Journal articles: 'Credit risk credit risk management'

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Spuchlakova, Erika, and Maria Misankova. "Risk management of Credit Default Swap." New Trends and Issues Proceedings on Humanities and Social Sciences 3, no. 4 (March 22, 2017): 229–34. http://dx.doi.org/10.18844/gjhss.v3i4.1573.

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Akram, Hassan, and Khalil ur Rahman. "Credit risk management." ISRA International Journal of Islamic Finance 10, no. 2 (December 10, 2018): 185–205. http://dx.doi.org/10.1108/ijif-09-2017-0030.

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PurposeThis study aims to examine and compare the credit risk management (CRM) scenario of Islamic banks (IBs) and conventional banks (CBs) in Pakistan, keeping in view the phenomenal growth of Islamic banking and its future implications.Design/methodology/approachA sample of five CBs and four IBs was chosen out of the whole banking industry for the study. Secondary data obtained from the banks’ annual financial reports for 13 years, starting from 2004 to 2016, were analyzed. Multiple regression, correlation and descriptive analysis were used in the examination of the data.FindingsThe results show that loan quality (LQ) has a positive and significant impact on CRM for both IBs and CBs. Asset quality (AQ), on the other hand, has a negative impact on CRM in the case of IBs, but has a significantly positive relation with CRM in the case of CBs. The impact of 16 ratios measuring LQ and AQ have also been individually checked on CRM, by making use of a regression model using a dummy variable of financial crises for robust comparison among CBs and IBs. The model proved significant, and CRM performance of IBs was observed to be better than that of CBs. Moreover, the mean average value of financial ratios used as a measuring tool for these variables shows that the CRM performance of IBs operating in Pakistan was better than that of CBs over the period of the study.Practical implicationsThe research findings are expected to facilitate bankers, investors, academics and policy makers to build a better understanding of CRM practices as adopted by CBs and IBs. The findings would be useful in formulating policy measures for the progress of the banking industry in Pakistan.Originality/valueThis research is unique in terms of its approach toward analyzing and comparing CRM performance of CBs and IBs. Such work has not been carried out before in the Pakistani banking industry.

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Freeman, Mark C., Paul R. Cox, and Brian Wright. "Credit risk management." Managerial Finance 32, no. 9 (September 2006): 761–73. http://dx.doi.org/10.1108/03074350610681952.

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Gazi, Boran. "Credit Risk Management." Journal of Applied Statistics 38, no. 6 (June 2011): 1314. http://dx.doi.org/10.1080/02664760903335083.

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Kealhofer, Stephen. "Credit Risk and Risk Management." AIMR Conference Proceedings 1999, no. 3 (August 1999): 80–91. http://dx.doi.org/10.2469/cp.v1999.n3.11.

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Boffey, R., and G. N. Robson. "Bank Credit Risk Management." Managerial Finance 21, no. 1 (January 1995): 66–78. http://dx.doi.org/10.1108/eb018497.

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BROLL, UDO, B. MICHAEL GILROY, and ELMAR LUKAS. "MANAGING CREDIT RISK WITH CREDIT DERIVATIVES." Annals of Financial Economics 03, no. 01 (June 2007): 0750004. http://dx.doi.org/10.1142/s2010495207500042.

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Credit risk is one of the most important forms of risk faced by national and international banks as financial intermediaries. Managing this kind of risk through selecting and monitoring corporate and sovereign borrowers and through creating a diversified loan portfolio has always been one of the predominant challenges in bank management. The aim of our study is to examine how a risky loan portfolio affects optimal bank behavior in the loan and deposit markets, when derivatives to hedge credit risk are available. In a stochastic continuous-time framework a hedging model is developed where the bank management can use derivatives to hedge credit risk. Optimal loan, deposit and hedging strategies are then studied. It is shown that the magnitude and the direction of hedging are determined by the bank manager's preferences, the corresponding risk premium and the variance of the loan rate and its hedging instrument respectively.

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Nayan J., Nayan J., and Dr M. Kumaraswamy Dr. M. Kumaraswamy. "Retail Credit Risk Management in Indian Public Sector Banks." Global Journal For Research Analysis 3, no. 8 (June 15, 2012): 31–37. http://dx.doi.org/10.15373/22778160/august2014/10.

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Moloi, Tankiso. "The nature of credit risk information disclosed in the risk and capital reports of the top-5 South African banks." Banks and Bank Systems 11, no. 3 (October 12, 2016): 87–93. http://dx.doi.org/10.21511/bbs.11(3).2016.09.

