• 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.

• Journal of Information Processing Systems
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• v.11 no.2
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• pp.173-183
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• 2015

At present, it is simple to the electronic commerce credit scoring model, as a brush credit phenomenon in E-commerce has emerged. This phenomenon affects the judgment of consumers and hinders the rapid development of E-commerce. In this paper, that E-commerce credit evaluation model that uses a Gaussian density function is put forward by density test and the analysis for the anomalies of E-commerce credit rating, it can be fond out the abnormal point in credit scoring, these points were calculated by nonlinear credit scoring algorithm, thus it can effectively improve the current E-commerce credit score, and enhance the accuracy of E-commerce credit score.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2153-2160
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• 2008

This paper develops an algorithm for probabilistic production cost credit evaluation of wind turbine generators(WTG) with multi-state. Renewable energy resources such as wind, wave, solar, micro hydro, tidal and biomass etc. are becoming importance stage by stage because of considering effect of the environment. Wind energy is one of the most successful sources of renewable energy for the production of electrical energy. Case study demonstrates that the wind speed credit in view point of economics can be assessed by using the proposed methodology.

• Korean Management Science Review
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• v.15 no.1
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• pp.97-115
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• 1998

This study aims to develop the methodology based on the AHP(Analytic Hierarchy Process) of evaluation for commercial paper. commercial paper is the ma product of merchant banks. commercial paper evaluation is annually performed by the credit-evaluation agency. Credit evaluation is performed by the informal judgemental system, which has potential to induce serious inconsistencies in decision-making. We present an objective scoring model which does not suffer from the weakness of the subjective judgement system. The model used is illustrated by analyzing the commercial paper evaluation for the 3 motor-companies(H, K and S motors).

• 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.

• Journal of Technology Innovation
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• v.25 no.4
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• pp.1-15
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• 2017

In 2014, the Financial Services Commission designated the Tech Credit Bureaus(TCB) to issue technical credit evaluation reports. The Five credit rating agencies, KEB Hana Bank and others have issued the technical credit reports since the summer in 2014. Meanwhile, the technology evaluation model of KEB Hana Bank consists of 25 detailed evaluation items. These item classes are weighted and the technology rating is systematically. The technology rating is combined with the credit rating to calculate the technology-credit rating. In this paper, we analyzed the 406 evaluation results issued by KEB Hana Bank. Based on the number of years of work experience, company managerial years, technical personnel score, the possession of R&D department, the amount of R&D investment, the number of certifications, and the number of patents, the Correlation between the above items and the technical grade was analyzed. It was found that quantitative indicators such as the presence of R&D department, patent numbers, and R&D investment expenses had a significant effect on the company's technology grade, and in particular, the presence of R&D department was shown a high correlation with the technology rating.

• 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.

• Information Systems Review
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• v.4 no.2
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• pp.361-376
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• 2002

This paper proposes a framework for integrated credit risk management system in domestic bank financial institutions. Credit evaluation system, loan processing system, credit monitoring system, and credit risk management system are integrated for efficient and effective risk-adjusted performance management in this framework. Risk exposures, not only for each credit, but also for bank's whole credit portfolio need to be measured and analyzed through the concept of Value-at-Risk (VaR). The effects of changes in credit ratings of individual loaners on bank's credit risk exposure are also considered. We tried to model this integrated credit risk management system by using object-oriented modeling language, UML.

• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.491-501
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• 2002

Case-based reasoning is emerging as a leading methodology for the application of artificial intelligence. CBR is a reasoning methodology that exploits similar experienced solutions, in the form of past cases, to solve new problems. Hybrid model achieves some convergence of the wide proliferation of credit evaluation modeling. As a result, Hybrid model showed that proposed methodology classify more accurately than any of techniques individually do. It is confirmed that proposed methodology predicts significantly better than individual techniques and the other combining methodologies. The objective of the proposed approach is to determines a set of weighting values that can best formalize the match between the input case and the previously stored cases and integrates fuzzy sit concepts into the case indexing and retrieval process. The GA is used to search for the best set of weighting values that are able to promote the association consistency among the cases. The fitness value in this study is defined as the number of old cases whose solutions match the input cases solution. In order to obtain the fitness value, many procedures have to be executed beforehand. Also this study tries to transform financial values into category ones using fuzzy logic approach fur performance of credit evaluation. Fuzzy set theory allows numerical features to be converted into fuzzy terms to simplify the matching process, and allows greater flexibility in the retrieval of candidate cases. Our proposed model is to apply an intelligent system for bankruptcy prediction.