• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1175-1186
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• 2015

Financial fraud has been increasing along with credit card usage. Magnetic stripe cards have vulnerabilities in that credit card information is exposed in plaintext and cardholder verification is untrustworthy. So they have been replaced by a smart card scheme to provide enhanced security. Furthermore, the FinTech that combines the IT with Financial product is being prevalent. For that reason, many mobile device based payment schemes have been proposed for card present transaction. In this paper, we propose a virtual credit card number payment scheme based on public key system for efficient authentication in card present transaction. Our proposed scheme is able to authenticate efficiently in card present transaction by pre-registering virtual credit card number based on cardholder's public key without PKI. And we compare and analyze our proposed scheme with EMV.

• Management & Information Systems Review
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• v.34 no.2
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• pp.113-130
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• 2015

This study considers the relation between firms' earnings management and credit rating. Unlike preceding papers only focusing earnings management by accrual(thereafter, AM), this paper examines the effect of accrual earnings management(AMs) and real earning management(thereafter, RM) on credit rating. RMs have more negative effects on firms' forward cash flow generation abilities and long term operating performances than AMs. So, RMs are more negative signals for credit analysts than AMs. But credit analysts have much difficulty in seeing through RM, because if credit analysts want to find out RMs, they have to understand firms' internal operating activities, cost structures, receivables collection practices, and review whether profit distortions are due to abnormal change of them. Sample of this study consists of 2,150firm-year data listed companies from 2002 to 2010. Empirical evidence shows that AMs and RMs are negatively related to credit rating. This result implies that credit analysts see through AMs and RMs in interpreting financial informations, that is to say, they discount credit rating in considering level of earnings management that consist of real activity and accrual earning management. This paper also finds that RMs are more negatively related to credit ratings than AMs. This result suggests that credit analysts don't take RMs into account in credit rating process as much as AMs.

• Korean Management Science Review
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• v.15 no.1
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• pp.137-148
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• 1998

We proposed a framework for computer-supported credit evaluation systems for the effective management of credit risks in Korean commercial banks. Especially for medium and small sized companies, credit evaluators used to depend much on past experience rather than formalized principles and rules. Therefore, we applied case-based reasoning. The credit grade of a company is roughly determined by searching for alreadygraded similar companies in terms of usually accepted evaluation items. And then the grade is refined and adjusted by considering additional information about exceptional facts or by reflecting other evaluation results from different methods or techniques. Booch's object-oriented analysis and design method, Visual Basic 5.0 and MS Access 97 are used for the development of this prototype system.

• Journal of Korean Society for Quality Management
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• v.36 no.1
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• pp.72-79
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• 2008

The policy for credit card approval/disapproval is based on the applier's personal and financial information. In this paper, we will analyze 2 credit card approval data with several classification methods. We identify which variables are important factors to decide the approval of credit card. Our main tool is an open-source statistical programming environment R which is freely available from http://www.r-project.org. It is getting popular recently because of its flexibility and a lot of packages (libraries) made by R-users in the world. We will use most widely used methods, LDNQDA, Logistic Regression, CART (Classification and Regression Trees), neural network, and SVM (Support Vector Machines) for comparisons.

• 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.

• International Commerce and Information Review
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• v.15 no.2
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• pp.25-47
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• 2013

In China, the commercial bank credit financing is the most important external financing channel for SMEs. However, the lack of credit guarantee significantly deters commercial banks to finance SMEs. which may generate a negative impact on the trade activities of SME in China. In this paper we examine the risk of credit guarantee for SMEs financing and the factors affecting this risk through a VAR (Value-at-Risk) model. Our analysis shows that the scale of enterprises' impact on the VAR (risk of financing guarantee) is not much relevant. We also find that the certainty of financing for SMEs, such as the fixed asset ratio, has a significant and negative effect on the VAR of Chinese credit guarantee institutions. The product uniqueness is positively correlated with the VAR, and operation risk is also positively related to the credit risk of Chinese credit guarantee institutions.

• Journal of Digital Contents Society
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• v.14 no.1
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• pp.81-88
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• 2013

The increase of the smartphone users has popularized the mobile payment and the mobile credit card users are rapidly getting increased. The mobile credit cards that currently used provide its users with the service through downloading mobile credit card information into USIM. The mobile credit card saved in USIM has the minimized information for the security and is based on PKI. However certificate-based payment system has a complicated procedure and costs a lot of money to manage the certificates and CRL(Certificate Revocation List). Furthermore, It can be a obstacle to develop local e-commerce in Korea because it is hard for foreigners to use them. We propose the secure and efficient mobile credit card payment protocol based on certificateless signcryption which solve the problem of certificate use.

• 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.

• Knowledge Management Research
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• v.21 no.3
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• pp.229-247
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• 2020

In the remarkable growth of P2P financial platform in the field of knowledge management, only companies with big data and machine learning technologies are surviving in fierce competition. The ability to analyze borrowers' credit is most important, and platform companies are also recognizing this capability as the most important business asset, so they are building a credit evaluation system based on artificial intelligence. Nonetheless, online P2P platform providers that offer related services only act as intermediaries to apply for investors and borrowers, and all the risks associated with the investments are attributable to investors. For investors, the only way to verify the safety of investment products depends on the reputation of P2P companies from newspaper and online website. Time series information such as delinquency rate is not enough to evaluate the early stage of Korean P2P makers' credit analysis capability. This study examines the credit analysis procedure of P2P loan platform using artificial intelligence through the case analysis method for well known the top three companies that are focusing on the credit lending market and the kinds of information data to use. Through this, we will improve the understanding of credit analysis techniques through artificial intelligence, and try to examine limitations of credit analysis methods through artificial intelligence.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.2
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• pp.193-208
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• 2020

In this study, we investigate the effects of technology innovation investments and employment on credit ratings of very small start-up businesses using the data period of 2009 till 2015 test two hypotheses: asymmetric information hypothesis or competence hypothesis. We use financial and non-financial data of 51,903 observations of 12,028 small businesses from a database of a commercial bank and fixed effects panel models and two-stage instrumental variable models. We find that in the short-run small size startups show lower credit ratings than non-startups, and that both technology innovation activities and employment capability improve their credit ratings. In the long-run, technology innovation investments do not improve their credit ratings of later years while employment capability improve their credit ratings of the subsequent year. In addition, the age of startups improves their credit ratings of the current year and until the subsequent two years while employee productivity, fixed ratio and ROA positively affect their credit ratings for up to three years. However, short-term and overall debt ratios, cost of borrowings and firm-size negatively affect their credit ratings for up to three years. The results of the study on credit ratings suggest that credit rating agencies seem to consider both technology innovation activities and employment capability in the credit ratings of small start-ups as 'competence factors' rather than 'asymmetric information factors' with inefficiency and cost burdens. The results also suggest that we must find ways to reflect properly the severe asymmetric information of the early-stage start-ups, and technology innovation activities and employment capability in the credit rating formula.