• 디지털산업정보학회논문지
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• 제7권3호
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• pp.63-74
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• 2011

Credit cards are more convenient than cash of heavy. Therefore, credit cards are used widely in on_line (internet) and off_line in nowadays. To use credit cards on internet is commonly secure because client identification based security card and authentication certificate. However, to use in off_line as like shop, store, department, restaurant is unsecure because of irregular accident. As client identification is not used in off_line use of credit cards, the irregular use of counterfeit, stolen and lost card have been increasing in number recently. Therefore, client identification is urgently necessary for secure card using in off_line. And the method of client identification must be simple, don't take long time, convenient for client, card affiliate and card company. In this paper, we study a reform measure of the structure and transaction process for the safety improvement of a credit cards. And we propose several authentication method of short-and long-term for client identification. In the proposal, the client authentication method by OTP application of smart-phone is efficient nowadays.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2004년도 추계학술대회 및 정기총회
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• pp.68-98
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• 2004

Although the internet is useful for transferring information, Internet auction environments make fraud more attractive to offenders because the chance of detection and punishment are decreased. One of fraud is phantom transaction which is a colluding transaction by the buyer and seller to commit illegal discounting of credit card. They pretend to fulfill the transaction paid by credit card, without actual selling products, and the seller receives cash from credit card corporations. Then seller lends it out buyer with quite high interest rate whose credit score is so bad that he cannot borrow money from anywhere. The purpose of this study is to empirically investigate the factors to detect of the phantom transaction in online auction. Based up on the studies that explored behaviors of buyers and sellers in online auction, bidding numbers, bid increments, sellers' credit, auction length, and starting bids were suggested as independent variables. We developed an Internet-based data collection software agent and collect data on transactions of notebook computers each of which winning bid was over 1,000,000 won. Data analysis with logistic regression model revealed that starting bids, sellers' credit, and auction length were significant in detecting the phantom transaction.

• 품질경영학회지
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• 제46권4호
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• pp.797-820
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• 2018

Purpose: It is to seek for an appropriate cost rate analysis methodology of credit card value propositions in Korea. For this issue, it is claimed that methodologies based on probability distribution is more suitable than methodologies based on data-mining. The analysis model constructed for the cost rate estimation is called VCPM model. Methods: The model includes two major variables denoted as S and P. S is monthly credit card usage amount. P stands for the proportion of usage amount at special merchants over the whole monthly usage amount. The distributions assumed for P are positively skewed distributions such as exponential, gamma and lognormal. The major inputs to the model are also derived from S and P, which are E(S) and the aggregate proportion of usage amount at special merchants over the total monthly usage amount. Results: When the credit card's value proposition is general discount, the VCPM model fits well and generates reasonable cost rate(denoted as R). However, it seems that the model does not work well for other types of credit cards. Conclusion: The VCPM model is reliable for calculating cost rate for credit cards with positively skewed distribution of P, which are general discount card. However, another model should be built for cards with other types of distributions of P.

• 응용통계연구
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• 제18권3호
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• pp.543-553
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• 2005

선형 로지스틱 모형은 신용위험 관리를 위한 신용평점 모형 구축에 있어서 널리 쓰이고 있는 방법론이다. 본 논문에서는 신용평점화를 위하여 로지스틱 회귀 방법에 기초한 스플라인 방법론을 다루고자 한다. 선형 스플라인과 자동적인 변수선택 방법을 채택하였다. 모의 실험을 통하여 스플라인 방법의 성능을 규명하였다.

• Journal of Information Technology Applications and Management
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• 제13권4호
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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.

