Affect Credit Risk Research Articles

Short-Term Costs and Long-Term Benefits of Environmental, Social, and Governance Performance on Corporate Credit Ratings

ESG (Environmental, Social and Corporate Governance) criteria are becoming increasingly important for companies to activate risk management and create long-term value within the framework of issues such as environmental concerns, expectations, reporting obligations, circular economy, regulatory compliance and investment demands. This study aims to explain how ESG scores and a company's financial performance affect credit risk. A company's ESG performance plays an important role in determining its credit score, which is directly reflected in its financial success and risk profile, and in strategic decision-making processes for investors and policy makers. The study determined that the balance between ESG performance and credit scores should be well understood, especially that high ESG scores may not always mean low financial risk, and that the long-term benefits of ESG investments should be well understood, as they will increase short-term costs.

Credit Risk Identification and Prevention Strategies of Small and Medium-sized Banks Based on Big Data Technology

The topic of this analysis is the application of big data technology to improve credit risk identification and prevention measures in SME banks. T¬he research, it highlights credit risk’s crucial role in banking and underscores the problems smaller banks encounter when dealing with this risk due to limited resources and less evolved tools. An extensive review is conducted, showing the progression of credit risk management and big data integration into financial risk management. It discusses the revolutionary aspect of big data in credit risk analysis as well as its practical applications in small and medium-sized banks. The secondary data from Kaggle datasets are used within a quantitative research approach. Regression analysis and hypothesis testing are some of the statistical tools used in EViews to uncover patterns and correlations related to credit risk. The study determines essential factors that affect credit risk, such as borrower credit score, loan amount, interest rate, and employment. It assesses the effectiveness of big data analytics in forecasting and mitigating this risk, focusing on accuracy and model resilience. This implies that the findings show that the borrowers have a moderate level of credit risk, and the traditional financial metrics have minimal influence on the Big Data risk score predictions, which require advanced analytical approaches. Big data technology can take more than traditional credit scoring, giving small and medium-sized banks a more advanced credit risk perspective. Limitations of the research include using a simulated dataset and the range of the analysed variables. Real-world data and variables should be used in future research studies.

Does ESG Affect Credit Risk?

Environmental, social, and governance (ESG) concerns became increasingly crucial for financial institutions, including banks, as they faced new types of risks associated with ESG factors. This study aims to investigate how these ESG dimensions affected non-performing loans (NPLs) in banks across ASEAN countries. With rising investor demand for sustainable practices and intensified regulatory pressures, incorporating ESG risks into risk management frameworks became imperative. This study uses panel data regression analysis along with the Chow and Hausman tests to assess the impact of ESG factors on NPL. The sample comprised banks from Indonesia, Malaysia, the Philippines, Singapore, and Thailand, selected based on Refinitiv Eikon's ESG values. The findings revealed that while environmental and governance performance had a statistically significant negative impact on NPLs, indicating that better environmental and governance practices were associated with fewer loan defaults, social performance showed a statistically significant positive effect on NPLs, suggesting that higher social scores correlated with increased loan defaults. This study highlighted the need for banks to carefully balance their social initiatives with financial stability and underscored the importance of integrating ESG factors into risk management strategies to enhance overall financial health and sustainability.

Factors Affecting Credit Risk In Commercial Banks Of Kenya; A Case Of Selected Commercial Banks In Kenya

Factors Affecting Credit Risk In Commercial Banks Of Kenya; A Case Of Selected Commercial Banks In Kenya

