• KDI Journal of Economic Policy
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• v.28 no.1
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• pp.1-47
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• 2006

Even after significant changes in the financial market due to the financial crisis the corporate debt markets have seen created turmoil caused such as by Daewoo, Hyundai, and credit card companies in the financial system. These lagging improvements of corporate debt markets are mainly due to inadequate market infrastructure. Specifically, the credit rating agencies have not been successful in providing proper and timely information on the loan repayment abilities of debtors. This study analyzes past performance of credit rating agencies in Korea and tries to develop policy implications to improve the role of credit rating agencies based on the recent discussions on credit rating agencies by academics and the SEC. In addition, this study focuses on unique operation environments of Korean credit rating agencies, which have kept credit rating agencies from providing fair, timely, and useful information. To warrant proper operation of credit rating agencies, it is essential to cope with unique problems in Korean credit rating agencies. We classify the unique problems of Korean credit rating agencies into ownership and governance structure, conflict of interests due to ancillary fee-based business, legal recognition of credit rating in the court, and code of conduct problem, etc. and propose policy directions to improve the quality and credibility of credit ratings.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.67-79
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• 2012

As credit cards are more convenient than cash of heavy and volume, they are used widely in on_line (internet) and off_line in nowadays. A head of the economically active population had 4.8 credit cards in the third quarter 2011. The economically active population in their 20's and 30's may have more than 10 credit cards. If the membership card and point card are included, the count rise to 20 cards in a man. Therefore, the their purse become thick by several cards. Nowadays, the smart-phone is widely used. Therefore, a lot of credit cards are come in smarty-phone from analog form to digital form. In this paper, we study a method of digital credit cards management in smart-phone. For the method, the hardware need in small and existing system have a few change. All kinds of credit cards are used in smarty-phone. We compare the method with Moneta card and NFC of smarty-phone. The digital credit cards feature a light, safety and convenience of mobile users in several ways.

• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.339-344
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• 2005

Currently the credit card settlement system which is used from off-line credit card joining point is composed of the credit card settlement terminal, the VAN company which do settlement relay service and the credit card company. When the credit card company occurs obstacle, the service VAN company executes as proxy an approval and it controls. When the service VAN company occurs obstacle, the credit card settlement service does not become accomplished in the normality. The dissertation which it sees when the service VAN company occurs obstacle, the possibility of doing a settlement relay service from the different VAN company in order to be, proposed the credit card settlement system which uses the Switching Agent.

• The Journal of Information Systems
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• v.29 no.1
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• pp.241-265
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• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.85-94
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• 2013

As the financial market becomes larger, the loss increases due to the failure of the credit risk managements from the poor management of the customer information or poor decision-making. Thus, the credit risk management also becomes more important and it is essential to develop a credit scoring model, which is a fundamental tool used to minimize the credit risk. Credit scoring models have been studied and developed only for binary target variables. In this paper, we consider other types of target variables such as ordinal multinomial data or longitudinal binary data and suggest credit scoring models. We then apply our developed models to real data and random data, and investigate their performance through Kolmogorov-Smirnov statistic.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11b
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• pp.1253-1256
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• 2003

유무선의 다양한 환경에서 신뢰할 수 있는 Accounting 을 할 수 있도록 AAA 프로토콜중에 하나인 DIAMETER 의 기능과 특성에 대해서 알아보고 기존의 Accounting 의 문제점을 보완한 Credit-Control 모델에 대해 알아 보도록 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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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 the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1027-1036
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• 2008

Value at Risk(VaR) is a fundamental tool for managing market risks. It measures the worst loss to be expected of a portfolio over a given time horizon under normal market conditions at a given confidence level. Calculation of VaR frequently involves estimating the volatility of return processes and quantiles of standardized returns. In this paper, we introduced and applied the CreditMetrics model to estimate the credit VaR of Korean Property and Casuality insurers.

• Journal of Families and Better Life
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• v.23 no.5 s.77
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• pp.15-26
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• 2005

The purpose of this study was to investigate the factors affecting credit card attitudes, financial management practices, and sound credit card use among college students, and to conceptualize a theoretical model. Earlier studies identified a number of antecedent variables (such as gender, year in college, job experience, amount of allowance, family income, living with parents, having taken a personal financial management course) and intervening variables (such as attitudes towards credit cards and financial management practices) as useful predictors of sound credit card practices. Four hundred and thirty four undergraduate students in Daejeon participated in this study. Stepwise multiple regression and path analysis were conducted. The results of this study were as follows: 1. Students' attitudes towards credit cards were affected by their you in college, whether they were living with their parents, and the amount of their allowance. Similarly, students' financial management practices were affected by their year in college, whether they were living with their parents, the amount of their allowance, and whether and not they had taken a personal financial management course. 2. Sound credit card practices were influenced by students' gender, their year in college, the amount of their allowance, attitudes towards credit cards, and financial management practices. 3. The path-analysis model demonstrates the relationships among the antecedent variables, intervening variables (credit card attitude, financial management practices), and sound credit card use.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.171-180
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• 2021

The purpose of this paper is to estimate the impact of credit rationing on the amount of trade credit used by farmers in Vietnam. This study employs a survey data collected through direct interviews with heads of 1,065 rice households randomly selected out of provinces and city in the Mekong River Delta (MRD). In each province or city, the village with the largest area of land devoted to rice production from the district with the largest area of land devoted to rice production was picked up for survey. In each village, 200 rice farmers were randomly chosen for interview. Based on a probit model and a semi-parametric propensity score matching (PSM) estimator while controlling socio-demographic traits of rice farmers, the estimated results show that non-credit rationed farmers use less trade credit to finance production compared to their credit rationed counterparts. Moreover, the amount of trade credit used by farmers decreases as the degree of credit rationing drops. This paper provides evidence of the substitutive relationship between bank credit and trade credit. It also implicitly suggests that banks can drive trade creditors out of the market if they manage to solve the problem of information asymmetry and transaction cost.