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This paper used the Credit Risk Disclosure Measurement Tool (CRDMT) constructed on the basis of six main areas, namely, banks own description of credit risk (i.e., as it applies to the banks operations), banks strategy of reducing credit risk exposure (i.e., objectives of credit management), banks approach to credit modelling or the internal rating system, banks approach and the manner in which they assess their exposure to credit risk, banks credit risk mitigation strategies employed (i.e., collateral and other credit enhancements), and banks approach to the valuation of pledged collateral and other credit enhancements to assess the information disclosed on the risk and capital management reports of the top-5 South African banks. Results demonstrated that the top-5 South African banks were fairly in line with the main six credit risk areas that would result in an informative risk and capital management report, as proposed by the CRMDT. It was observed that there were, however, pockets of information that could be improved to enhance these risk and capital management reports, particularly the credit risk information made available to public. These areas included the information relating to banks credit risk mitigation strategies employed and banks strategy of reducing credit risk exposure, as well as the information relating to banks approach to the valuation of pledged collateral and other credit enhancements. These areas were noted for their partial or non-disclosure of information. Keywords: banks, credit risk, Credit Risk Disclosure Measurement Tool (CRDMT), disclosure analysis and risk and capital reports. JEL Classification: G21, G32

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Ndegwa, Michael K., Apurba Shee, Calum G. Turvey, and Liangzhi You. "Uptake of insurance-embedded credit in presence of credit rationing: evidence from a randomized controlled trial in Kenya." Agricultural Finance Review 80, no. 5 (June 22, 2020): 745–66. http://dx.doi.org/10.1108/afr-10-2019-0116.

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PurposeDrought-related climate risk and access to credit are among the major risks to agricultural productivity for smallholder farmers in Kenya. Farmers are usually credit-constrained due to either involuntary quantity rationing or voluntary risk rationing. By exploiting randomized distribution of weather risk-contingent credit (RCC) and traditional credit, the authors estimate the causal effect of bundling weather index insurance to credit on uptake of agricultural credits among rural smallholders in Eastern Kenya. Further, the authors assess farmers' credit rationing, its determinants and effects on credit uptake.Design/methodology/approachThe study design was a randomized controlled trial (RCT) conducted in Machakos County, Kenya. 1,170 sample households were randomly assigned to one of three research groups, namely control, RCC and traditional credit. This paper is based on baseline household survey data and the first phase of loan implementation data.FindingsThe authors find that 48% of the households were price-rationed, 41% were risk-rationed and 11% were quantity-rationed. The average credit uptake rate was 33% with the uptake of bundled credit being significantly higher than that of traditional credit. Risk rationing seems to influence the credit uptake negatively, whereas premium subsidies do not have any significant association with credit uptake. Among the socio-economic variables, training attendance, crop production being the main household head occupation, expenditure on food, maize labour requirement, hired labour, livestock revenue and access to credit are found to influence the credit uptake positively, whereas the expenditure on non-food items is negatively related with credit uptake.Research limitations/implicationsThe study findings provide important insights on the factors of credit demand. Empirical results suggest that risk rationing is pervasive and discourages farmers to take up credit. The study results also imply that credit demand is inelastic although relatively small sample size for RCC premium subsidy groups may be a limiting factor to the authors’ estimation.Originality/valueBy implementing a multi-arm RCT, the authors estimate the factors affecting the uptake of insurance bundled agricultural credits along with eliciting credit rationing among rural smallholders in Eastern Kenya. This paper provides key empirical findings on the uptake of RCC and the effect of credit rationing on uptake of agricultural credits, a field which has been majorly theoretical.

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Tigges, Christoph. "Buchbesprechung: Die Optimierung der Performance im Credit Management. Verein für Credit Management e.V. (Hrsg.: Schneider-Maessen, Schumann, Skier, Weiß)." RISKNEWS 2, no. 6 (December 2005): 76. http://dx.doi.org/10.1002/risk.200590127.

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Jalilian, Negar, Seyed Mahmoud Zanjirchi, and Mark Goh. "Interactive scenario analysis of banking credit risks in intuitive fuzzy space." Journal of Modelling in Management 15, no. 1 (November 18, 2019): 257–75. http://dx.doi.org/10.1108/jm2-01-2019-0011.

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Purpose The purpose of the paper is to bring attention to documentary credits and the efforts to reduce debt obligations in credit history is recognized as an important source of uncommitted bank earnings. Credit risk has a significant impact on the stability of the banking system. This paper identifies the types of credit risk in the banking supply chain. Design/methodology/approach The authors model the types of credit risk using the intuitive fuzzy failure modes and effects analysis (IFMEA) and intuitive fuzzy cognitive mapping. The population of the study that is needed for the interviews and expert panels comprises senior managers and experts of a leading bank in Iran. The respondents are experienced in credit and banking risk and were selected through judgment sampling and snowballing. Findings The findings suggest that reducing the risks of the foreign letters of credit contracts can mitigate the risk in the agricultural sector, the specific risks of rent-to-own contracts, the risk of the long-term facilities and the specific risk of the domestic letter of credit contracts. Originality/value This research investigates Iran Tejart Bank’s credit risk, formulates a model of the types of credit risk present and analyzes them using the intuitive fuzzy failure modes and effects analysis and intuitive fuzzy cognitive map. Through this credit risk model, one can then facilitate risk management for better financial stability. Also, the model can be used to evaluate the risk indicators.