МОДЕЛЮВАННЯ КРЕДИТНОГО СКОРИНГУ МЕТОДАМИ ІНТЕЛЕКТУАЛЬНОГО АНАЛІЗУ ДАНИХ

The paper substantiates a methodological approach to improving the credit scoring system for individuals based on data mining methods. A classification of the main types of scoring models is provided, and the conditions for their application for assessing the borrower’s creditworthiness are determined. The authors paid great attention to the formation of a list of characteristics-indicators that make it possible to assess the borrower. The feasibility of using both quantitative characteristics that reveal the client’s financial capabilities and qualitative ones that relate to demographic and social factors is proven. The methods of constructing scoring models were analyzed, from classical ones – logistic regression, decision tree and cluster analysis, which are the basic algorithms of intelligent data analysis, to machine learning and building neural network models, which allow obtaining a more holistic picture of the financial behavior of a potential client. The paper analyzes a database of borrowers to identify key factors affecting credit risk. The use of clustering allowed us to identify significant differentiation of credit behavior of different groups of bank clients. To improve the accuracy of creditworthiness forecasting, a neural network based on a multilayer perceptron was developed. The network architecture includes a hidden layer, for the formation of which the hyperbolic tangent function was used. The output layer was constructed using a sigmoid activation function, which provides an estimate of the probability of a borrower belonging to creditworthiness classes. The model showed high efficiency in identifying reliable borrowers, but requires optimization for better prediction of defaults due to the imbalance of training data. The proposed scoring model contributes to reducing the level of problem loans, reducing operating costs for processing applications, and improving customer segmentation. The feasibility of a combined approach is substantiated: the use of logistic regression and cluster analysis for preliminary selection of clients with the subsequent use of a neural network to make final decisions in complex cases.

Bank Specific Factors that Affect Credit Risk

Indonesian’s banking has taken steps to improve financial performance by managing credit risk and supporting economic recovery in 2023, efforts carried out by banks include providing credit and acting as intermediaries with very vulnerable credit risk challenges.This research has purpose to find out which bank spesific internal factor can significantly affect the credit risk in a bank. The independent variables in this study are efficiency, leverage, liquidity, ROA, ROE, solvency, banking spread, banking size, and ownership concentration. While the dependent variable is credit risk using NPL and Z-Score. The study used panel data regression models to test the data collected from 98 banks registered with OJK over three years (2020-2022). The results stated that leverage, ROA, ROE, solvency, bank size, have a positive influence on ZScore, while efficiency, liquidity, and bank spread have no influence on Zscore. The results also show that efficiency and ROA have a positive influence on NPL. Liquidity, ROE, solvency, bank spread, bank size, and ownership concentration variables have an influence on NPLs and leverage has no influence on NPL. Additionally, investors should be able to use this research's insights about company performance to get a general understanding of the credit risk associated with linked companies, which they can use to inform their investment decisions.

How Female Representation in Indonesian Banks Affects Credit Risk: Evidence from Indonesia

This study examines the impact of female representation on the Board of Directors (DDP), Board of Commissioners (DKP), and Audit Committee (KAP) on credit risk in commercial banks in Indonesia. Utilizing panel data with 399 observations from various banks over a specified period, the Random Effect Model (REM) was applied to analyze the relationship between the independent variables (DDP, DKP, and KAP) and the dependent variable (credit risk). The results indicate that DDP has a significant negative impact on credit risk (coefficient -4.331768, p = 0.0000), suggesting that increasing the proportion of women on the Board of Directors tends to reduce credit risk. This could be attributed to the diversity of perspectives and caution in decision-making brought by women, as well as a push for higher transparency and accountability. The DKP shows a nearly significant negative impact on credit risk (coefficient -1.371344, p = 0.0593). Although its impact is not as strong as DDP, the presence of women on the Board of Commissioners can also reduce credit risk through enhanced supervision and control. Conversely, KAP does not have a significant impact on credit risk (coefficient 0.508613, p = 0.5055). This suggests that while gender diversity on the audit committee is important for regulatory compliance and internal control, it may not directly influence credit risk management. Theoretically, these findings support the literature that gender diversity on boards improves the quality of decision-making and risk management. Managerial implications emphasize the importance of increasing female representation on the Board of Directors and Board of Commissioners to reduce credit risk and enhance the financial stability of banks. Gender diversity policies should be implemented at all organizational levels to maximize their benefits in corporate governance. This study provides insights for policymakers and practitioners in the banking sector on the importance of gender diversity in managing risk and improving the financial performance of banks.

The effect of industry sector and profit-loss sharing financing on credit risk of Islamic banks in Indonesia considering COVID-19 pandemic

Purpose The COVID-19 pandemic has affected economic activity both globally and nationally, which also has an impact to banking sector and Islamic banking is no exception. This study aims to see how the impact of Islamic bank financing in seven sectors affected by the COVID-19 to the credit risk of Indonesian Islamic banks. In addition, this study also tries to see whether the proportion of mudharabah-musharaka or profit-loss sharing (PLS) financing also affects credit risk in Indonesian Islamic banks. Design/methodology/approach This study uses fixed effect panel data regression over the period 2011–2020. Findings The results of this study show that wholesale and retail trade financing will increase credit risk in Indonesian Islamic banks as a policy implication. In terms of the proportion of PLS financing, it shows that a larger share of PLS financing will reduce credit risk in Islamic banks. Originality/value This paper demonstrates that despite the industry’s perception of PLS as riskier than murabaha-based instruments. According to the research, PLS financing will lower credit risk in Islamic banks. This study found that PLS contributes to overall economic stability by shifting the function of Islamic banks from a simple lending body to an active market catalyst/manager/consultant to market players seeking financial aid.

