Giving credit to debtors can pose a default risk. This risk arises because of an error in analyzing the credit risk rate of the debtor. Therefore, this study aims to design a framework for analyzing the credit risk rate of debtors so that the default risk can be reduced. This framework is created using the integration of factor analysis and Tsukamoto’s fuzzy logic method. This integration method can group many credit assessment variables into several decisive factors. In addition, the integration method can estimate credit risk rate firmly based on the α-predicate of each basic rule. This analytical framework is simulated on credit application data at a Rural Bank, in Indonesia. The simulation results show that there are three factors and one variable to measure the credit risk rate, namely: factor 1 represents repayment capacity, business length, working capital, and liquidity value; factor 2 represents the age and the difference between the granted and the proposed loan amount; factor 3 represents the stay length, character, and credit history; and one variable represents a dependent number. This research is expected to help credit institutions measure the credit risk rate in making credit decisions for prospective debtors.

The purpose of this study is to examine the effect of credit risk and liquidity risk on profitability with loan restructuring and income diversification as moderating variables. The research population is all general banking companies, which were listed on the Indonesia Stock Exchange (IDX) during the period 2018-2021. The research sample was created using the purposive sampling technique and 160 observations were obtained. This study conducts panel data regression analysis using EViews 12 software. The results of this study indicate that an increase in credit risk reduces profitability, liquidity risk does not affect profitability, a loan-restructuring strategy can reduce the effect of credit risk on profitability, and an income-diversification strategy can reduce the effect of liquidity risk on bank profitability. The research findings provide an understanding of banking strategy, namely loan restructuring and income diversification can increase banking profitability under urgent conditions. This study provides support for contingency theory and stakeholder theory. The limitation of this research is that it does not discuss Islamic banking because the policies of those companies are different in terms of rules and there are limited data.

In order to identify the determinants that influence the credit risk of Peruvian municipal savings banks, this quantitative research uses a nonexperimental design and a longitudinal sample to analyze monthly data corresponding to macroeconomic variables and microfinance institutions’ internal variables from 2011 to 2020. Using multiple linear regression, the results show that the interest rate, unemployment rate, and liquidity ratio positively influence the credit risk of Peruvian municipal savings banks; the study also shows that gross domestic product, efficiency of administrative expenses, solvency, and coverage of provisions exert a negative influence on credit risk. It is concluded that seven of the eight independent variables studied influence the credit risk of Peruvian municipal savings banks; only the inflation variable does not significantly influence credit risk.

This study examines the impact of unsystematic financial risk, including liquidity risk, credit risk, and operational risk, on financial stability in conventional banks listed on the Amman Stock Exchange in Jordan. Understanding and managing these risks is crucial for protecting investors, maintaining financial stability, encouraging foreign investment, and strengthening the financial sector in Jordan. The study adopts a descriptive approach to collect and describe data and utilizes cross-sectional panel data over five years from 2016 to 2021 to establish cause-and-effect relationships between study variables, while controlling for other relevant factors that may influence the relationship. The findings suggest that while liquidity risk may not directly impact financial stability, it remains a critical risk factor that requires attention in risk management strategies. Credit risk has a negative impact on financial stability, highlighting the importance of effective credit risk management strategies to maintain a stable financial system. The study finds that operational risk has no direct impact on financial stability. Still, unsystematic operational risks can have significant implications for individual financial institutions and may indirectly affect overall stability. The study underscores the importance of comprehensive risk management strategies to mitigate the negative impact of unsystematic financial risk on financial stability. Future research may consider analyzing the impact of other types of risks on financial stability.

The study examines how loan growth affects performance of banks, in the form of credit risk, bank profitability and bank solvency in Vietnam during the period from 2006 to 2017. Overall, the regression results by both static and dynamic panel data models provide some evidence that loan growth indicators could have the great impacts on bank performance. In particular, growth in lending increases loan loss provisions from 2 to 3 subsequent years, lowers bank capital ratio the next year; while bank profitability gains positive effects from loan growth both in the short term and long term. These findings show the robustness when applying alternative estimation techniques. The study emphasizes the importance of caution in expanding lending activities aggressively as well as it provides implications for banks in terms of risk governance and capital management.

