• The Journal of Industrial Distribution & Business
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• v.13 no.10
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• pp.1-8
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• 2022

Purpose: Support vector machines (SVMs) ensemble has been proposed to improve classification performance of Credit risk recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. To deal with this problem, this paper designs a support vector machines (SVMs) ensemble method based on fuzzy integral, which aggregates the outputs of separate component SVMs with importance of each component SVM. Research design, data, and methodology: This paper designs a personal credit risk evaluation index system including 16 indicators and discusses a support vector machines (SVMs) ensemble method based on fuzzy integral for designing a credit risk assessment system to discriminate good creditors from bad ones. This paper randomly selects 1500 sample data of personal loan customers of a commercial bank in China 2015-2020 for simulation experiments. Results: By comparing the experimental result SVMs ensemble with the single SVM, the neural network ensemble, the proposed method outperforms the single SVM, and neural network ensemble in terms of classification accuracy. Conclusions: The results show that the method proposed in this paper has higher classification accuracy than other classification methods, which confirms the feasibility and effectiveness of this method.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.873-880
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• 2021

The purpose of this study is to investigate the impact of internal control on credit risk of joint stock commercial banks in Vietnam from 2007 to 2018. Furthermore, we specify bank-specific characteristics and macroeconomic conditions, and analyze how these factors affect credit risk of banks: the number of board members, the number of board members with banking or finance background as ratio of total board members, loans to total assets ratio, loans to deposit ratio, the number of days between the year-end and the publication of the financial statements, and the use of top four auditing firms proxy for five elements of internal control. By using the dataset of 30 Vietnamese joint stock commercial banks and Bayesian linear regression via Random-walk Metropolis Hastings algorithm, the results of this study show that five elements of internal control have a impact on bank credit risk, namely, control environment, risk assessment, control activities, information and communication, and monitoring activities. For factors of banks' characteristics, bank size and financial leverage have a negative impact on banks' credit risk, and bank age has a positive effect. For macroeconomic factors, inflation has a positive impact and economic growth has a negative impact on banks' credit risk.

• Korean Journal of Construction Engineering and Management
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• v.21 no.5
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• pp.64-73
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• 2020

The government announced a plan for fund support to the enterprises with high possibility of recovery and early restructuring for the enterprises with low recovery by objectifying credit assessment system. Such announcement of government could be extended to restructuring risk of middle standing enterprises with low financial soundness by establishing the basis to prepare prompt restructuring by reinforcing the basis for restructuring through capital market. This research analyzed financial soundness based on the financial evaluation of bank by selecting 10 middle standing construction companies which focused on housing business in 2019, based on such analysis result, it was confirmed that there was a high possibility of restructuring risk. This research determined that there would be a decrease in growth rate of construction industry on the whole in 2020 due to fall of economic growth rate and reinforced real estate regulation, accordingly, there's a big possibility for middle standing construction companies with paid-in capital ratio due to its low possibility of maintenance of stable credit rating. This research established KCSI assessment model by utilizing the material of a reliable research institute in order for middle standing construction companies to evade restructuring risk, and indicated risk ratio differentiated per each item through a working-level expert survey. Such research result could suggest credit risk reduction method to middle standing construction company management staffs, and prepare a basis to evade restructuring risk.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.347-354
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• 1999

The credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this paper, we present a new approach to credit rating of customers based on the rough set theory. The concept of a rough set appeared to be an effective tool for the analysis of customer information systems representing knowledge gained by experience. The customer information system describes a set of customers by a set of multi-valued attributes, called condition attributes. The customers are classified into groups of risk subject to an expert's opinion, called decision attribute. A natural problem of knowledge analysis consists then in discovering relationships, in terms of decision rules, between description of customers by condition attributes and particular decisions. The rough set approach enables one to discover minimal subsets of condition attributes ensuring an acceptable quality of classification of the customers analyzed and to derive decision rules from the customer information system which can be used to support decisions about rating new customers. Using the rough set approach one analyses only facts hidden in data, it does not need any additional information about data and does not correct inconsistencies manifested in data; instead, rules produced are categorized into certain and possible. A real problem of the evaluation of the evaluation of credit rating by a department store is studied using the rough set approach.

• Asia pacific journal of information systems
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• v.10 no.2
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• pp.149-176
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• 2000

As companies become more dependent upon information systems(IS), the potential losses of IS resources become critical. IS management must assume the increasing responsibility for protection of IS resources as the IS and business environments become more vulnerable to various threats. The major issues facing management, when attempting to manage risks, include the assessment of the impact of risks on business objectives and the design of security safeguards to reduce the unacceptable risks to an acceptable level. This paper provides a case study of the risk management for IS. A Korean credit card company which has the high sensitivity for customers security was selected as a case. The risk management procedure using a powerful tool, CRAMM(the Central Computer and Telecommunications Agencys Risk Analysis and Management Method) was applied for this company.

