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Journal articles on the topic 'Credit risk'

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Published: 4 June 2021

Last updated: 1 June 2024

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1

Michalkova, Lucia, and Katarina Frajtova Michalikova. "Credit risk measurement." New Trends and Issues Proceedings on Humanities and Social Sciences 3, no. 4 (March 22, 2017): 168–74. http://dx.doi.org/10.18844/gjhss.v3i4.1562.

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2

Hatchett, J. P. L., and R. Kühn. "Credit contagion and credit risk." Quantitative Finance 9, no. 4 (June 2009): 373–82. http://dx.doi.org/10.1080/14697680802464162.

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3

Brown, Christine A., and Sally Wang. "Credit risk." International Review of Financial Analysis 11, no. 2 (January 2002): 229–48. http://dx.doi.org/10.1016/s1057-5219(02)00076-5.

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4

Eko Muliansyah and Nurmala. "Credit risk, operational risk, and liquidity risk on profitability." World Journal of Advanced Research and Reviews 19, no. 1 (July 30, 2023): 744–52. http://dx.doi.org/10.30574/wjarr.2023.19.1.1426.

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Profitability is the ability of the Village Credit Institution to generate profits and is a ratio that can assess how the Village Credit Institution's ability to generate profits. The high profitability of the Village Credit Institution indicates the good performance of the Village Credit Institution. This study aims to determine the effect of credit risk, operational risk, and liquidity risk on profitability. This research was conducted at the Village Credit Institution for the period 2017-2021. The data collection method used is the non-behavioral observation method with multiple linear regression data analysis techniques. The results showed that Credit Risk has a negative and significant effect on Profitability. Operational Risk has a negative and significant effect on Profitability. Liquidity Risk has a positive and significant effect on Profitability. The profitability of the Village Credit Institution can be maximized by applying the precautionary principle, monitoring and supervising the operations of the Village Credit Institution to minimize costs and provide sufficient liquidity and balanced with good lending.

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5

Tsintsadze, Asie, Lela Oniani, and Tamar Ghoghoberidze. "Determining and predicting correlation of macroeconomic indicators on credit risk caused by overdue credit." Banks and Bank Systems 13, no. 3 (September 19, 2018): 114–19. http://dx.doi.org/10.21511/bbs.13(3).2018.11.

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The banking system guarantees the economic strength of the country. Its sustainability is due to the sustainability of the credit portfolio. Therefore, scientific research on banking risks is always relevant. Basel recommendations and central bank regulations provide risk minimization in case of default of borrower by creating risk reserve, but the high range of macroeconomic factors creates a basis for creating credit risk. The model, which determines the risk factors, may be structurally the same, but the quality of the influence of factors is different in various countries. The influence of macroeconomic factors is particularly evident in developing countries. The impact of economic factors in different countries is high in GDP of these countries. The article focuses on determining the influence of macroeconomic factors on credit risk of systematic banks in Georgia. The coefficients of individual macroeconomic indicators are calculated by using Pearson’s correlation. The credit risk ratio is taken from the bank’s overdue credits and credit portfolio ratio. Based on the correlation coefficients obtained, the expected risk of shock changes is calculated.

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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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7

K, Roopa. "Credit Risk Management - A Case Analysis." International Journal of Science and Research (IJSR) 12, no. 12 (December 5, 2023): 361–66. http://dx.doi.org/10.21275/sr231128152822.

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8

Arnold, Lutz G., Johannes Reeder, and Stefanie Trepl. "Single-name Credit Risk, Portfolio Risk and Credit Rationing." Economica 81, no. 322 (February 10, 2014): 311–28. http://dx.doi.org/10.1111/ecca.12075.

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9

Redondo, Helena, and Elisa Aracil. "Climate‐related credit risk: Rethinking the credit risk framework." Global Policy 15, S1 (March 2024): 21–33. http://dx.doi.org/10.1111/1758-5899.13315.