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Al-Shawabkeh, Abdallah, and Rama Kanungo. "Credit risk estimate using internal explicit knowledge." Investment Management and Financial Innovations 14, no. 1 (March 31, 2017): 55–66. http://dx.doi.org/10.21511/imfi.14(1).2017.06.

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Jordanian banks traditionally use a set of indicators, based on their internal explicit knowledge to examine the credit risk caused by default loans of individual borrowers. The banks are reliant on the personal and financial information of the borrowers, obtained by knowing them, often referred as internal explicit knowledge. Internal explicit knowledge characterizes both financial and non-financial indicators of individual borrowers, such as; loan amount, educational level, occupation, income, marital status, age, and gender. The authors studied 2755 default or non-performing personal loan profiles obtained from Jordanian Banks over a period of 1999 to 2014. The results show that low earning unemployed borrowers are very likely to default and contribute to non-performing loans by increasing the chances of credit risk. In addition, it is found that the unmarried, younger borrowers and moderate loan amount increase the probability of non-performing loans. On the contrary, borrowers employed in private sector and at least educated to a degree level are most likely to mitigate the credit risk. The study suggests improving the decision making process of Jordanian banks by making it more quantitative and dependable, instead of using only subjective or judgemental based understanding of borrowers.

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Luo, Na, Jiayi Yang, Yuanfeng Zhu, and Yu Zhang. "The Risk Management of Commercial Banks——Credit-Risk Assessment of Enterprises." International Journal of Economics and Finance 8, no. 9 (August 24, 2016): 69. http://dx.doi.org/10.5539/ijef.v8n9p69.

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With the diversified developments of the financial market, commercial banks are confronted with various risks, among which the credit risk is the core, and thus the assessment of enterprises’ credit risks is especially important in the credit process of the commercial banks. Based on the relevant researches about commercial banks’ credit risk management, the paper carries out a deep analysis on the factors that may affect the credit risk assessment and then establishes a relatively comprehensive credit risk assessment system. In this paper, we apply our risk assessment model, which is established on the basis of GRNN neural network model, to make an empirical analysis with the selected sample data. And the results suggest that the hit rates of identifying high quality enterprises and low quality enterprises are 92.16 percent and 93.75 percent, respectively, indicating that the model has realized a good prediction.

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Lin, Xi, Yafeng Yin, and Fang He. "Credit-Based Mobility Management Considering Travelers’ Budgeting Behaviors Under Uncertainty." Transportation Science 55, no. 2 (March 2021): 297–314. http://dx.doi.org/10.1287/trsc.2020.1014.

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This study analyzes the performance of a credit-based mobility management scheme considering travelers’ budgeting behaviors for credit consumption under uncertainty. In the scheme, government agencies periodically distribute a certain number of credits to travelers; travelers must pay a credit charge for driving to complete their trips. Otherwise, they can take public transit free of credit charge. Consequently, within a credit-releasing cycle, travelers must budget their credit consumption to fulfill their mobility needs. Such budgeting behaviors can be viewed as a multistage decision-making process under uncertainty. Considering a transportation system with a credit scheme, we propose parsimonious models to investigate how the uncertainty associated with individual mobility needs and the subsequent travelers’ credit-budgeting behavior influence the multistage equilibrium of the transportation system, as well as the performance of the credit scheme on managing the transportation system. Both analytical and numerical results suggest that travelers tend to restrict their credit consumption in the early stage of a credit-releasing cycle to hedge against the risks associated with using up all credits, which compromises the performances of credit-based schemes. Moreover, a negative attitude toward risk aggravates the discrepancy between the credit consumption of the early and late stages. Last, we propose a contingency credit scheme to mitigate the negative impact incurred by travelers’ budgeting behaviors.

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Pradhan, Shreya, and Ajay K. Shah. "Credit Risk Management of Commercial Banks in Nepal." Journal of Business and Social Sciences Research 4, no. 1 (June 30, 2019): 27–37. http://dx.doi.org/10.3126/jbssr.v4i1.28996.

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The study is primarily focused on credit risk assessment practices in commercial banks on the basis of their internal efficiency, assessment of assets and borrower. The model of the study is based on the analysis of relationship between credit risk management practices, credit risk mitigation measures and obstacles and loan repayment. Based on a descriptive research approach the study has used survey-based primary data and performed a correlation analysis on them. It discovered that credit risk management practices and credit risk mitigation measures have a positive relationship with loan repayment, while obstacles faced by borrowers have no significant relationship with loan repayment. The study findings can provide good insights to commercial bank managers in analysing their model of credit risk management system, policies and practices, and in establishing a profitable and sustainable model for credit risk assessment, by setting a risk tolerance level and managing credit risks vis-a-vis the prevailing market competition.

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Adam, Tim René. "Risk management and the credit risk premium." Journal of Banking & Finance 26, no. 2-3 (March 2002): 243–69. http://dx.doi.org/10.1016/s0378-4266(01)00221-7.

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Niklis, Dimitrios, Michalis Doumpos, and Constantin Zopounidis. "Credit Risk Modelling." International Journal of Sustainable Economies Management 7, no. 3 (July 2018): 50–64. http://dx.doi.org/10.4018/ijsem.2018070105.

