure.

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 chapter reassesses the economics of interest rate risk management in light of the global financial crisis by developing a derivative-based integrated treatment of interest rate and credit risk interrelation. The decade-long historical data on credit default swap spreads and interest rate swap rates are used as proxy measures for credit risk and interest rate risk, respectively. An elasticity of interest rate risk and credit risk, considered a function of the business cycle phases, maturity of instruments, economic sector, creditworthiness, and other macroeconomic parameters, is investigated for optimizing economic capital. This chapter sheds light on how financial institutions may address hedge strategies against downside risks implementing the proposed derivative-based integrated treatment of interest rate and credit risk assessment allowing for optimization of interest rate swap contracts. The developed framework of integrated interest rate and credit risk management is of special importance for emerging markets heavily dependent on foreign capital as it potentially allows emerging market banks to improve risk management practices in terms of capital adequacy and Basel III rules. Analyzing diversification versus compounding effects, it allows enhancing financial stability through jointly optimizing Pillar 1 and Pillar 2 economic capital.

Since sovereign ratings provided by credit rating agencies (CRAs) are a key determinant of the interest rates a country faces in the international financial market and once sovereign ratings may have a constraining impact on the ratings assigned to domestic banks or companies, some studies have focused on identifying the determinants of sovereign credit risk assessments provided by CRAs. In particular, this study estimates the effect of fiscal credibility on sovereign risk using four different comprehensive credit rating (CCR) measures obtained from CRAs' announcements and two different fiscal credibility indicators.

We build comprehensive credit rating (CCR) measures to capture sovereign risk. These measures are calculated using sovereign ratings, the rating outlooks and credit watches issued by the three main credit rating agencies (S&P, Moody's and Fitch) for long-term foreign-currency Brazilian bonds. Based on monthly data from 2003 to 2018, we use different econometric estimation techniques in order to provide robust results.

The results indicate that fiscal credibility exerts both short- and long-run effects on sovereign risk perception, and macroeconomic fundamentals are important long-run determinants.

Since fiscal credibility reflects the government's ability to maintain budgetary balance and sustainable public debt, the government should keep its commitment to responsible fiscal policies so as not to deteriorate expectations formed by financial market experts about the fiscal scenario and, thus, to achieve better credit assessments issued by CRAs with respect to sovereign debt bonds. Sovereign credit rating assessment is a voluntary practice. It is up to the country whether they want to apply for a rating assessment or not. Thus, without a sovereign rating, one must find an alternative to measure the sovereign risk of a country. In this sense, an important practical implication that this study provides is that fiscal credibility can be used as a leading indicator of sovereign risk perceptions obtained from CRAs or even as a proxy for sovereign risk.

This paper is the first to verify how important the expectations of financial market experts in relation to the fiscal effort required to keep public debt at a sustainable level (i.e. fiscal credibility) are to sovereign risk perception of credit rating agencies. In this sense, the study is the first to address this relation, and thus it contributes to the literature that seeks to understand the determinants of sovereign ratings in emerging countries.

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.

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.

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.