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AbstractClimate change and the challenges associated with the transition to a zero‐carbon economy pose significant financial risks. Climate‐related risks (CRR) indirectly impact banks through their loan portfolios. To examine the integration of CRR into banks' credit risk assessment and monitoring, this article reviews academic and institutional literature using quantitative bibliometric techniques and content analysis of 145 academic documents from policymakers and financial supervisors. A framework emerges that incorporates CRR into credit risk management. We find four thematic areas in the literature: CRR drivers, CRR tools, CRR data and CRR pricing. Overall, uncertainty, non‐linearity, geographic and industrial dependency and non‐reversibility of CRR difficult climate‐related credit risk assessment. Moreover, CRR data present comparability, availability and reliability issues, which Artificial Intelligence can improve. Finally, evidence reveals that current financial prices do not fully reflect CRR. Our findings provide important implications to policymakers for assessing ex‐ante the financial impacts of climate transition regulations, the potential for prudential regulatory action, and the need for supra‐national policies that facilitate access to reliable and comparable climate data.

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10

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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11

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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12

Misankova, Maria, and Erika Spuchlakova. "Application of conditional value at risk for credit risk optimization." New Trends and Issues Proceedings on Humanities and Social Sciences 3, no. 4 (March 22, 2017): 146–52. http://dx.doi.org/10.18844/gjhss.v3i4.1540.

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13

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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14

Das, Sanjiv Ranjan. "Credit Risk Derivatives." Journal of Derivatives 2, no. 3 (February 28, 1995): 7–23. http://dx.doi.org/10.3905/jod.1995.407914.

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15

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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16

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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17

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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18

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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19

Kountzakis, Christos E. "Credit risk transformations." Applied Mathematical Sciences 8 (2014): 1855–64. http://dx.doi.org/10.12988/ams.2014.312724.

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20

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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21

Ghamami, Samim. "Counterparty Credit Risk." Quantitative Finance 13, no. 12 (December 2013): 1863–65. http://dx.doi.org/10.1080/14697688.2013.789546.

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22

Priest, Maura. "Risk Sensitive Credit." Erkenntnis 84, no. 3 (February 21, 2018): 703–26. http://dx.doi.org/10.1007/s10670-018-9978-7.

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23

Paroush, Jacob. "Credit risk measurement." International Review of Economics & Finance 1, no. 1 (January 1992): 33–41. http://dx.doi.org/10.1016/1059-0560(92)90004-v.

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24

Jarrow, Robert A. "Credit Risk Models." Annual Review of Financial Economics 1, no. 1 (December 5, 2009): 37–68. http://dx.doi.org/10.1146/annurev.financial.050808.114300.

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25

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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26

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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27

Michalkova, Lucia, and Katarina Frajtova Michalikova. "Credit risk measurement." New Trends and Issues Proceedings on Humanities and Social Sciences 3, no. 4 (March 22, 2017): 168–74. http://dx.doi.org/10.18844/prosoc.v3i4.1562.

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Focused on globalizaing of economics and still actual financial crisis credit risk becomes one of the most discussing topic in business world. Every investment decision should be accompanied by analysis of the possibility of default. Through the years there were developed many credit risk measures, so research and quantification of them are a subject of interest of many economic publications and studies. So nowadays there are many approaches which can be used by investors to monitor credit risk and it can be calculated through various models and methods. The aim of the article is to present the basic ones as well as the most often used models based on them such like CreditMetrics, CreditRisk or KMV model. There is given a comparison of these models in dimension as risk definition, risk source, recovery rate, types of model etc. Then we also describe pros and cons of them. Eventually we apply the CreditMetrics model for a single bond. Â Â Keywords: risk; credit risk; model; CreditMetrics

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28

Parbat, Tejas. "Credit Risk Modelling." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (February 29, 2024): 595–98. http://dx.doi.org/10.22214/ijraset.2024.58397.

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Abstract: Modern society recognizes the significance of Identity and Access Management. It's the method through which information is controlled in terms of who gets access to what and when. Creation of user and system identities is an IAM activity. Data and information sharing relies heavily on safe user access. In addition, most businesses are realizing the growing value of electronic data. Strong authentication is a common solution to this problem and is becoming more and more necessary as the standards for access protection rise. The two most critical IAM concepts that must be handled by the business are identity and access. More and more businesses are turning to an automated system to handle these tasks. But it opens up a new danger. Since these technologies lack the wit to make judgements on their own, we must supplement them with our own brainpower employing a variety of data mining algorithms. This allows us to save data for later model building. Everything you need to know about the difficulties of Identity and Access Management may be found in this document. A potential answer is provided for these problems.

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29

Iulia, Iuga. "Dimensions Of The Credit Risk Analysis Process And Credit Risk." Annales Universitatis Apulensis Series Oeconomica 3, no. 8 (July 31, 2006): 48–53. http://dx.doi.org/10.29302/oeconomica.2006.8.3.8.