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The assessment of businesses' credit risk is a difficult and important process in the area of financial risk management. In a classical multivariate model, financial ratios are combined in order to achieve a credit risk score, which signals if a loan application is approved or discarded. Despite their good performance, the developed multivariate models using statistical methods have been widely criticized. They are based on models that use accounting data, which have the disadvantage of being static and so often fail to follow the changes in the economic and business environment. In recent years, market models (structural and reduced form models) have become popular among banks and financial institutions, because of their theoretical background and the use of updated information. The aim of this article is to present an overview of basic market models (structural models, reduced form models and market models used from credit institutions) together with their characteristics in order to outline their development throughout the last decades.

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Gibson, Michael S. "Credit Derivatives and Risk Management." Finance and Economics Discussion Series 2007, no. 47 (2007): 1–20. http://dx.doi.org/10.17016/feds.2007.47.

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Kealhofer, Stephen. "Portfolio Management of Credit Risk." AIMR Conference Proceedings 2003, no. 5 (February 10, 2003): 19–29. http://dx.doi.org/10.2469/cp.v2003.n5.3318.

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Vu, Joseph. "Trends in Credit Risk Management." CFA Digest 29, no. 3 (August 1999): 34–35. http://dx.doi.org/10.2469/dig.v29.n3.515.

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Anna, Siekelova, Kollar Boris, and Weissova Ivana. "Impact of Credit Risk Management." Procedia Economics and Finance 26 (2015): 325–31. http://dx.doi.org/10.1016/s2212-5671(15)00860-6.

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Erben, Roland Franz. "Buchbesprechung: Standortbestimmung im Credit Management - Der Credit Manager als der Erfolgsfaktor zur Steigerung des Unternehmenswertes vom Verein für Credit Management e. V.; Jan Schneider-Maessen; Matthias Schumann und Bernd Weiß (Hrsg.)." RISKNEWS 2, no. 2 (March 24, 2005): 79. http://dx.doi.org/10.1002/risk.200590044.

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Chen, Jiongyong, and Li Ma. "Bank Credit Risk Avoidance and Countermeasures Based on Wireless Communication." Scientific Programming 2022 (March 17, 2022): 1–7. http://dx.doi.org/10.1155/2022/7793088.

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Credit risk management refers to the main operating risks faced by banks, and its effective management is directly related to the bank’s operating performance. The main link of the credit risk management system is the measurement of bank credit risk, and this connection will run through the entire process of the bank’s credit risk management system. The measurement results directly affect the actual operation of the bank and therefore also affect risk management and business management. This article aims to study bank credit risk management in the context of wireless communication and big data. Based on the analysis of the characteristics of credit risk, the theory of credit risk and information asymmetry, and the credit risk measurement model, Bank S is taken as an example to construct Bank S’s credit risk influencing factor model. Finally, the bank is compared with three banks.

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Kercheval, Alec, Lisa R. Goldberg, and Ludovic Breger. "Modeling Credit Risk." Journal of Portfolio Management 29, no. 2 (January 31, 2003): 90–100. http://dx.doi.org/10.3905/jpm.2003.319876.

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Cheyette, Oren, and Boris Postler. "Empirical Credit Risk." Journal of Portfolio Management 32, no. 4 (July 31, 2006): 79–92. http://dx.doi.org/10.3905/jpm.2006.644199.

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Orlova, E. V. "Mechanism and model of credit portfolio diversification." Issues of Risk Analysis 17, no. 1 (February 27, 2020): 78–89. http://dx.doi.org/10.32686/1812-5220-2020-17-1-78-89.

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Under conditions of demand for credit resources growing in Russian economy the importance of credit risks assessment and their influence on the credit organizations efficiency is increased. Empirical studies show that credit risks in the banking today are increasing nonlinearly relative to the main characteristics of the credit — the level of credit risk, credit terms, interest rate. Therefore, the formation of the most acceptable from the point of view of risk reducing of the bank’s credit portfolio is a scientifically based and practically important problem. The aim of the work is to justify the need for and develop a new mechanism for managing the bank's credit portfolio, ensuring its diversification and reduction of credit risks. The materials of the study were the statistical data of the Bank of Russia and Rosstat. Methods used in the work are: system analysis, control theory, statistical data processing and operational research. A mechanism for managing the quality of a bank credit portfolio is proposed, featuring a combination of quantitative and qualitative criteria for assessing the quality of the credit portfolio and allow to monitor of the credit portfolio, to make decisions on approving or rejecting a credit application in accordance with the permissible values of risk factors. A model has been developed for optimizing the structure of the credit portfolio, which makes it possible to form an optimal ratio of long-term and short-term credits, ensuring the maximum yield of the credit portfolio taking into account credit risk in the context of various credit policy types. A practical importance of the investigation are the positive results of the implementation of the proposed mechanism and model of credit portfolio management into the credit organization, ensuring the growth of its profitability and promoting an increase in competitiveness.

