• Journal of the Korean Home Economics Association
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• v.42 no.3
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• pp.119-130
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• 2004

The purpose of this study is to estimate the rate of credit card payment for private education. The results of study can be used to improve credit card handling problems of private educational institutes, leading toward improvements in income transparency, increase in tax burden equity and long-term economic welfare improvement for individual households. 424 households out of 586 household that were surveyed in September of 2002 had 1,700 cases private extracurricular education. 67 of the 1,700 cases that did not have expenditure records were removed from the analysis. Only 3.67％ out of 1,633 cases were paid by a credit cards and the amount of credit card payment were only 5.65％ of the total amount spent for private education. The average fee of private educational institutes that allow credit card payment was higher than the fees of private institutes which don't allow a credit card payment or those of private institutes where consumers don't know whether a credit card payment was allowed. The average fee of private education paid by credit cards was 34,465.46 won higher than that paid by cash. Credit card payments to private educational institutions is an important social issue with respect to fair tax collection and tax burden equity since most private educational services operate in fairly small sizes and are offered by the self-employed, and the expense of private education is a fairly large proportion of the household income. It is also important for consumers if credit card acceptance expands alternatives that consumers can choose in private education. Therefore, credit card payment should be encouraged in private extracurricular education. To do this, private education providers should be forced to join a credit card payment service by the National Tax Service. A regulation that prohibits the refusal of credit card payments should be required, and credit card service charges of private education providers should be incrementally decreased. Also, consumer education and public promotions for credit card use instead of cash in paying for private education fees are recommended.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.789-800
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• 2008

Current credit evaluation models based on financial data make use of smoothing estimated default ratios which are transformed from each financial variable. In this work, some problems of the credit evaluation models developed by financial experts are discussed and we propose improved credit evaluation models based on the stepwise variable selection method and Box-Cox transformed data whose distribution is much skewed to the right. After comparing goodness-of-fit tests of these models, the validation of the credit evaluation models using statistical methods such as the stepwise variable selection method and Box-Cox transformation function is explained.

• The Journal of Small Business Innovation
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• v.20 no.3
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• pp.19-30
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• 2017

Today's new engine of economic growth is innovative technology intensive SMEs. However, they have limited access to funding because of asymmetric information problems. Sharing of SME information helps reduce information asymmetry. This paper explains the Korean system of SME technology information sharing, as well as SME credit information sharing. It also provides theoretical analysis about the effects of the SME information sharing on banks' credit analysis activities, based on Karapetyan and Stacescu (2013). Sharing of SME credit and technology information expands the data set of banks and it will enhance their credit analysis. In addition, SME information sharing increases banks' investments in credit analysis activities. To encourage SME information sharing and production, the government can subsidize the production of SME technology information.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.1044-1051
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• 2002

This paper proposes a two-phase mathematical programming approach by considering classification gap to solve the proposed credit scoring problem so as to complement any theoretical shortcomings. Specifically, by using the linear programming (LP) approach, phase 1 is to make the associated decisions such as issuing grant of credit or denial of credit to applicants. or to seek any additional information before making the final decision. Phase 2 is to find a cut-off value, which minimizes any misclassification penalty (cost) to be incurred due to granting credit to 'bad' loan applicant or denying credit to 'good' loan applicant by using the mixed-integer programming (MIP) approach. This approach is expected to and appropriate classification scores and a cut-off value with respect to deviation and misclassification cost, respectively. Statistical discriminant analysis methods have been commonly considered to deal with classification problems for credit scoring. In recent years, much theoretical research has focused on the application of mathematical programming techniques to the discriminant problems. It has been reported that mathematical programming techniques could outperform statistical discriminant techniques in some applications, while mathematical programming techniques may suffer from some theoretical shortcomings. The performance of the proposed two-phase approach is evaluated in this paper with line data and loan applicants data, by comparing with three other approaches including Fisher's linear discriminant function, logistic regression and some other existing mathematical programming approaches, which are considered as the performance benchmarks. The evaluation results show that the proposed two-phase mathematical programming approach outperforms the aforementioned statistical approaches. In some cases, two-phase mathematical programming approach marginally outperforms both the statistical approaches and the other existing mathematical programming approaches.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.515-521
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• 2020

Recently, data traffic has exploded due to development of various industries, which causes problems about losing of efficiency and overloaded existing networks. To solve these problems, network slicing, which uses a virtualization technology and provides a network optimized for various services, has received a lot of attention. In this paper, we propose a resource allocation method using credit value. In the method using the clustering technology, an operation for selecting a cluster is performed whenever an allocation request for various services occurs. On the other hand, in the proposed method, the credit value is set by using the residual capacity and balancing so that the slice request can be processed without performing the operation required for cluster selection. To prove proposed method, we perform processing time and balancing simulation. As a result, the processing time and the error factor of the proposed method are reduced by about 13.72% and about 7.96% compared with the clustering method.

• Journal of the Korean Home Economics Association
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• v.44 no.3 s.217
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• pp.1-11
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• 2006

Although the personal credit rating has become more important than ever before in our era, a significant number of social problems have occurred due to the rising number of individuals and households with low credit ratings. The main objectives of this research are to determine effective policies of social remedies through an investigation of recognition, expectation, and utilization levels of relevant public policies available to assist individuals with low credit ratings. The sample population was taken from the credit defaulters who had visited the Credit Recovery Commission. The research was undertaken from April 28 to May 4, 2004. This study focused on the related variables concerning the degree of utilization of remedial public policies. The results showed that females, less educated individuals, and those with higher levels of expectation and recognition were more likely to utilize remedial policies. Based on the research, conclusions regarding the usage of public remedial policies for credit defaulters are as stated below. Education for households should be conducted in order to increase the expectation and recognition levels of relevant policies.

• 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 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.

• Journal of Fisheries and Marine Sciences Education
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• v.20 no.1
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• pp.68-77
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• 2008

The purpose of this study is to examine the organization of the standard liberal arts curriculum in accordance with the academic credit bank system (ACBS). With the growth of the system, the standard curriculum of the ACBS turned out to have many problems which have to be solved for securing and maintaining the quality of the system. In an attempt to improve the standard liberal arts curriculum of the ACBS, the study analyzed the problems of standard liberal arts curriculum of the ACBS according to three criteria: the organization of subjects in the six liberal arts curriculum areas, the completion of liberal arts and the exchange subjects between majors and the liberal arts. Based on the results of the analysis, the study suggests ways to improve standard liberal arts curriculum. In the future, research needs to be conducted to explore ways to implement the proposed standard liberal arts curriculum of the ACBS in real contexts.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.615-631
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• 2008

Current credit evaluation models based on only financial data except non-financial data are used continuous data and produce credit scores for the ranking. In this work, some problems of the credit evaluation models based on transformed continuous financial data are discussed and we propose improved credit evaluation models based on categorized financial data. After analyzing and comparing goodness-of-fit tests of two models, the availability of the credit evaluation models for categorized financial data is explained.