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30

Molins, J., and E. Vives. "Model risk on credit risk." Risk and Decision Analysis 6, no. 1 (January 14, 2016): 65–78. http://dx.doi.org/10.3233/rda-150115.

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31

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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32

MacIsaac, Keith Joseph. "Rollover Risk and Credit Risk." CFA Digest 42, no. 3 (August 2012): 115–17. http://dx.doi.org/10.2469/dig.v42.n3.62.

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33

HE, ZHIGUO, and WEI XIONG. "Rollover Risk and Credit Risk." Journal of Finance 67, no. 2 (March 27, 2012): 391–430. http://dx.doi.org/10.1111/j.1540-6261.2012.01721.x.

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34

Gourio, François. "Credit Risk and Disaster Risk." American Economic Journal: Macroeconomics 5, no. 3 (July 1, 2013): 1–34. http://dx.doi.org/10.1257/mac.5.3.1.

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Credit spreads are large, volatile, and countercyclical, and recent empirical work suggests that risk premia, not expected credit losses, are responsible for these features. Building on the idea that corporate debt, while fairly safe in ordinary recessions, is exposed to economic depressions, this paper embeds a trade-off theory of capital structure into a real business cycle model with a small, exogenously timevarying risk of economic disaster. The model replicates the level, volatility and cyclicality of credit spreads, and variation in the corporate bond risk premium amplifies macroeconomic fluctuations in investment, employment, and GDP. (JEL E13, E22, E23, E24, E32, E44, G32)

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35

PYKA, Irena, and Jan PYKA. "Corporate green investment imperative and risk of a credit crunch in Poland." Scientific Papers of Silesian University of Technology. Organization and Management Series 2021, no. 154 (2021): 233–48. http://dx.doi.org/10.29119/1641-3466.2021.154.17.

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Purpose: The main subject of the article is a phenomenon that is increasingly common in countries of the global economy referred to as the so-called credit crunch. The study analyses the reasons that favour the escalation of risk of a credit crunch in the banking systems. The main objective of the article is to expose them as widely as possible, combining it with verification of the determinants of a credit crunch. Design approach: The empirical research conducted in this study focuses on the Polish banking system. For the first time the credit crunch was observed there in the second half of 2008. It was then that lending to households decreased by 25% and to enterprises by as much as 33%. In the Polish banking system, a drop in the volume of loans to enterprises has been observed for a long time, favouring the increase in risk of a credit crunch. Findings: The article evaluates the potential risk of a credit crunch in the Polish banking system pointing out their links resulting from the implementation of the new climate policy in the European Union as well as the COVID-19 pandemic. This is caused by the fact that during the COVID-19 crisis, credit rating of Polish enterprises decreased significantly, causing partial restrictions or even elimination of bank loan in industries threatened by the crisis. Research implication: The Polish economy is facing a significant challenge of meeting the EU criteria for limiting CO2 emissions, which will force domestic enterprises to invest considerably in environmental protection and will increase their demand for debt financing, including bank loans. Banks are preparing for green lending to the Polish economy which signifies a strong transition of loans to investments which meet the taxonomy criteria and are therefore subject to climate objectives. Practical and social implication: Industry risk will determine lending of Polish enterprises under the conditions of the European Green Deal. Green financing of investments of Polish enterprises is therefore becoming a significant potential cause of increasing risk of a credit crunch in the Polish banking sector. Originality/value: Presentation of the enterprise credit dilemmas in the conditions of financial instability of the global economy in the perspective of credit-crunch in Poland is a novel, original and contemporary subject. The diagnosis of the determinants of this threat has facilitated their positioning relatively to the risk of credit-crunch in the Polish banking sector. The results of this analysis underline the risks in this sector and the consequences of introducing European taxonomy of green investments as factors limiting credit actions and enterprise credits in banks.

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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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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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Henisz, Witold J., and James McGlinch. "ESG, Material Credit Events, and Credit Risk." Journal of Applied Corporate Finance 31, no. 2 (June 2019): 105–17. http://dx.doi.org/10.1111/jacf.12352.

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39

Alam, MD Waquar. "INVESTIGATING THE IMPACT OF CREDIT RISK ON FINANCIAL PERFORMANCE OF COMMERCIAL BANK IN INDIA." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 2, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem33025.