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Wang, Huibo. "Credit Risk Management of Consumer Finance Based on Big Data." Mobile Information Systems 2021 (July 22, 2021): 1–10. http://dx.doi.org/10.1155/2021/8189255.

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In recent years, China’s consumer finance has developed rapidly, but the foundation is unstable, and the industry has serious problems of violent competition, excessive credit, and fraud. Therefore, we should attach great importance to the healthy development of consumer finance, especially the management of its credit risk. The application of big data credit investigation can provide early warning of potential risks and prevent the risk of excessive credit investigation. This paper starts with the definition of basic core concepts, such as traditional credit investigation, big data credit investigation, and consumer finance, analyzes the performance and causes of consumer finance credit risk, and combs in detail the relevant theories of the application of big data credit investigation in consumer finance credit risk management. The application of big data credit investigation has optimized the risk management process of consumer financial institutions, deepened the concept of Internet consumer finance, improved the risk management system, created a diversified credit information system, and strengthened the innovation of Internet consumer finance products and services. For example, credit scores provide the most intuitive quantification of consumer credit risk. For consumers with different levels of credit scores, different credit approval processes can be matched. For customers with high scores, the work process can be simplified without affecting the work results. It can reduce the workload of employees by 20% and increase the accuracy of customer credit risk prediction by 16%.

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Apanga, Michelle Ayog-Nying, Kingsley Opoku Appiah, and Joseph Arthur. "Credit risk management of Ghanaian listed banks." International Journal of Law and Management 58, no. 2 (March 14, 2016): 162–78. http://dx.doi.org/10.1108/ijlma-04-2014-0033.

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Purpose – The study aims to assess credit risk management practices within financial institutions in Ghana. Specifically, the study compares credit risk management practices of listed banks in Ghana with Basel II (1999). Design/methodology/approach – The analysis is based on data gathered from varied sources, namely, use of questionnaires, analysis of internal credit policies and procedure manuals and semi-structured interviews and discussions with credit risk managers of the selected banks in May 2007 and October 2014. Findings – Overall, the credit risk management practices within listed banks in Ghana are in line with sound practices. The only dissimilarity, however, is the role of the board of directors in defining acceptable types of loans and maximum maturities for the various types of loans. The listed banks in Ghana are also exposed to credit risks associated with granting both corporate and small business commercial loans and the use of collaterals to mitigate their credit risk exposures. Practical implications – Banks in Ghana should consider developing the skills of all their personnel and appropriately motivating those involved in the credit risk management processes to ensure that they carry out this process efficiently. Originality/value – Research into credit risk management in the banking industry from the Ghanaian perspective remains scant. This study is, therefore, timely, and its findings are invaluable for the efficient management of credit risk in the banking industry. This study provides policy recommendations which will enhance shareholder value and, in this way, contribute to greater stability in the banking sector in developing countries, in particular.

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Панова and T. Panova. "Determination of Credit Border – Basis of Credit Risk Management of Consumer Credit." Economics of the Firm 4, no. 3 (September 17, 2015): 44–48. http://dx.doi.org/10.12737/17595.

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The article discusses the problem of determining the boundaries of credit and credit risk management. Defining the boundaries of reasonable use of credit and compliance is of great importance for the economy as a whole. Only with an optimal level of credit investments of credit impact on the economy can be positive.

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Gustafson, Cole R., Glenn D. Pederson, and Brent A. Gloy. "Credit risk assessment." Agricultural Finance Review 65, no. 2 (November 2005): 201–17. http://dx.doi.org/10.1108/00214660580001173.

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Toufaili, Bilal. "THE IMPACT OF RISK MANAGEMENT ON FINANCIAL PERFORMANCE." EUrASEANs: journal on global socio-economic dynamics, no. 3(28) (May 31, 2021): 7–23. http://dx.doi.org/10.35678/2539-5645.3(28).2021.7-23.

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Commercial banks that control a large proportion of overall assets of the financial sector primarily rely on extending credits, and banks may raise their earnings through this function which constitutes one of the major functions of commercial banks. Consequently, and due to the wide multiple risk exposures of commercial banks, the issue of capital structure has become a vital element in determining the viability of banks and their ability to withstand various risks involved. Hence, risk management as such has become an essential part of evaluating various risks, including credit risks, liquidity risks, solvency risks and so forth. It is necessary to remember that banks differ from one to another in many respects, namely, their goals, services and strategies. Thus, banks are facing various risks in their day-to-day operations. The research here has implemented a quantitative methodology throughout distributing a survey over a defined number of respondents, and the results were viewed through the prism of regression analysis and Pearson correlations. The obtained results prove there is a direct relationship between market risk, liquidity risk, credit risk, and solvency risk. The results also prove that the higher the risk management ratios are managed, the higher the net income will be.

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Agaba, Francis, Caleb Tamwesigire, and Marus Eton. "Credit Risk Management Practices and Loan Performance of Commercial Banks in Uganda." Business Perspective Review 4, no. 1 (March 31, 2022): 16–28. http://dx.doi.org/10.38157/bpr.v4i1.394.