Green bond issuance and credit risk: International evidence

Green bond issuance and credit risk: International evidence

Corporate governance, social responsibility and sustainability commitments by banks: Impacts on credit risk and performance

AbstractWith the increasing pressures to commit to social and environmental issues, banks align with stakeholders' expectations via corporate social responsibility (CSR), and sustainable development goals (SDGs) initiatives. Understanding how these initiatives affect credit risk and performance of banks is crucial for the economy. Relying on the dynamic generalized method of moments and data on Thai listed commercial banks from 2015 to 2020, we find commitments to good governance, CSR and SDGs largely have an opposite effect on bank credit risk and performance. Consistent with stewardship or resource dependency hypothesis, together with stakeholder and good management hypothesis, banks with more female directors, higher levels of anti‐corruption and SDGs commitments are associated with a reduction in credit risk. However, consistent with queen bee hypothesis and agency and trade‐off hypothesis, banks with larger board size, more female directors and higher levels of CSR and SDGs commitments underperform. This apparent contradiction between negative impacts on bank performance and positive impacts on credit risk can be reconciled by understanding the trade‐offs involved, the long‐term focus of sustainability initiatives, leading to a decline in short‐term performance but enhancing their resilience to adverse events, ultimately reducing credit risk. Our findings enable stakeholders to make informed decisions, improve sustainable business practices, and contribute to the long‐term success of banks and the broader society.

The Impact of Geographic Factors on Credit Risk: A Study of Chinese Commercial Banks

Abstract Controlling credit risk is crucial for maintaining financial stability, and the role of geographic factors in this regard is a significant concern for scholars and policymakers. Drawing on the concept of information asymmetry, we developed a theoretical model to analyze how geographic factors influence credit risk. Our theoretical proposition suggests that the spatial organization of banks affects the efficiency of collecting and processing soft information, ultimately impacting the credit risk. To test this proposition, we collected microdata from Chinese commercial banks spanning the period from 2011 to 2022. Employing a mediating effect model, we empirically examined the relationship between spatial organizational structure and credit risk. Our results indicate that the distance between bank operations and functional distance impedes the collection and processing of soft information, thereby exacerbating credit risk in banks. The study focuses on examining how the spatial organizational structure of Chinese commercial banks affects credit risk. By analyzing geographic factors and information asymmetry, the study aims to understand how the organization of banks influences the collection and processing of soft information, which in turn impacts the credit risk. Furthermore, our analysis of the sample reveals that the mediating role of soft information varies between state-owned banks and joint-stock banks due to their distinct customer profiles. On the basis of these findings, we propose several policy recommendations, including a focus on enhancing the collection and processing of soft information, promoting the growth of locally based small and medium-sized banks, and reducing information barriers within bank hierarchies.

Bank ownership and credit risk: an empirical study of Indonesian Islamic local banks

Purpose This study aims to determine the effect of bank ownership on the credit risk of Indonesian Islamic local banks (ILBs). Design/methodology/approach This study uses the system generalized method of moments (GMM) estimation technique with a sample of 155 Islamic local banks in Indonesia from 2012 to 2019. Findings The results show that commissioner board (D.COW) ownership has a negative effect on credit risk. This indicates that an increase in the number of shares of Islamic local banks owned by the commissioner board reduces credit risk. On the other hand, government ownership (D.GOW), the Sharia supervisory board (D.SOW) and the director board (D.DOW) do not affect credit risk. Practical implications The government, Sharia supervisory board and director board need opportunities to easily own more Islamic local bank shares. Therefore, the provisions regarding the share ownership rights of the government, Sharia supervisory board and director board need to be improved to increase their role in reducing credit risk. Originality/value Previous researchers have not studied the effect of government ownership, the commissioner board, the Sharia supervisory board and the ownership of directors on credit risk at the ILB in Indonesia.

