Credit Risk Control Research Articles

Predicting determinants of industrial firms’ loan payment default: Logit model versus probit model

Appropriate control and management of credit risk has become the main concern of financial institutions, which are constantly developing models for analyzing, assessing and predicting this risk, particularly in view of the prudential standards required by central banks. The current research paper aims to specify the determinants of payment defaults in Tunisian companies in the industrial sector so as to develop a model with the optimal predictive precision of forecasting payment default when it relates to industrial firms functioning in the Tunisian environment. Within this framework, we compared two generalized linear models which correspond to the logit and probit models based on a sample of Tunisian industrial companies. The results revealed that debt ratio is the most important variable that increases the probability of default payment, while the solvency ratio reduces the probability of default payment. This study highlights the vital contribution of macroeconomic agents, such as GDP growth rate and inflation rate, in terms of the prediction of default payment.

Prediction of bank credit worthiness through credit risk analysis: an explainable machine learning study

AbstractThe control of credit risk is an important topic in the development of supply chain finance. Financial service providers should distinguish between low- and high-quality customers to predict credit risk accurately. Proper management of credit risk exposure contributes to the long-term viability and profitability of banks, systemic stability, and efficient capital allocation in the economy. Moreover, it benefits the development of supply chain finance. Supply chain finance offers convenient loan transactions that benefit all participants, including the buyer, supplier, and bank. However, poor credit risk management in supply chain finance may cause losses for finance providers and hamper the development of supply chain finance. Machine learning algorithms have significantly improved the accuracy of credit risk prediction systems in supply chain finance. However, their lack of interpretability or transparency makes decision-makers skeptical. Therefore, this study aims to improve AI transparency by ranking the importance of features influencing the decisions made by the system. This study identifies two effective algorithms, Random Forest and Gradient Boosting models, for credit risk detection. The factors that influenced the decision of the models to make them transparent are explicitly illustrated. This study also contributes to the literature on explainable credit risk detection for supply chain finance and provides practical implications for financial institutions to inform decision making.

Credit risk management: An imperative for profitability of Centenary Bank Kabale Branch

Purpose: This study focuses on establishing the effect of credit risk management on the profitability of the Centenary Bank Kabale Branch. Credit Risk Management was operationalized as credit risk identification, risk assessment, and risk control, while profitability was operationalized as Return on Equity, Return on Assets, and Non-Performing Loans. Research methodology: The study population comprised of 140 respondents. A sample size of 103 respondents was obtained using the Krejcie and Morgan 1970 table for sample determination. This study adopted a mixed method approach. Quantitative data were collected using Self-Administered Questionnaires and analyzed using Pearson’s linear correlation coefficient. Qualitative data were collected through in-depth interviews and analyzed using content analysis. Results: The findings indicate that the majority of the respondents were male, aged 31–40 years, and bachelor’s degree holders. Risk identification (r=0.882), risk assessment (r=0.776), and risk controls (r=0.829) have a significant positive relationship with profitability at the central bank. Limitations: The limitations include bias from the respondents and the study being conducted in only one branch, making generalization difficult. Contribution: These investigations have informed Centenary Bank managers of the importance of credit risk identification, risk assessment, and risk control. Managers should focus on mitigation measures to reduce risks, create a credit risk assessment team to evaluate risks, establish strategies, and prioritize risk management practices by implementing policies in place. The findings contribute to the literature on credit risk management in terms of the central bank. Novelty: Previous similar research only studied how environmental accounting is implemented in a hospital and did not compare its implementation before, during, and after a pandemic.

Credit Risk Game Analysis and Control Model of Rural Small and Micro Enterprises——Based on the perspective of green credit

RMSEs (rural micro and small enterprises) absorb the rural surplus labor force for employment and entrepreneurship, which is an important economic growth point in rural areas in the future. However, RMSEs have narrow financing channels and difficult financing problems, which hinder the development of the rural economy. If RMSEs want to develop sustainably, they need to find effective financing channels. RMSEs no longer use traditional means of financing through commercial bank loans but are looking for newer financing modes, such as online loan platforms, investors, and so on. This paper takes RMSEs as the main body, RMSEs target rural commercial banks, online loans, and investors as funding instruments, and through building a benchmark model of the evolution game between funding instruments and RMSEs, banks, based on the perspective of green credit other investors industry competition and market regulatory mechanisms and to build a control model of credit risk of RMSEs to reduce the credit risk of RMSEs.