• Journal of Information Technology Applications and Management
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• v.14 no.2
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• pp.151-168
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• 2007

The corporate credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this study, the corporate credit rating model employs artificial intelligence methods including Neural Network (NN) and Case-Based Reasoning (CBR). At first we suggest three classification models, as partitioned neural networks, all of which convert multi-group classification problems into two group classification ones: Ordinal Pairwise Partitioning (OPP) model, binary classification model and simple classification model. The experimental results show that the partitioned NN outperformed the conventional NN. In addition, we put to use CBR that is widely used recently as a problem-solving and learning tool both in academic and business areas. With an advantage of the easiness in model design compared to a NN model, the CBR model proves itself to have good classification capability through the highest hit ratio in the corporate credit rating.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.71-78
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• 2019

This study aims to identify good borrowers within the context of P2P lending. P2P lending is a growing platform that allows individuals to lend and borrow money from each other. Inherent in any loans is credit risk of borrowers and needs to be considered before any lending. Specifically in the context of P2P lending, traditional models fall short and thus this study aimed to rectify this as well as explore the problem of class imbalances seen within credit risk data sets. This study implemented an over-sampling technique known as Synthetic Minority Over-sampling Technique (SMOTE). To test our approach, we implemented five benchmarking classifiers such as support vector machines, logistic regression, k-nearest neighbor, random forest, and deep neural network. The data sample used was retrieved from the publicly available LendingClub dataset. The proposed SMOTE revealed significantly improved results in comparison with the benchmarking classifiers. These results should help actors engaged within P2P lending to make better informed decisions when selecting potential borrowers eliminating the higher risks present in P2P lending.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.893-904
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• 2020

The study examines the relationship between credit risk and operational risk (understanding of risk management, risk identification, risk assessment and control, and risk monitoring) on risk management practices followed by private and public sector commercial banks. The cross-sectional data method was used to check the impact of risk management practices. Data was collected from the bank employees and a total of 284 respondents were finally selected for further analysis. Measurement Invariance of Composite Models analysis is used to test the quality of the measurement model for sub-samples, and multi-group analysis is used for path analysis in sub-sample through PLS-SEM. The findings of the study as the total sample show that both types of banks are managing adequate and significant risk management practices. On the other hand, sub-groups' results show private sector banks are more momentous than public sector banks. Risk identification is significantly different at the sub-group level, which shows public sector banks are more concentrating on this type of risk. Understanding of risk management has no significant effect on both types of banks and risk assessment & control for public sector banks, and there is a difference in the risk management practices among private and public sector commercial banks.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.923-933
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• 2016

This paper proposes a new method for evaluating Effective Load Carrying Capability(ELCC) and capacity credit(C.C.) of power system including Wind Turbine Generator(WTG) combined with Battery Energy Storage System(BESS). WTG can only generate electricity power when the fuel(wind) is available. Because of fluctuation of wind speed, WTG generates intermittent power. In view point of reliability of power system, intermittent power of WTG is similar with probabilistic characteristics based on power on-off due to mechanical availability of conventional generator. Therefore, high penetration of WTG will occur difficulties in power operation. The high penetration of numerous and large capacity WTG can make risk to power system adequacy, quality and stability. Therefore, the penetration of WTG is limited in the world. In recent, it is expected that BESS installed at wind farms may smooth the wind power fluctuation. This study develops a new method to assess how much is penetration of WTG able to extended when Wind Turbine Generator(WTG) is combined with Battery Energy Storage System(BESS). In this paper, the assessment equation of capacity credit of WTG combined with BESS is formulated newly. The simulation program, is called GNRL_ESS, is developed in this study. This paper demonstrates a various case studies of ELCC and capacity credit(C.C.) of power system containing WTG combined with BESS using model system as similar as Jeju island power system. The case studies demonstrate that not only reasonable BESS capacity for a WTG but also permissible penetration percent of WTG combined with BESS and reasonable WTG capacity for a BESS can be decided.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.4
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• pp.9-20
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• 2018

Global rating agencies, such as Moody's and S&P, have assigned credit ratings to corporate bonds issued by Japanese firms since 1980s. Local Japanese rating agencies, such as R&I and JCR, have more market share than the global raters. We examine the yield spreads of 1,050 yen-denominated corporate bonds issued by financial firms in Japan from 1998 to 2014 and find no evidence that bonds rated by at least one global agency are associated with a significant reduction in the cost of debt as compared to those rated by only local rating agencies. Unlike non-financial firms, the reputation effect of global rating agencies does not exist for Japanese financial firms. We also observe that firms with less information asymmetry are more likely to acquire ratings from Moody's or S&P. Additionally, the firm's financial profile does not affect its choice to seek out ratings from global raters. Our findings are contradictory to those by Han, Pagano, and Shin (2012), who employ bonds issued by non-financial firms in Japan. Our conjecture is that the asymmetric nature of financial firms makes investors less likely to depend on a credit risk assessment by rating agencies in determining the yields of new bonds.