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Purpose: The study examined the relationship between Credit Risk Management Practices and Loan Performance of Commercial Banks in Mbarara City. The study covered 19 commercial banks. Method: A correlational design was used to establish the relationship between different credit risk management practices and Loan Performance in selected commercial banks in the city. The study used a structured questionnaire to collect numerical data from the credit staff and management of 19 commercial banks. Correlation and regression tests to analyze the relationships and effects of Credit risk management and Loan Performance of commercial banks in Mbarara city Findings: The study found a significant relationship between credit risk identification and loan performance; credit risk assessment and loan performance; credit risk monitoring and loan performance; and credit risk control and loan performance. The study also found that some commercial banks did not have experts to accurately predict credit risks nor evaluate the consequences of the decisions taken by loan officers. Implication: Banks should source experts who can analyze and predict risks and evaluate their consequences on the bank. The bank should adopt the tool of 5cs of credit management, with this it will develop a good loan book that shall lead to good loan performance. Limitations: We still don't know clients' perceptions of the different credit risk management practices. Therefore, a qualitative study to assess clients' perception of the credit management practices in commercial banks should be conducted.

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HORODETSKA, Tetiana, Kateryna ZAICHENKO, and Alla IVASHCHENKO. "Methodical approaches for reducing the credit risk." Economics. Finances. Law 11/1, no. - (November 26, 2021): 16–20. http://dx.doi.org/10.37634/efp.2021.11(1).3.

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Banking is inevitably associated with risks. No matter what efforts the bank makes to minimize risks, they will always exist – the only question is to what extent. Lending operations are among the most profitable types of banking, but they are associated with a high level of risk. The instability of the economic situation in the country, the imperfection of the legal framework in this area necessitate a detailed study of the problems of minimizing credit risks. It should be noted that the choice of methods of credit risk management in the bank is quite relevant today. Credit risk management is the most important task of any bank, and choosing the right method of credit risk management will increase the stability, reliability and competitiveness of the banking system, which will positively affect the overall economic condition of the country. Credit risk is the oldest in the system of banking risks and occupies a prominent place. It is necessary to work out an effective system of using the tools recognized by the world banking community to minimize risks, given the possibility of their transfer from the bank to investors. The starting point in the development of the latest risk management tools of the bank should be the creation of a regulatory framework that will regulate this process. It is necessary to improve the existing methodological framework and develop a new methodological framework for credit risk management of the bank, concentrating the advantages of existing assessment methods, create a single method of assessing the borrower's creditworthiness, not to mention a certain algorithm for banks to form credit procedures. It is necessary to adopt the experience of foreign banks in credit risk management. The experience of foreign banks in developed countries, based on a detailed study of all credit procedures, multifactor analysis of the creditworthiness of potential borrowers.

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Kim. "The Effect of Systematic Default Risk on Credit Risk Premiums." Sustainability 11, no. 21 (October 30, 2019): 6039. http://dx.doi.org/10.3390/su11216039.

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This study examines whether systematic default risks affect a cross section of credit risk premiums. Using a structural framework, I derive a theoretical cross-sectional relationship, develop a testable hypothesis, and provide a method to estimate the systematic default risk. The empirical results of US corporate credit default swap data are consistent with my hypothesis. The findings show that, while credit market factors have positive effects on a cross section of credit risk premiums, stock market factors have a negative impact. Regression analyses reveal that the market’s average default probability and the value factor have a significant effect on the credit risk premium. In addition, credit market factors are more influential than equity market factors as systematic default risk factors. The results suggest that systematic and idiosyncratic default risks are priced differently in a cross section of credit risk premiums.

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Santana, Patricia Jimbo, Laura Lanzarini, and Aurelio F. Bariviera. "Fuzzy Credit Risk Scoring Rules using FRvarPSO." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, Suppl. 1 (December 2018): 39–57. http://dx.doi.org/10.1142/s0218488518400032.

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There is consensus that the best way for reducing insolvency situations in financial institutions is through good risk management, which involves a good client selection process. In the market, there are methodologies for credit scoring, each analyzing a large number of microeconomic and/or macroeconomic variables selected mostly depending on the type of credit to be granted. Since these variables are heterogeneous, the review process carried out by credit analysts takes time. The objective of this article is to propose a solution for this problem by applying fuzzy logic to the creation of classification rules for credit granting. To achieve this, linguistic variables were used to help the analyst interpret the information available from the credit officer. The method proposed here combines the use of fuzzy logic with a neural network and a variable population optimization technique to obtain fuzzy classification rules. It was tested with three databases from financial entities in Ecuador — one credit and savings cooperative and two banks that grant various types of credits. To measure its performance, three benchmarks were used: accuracy, number of classification rules generated, and antecedent length. The results obtained indicate that the hybrid model that is proposed performs better than its previous versions due to the addition of fuzzy logic. At the end of the article, our conclusions are discussed and future research lines are suggested.