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.

Pengaruh CAR, BOPO, ROA dan BI RATE terhadap NPL Bank

This research aims to evaluate the impact of Capital Adequacy Ratio (CAR), Operational Expenses on Operating Income (BOPO), Return on Assets (ROA), and BI Rate on Non-Performing Loans (NPL) of Banks in Indonesia from 2019 to 2021. Method used is panel regression analysis with data from the financial reports of 43 banks published by the Financial Services Authority (OJK). The sample includes banks that are listed on the Indonesian Stock Exchange and have complete data for that period. The research results show that CAR does not have a significant influence on bank NPL, indicating that the availability of bank capital does not significantly influence the level of bank non-performing loans. On the other hand, BOPO has an effect on bank NPLs, where high operational costs and low operating income tend to increase the bank's level of non-performing loans. ROA also does not have a significant effect on bank NPL, indicating that the rate of return on bank assets does not substantially influence the level of bank non-performing loans. However, the BI Rate has a significant influence on bank NPLs, indicating that Bank Indonesia's benchmark interest rate (BI Rate) has a significant impact on the level of bank non-performing loans. In conclusion, the research emphasizes the importance of managing operational efficiency and paying attention to changes in the BI Rate to control credit risk and maintain a healthy NPL level. Keywords: CAR, BOPO, BI RATE, NPL, ROA

Banks and Financial Institutions: Assessment of Risk Management Strategies

Taking the banking sector as a perspective, this article comprehensively analyzes risk management from four perspectives: liquidity, credit, market, and systemic risk. This paper points out that banks need to closely monitor these risks and ensure their stability and continuity of operations during the epidemic through effective risk management measures. In terms of liquidity risk, banks need to ensure adequate liquidity reserves and maintain funding flexibility through the establishment of an effective management framework and regular stress tests. The key to controlling credit risk lies in rigorous credit review and assessment to ensure that loans are in line with sustainability and repayment capacity. It requires active risk hedging strategies to reduce the market volatility shock to the portfolio by countering market risk. Systemic risk is guarded against by complying with regulations, maintaining adequate capital buffers, and cooperating with regulators. The further research could delve into specific programs to combat these risks.

Research on the Financial Credit Risk Management Model of Real Estate Supply Chain Based on GA-SVM Algorithm: A Comprehensive Evaluation of AI Model and Traditional Model

By combining machine learning AI model with traditional credit risk measurement model, this paper aims to more accurately measure the credit default risk of real estate enterprises. Through professional credit management knowledge, the integration of these two models can fill the gaps of traditional models outside of financial data, and use the research experience of traditional models in the financial field to provide guidance for machine learning. This paper focuses on the key issues of capital intensity, exploration of Supply Chain Finance Growth and Credit Risk Control within the Real Estate Sector. By combining qualitative and quantitative methods. Through literature research and empirical analysis, this paper develops a supply chain finance credit risk assessment index system tailored for small and medium-sized enterprises operating in China's real estate sector. The index system comprises 23 risk assessment indicators, and comprehensively considers many factors such as corporate financial status and market environment. Then, a credit risk assessment model for small and medium-sized real estate enterprises is developed from a supply chain finance perspective, utilizing genetic algorithm optimization for Support Vector Machine (GA-SVM)., and the advantages of this model compared with the traditional model in accuracy and generalization ability are verified through comparative analysis of experiments. The research results can help reduce the information asymmetry in the real estate industry, enhance the credit rating evaluation process for small and medium-sized enterprises, and furnish references to aid commercial banks in credit and credit risk management within the real estate supply chain finance sector.which has important theoretical and practical value.