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

• 산업경영시스템학회지
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• 제40권4호
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• pp.154-163
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• 2017

Collaborative filtering, one of the most widely used techniques to build recommender systems, is based on the idea that users with similar preferences can help one another find useful items. Credit card user behavior analytics show that most customers hold three or less credit cards without duplicates. This behavior is one of the most influential factors to data sparsity. The 'cold-start' problem caused by data sparsity prevents recommender system from providing recommendation properly in the personalized credit card recommendation scenario. We propose a personalized credit card recommender system to address the cold-start problem, using multiple user profiles. The proposed system consists of a training process and an application process using five user profiles. In the training process, the five user profiles are transformed to five user networks based on the cosine similarity, and an integrated user network is derived by weighted sum of each user network. The application process selects k-nearest neighbors (users) from the integrated user network derived in the training process, and recommends three of the most frequently used credit card by the k-nearest neighbors. In order to demonstrate the performance of the proposed system, we conducted experiments with real credit card user data and calculated the F1 Values. The F1 value of the proposed system was compared with that of the existing recommendation techniques. The results show that the proposed system provides better recommendation than the existing techniques. This paper not only contributes to solving the cold start problem that may occur in the personalized credit card recommendation scenario, but also is expected for financial companies to improve customer satisfactions and increase corporate profits by providing recommendation properly.

• 한국의류학회지
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• 제23권2호
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• pp.250-261
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• 1999

This study is designed to classify consumer groups based on their perception toward department store credit cards and the behavior they exhibit during the purchase of clothing. This classification is based on the study of factors taken into consideration during shopping and disparities in credit cared usage., The specific goals of this study are the following : First it is to classify female consumers over age 20 into "shopping orientation" types and "clothing purchase behavior" types according to their perception towards department store credit care usage. Second it is to discover the degree of perceived utility of department store credit card in clothing purchases. Third finally it is to assist a department store credit card market researcher establish a marketing strategy to best address consumers; needs and wants in credit card purchases The study methodology utilized and the results found were that : 1. The division of consumers into positive and negative groups based on factor analysis with the positive group found to have favorable attitudes towards department store credit card usage. 2. Classification of female consumers into three " shopping orientations" : fashion purchasing economic value purchasing and convenience purchasing. The positive group were predominantly fashion convenience purchasers who valued low cost and convenience over "fashionability" 3. The three classes of "purchase behavior" used were impulse buying planned buying and unplanned buying. The positive group those who had favorable attitudes toward department store credit cards. made mostly impulse and unplanned purchases while the negative group made largely planned purchasee the negative group made largely planned purchase.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2003년도 추계학술발표논문집 (중)
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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.

• 패션비즈니스
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• 제19권6호
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• pp.74-81
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• 2015

The purpose of this study is to analyze the operation situation & academic research trends in Seoul and Gyeonggi area, based on theoretical consideration on academic credit bank system, focusing on academic credit bank system where a lifelong education institute affiliated with a university produces graduates with associate's degree. To find out about how academic credit bank institutes are operated in cosmetology field, the analysis of literature review was used, in reference to the literatures as well as administrative data from the Ministry of Education and institutes for lifelong education with respect to academic credit bank system. Further, dissertations and articles in journals were also reviewed for analysis, in order to see academic research trends with respect to academic credit bank system in cosmetology, and finally to provide the directions for a follow-up study in the future. It was found that about 120 junior colleges have cosmetology departments, while only about 20 4-year universities have them, where lifelong education systems such as lifelong education are essential for learners to have bachelor's degree to go to a graduate school in reality. Every year more people want to learn and acquire the degree through a lifelong education institute affiliated with a university. In this regard, it is thought that there should be first positive social awareness towards a degree recipient from such educations and more administrative promotion and active engagement of government, businesses and schools, in order to vitalize academic credit bank system. Meanwhile, there are only about 10 academic literatures including the dissertations on the operation of academic credit bank system with respect to cosmetology, which is not sufficient number in academic research, compared to the increasing number of people who want to acquire the degree. Most of the preceding studies have been limited to education services and learners' satisfaction level. Therefore, continuous follow-up study is required on how to improve social awareness as well as teachers and instructors' satisfaction level, as well as how to develop industry-customized curriculum, in order to ensure active academic credit bank system.