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In this liberalization period, credit Risk Management has got much importance in the Indian Economy. The main challenges faced by the banking sector today are the challenge of identifying the risk and managing it. The risk is imbibed nature of the banking business. The main role of a bank is of intermediate for those having resources and requiring resources. For risk management various risks like credit risk, market risk or operational risk have to be converted into one composite measure. The importance of credit risk management and its impact on profitability has motivated us to pursue this study. We assume that if the credit risk management is sound, the profit level will be satisfactory. The other way around, if the credit risk management is poor, the profit level will be relatively lower. Because the less the banks loss from credits, the more the banks gain. Therefore, it is necessary that measurement of credit risk should be in tandem with other measurements of operation and market risk so that the requisite composite estimate can be worked out. So, in banking sector credit risk management is being most important task of all. Moreover, the central question is how significant the impact of credit risk management on profitability is. This thesis is an endeavor to find the answer. The principal concern of this thesis is to ascertain to what extent banks can manage their credit risks, what tools or techniques are at their disposal and to what extent their performance can be augmented by proper credit risk management policies and strategies.

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40

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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Qasem, Mais Haj, and Loai Nemer. "Extreme Learning Machine for Credit Risk Analysis." Journal of Intelligent Systems 29, no. 1 (June 18, 2018): 640–52. http://dx.doi.org/10.1515/jisys-2018-0058.

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Abstract Credit risk analysis is important for financial institutions that provide loans to businesses and individuals. Banks and other financial institutions generally face risks that are mostly of financial nature; hence, such institutions must balance risks and returns. Analyzing or determining risk levels involved in credits, finances, and loans can be performed through predictive analytic techniques, such as an extreme learning machine (ELM). In this work, we empirically evaluated the performance of an ELM for credit risk problems and compared it to naive Bayes, decision tree, and multi-layer perceptron (MLP). The comparison was conducted on the basis of a German credit risk dataset. The simulation results of statistical measures of performance corroborated that the ELM outperforms naive Bayes, decision tree, and MLP classifiers by 1.8248%, 16.6346%, and 5.8934%, respectively.

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Maria Antony, Tisa, and Suresh G. "Determinants of credit risk: Empirical evidence from Indian commercial banks." Banks and Bank Systems 18, no. 2 (May 22, 2023): 88–100. http://dx.doi.org/10.21511/bbs.18(2).2023.08.

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Credit risk is a significant factor affecting the financial stability of banks. Keeping the credit risk under control is essential to maintain a bank’s cash flow. This paper examines the various profitability, microeconomic and macroeconomic indicators that affect a bank’s credit risk. The study uses the dataset of 31 banks from 2012 to 2021 and employs a panel data modelling approach to account for any variations in risk-taking behavior. The results revealed a statistically significant negative relationship between return on equity and credit risk when nonperforming loans proxy credit risk. This finding was consistent across fixed effect, random effect, and pooled OLS methods, at 1 percent significance (P value &lt; 0.00), indicating that the extent of credit risk decreases as profitability increases. It was further found that bank age and ownership type positively affect a bank’s credit risk, while factors such as bank size and operational efficiency negatively affect credit risk when nonperforming loans proxy credit risk. Further, macroeconomic variables showed that gross domestic product is positively associated with credit risk, while inflation negatively affects credit risk. Overall, the findings of this paper demonstrated that credit risk is affected by both micro and macroeconomic factors. The paper also addresses significant policy implications as it helps various stakeholders to examine the determinants of credit risk, make credit decisions, and ultimately lower their credit risk.

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Yi, Ka-Youn. "The Effect of Corporate Credit Risk on Trade Credits." Journal of CEO and Management Studies 23, no. 1 (April 30, 2020): 257–74. http://dx.doi.org/10.37674/ceoms.23.1.14.

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Gusti Ngurah Agung Suaryana, I., Naniek Noviari, and I. Gusti Ayu Eka Damayanthi. "The impact of Indonesian financial accounting standard implementation, credit risk, and credit restructuring on allowance for credit losses in Indonesia." Banks and Bank Systems 17, no. 3 (September 27, 2022): 177–87. http://dx.doi.org/10.21511/bbs.17(3).2022.15.