The Impact of Credit and Liquidity Risk on Bank Performance

Banks exist to make a profit so that they can continue to operate, grow, and expand. Bank stability has been challenged over the previous two decades as a result of political intervention, nonperforming loans (NPLs), and interest rate changes. They need to monitor these micro and macro-level factors to identify and manage risk. Thus, the current study explores the impact of credit risk (NPL, Z-Score) and liquidity risk (LR) on the performance of South Asian banks measured with ROA and ROE. The sample of the study comprised a total of 35 listed banks of South Asian Countries (Pakistan (20) and India (15)) and the sample period spans 10 years from 2011 to 2020. The information was obtained from data stream and the financial statements of selected banks listed on the Pakistan Stock Exchange (PSX) and Bombay Stock Exchange (BSE). The findings show that credit and liquidity risk has a major impact on the performance of South Asian banks. Overall, credit risk has a negative impact on bank performance. However, the Z-score value in the estimation derived with ROE, on the other hand, demonstrates a positive connection with bank performance. The current study’s findings suggest that bank management in developing countries should provide timely monitoring and supervision of their long-term borrowers to control credit risk.

Research on the Application of Blockchain Technology in Credit Risk Control of Chinese Commercial Banks

China is actively promoting the development of blockchain-related technologies, and hopes to apply the technology to commercial banks. Nowadays, blockchain technology has become one of the most important research directions of financial technology for Chinese commercial banks. Under the traditional model, it is difficult for banks to reasonably evaluate customers before lending, update customer information during lending, and make timely adjustments to unfavorable conditions after lending. These are the difficulties faced by banks in the traditional mode. Blockchain technology can screen customers for banks before lending, monitor customers status in real time during lending, and deal with problems in time after lending. However, if this technology is to be introduced perfectly, China needs to solve the problems of imperfect laws and mechanisms, low maturity of technology, insufficient performance and scalability, unprotected security, and excessive cost and mismatch with revenue.

Credit Risk Management and Financial Performance of Savings and Credit Co-operative Organizations: A Case of Selected SACCOs in Makindye Division, Kampala Uganda

The purpose of this paper is to assess the effect of credit risk management on the financial performance of Savings and Credit Co-operative Organizations (SACCOs) in Makindye division, Kampala. Specifically, the paper looked at how credit risk identification, credit risk assessment and credit risk control affect the way that SACCOs perform financially. A cross-sectional field survey design that adopted both qualitative and quantitative approaches to collecting and analyzing data was adopted. Twenty (20) registered active SACCOs were purposively selected from Makindye Division for data collection using both questionnaires and interview guides. Data was collected from managers, treasurers or accountants, and secretaries of the registered SACCOs. Descriptive and inferential statistics were generated and interpreted to meet the study objectives. Findings showed that credit risk management has a positive and significant effect on the financial performance of SACCOs. This implies that the way a SACCO performs financially is influenced by the way credit risk is identified, assessed and controlled within that SACCO. SACCOs should train their members on strategies of identifying, assessing as well as controlling risks if they are to register and sustain good financial performance. This paper contributes to literature in regard to credit risk management strategies within SACCOs in Uganda.

The Influence of the Network on the Financial Market: Taking Financial Intermediaries and Internet Finance as Examples

The article investigates the impact of financial intermediaries and internet finance on financial markets. By comparing the existing research data and materials, this paper analyzes the advantages of financial intermediaries in dealing with information asymmetry, controlling credit risk, capital allocation, and conversion. The research shows that both the advantages and disadvantages of financial intermediaries affect financial markets to some extent. The author also found that with the development of search engines and social networks, the rise of Internet finance has a significant status in promoting the efficiency of resource allocation. The results are supported by data generated from previous studies and are consistent across both internal and external conditions. From both positive and negative aspects, the influence of social networks and informatization on financial markets can also provide a positive reform effect on networks and financial intermediaries and put forward some further ideas. Furthermore, based on the analysis, the author also puts forward some measures to utilize financial intermediaries and internet finance.

Technology Innovation, Financing Risk and Control in Small and Medium-Sized Enterprises Supply Chain

With the development of supply chain finance, micro and small enterprises can effectively solve their financing difficulties and improve the flow of funds in the supply chain. This paper reviews the work done by past research in solving the financing problems of MSMEs, credit risk assessment and control, and the development of new technologies in supply chain finance. It discusses how technology affects the efficiency and security of supply chain finance and takes small and medium-sized enterprises as carriers to carry out a broader theoretical analysis of risk control and assessment. At the same time, the paper analyses the application of currently popular and advanced technologies, such as blockchain, artificial intelligence, and the Internet of Things, in solving the financing challenges of SMEs. In addition, the paper discusses and compares the risk assessment and control of micro and small enterprise financing and supply chain finance risk control and proposes corresponding solutions and models.