Моделирование суверенного кредитного риска с учетом экологической эффективности

This study contributes to the evaluation of the role of the eco-efficiency factor in explaining sovereign credit risk. The purpose of the study is to develop a model of sovereign credit risk taking into account the environmental efficiency factor. The sample includes the following countries: Argentina, Belgium, China, China, Egypt, Germany, Greece, Nigeria, Pakistan, Portugal, Spain. 58 % of the world’s countries are categorized as investment grade, indicating that more than half of the countries have high and very high solvency. Using the multiple regression method, the factors affecting the sovereign credit rating are identified: GDP per capita, annual change in inflation, international reserves, private sector credit, total public debt, and environmental performance. The results show a strong positive relationship between credit rating and GDP per capita, private sector lending volume, indicating that countries with better GDP per capita have lower credit risk. To prove the relationship between environmental and credit risk, two multiple regression models were constructed: without and with the environmental factor. The results show that including the environmental factor in modeling a country’s credit rating is appropriate because it improves the quality of the model. According to the two comparison criteria, the model including the environmental factor had better results; the coefficient of determination was larger and the approximation error was smaller. Countries have different environmental situations and different degrees of environmental policy management. There is a relationship between environmental and credit risk of countries, but the environmental situation of a country is not the only determinant factor affecting credit risk. If a country has a high environmental performance index value, it may have an average sovereign rating and vice versa. The scientific usefulness of the study lies in the fact that the constructed model can be used by investors, creditors, rating objects to calculate their own values of sovereign credit ratings.

Pengaruh Kebijakan Moneter dan Makroprudensial terhadap Risiko Kredit di Bank Umum Syariah Periode 2018-2022

Abstract 
 The impact of the financial crisis at the end of 2008 resulted in the financial sector losing its ability to mitigate risk, in turn causing increased credit risk. The aim of the study is, of course, to understand the impact of monetary policy and macroprudential on credit risks in Indonesia's non-allocation financing (NPF). The study USES the data panel that has been processed through eviews 10 and applied with multiple linear regression analysis, specifying five different types of cellular general Banks that fit the criteria as a sample of observation during the 2018-2022 period. Research shows that bi rate as a monetary policy proxy and mandatory minimum requirement (GWM) as a macroprudensian policy proxy does not significantly affect credit risk. However, those capital inequality ratios have a significant negative impact on credit risk, while those variables financing to depositite ratios have significant positive effects on credit risk. Monetary policy and macroprudencies are crucial to systemic risk, particularly credit risk in the financial sector, especially at sharia general bank.

CREDIT RISK DETERMINANTS OF COMMERCIAL BANKING

Studies on non-performing loans in banking provide mixed results regarding the bank-specific factors that influence them. This research aims to analyze the determinants of credit risk in commercial banking in Indonesia. Testing was conducted on 32 banking companies listed on the Indonesia Stock Exchange for 2019-2021 with 96 observations using panel data regression. The research results show that increasing bank diversification and external audit quality significantly reduce the risk of credit non-performing loans (NPL). Other findings of this research show that credit growth, operational efficiency, and bank size do not significantly affect credit risk. To overcome the problem of endogeneity and dynamic panel bias, a series of robustness tests were carried out using the 2SLS, LIML, and GMM approaches provided significant results. The results of this research imply that bank risk management plays a role in reducing problem loans, which must align with the business entity's efforts to increase profits.

Data analysis with different variables and credit risk assessment

Nowadays, credit payment is a very common way to pay, such as credit cards, loans, many people can use their credit as a guarantee to borrow money from the bank, however some people will default. So we have to predict whether the borrower will pay on time, it is known as credit risk assessment. In this paper, we analyze a data set on credit risk to predict whether individuals will be late on their payments, helping financial firms improve their earnings and reduce their losses. We not only made predictions on the data, but also analyzed the relationship between the variables that affect the overdue probability to find some specific associations. Specifically, we performed ANOVA analysis and found that married people borrowed significantly more than other groups, and the delinquency rate of people with higher education was lower, and the delinquency rate of married people was higher than that of unmarried people. In addition, we conducted a binary logistic regression and found that gender had no significant impact on the prediction results, but an individuals amount of bill statement, amount of previous payment, past repayment situation and Amount of the given credit had an impact on the prediction results. Other variables, such as marital status and education, can also impact the predicted results. Our research puts forward more factors affecting credit risk and also different angles that can be used to analyzes individual credit risk. This has a guiding role for financial firms like banks and other companies in the financial industry, providing more ways to help them analyze the credit risk of borrowers.

