• 한국산업정보학회:학술대회논문집
• /
• 한국산업정보학회 2008년도 추계 공동 국제학술대회
• /
• pp.164-169
• /
• 2008

The increasing late-payment rate of credit card customers caused by a recent economic downturn are incurring not only reduced profit of department stores but also significant loss. Under this pressure, the objective of credit forecasting is extended from presumption of good or bad customers to contribution to revenue growth. As a method of managing defaults of department store credit card, this study classifies credit delinquents into some clusters, analyzes repaying patterns of customers in each cluster, and develops credit forecasting system to manage delinquents of department store credit card using data of Korean D department store's delinquents. The model presented by this study uses Kohonen network, a kind of artificial neural network of data mining techniques to cluster credit delinquents into groups. Logistic regression model is also used to predict repayment rate of customers of each cluster per period. The accuracy of presented system for the whole clusters is 92.3%.

• Journal of the Korean Data and Information Science Society
• /
• 제19권3호
• /
• pp.789-800
• /
• 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.

• 경영과정보연구
• /
• 제3권
• /
• pp.439-460
• /
• 1999

The purpose of this paper is to compare the characteristics between group that has only one credit card and group that has multiple credit cards in each stage of purchase decision making based on the literature in consumer behavior. Results indicated that (1) The number of credit cards that consumers has was affected by internal factor and reference factor, (2) depending on the number of credit cards, usage frequency, amount, and duration were differently showed and (3) inertia, point accumulation, and convenience were founded as determinants of using credit cards. Finally, theoretical and managerial implications of these findings are discussed.

• Journal of Information Technology Applications and Management
• /
• 제13권4호
• /
• pp.273-290
• /
• 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.

• 한국정보통신학회논문지
• /
• 제6권5호
• /
• pp.762-767
• /
• 2002

이 논문에서는 이동통신단말기를 이용한 신용카드 및 온-라인 금융거래에 관한 방법을 제안한다. 제안한 방법은 먼저, 신용카드 회원이 신용카드사 거래승인시스템으로부터 보안코드를 이동통신 단말기로 수신 받는 보안코드수신단계, 보안코드를 가맹점의 인증 단말기에 입력하는 단계, 입력된 보안코드의 일치여부를 비교하는 단계 및 최종 승인단계의 과정으로 구성된다.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2009년도 춘계학술대회
• /
• pp.797-800
• /
• 2009

RFID enabled credit cards are becoming increasingly popular as contactless credit cards. We envision a future where RFID credit cards will be used for online shopping. RFID system has tremendous potential to render electronic payments more secure than normal credit cards. The word RFID enabled credit cards may bring in mixed passion, enthusiasm and perhaps even rage! This is partly paranoia and partly reality. The reality is that an intruder can read RFID cards without the user even noticing it. This brings in a zone of discomfort and leads to paranoia. Certain interactivity should exist to bring back this comfort to the user. This paper tries to make an effort in that direction. In this paper we propose mobile phone based architecture for secured electronic payments using RFID credit cards.

• Journal of Information Processing Systems
• /
• 제17권1호
• /
• pp.75-88
• /
• 2021

Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.

• 한국항행학회논문지
• /
• 제11권3호
• /
• pp.337-342
• /
• 2007

본 논문에서는 현재 국내 ETCS(Electrical Toll Collection System)에 적용될 예정에 있는 후불식 지불 방식에 있어서 수행해야할 전자 신용카드(EMV) 처리절차를 제안한다. 현재 국내에서는 한국도로공사의 하이패스, 터치패스 시스템이 비접촉식 ETCS로 운영되고 있으며, 현재의 운영 방식은 일종의 전자화폐 개념의 선불(pre-paid)식이다. 이에 반해 후불(credit)식은 신용카드 기반의 지불을 수행함으로써 미리 지불할 필요가 없다는 장점이 있다. 현재 국내의 ETCS에 후불식의 도입이 준비 중에 있으며, 본 논문에서는 이러한 후불식 지불에서 고려해야 할 EMV 처리 방식을 제안한다.

• 한국정보시스템학회지:정보시스템연구
• /
• 제14권1호
• /
• pp.41-65
• /
• 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.

• The Journal of Asian Finance, Economics and Business
• /
• 제7권2호
• /
• pp.33-42
• /
• 2020

The paper investigates the mechanism through which corporate credit ratings affect dividend payments by decomposing the mean difference of dividends into a part that is explained by the determinants of dividends and a residual part that is contributed by the pure credit group effect, in the framework of the traditional dividend model of Fama and French (2001). Historically, better credit rated firms have shown consistently higher propensity to pay dividends especially during the economic crisis period. According to the counter-factual decomposition technique of Jann (2008), better rated firms are more responsive to the firm characteristics that have positive impact on dividends and poor rated firms are more responsive to the negative dividend predictors. As a result, good (bad) credit ratings make corporate managers become more bold (timid) in their dividend payments and they tend to pay more (less) dividends than what their firm characteristics prescribe. The degree of information asymmetry increases for the poor group firms during crisis periods and they attempt to reserve more cash in preparation for future investments. The decomposition results suggest that the credit group effect can potentially exceed the effect of firm characteristics because firms of different credit ratings can respond to the very same firm characteristics in a different manner.