Pathways to self-sufficiency in the microfinance ecosystem

We used latent class growth analysis to study the trajectories followed by microfinance institutions for 10 years. This technique can detect groups of firms that follow different patterns of change over time. We identified groups of institutions that followed the same strategy and iso-performance groups of institutions with the same outcome trajectory. The trajectories were analyzed with categorical regression and decision trees, which constitutes a novel approach to latent class growth analysis. Lending money to the poorest while making a profit is not straightforward and it is challenging for microfinance institutions to be self-sufficient. We found that the most useful strategy was to improve efficiency by lowering operating costs, followed by the control of credit risk. Deviating from the mission also had a positive effect on self-sufficiency, but was a strategy followed by few institutions. Rarely did changes in interest rates or not lending to women prove valuable. The findings are useful for the stakeholders of these institutions and particularly for managers.

Analysis of the Effectiveness of Banking Credit Risk Control with Macroprudential Policy in Indonesia

High credit risk will affect the health of the bank which will be a factor causing systemic risk. This research aims to analyze the effectiveness of macroprudential policy in controlling banking credit risk in Indonesia. This research uses Non-Performing Loans (NPL) as an indicator of credit risk. Meanwhile, as indicators of macroprudential policy, policy instruments such as LTV (Loan to Value), RIM (Macroprudential Intermediation Ratio), DTI (Debt to Income), and COC (Ceilings on Credit). The analytical method used in this research is panel data regression analysis with a fixed effect model (FEM). The research results show that simultaneously each macroprudential policy instrument, be it RIM, DTI, and COC, influences banking credit risk in Indonesia, and easing policy on LTV is unable to reduce NPLs.

Analyzing the Effects of Credit Management Practices on Loan Repayment Performance of Microfinance Institutions in Makamba, Burundi

The ability to obtain funding is crucial to the success of microfinance institutions. To give credit to micro entrepreneurs, an individual’s credit history and worthiness is pivotal. However, loan payback performance is a barrier. Burundi's financial institutions have collapsed as a result of poor credit management. The purpose of this study was analyzing the effects of credit management practices on loan performance in Microfinance institutions. The study was based on the subsequent goals: to ascertain the impact of client evaluation on loan repayment performance benchmark; to assess how credit risk management control affect MFI loan payback performance; to determine how credit terms affects MFI’ ability to collect loan; and to ascertain how credit collection practices affect MFI loan repayment performance. Ten microfinance Institutions registered in the Makamba province of Burundi were used. Purposive sampling was employed as the sample method in this investigation. Target population of study was 134 and sample size was 100 from using Yamane’s formula. Mean, frequency and standard deviation were also the main descriptive statics employed. Primary data from credit officers, managers’ officers, finance and accounting officers, cashiers, control officers and recovery officers of all microfinance Makamba, Burundi were used. To be utilized to gather main information on credit management techniques, structured questionnaires were used to gather primary data on existing microfinance loan records. The secondary data about the documentation reports related to the loan performance of microfinance institutions for the period 2018-2021 was verified to ensure whether those MFIs recorded loan repayment performance or not. Findings revealed that the Pearson Chi-Square value is 85.226, and the Likelihood Ratio value is 85.656. These values represent the chi-square test statistics for the association between the variables of interest (credit collection policy and loan repayment performance). In both cases, the p-value is 0.000, indicating a statistically significant association between the variables. Multiple linear regressions indicated that client evaluation had the highest positive correlation of 0.141 proves that there existed a positive significant relationship between client evaluation and loan repayment performance while credit risk control had a negative correlation of -0.014, credit terms had no correlation since its beta value was found to be 0.00 and credit collection policy had a negative correlation of -0.014. Evaluating clients' characteristics, such as moral character and creditworthiness, before granting loans is important for microfinance institutions. Microfinance institutions should focus on implementing robust credit risk control mechanisms to mitigate default risk and improve overall repayment rates. There is need for investigate the long-term effects of credit management practices on loan repayment performance by conducting longitudinal studies that track borrowers and analyze their repayment behavior over an extended period.

