• 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.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2013년도 제48차 하계학술발표논문집 21권2호
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• pp.319-320
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• 2013

In these days, an individual is evaluated by his or her credit. So, it is very critical for an individual to know his or her credit rating and then to try to improve the credit rating. But, there are few services to analyze credit status for an individual and inform the person of the credit. In fact, it could be impossible to let a person know how to improve his or her credit rating. Against this backdrop, we research on a credit management model to analyze credit status of an individual rapidly and propose individual-customized method to improve the credit rating. We set up the model and design it in detail. This servie would certainly make it convenient for an individual to retrieve credit rating and improve it.

• 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.

• 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.

• 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.

• 한국정보통신학회논문지
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• 제9권7호
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• pp.1569-1574
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• 2005

In this paper, I propose an intelligent credit rating system using a bankruptcy prediction model based on support vector machines (SVMs). SVMs are promising methods because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study examines the feasibility of applying SVM in Predicting corporate bankruptcies by comparing it with other data mining techniques. In addition. this study presents architecture and prototype of intelligeht credit rating systems based on SVM models.

• Journal of applied mathematics & informatics
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• 제31권5_6호
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• pp.711-732
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• 2013

In this paper, we are interested in finding explicit numerical formulas to evaluate defaultable bonds prices of firms. For this purpose, we use a default intensity whose values depend on the credit rating of these firms. Each credit rating corresponds to a state of the default intensity. Then, this regime switches as soon as one of the credit rating of a firm also changes. Moreover, this regime switching default intensity model allows us to capture well some market features or economics behaviors. Thus, we obtain two explicit different formulas to evaluate the conditional Laplace transform of a regime switching Cox Ingersoll Ross model. One using the property of semi-affine of the model and the other one using analytic approximation. We conclude by giving some numerical illustrations of these formulas and real data estimation results.

• KDI Journal of Economic Policy
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• 제33권2호
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• pp.81-110
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• 2011

서브프라임 모기지 및 구조화 상품 등에 대한 부정확한 신용등급은 최근 금융위기 확산의 주요 요인으로 지적되고 있다. 본 논문은 신용평가노력을 관찰할 수 없는 숨겨진 행동모형(hidden action model)을 통해 신용평가회사의 행태 및 규제에 대한 분석을 시도하여 현재 논의되고 있는 신용평가기능 개선을 위한 논의에 보완적인 기여를 하고자 한다. 분석 결과, 도덕적 해이가 존재하면 신용평가노력이 관찰 가능하지 않음으로 인해 사회적으로 최적인 수준보다 낮은 수준의 신용평가노력을 기울임을 확인하였다. 경쟁 및 평판효과를 고려한 확장된 모형의 경우에도 신용평가회사에 사회적으로 최적의 유인을 제공하는 데는 한계가 존재한다. 그리고 부수업무의 존재는 신용평가회사의 노력수준과 사회적 최적 수준 간의 괴리를 확대함을 확인하였다. 따라서 경쟁과 평판에 의한 규율이 불완전한 경우 신용평가회사에 대한 감독 및 잘못된 정보의 제공에 따른 책임의 부과가 필요하다

• Journal of the Korean Data and Information Science Society
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• 제28권3호
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• pp.605-615
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• 2017

국내 외에서 지급보증과 관련 모회사의 지원 중단으로 신용평가사로부터 높은 등급을 받았던 자회사가 법정관리에 갔던 사태로 투자자의 피해가 발생한 사례가 존재하여 이에 모기업 계열사의 지원 가능성을 배제한 기업의 자체신용도 또는 독자신용등급에 대한 관심이 높아지고 있다. 본 연구에서는 해외자회사를 둔 국내 기업을 대상으로 판별력 분석, 등급화 분석 그리고 안정성 분석을 통해 기업 신용평가모형의 적합성검증을 실시하였으며 주요 실증분석결과 해외자회사의 부도 현황을 볼 때 부도율측면에 있어서 국내모회사보다 상대적으로 낮은 부도율을 나타내고 있는 것을 확인할 수 있었고, 한국모회사가 지급보증을 하는데 있어 해외자회사보다 신용등급이 일반적으로 높은 것으로 나타났다.