• 대한안전경영과학회지
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• 제10권2호
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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.

• 한국정보시스템학회지:정보시스템연구
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• 제16권3호
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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.

• 경영과학
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• 제20권2호
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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.

• 한국융합학회논문지
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• 제9권1호
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• pp.1-8
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• 2018

본 연구는 기업의 사회적 책임과 감사인 규모가 기업의 신용등급에 어떠한 영향을 미치는지를 실증적으로 분석하였다. 본 연구는 관심변수를 기준으로 2005년부터 2011년까지 경실련 경제정의연구소가 경제정의기업으로 선정한 기업을 대상으로 최종표본 159개 기업/연 자료를 분석하였다. 분석결과는 다음과 같다. 첫째, CSR 항목 중 건전성과 기업의 사회적 책임활동 총점이 높을수록 기업신용등급은 유의하게 높게 나타났다. 이러한 결과는 신용평가기관이 기업의 사회적 책임활동을 긍정적으로 평가하여 신용등급에 반영한다는 것을 의미한다. 그러나 환경보호만족도와 기업신용등급 간에는 반대의 결과가 나타났다. 둘째, CSR 활동 중 공정성과 경제발전기여도가 높고 대형감사인이 감사할수록 기업신용등급은 유의하게 높게 나타났다. 반면, 건전성이 높고 대형감사인이 감사할수록 기업신용등급은 낮게 나타나 신용평가기관이 신용등급을 평가함에 있어 CSR 활동 간에 차별적인 반응이 있는 것으로 파악된다.

• Journal of Information Technology Applications and Management
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• 제14권2호
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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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• 제25권3호
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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.

• 한국정보시스템학회지:정보시스템연구
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• 제14권1호
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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.

• 디지털융복합연구
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• 제13권2호
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• pp.105-113
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• 2015

기업지배구조가 우수하면 기업경영자의 감시역할을 하고 대리비용과 정보비대칭을 감소시킨다. 기업지배구조점수가 높을수록 기업 내부통제시스템과 재무보고 체계가 잘 갖추어져 있으므로 기업 경영이 활성화되고 기업성과가 높아지므로 신용재무평점이 높아질 것이다. 이러한 전제하에 가설을 설정하고 본 연구는 기업지배구조(CGI)가 신용재무평점(CFR)에 어떠한 영향을 미치는지 실증 연구하였다. 연구결과, 기업지배구조(CGI)와 신용재무평점의 관련성은 유의한 양(+)의 영향을 미치는 것으로 나타났다, 회귀계수부호는 기대부호인 양(+)이 값이 나타났다. 이러한 결과 기업지배구조(CGI)가 우수할수록 신용재무평점의 점수가 커질 것이라는 예측과 같은 결과가 나타났다. 본 연구결과는 CGI가 우수할수록 신용재무평점이 커진다는 것이다. 본 연구는 기업의 사회적 책임, 건전한 지배구조와 감시기구를 갖춘 기업이 보다 높은 신용등급을 받을 수 있다는 유용한 지침을 실무 및 연구 분야에 제공해 줄 것으로 기대한다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2005년도 공동추계학술대회
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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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• 제28권1호
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• pp.1-47
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• 2006

우리나라의 회사채시장은 양적으로 꾸준한 성장세를 지속하였으나 여전히 질적 성숙이 그에 미치지 못하는 것으로 평가되고 있으며 외환위기 이후에도 대우채, 현대채, 카드채 사태 등의 금융시장 불안을 반복적으로 초래하였다. 회사채시장의 질적 발전이 이루어지지 못한 것은 무엇보다도 관련 인프라의 적절한 구축이 이루어지지 못한데 크게 영향 받은 것으로 판단된다. 특히 신용평가산업은 실제 발행기업의 채무상환능력을 평가하는 정보의 생성기능을 적절하게 담당하지 못한 채 제도의 이식 수준에 머물고 있다. 본 연구는 미국 SEC 및 미국학계에서 제기되고 있는 신용평가사제도 개선 논의를 고려하여 우리나라의 신용평가사제도 개선의 가능성을 모색한다. 특히, 우리나라 특유의 상황에 의해 발생하고 있는 우리나라 신용평가산업 특유의 문제들을 소유 지배구조 및 부수업무 수행에 따르는 이해상충, 역사적 발전과정, 신용등급에 대한 법리적 해석 및 경제 사회적 차이에 따르는 문제로 분류하여 지적하고 대응방향을 제시하였다.