Analisis Penerapan Manajemen Risiko Kredit pada Perusahaan Start-Up Fintech PT A

Fintech PT A is one of the pioneers in Indonesia that serves business people with lenders on a digital platform. One of the problems faced by fintech PT A is the increase in non-performing loans. This research is a case study at a fintech startup to analyze the application of credit risk management and provide recommendations for improving credit risk management to improve company performance. The methodology used in this research is descriptive qualitative analysis by collecting data from interviews and documentation studies. Interviews were conducted with the Director of Credit and Collection and Director of Risk who have a crucial role in risk management in the company. The results of the study show that the application of risk management to PT A is in accordance with the four stages of the Indonesia’s Financial Services Authority regulation number 04/POJK.05/2021 namely credit risk identification, credit risk measurement, credit risk control, and credit risk monitoring. However, the application of credit risk management by PT A based on data analysis obtained through interviews and documentation studies shows that the results have not operate optimally. Because at the stages of monitoring credit risk and controlling credit risk there are still several aspects that have not been fulfilled based on the Indonesia’s Financial Services Authority regulation number 04/POJK.05/2021. So the recommendation that can be given is that the company can create a more structured and sustainable risk management system with all related divisions so that it can accommodate all aspects of risk up to the risk monitoring and control process.

Effect of Credit Management Systems on Loan Recovery Efforts of Microfinance Banks in Akwa Ibom State

This study set out to ascertain the effect of credit management techniques on loan recovery efforts by microfinance banks. To guide the study, 3 objectives specific objectives, 3 research questions and three null hypotheses tested at 0.05 alpha levels were formulated. The study covers all microfinance companies operating in Akwa Ibom State. A descriptive survey research design was employed for the study. The population was 85 operations and marketers in microfinance banks in Akwa Ibom State that have been in existence from 2014 to 2017. The sample size was 60 and the stratified random sampling technique was employed. The research instrument used for the study was a researcher- made questionnaire tagged “Credit Management and Loan Recovery Questionnaire” (CMLRQ). The instrument developed by the researcher was face and content validated by three experts in the Faculty of Social sciences, Akwa Ibom State University. All corrections and inputs were built in to the final version of the instrument. The reliability coefficient of the instrument was determined using Cronbach alpha method after the instrument was trial-tested on 20 respondents who were part of the population but not part of the sample. The result showed reliability co-efficient of 0.78. The data generated was analyzed using regression analysis to answer the research questions and to test the null hypotheses at .05 alpha level. It was concluded that Credit appraisal, credit risk control and collection policy were found to be very important in influencing loan recovery. It was recommended among others that Microfinance banks should ensure that credit appraisal is carried out by the technical people who are experienced and competent credit officer in order to stem out those with intolerable credit risk at the earliest possible opportunity.

The impact of public health emergencies on small and medium-sized enterprises: Evidence from China

This article proposes a machine learning-based method to quantify the impact of public health emergencies on the economic benefits and credit risks of small and medium-sized enterprises (SMEs). Using natural language processing and text analysis methods, we construct the degree of public health emergencies index using Chinese newspaper data. Based on the panel data of Chinese SMEs, we find that public health emergencies have a strong negative impact on the economic benefits of SMEs, and the impact is uneven across industries. In addition to declining economic benefits, we determine that SMEs' credit default risk is a more serious problem. Our predictive analysis of SMEs' credit default risk after COVID-19 indicates the benefits of using machine learning methods in credit risk assessment and control, providing precise assistance for SMEs.

Credit risk management and effectiveness of credit activities: Evidence from Vietnam

The research aims to assess the effects of credit risk management on the effectiveness of credit activities at Vietnamese state-owned commercial banks. Based on the literature review and empirical studies, the aspects of credit risk management practices include four factors proposed in the research model, such as: credit terms, client appraisal, credit risk control, and collection policies. The author implements the descriptive statistical method and linear regression model on SPSS with data from a survey of credit officers from state-owned commercial banks in Vietnam. The findings show that credit term, client appraisal, and credit risk control positively influence loan performance at a 95% confidence interval. The paper also highlights the role of internal control as the mechanism for reducing credit risk and improving the effectiveness of credit activities.