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Yanenkova, Iryna, Yuliia Nehoda, Svetlana Drobyazko, Andrii Zavhorodnii, and Lyudmyla Berezovska. "Modeling of Bank Credit Risk Management Using the Cost Risk Model." Journal of Risk and Financial Management 14, no. 5 (May 7, 2021): 211. http://dx.doi.org/10.3390/jrfm14050211.

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This article deals with the issue of managing bank credit risk using a cost risk model. Modeling of bank credit risk management was proposed based on neural-cell technologies, which expand the possibilities of modeling complex objects and processes and provide high reliability of credit risk determination. The purpose of the article is to improve and develop methodical support and practical recommendations for reducing the level of risk based on the value-at-risk (VaR) methodology and its subsequent combination with methods of fuzzy programming and symbiotic methodical support. The model makes it possible to create decision support subsystems for nonperforming loan management based on the neuro-fuzzy approach. For this paper, economic and mathematical tools (based on the VaR methodology) were used, which made it possible to analyze and forecast the dynamics of overdue payment; assess the quality of the credit portfolio of the bank; determine possible trends in bank development. A scientific and practical approach is taken to assess and forecast the degree of credit problematicity by qualitative criteria using a mathematical model based on a fuzzy technology, which can forecast the increased risk of loan default at an early stage in the process of monitoring the loan portfolio and model forecasting changes in the degree of credit problematicity on change of indicators. A methodology is proposed for the analysis and forecasting of indicators of troubled loan debt, which should be implemented as software and included in the decision support system during the process of monitoring the risk of the bank’s credit portfolio.

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Sokolovska, Olena. "Trade Credit Insurance and Asymmetric Information Problem." Scientific Annals of Economics and Business 64, no. 1 (March 1, 2017): 123–37. http://dx.doi.org/10.1515/saeb-2017-0008.

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Abstract The presence of different risk factors in international trade gives evidence of the necessity of support in gaps that may affect exporters’ activity. To maximize the trade volumes and in the same time to minimize the exporters’ risks the stakeholders use trade credit insurance. The paper provides analysis of conceptual background of the trade credit insurance in the world. We analyzed briefly the problems, arising in insurance markets due to asymmetric information, such as adverse selection and moral hazard. Also we discuss the main stages of development of trade credit insurance in countries worldwide. Using comparative and graphical analysis we provide a brief evaluation of the dynamics of claims and recoveries for different forms of trade credit insurance. We found that the claims related to the commercial risk for medium and long trade credits in recent years exceed the recoveries, while with the political risk the reverse trend holds. And we originally consider these findings in terms of information asymmetry in the trade credit insurance differentiated by type of risk.

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Sahani, Kalpana, Soni Sahani, Sundip Bansal, Deepak Shakya, and Binay Shrestha. "Credit Risk Management of Kumari Bank Ltd. Nepal." International Journal of Emerging Research in Management and Technology 7, no. 4 (April 20, 2018): 14. http://dx.doi.org/10.23956/ijermt.v7i4.2.

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Banks are always faced with different types of risks that may have a potentially negative effect on their business. Risk-taking is an inherent element of banking and, indeed, profits are in part the reward for successful risk taking in business. On the other hand, excessive and poorly managed risk can lead to losses and thus endanger the safety of a bank's depositors. Risks are considered warranted when they are understandable, measurable, controllable and within a bank’s capacity to readily withstand adverse results. Sound risk management systems enable managers of banks to take risks knowingly, reduce risks where appropriate and strive to prepare for a future, which by its nature cannot be predicted.Financial institutions are subject to a number of risks such as Credit risk, Market risk management, Foreign exchange risk, Operational risk, and Liquidity risk. Although credit risk has always been of primary concern to these institutions, its importance became paramount during the recent financial crisis. The crisis exposed the shortcomings of existing risk management systems, and several firms saw significant losses resulting from failure of their counterparties to deliver on contracts. Firms may also be worried about a second recession, which makes credit risk a top priority.

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Tchernykh, S. "Risk Management in Banks." Voprosy Ekonomiki, no. 8 (August 20, 2004): 120–27. http://dx.doi.org/10.32609/0042-8736-2004-8-120-127.

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Problems of managing risks of partnership in banks taking into account the new Central Bank of Russia document "On Organization of Internal Control in Credit Organizations and Bank Groups" are considered in the article. It is pointed out that effective bank risk management including risks of partnership сan be realized only under condition of bona fide competition. Functioning of banks in competitive environment is impossible without risks, their monitoring allows to become competitive on the banking services market if various "black lists" and other unsound negative information leading to lowering the level of liquidity of a credit organization are absent. Methods of managing risks of partnership that become all the more complex under the influence of technological innovations (in particular, the development of operations with credit derivatives) are also analyzed.

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Liu, Ming, Jie Zhu, Yuanchao Bian, and Liping Chen. "Comprehensive Credit Risk Management of Policy Export Credit Insurance Institutions." Journal of Accounting, Business and Finance Research 4, no. 2 (2018): 66–73. http://dx.doi.org/10.20448/2002.42.66.73.

