• Journal of the Korea Safety Management & Science
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• v.10 no.2
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• pp.71-83
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• 2008

Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, in this study we present a medium sized corporate credit rating system by using Artificial Neural Network(ANN) and Analytical Hierarchy Process(AHP). Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the ANN and AHP model using both financial information and non-financial information. Finally, the credit ratings of each firm are assigned by the proposed method.

• The Journal of Information Systems
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• v.16 no.3
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• pp.1-20
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• 2007

Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, this study presents a corporate credit rating method using business failure probability map(BFPM) and AHP(Analytic Hierarchy Process). The BFPM enables us to rate the credit of corporations according to business failure probability and data distribution or frequency on each credit rating level. Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the BFPM and the AHP model using both financial and non-financial information. Finally, the credit ratings of each firm are assigned by our proposed method. This method will be helpful for the loan evaluators of financial institutes to decide more objective and effective credit ratings.

• Korean Management Science Review
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• v.20 no.2
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• pp.165-177
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• 2003

This paper presents the prototype of corporate credit rating system using analytic hierarchy process (AHP). Prior studios have proposed various models of credit rating system, but most studies considered only financial information. Financial information, however, is only a small part of corporate information. In this study, the proposed credit rating system integrates both financial and non-financial information. Fifteen corporations are tested for the usefulness of the proposed system.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.1-8
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• 2018

This study analyzed annual final sample data from 159 companies based on firms selected as economic justice companies by Economic Justice Institute in Citizens' Coalition for Economic Justice in South Korea according to interest variables from 2005 until 2011. Analyzed results are as follows. First, higher scores in soundness and corporate social activities among CSR items suggested that corporate credit rating upgraded. This indicates that credit rating institutions give a good evaluation on their social activities and reflect them in credit rating assessment. However, environmental protection satisfaction and corporate credit rating showed the opposite results. Second, high objectivity and contribution to the economic development as well as supervision by giant auditors had substantial effects on higher corporate credit rating. In contrast, high soundness and supervision by giant auditors reduced corporate credit rating. Based on this outcome, it is estimated that there is a discriminatory response among CSR activities in terms of credit rating evaluation conducted by credit rating institution.

• 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.

• Asia pacific journal of information systems
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• v.25 no.3
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• pp.579-596
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• 2015

Many studies have focused on the prediction of corporate credit rating using various data mining techniques. One of the most frequently used algorithms is support vector machines (SVM), and recently, novel techniques such as SVM+ and SVM+MTL have emerged. This paper intends to show the applicability of such new techniques to multi-classification and corporate credit rating and compare them with conventional SVM regarding prediction performance. We solve multi-class SVM+ and SVM+MTL problems by constructing several binary classifiers. Furthermore, to demonstrate the robustness and outstanding performance of SVM+MTL algorithm over other techniques, we utilized four typical multi-class processing methods in our experiments. The results show that SVM+MTL outperforms both conventional SVM and novel SVM+ in predicting corporate credit rating. This study contributes to the literature by showing the applicability of new techniques such as SVM+ and SVM+MTL and the outperformance of SVM+MTL over conventional techniques. Thus, this study enriches solving techniques for addressing multi-class problems such as corporate credit rating prediction.

• The Journal of Information Systems
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• v.14 no.1
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• pp.41-65
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• 2005

The credit ration is a significant area of financial management which is of major interest to practitioners, financial and credit analysts. The components of credit rating are identified decision models are developed to assess credit rating an the corresponding creditworthiness of firms an accurately ad possble. Although many early studies demonstrate a priori which of these techniques will be most effective to solve a specific classification problem. Recently, a number of studies have demonstrate that a hybrid model integration artificial intelligence approaches with other feature selection algorthms can be alternative methodologies for business classification problems. In this article, we propose a hybrid approach using rough set theory as an alternative methodology to select appropriate attributes for case-based reasoning. This model uses rough specific interest lies in lthe stable combining of both rough set theory to extract knowledge that can guide dffective retrevals of useful cases. Our specific interest lies in the stable combining of both rough set theory and case-based reasoning in the problem of corporate credit rating. In addition, we summarize backgrounds of applying integrated model in the field of corporate credit rating with a brief description of various credit rating methodologies.

• Journal of Digital Convergence
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• v.13 no.2
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• pp.105-113
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• 2015

If the watchdog role of good corporate governance, corporate executives and reduce agency costs and information asymmetries. Corporate governance score higher because enterprise internal control systems and financial reporting system is well equipped with the company management is enabled and corporate performance is higher because the high financial credit rating. Under these assumptions and hypotheses set up this study corporate governance (CGI) has been studied demonstrated how the financial impact on the credit rating (CFR). Findings,

relevant corporate governance (CGI) and financial credit rating was found to significantly affect the positive (+), Regression coefficient code is expected code of positive (+), the value

indicated by the value of all positive. The results of corporate governance (CGI) has showed excellent results, such as the more predictable will increase the credit score financial rating. The results of this study will have more CGI-credit financial rating the greater good. This study might be expected to provide a useful guide that corporate social responsibility, the company with a good governance and oversight systems enable to to get a higher credit rating in practice and research.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.571-578
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• 2005

Corporate credit rating analysis has drawn a lot of research interests in previous studies, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVMs) to the corporate credit rating problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, the researcher uses a grid-search technique using 5-fold cross-validation to find out the optimal parameter values of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, the researcher compares its performance with those of multiple discriminant analysis (MDA), case-based reasoning (CBR), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

• KDI Journal of Economic Policy
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• v.28 no.1
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• pp.1-47
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• 2006

Even after significant changes in the financial market due to the financial crisis the corporate debt markets have seen created turmoil caused such as by Daewoo, Hyundai, and credit card companies in the financial system. These lagging improvements of corporate debt markets are mainly due to inadequate market infrastructure. Specifically, the credit rating agencies have not been successful in providing proper and timely information on the loan repayment abilities of debtors. This study analyzes past performance of credit rating agencies in Korea and tries to develop policy implications to improve the role of credit rating agencies based on the recent discussions on credit rating agencies by academics and the SEC. In addition, this study focuses on unique operation environments of Korean credit rating agencies, which have kept credit rating agencies from providing fair, timely, and useful information. To warrant proper operation of credit rating agencies, it is essential to cope with unique problems in Korean credit rating agencies. We classify the unique problems of Korean credit rating agencies into ownership and governance structure, conflict of interests due to ancillary fee-based business, legal recognition of credit rating in the court, and code of conduct problem, etc. and propose policy directions to improve the quality and credibility of credit ratings.