Non-Performing Loans and Credit Risk Management in Listed Deposit Money Banks in Nigeria

The study investigates the effect of non-performing loans (NPLs) on credit risk management in listed deposit money banks in Nigeria, focusing on three banks: First Bank Nig Plc, Zenith Bank Plc, and First City Monument Bank Plc within the period of 2008 to 2022. The method of data analysis is the panel cointegrating method using fully modified ordinary least square regression analysis. The results show that the non-performing loan to deposit ratio (NPTDR) significantly impacts credit risk management, with a coefficient of -6.499415, a t-statistic of -3.208157, and a probability value of 0.0000. This indicates that changes in NPTDR have a significant long-term impact on credit risk management. Conversely, the non-performing loan ratio (NPTLR) does not significantly influence credit risk management, with a coefficient of 4.439397, a t-statistic of 0.849112, and a probability value of 0.4016. The leverage ratio (LEVRA) has a significant impact on credit risk management, with a coefficient of 35.13657, a t-statistic of 4.380698, and a probability value of 0.0001, suggesting that higher leverage ratios are associated with better credit risk management practices. Additionally, firm size (FMSZ) significantly affects credit risk management, with a coefficient of -1.283115, a t-statistic of -2.117403, and a probability value of 0.0414, indicating that larger firm sizes are linked to lower credit risk management scores. These findings underscore the importance of capital adequacy, leverage, and firm size in shaping credit risk management practices in Nigeria’s banking sector, providing valuable insights for policymakers and banking regulators to enhance the stability and resilience of the banking sector.

FACTORS AFFECTING CREDIT RISK FOR INDIVIDUAL CUSTOMERS AT VIETNAM BANK FOR AGRICULTURE AND RURAL DEVELOPMENT, BINH THANH BRANCH, HO CHI MINH CITY

The main objective of the study is to determine the factors affecting credit risk for individual customers at Vietnam Bank for Agriculture and Rural Development, Binh Thanh Branch, HCMC. In order to achieve the research objectives, the author used comparative and absolute numerical analysis methods; Descriptive statistics method; Probit regression method to identify factors affecting credit risk. The research results show that of 09 independent variables included in the research model, 05 variables correlated with the dependent variable which is the borrower's financial ability (X1); Industry (X3); Checking and supervising loan debts (X4); Experience of credit officer (X5); Number of dependents (X8). In particular, the variable that is positively correlated with the dependent variable is the number of dependents; The borrower's industry variable is the one that has the strongest impact on the dependent variable, followed by the borrower's financial ability. Based on the research results, the author proposes a number of solutions to improve credit efficiency for Vietnam Bank for Agriculture and Rural Development, Binh Thanh Branch as follows: Selection of credit officers experienced; Assess the number of dependents; Credit officers enhance inspection and supervision of loans; Increasing the financial security of borrowers; Focus on the main business that generates the income of customers when reviewing loan applications.

FACTORS AFFECTING CREDIT RISK OF INDIVIDUAL CUSTOMERS AT BANK FOR FOREIGN TRADE OF VIETNAM CAN THO BRANCH

The study "Factors affecting individual customer credit risk at Can Tho VCB will be conducted from 2021-2023. The study used comparative and absolute numerical analysis methods; Descriptive statistics and Probit regression methods to identify factors affecting credit risk. The research results show that of the 09 independent variables included in the research model, 05 variables correlate with the dependent variable, which is the borrower's financial ability (X1); Occupation (X3); Check and monitor loan debt (X4); Experience of credit officers (X5); Number of dependents (X8). In particular, the variable that has a positive correlation with the dependent variable is the number of dependents; The borrower's industry variable is the variable that has the strongest impact on the dependent variable, followed by the borrower's financial ability variable. Based on the research results, the author proposes several solutions to improve credit efficiency for Can Tho VCB such as selecting experienced credit officers; Assessing the number of dependents; Credit officers strengthening inspection and supervision of loan debt; Increasing the financial security of borrowers; Pay attention to the main industry that generates customers' income when reviewing loan applications.

Does sentiment of investor protection information disclosures affect credit risk premium? Evidence from China

Does sentiment of investor protection information disclosures affect credit risk premium? Evidence from China