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Nayak, Bhabani Shankar, and Jia Xu. "Historical Trends and Transitions in Credit Risk Management of Chinese Commercial Banks." International Journal of Business Administration 9, no. 5 (August 3, 2018): 96. http://dx.doi.org/10.5430/ijba.v9n5p96.

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The paper outlines different trends and transitions in the history of credit risk management of Chinese commercial banks. By critically reviewing different stages of credit management and its historical evolution, it helps in understanding the nature of subjective challenges faced by Chinese commercial banks to manage credit risks. It reviews post reform policies in particularly after 1978 to locate the policy transitions and trajectories of credit risk management of commercial banks in China. It helps to understand the problems and prospects of effective credit management of risks by Chinese commercial banks. It argues that Chinese commercial banks are facing greater challenges in managing risk after the entry of foreign banks to China. Therefore, it is important for the commercial banks in China to develop its own credit management mechanisms within the context of Chinese banking environment.

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Kogeda, Okuthe Paul, and Nicknolt N. Vumane. "A Model Augmenting Credit Risk Management in the Banking Industry." International Journal of Technology Diffusion 8, no. 4 (October 2017): 47–65. http://dx.doi.org/10.4018/ijtd.2017100104.

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A lack of reliable credit risk measurements and poor control of credit risks has caused massive financial losses across a wide spectrum of business. Financial institutions like banks have not been able to control and contain the rapid increases of the credit defaulting. In this paper, we address the credit lending challenges by eliminating credit defaulting faced by the banking industry. Data from bank of previously accepted and rejected loan applicants was used to construct a credit risk evaluation network. The artificial neural network technique with back-propagation algorithm was applied to develop a model that supports the banks in the credit granting decision-making. The model was trained to categorize applicants as either good (credit granted) or bad (credit denied) based on the credit record. The model was able to predict whether a particular applicant is likely or unlikely to repay the credit. The training of neural network model and validation testing was done using data obtained from the bank. The results show a greater performance, classification and prediction accuracy.

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Morozko, Nat I., N. I. Morozko, and V. J. Didenko. "Risk Minimization in Consumer Credit Cooperative Activities." Economics, taxes & law 12, no. 4 (September 6, 2019): 60–67. http://dx.doi.org/10.26794/1999-849x-2019-12-4-60-67.

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The subject of the research is risks in consumer credit cooperative activities in the context of economic instability. The purpose of the article is developing methods of risk minimization in terms of the authors’ approach to cooperative risks classification. The importance of the research lies in the fact that risks are an integral part of the consumer credit cooperative process of functioning; therefore there is a necessity of appropriate risk assessment that will enable businesses to mitigate possible losses. Consumer credit cooperatives have some distinctive features in comparison with other credit institutions. The authors identify risks of consumer credit cooperatives, and analyze different approaches to risk assessment. While assessing the risks of cooperatives the authors suggest using scoring method according to international standards for building risk management system. It is proved that scoring is the most optimal and effective instrument for the consumer credit market. It is noted that probability of material losses arises as a result of financial risks, while nonfinancial risks result in both material and nonmaterial losses. The authors also pay attention to the fact that the guiding lines in determining the limit value of risks appear in financial norms of consumer credit cooperatives established by the Central Bank. Methods of minimization of consumer credit cooperative risks are proposed. The authors come to the conclusion that monitoring of credit risks must be conducted at every stage of crediting in order to detect problems with loan clearing off at due dates, which allows to provide effective management of the cooperative loan portfolio and to mitigate risks.

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Bai, Hang. "Unemployment and credit risk." Journal of Financial Economics 142, no. 1 (October 2021): 127–45. http://dx.doi.org/10.1016/j.jfineco.2021.05.046.

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Konovalova, Natalia, Ineta Kristovska, and Marina Kudinska. "CREDIT RISK MANAGEMENT IN COMMERCIAL BANKS." Polish Journal of Management Studies 13, no. 2 (June 2016): 90–100. http://dx.doi.org/10.17512/pjms.2016.13.2.09.

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Beatty, Nigel. "Credit risk management with EMV cards." Card Technology Today 18, no. 5 (May 2006): 9. http://dx.doi.org/10.1016/s0965-2590(06)70486-8.

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MACLACHLAN, IAIN. "RECENT ADVANCES IN CREDIT RISK MANAGEMENT." Economic Papers: A journal of applied economics and policy 18, no. 4 (December 1999): 92–106. http://dx.doi.org/10.1111/j.1759-3441.1999.tb01165.x.

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Srikanth, Maram, and Braj Kishore. "Credit Risk Management in Indian Banks." Indian Economic Journal 62, no. 1 (April 2014): 751–67. http://dx.doi.org/10.1177/0019466220140104.

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Acharya, Viral. "Credit Risk: Pricing, Measurement, and Management." Economica 72, no. 285 (February 2005): 181–82. http://dx.doi.org/10.1111/j.0013-0427.2005.408_3.x.

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Journal articles on the topic 'Credit risk credit risk management'

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