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This study examines the impact of the implementation of the Indonesian Financial Accounting Standard, credit risk, and credit restructuring on the formation of Allowance for Credit Losses (ACL) of commercial banks listed on the Indonesia Stock Exchange. The formation of ACL is regulated in PSAK 71 which is part of the Indonesian Financial Accounting Standard. The implementation of PSAK 71, and credit risk are expected to increase the ACL of commercial banks, however, credit restructuring programs will reduce the ACL. The research population is commercial banks listed on the Indonesia Stock Exchange in 2019–2020. The research sample is the entire research population. This study uses panel data regression analysis to examine the effect of the application of PSAK 71, credit risk, and credit restructuring on ACL for commercial bank loans. The findings show that the implementation of PSAK 71 and credit risk have a positive effect on the ACL, meanwhile, credit restructuring has a negative effect on the ACL.

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Hamraevich, Tashmatov Shuhrat. "ASSESSMENT OF CREDIT RISK OF A COMMERCIAL BANK." International Journal Of Management And Economics Fundamental 3, no. 12 (December 1, 2023): 92–97. http://dx.doi.org/10.37547/ijmef/volume03issue12-16.

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This article presents a method for assessing the risks of commercial banks, mainly analyzing the credit risk, interest rate risk and capital risk faced by commercial banks. A model for assessing the credit risk of commercial banks in Uzbekistan is also reviewed.

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Tunay, K. Batu, Hasan F. Yuceyılmaz, and Ahmet Çilesiz. "An International Comparison on Excessive Credit Expansion, Credit Guarantee Programs and The Risks Arising." Khazar Journal of Humanities and Social Sciences 23, no. 1 (2020): 83–102. http://dx.doi.org/10.5782/2223-2621.2020.23.1.83.

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Crediting in the banking sector plays an important role in all developed and developing countries. For this reason, it is monitored continuously by public authorities and measures are taken to control credit supply in economic growth periods. On the other hand, in an economic slowdown, when banks are reluctant to increase their credit portfolio, public credit guarantee programs are put into use to increase the credit supply. In this study, a sample covering 26 advanced and emerging economies was analyzed, and the effects of credit gap, credit guarantees and economic growth on credits and arising credit risks were investigated. The findings show that both credits and non-performing loans, an important measure of credit risk, are affected by credit gap, credit guarantees, and economic growth. On the one hand, public credit guarantees positively affect economic growth. On the other hand, though they are widely used for supporting small and medium-sized enterprises, our findings suggest that such expansive credit policies might negatively affect the riskiness of the credit portfolios and soundness of the banking sector.

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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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이종용. "Credit Risk and Underlying Asset Risk." Seoul Journal of Business 24, no. 2 (December 2018): 39–52. http://dx.doi.org/10.35152/snusjb.2018.24.2.002.

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Tayachi, Tahar, and Ahmed BenSaïda. "Modeling SMEs Credit Default Risk: The Case of Saudi Arabia." Journal of Reviews on Global Economics 11 (November 21, 2022): 32–48. http://dx.doi.org/10.6000/1929-7092.2022.11.04.

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This study assesses the credit risk of small and medium-sized enterprises (SMEs) to minimize unexpected risk events. We construct a hybrid statistical model based on factor analysis and logistic regression to predict enterprise default on loans and determine the factors predicting SMEs default. We assess the credit risk of SMEs listed on the Saudi stock market. The results indicate that the SMEs acid-test ratios are the most influential factors in predicting SMEs credit risk. Therefore, the designed logistic model can be used by financial institutions during the decision-making process of granting loans to SMEs. This study sheds light on challenging access to bank credits due to the lack of financial transparency of most Saudi SMEs.

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Lemonjava, Givi. "Bank’s Credit Risk Modeling." Caucasus Journal of Social Sciences 6, no. 1 (November 6, 2023): 81–91. http://dx.doi.org/10.62343/cjss.2013.123.

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The article reviews the Bank’s credit risk modeling issues. The substance of the article analyzes the credit risk structure and methods for measuring its components. Credit risk is measured as a loss, that is the function of several variables. The amount of open credit risk position in case of default, expected proba-bility of credit default and recovery ratio after the default are the main variables of the given function presented in the arti-cle. These variables are reviewed as random values and meth-ods are given for its evaluation and integration as one indica-tor.The article also reviews the tasks of forming the bank’s internal credit ratings and issues related to the use of these ratings in credit risk evaluation model.

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