• Journal of Digital Convergence
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• v.15 no.1
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• pp.199-208
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• 2017

This study investigates the components and rating system of reliable technology credit information for a technology finance donor who is a consumer of the information and aims to create an effective and optimal technology credit appraisal system to enlarge technology finance supply. Firstly, we calculate the optimal TCAR which becomes the maximum AUROC through the combination of ratio change, verify the substitution possibility between TAR and CR through the existing CR and system gap simulation, and propose a rating system by which financial institutes can utilize the TCAR as a credit rating. As a result, 70% : 30% is the most suitable as the weighted combination ratio of credit rating : technology rating. As a result of this study, we confirmed the possibility that the technical credit rating information could be substituted by the credit rating or the technology appraisal rating. Furthermore, it also suggests that sophisticated risk management is possible through using technology credit rating that are combined with credit and technology appraisal rating.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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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 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 Contemporary Eastern Asia
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• v.20 no.1
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• pp.102-119
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• 2021

Social credit rating in China (SCRC) has been criticized as "dystopian" and "Orwellian," an attempt by the Communist Party to hold onto power by exerting ever greater control over its citizens. To explain such measures, value differences are often invoked, that Chinese value stability and cooperation over privacy and freedom. However, these explanations are oversimplifications that result in ethical impasses. This article argues social credit rating should be understood in terms of the commonly human problem of large-scale cooperation. To do so, this paper relies on a cultural evolutionary framework and is an exercise in interpretive pros hen ethical pluralism, attempting to understand how apparently irresolvable cultural differences stem from common human concerns. Wholesale condemnation of SCRC fails to acknowledge the serious, intractable nature of problems resulting from a lack of trust in China. They take for granted the existence of institutions ensuring largescale, anonymous cooperation characteristic of - but somewhat unique to - Western Educated Industrialized Rich and Democratic (WEIRD) cultures. Because of its history and rapid development, China lacks the institutions necessary to ensure such cooperation, and because of anti-social punishment, social credit rating might be one of the few ways to ensure cooperation at this scale. The point is not to defend social credit rating in general, but to raise the possibility of its defense in China and show one way this would be done.

• Journal of Technology Innovation
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• v.26 no.2
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• pp.129-149
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• 2018

Technology finance is an area in which financial authorities have introduced and implemented a strong policy will for the advancement of the financial industry and the development of SMEs. As a result, the Bank's own technology evaluation was conducted from September 2016. Technically superior companies are upgrading their credit ratings, and as a result, they benefit from financial transactions as much as their higher credit ratings through technology evaluation. Based on the data generated during this process, we analyze the degree to which credit ratings was upgraded by technology evaluation. The pre study handles 406 data from KEB Hana Bank's technology evaluation conducted in the second half of 2016. As a result of combining the credit rating with the calculated technology rating, J58 'Publishing Activities' technology-credit rating is raised by 1.05 rating, which is the highest, and C10 'Manufacture of Food Products' is the second highest. As a result, we were able to identify the sectors that benefited from the technology evaluation and confirmed the usefulness of technology evaluation by industry(KSIC). To expanding the study, 2,719 companies evaluated during the entire period were analyzed by technology grade, business experience and promising growth industry code. As a result of the analysis, technological power over T-4 grade companies had the highest credit rating upgrades. The companies belonging to promising growth industries designated for efficiency of policy support, it is confirm that the support of the promising business type was useful because the credit grade was upgraded through technology evaluation. The validity of the technology evaluation based on the five-year business experience was found to be insignificant. In the future, it will be possible to maximize the support effect by concentration on the companies with over T-4 grade and growth potential companies when supporting SMEs.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.1-12
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• 2022

Our study adds to the body of knowledge about the relationship between credit ratings and the capital structure of bond issuers. Using Bloomberg and Datastream databases and employing panel regression models, we study the capital structure changes of Japanese enterprises after credit rating changes by global rating agencies (S&P and Moody's) as well as their local counterparts (R&I and JCR) from 1998 to 2016. We find that after rating downgrades, Japanese enterprises considerably reduce net debt or net debt relative to net equity, similar to the findings of Kisgen (2009), who focused on U.S. industrial firms. They do not, however, make adjustments to their financial structure as a result of rating improvements. In comparison to downgrades by S&P and Moody's, Japanese corporations issue 1.89 percent less net debt and 1.50 percent less net debt relative to net equity after R&I and JCR rating downgrades. To put it another way, Japanese companies consider rating adjustments made by local agencies to be more significant than those made by global rating organizations. Our findings contradict earlier research that suggests S&P and Moody's are more prominent in the investment community than R&I and JCR in Japan.

• 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.

• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.273-290
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• 2006

This study suggests a frequency matrix technique to predict personal credit rate more efficiently using incomplete data sets. At first this study test on multiple discriminant analysis and logistic regression analysis for predicting personal credit rate with incomplete data sets. Missing values are predicted with mean imputation method and regression imputation method here. An artificial neural network and frequency matrix technique are also tested on their performance in predicting personal credit rating. A data set of 8,234 customers in 2004 on personal credit information of Bank A are collected for the test. The performance of frequency matrix technique is compared with that of other methods. The results from the experiments show that the performance of frequency matrix technique is superior to that of all other models such as MDA-mean, Logit-mean, MDA-regression, Logit-regression, and artificial neural networks.

• 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.