This study conducts the theme of The Causes of Financial Distress conditions by samples from Indonesian banking sector registered in the Financial Services Authority of Indonesia within the period of 2015-2019. The title of this study: "Indicators of Financial Distress condition in Banking sector in Indonesia” during the period of 2015-2019" with a multiple correlation approach. The purpose of this study is to determine the effect of leverage of Credit Risk, CAR, ROA, and LDR to the Financial Distress conditions. The sample of population in this study are all conventional commercial banks in Indonesia registered in the Financial Services Authority of Indonesia. The number of samples in this study were included 37 commercial banks that their profitabilities were being declined, with a total number 146 observations. The method carried out in determining the sample is “Purposive” sampling. Based on the results of study and data analysis using the panel data method, it shows that capital, credit risk, profitability and liquidity have a positive effect on Financial Distress. The implication of the above conclusion is that it required further research to perform preventive actions to anticipate the measures of financial performance of the Bank, and it is expected to select a larger population of samples and variables that might have not been included in research on banking Financial Distress in Indonesia.

The study aimed to identify the impact of the credit query in reducing credit risks from the viewpoint of workers in Jordanian banks, and to achieve the goal of the study, the researchers adopted the descriptive analytical approach, where a questionnaire is designed and distributed to a sample of (176) individuals within the upper and middle administrative levels in Jordanian banks. The study reached a set of results, the most important of which is the existence of a negative correlation between the level of use of the credit query system and each of the financial, administrative and legal credit risks, in addition a statistically significant effect of using the credit query system in reducing the financial, administrative and legal credit risks. The study recommended that Jordanian banks should continue to use the credit system because of its positive effects on the efficiency and effectiveness of granting credit to business organizations or individuals alike.

This study aims to examine the influence of credit risk and capital adequacy of a rural bank on financial distress, proxied by interest coverage ratio (ICR). Samples used in this research are 123 rural banks located in the Jakarta metropolitan area from 2013 to 2018. In this area, almost 70% of cash flow circulation in Indonesia was happening. The logistic regression model was employed to analyze the collected data. The findings show that both credit risk and capital adequacy had significant influences on financial distress, with positive and negative effects, respectively. Realizing the important role of credit risk and capital adequacy, this study makes some suggestions that rural banks should utilize both variables as a measure to monitor their financial performance.

Credit risk consists of probability of non-return, which may be in the form of bankruptcy or a decrease in financial and credit situation of the lessee. The variables are extracted from the Central Bank. In this study the independent variables are measured with six factors that are called external factors. The external factors are size of leasing, ownership interest rate, foreign exchange, inflation, and Gross Domestic Product (GDP). The present study uses related observations from 31 leasing companies from 2008 to 2013 to find out the determinants of the credit risk. The combined evidences suggest that internal factors such as upfront prepayment, credit insurance contract, security deposits, time and period contract, collateral and guarantees, contract amount, as well as external factors such as interest rate, inflation, foreign exchange, Gross Domestic Product infrastructure, and credit risk are determinants in the policy-making process involving the industrial leasing. Furthermore, the empirical results indicate the size of leasing and ownership are not the significant determinants of credit risk. The results of this dissertation provide several implications for policy-makers in the leasing industry. Policy-makers will be better off employing different procedures for leasing activities in the leasing industry.

Measuring different risk factors such as credit risk in banking industry has been an interesting area of studies. The artificial neural network is a nonparametric method developed to succeed for measuring credit risk and this method is applied to measure the credit risk. This research’s neural network follows back propagation paradigm, which enables it to use historical data for predicting future values with very good out of sample fitting. Macroeconomic variables including GDP, exchange rate, inflation rate, stock price index, and M2 are used to forecast credit risk for two Iranian banks; namely Saderat and Sarmayeh over the period 2007-2011. Research data are being tested for ADF and Causality Granger tests before entering the ANN to achieve the best lag structure for the research model. MSE and R values for the developed ANN in this research respectively are 86×?10?^(-4) and 0.9885, respectively. The results showed that ANN was able to predict banks’ credit risk with low error. Sensibility analyses which has accomplished on this research’s ANN corroborates that M2 has the highest effect on the ANN’s credit risk and should be considered as an additional leading indicator by Iran’s banking authorities. These matters confirm validation of macroeconomic notions in Iran’s credit systematic risk.