• 대한안전경영과학회지
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• 제11권4호
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• pp.261-267
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• 2009

The purposes of this article is to analyse how market competition of credit card company affect price(interest rate) and survival length of card users. This paper uses individual account data from a large Korean credit card company during the periods from 2002 to 2006. The findings of our study are as follows. First, market competition of credit card company have a negative effect with interest rate of credit card. Second, market competition of credit card company have a affirmative effect with survival length. Finally, The effect of Increasing delinquency rate due to price increase is smaller than decreasing delinquency rate due to extending survival length.

• 가정과삶의질연구
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• 제27권6호
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• pp.95-109
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• 2009

This study focused on the development of a scale to measure the ability of consumers to use credit cards. The purposes of this study were to develop a tool which would be able to measure consumer knowledge, consumer skills and consumer attitudes. Data were collected from 313 credit card using consumers and were analyzed by employing a goodness of fit test, principal component analysis & confirmatory factor analysis(Amos 5.0), multiple regression. The results from this study were as follows: 1) Six factors of consumer knowledge(16-items) were identified: damage salvation; credit delinquency; personal credit information; credit provision period; credit & credit card issuance; credit delinquent striking out a record & credit rating. The total variance was 55.86%. 2) Three factors of consumer skills(17-items) were identified: credit delinquency & over-consumption; credit card management; and loss & damage salvation. The total variance was 62.90%. 3) Three factors of consumer attitudes(16-items) were identified: credit delinquency & credit; credit card issuance & use; and credit card management. The total variance was 58.75%.

• 한국인공지능학회지
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• 제9권1호
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• pp.21-27
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• 2021

In this Paper, Since the 1990s, Korea's credit card industry has steadily developed. As a result, various problems have arisen, such as careless customer information management and loans to low-credit customers. This, in turn, had a high delinquency rate across the card industry and a negative impact on the economy. Therefore, in this paper, based on Azure, we analyze and predict the delinquency and delinquency periods of credit loans according to gender, own car, property, number of children, education level, marital status, and employment status through linear regression analysis and enhanced decision tree algorithm. These predictions can consequently reduce the likelihood of reckless credit lending and issuance of credit cards, reducing the number of bad creditors and reducing the risk of banks. In addition, after classifying and dividing the customer base based on the predicted result, it can be used as a basis for reducing the risk of credit loans by developing a credit product suitable for each customer. The predicted result through Azure showed that when predicting with Linear Regression and Boosted Decision Tree algorithm, the Boosted Decision Tree algorithm made more accurate prediction. In addition, we intend to increase the accuracy of the analysis by assigning a number to each data in the future and predicting again.

• 지능정보연구
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• 제9권2호
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• pp.135-154
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• 2003

Credit scoring system (CSS) starts from an analysis of delinquency trend of each individual or industry. This paper conducts a research on credit card delinquency of bank customers as a preliminary step for building effective credit scoring system to prevent excess loan or bad credit status. To serve this purpose, we use association rules as a rule generating data mining technique. Specifically, we generate sets of rules of customers who are in bad credit status because of delinquency by association rule mining. We expect that the sets of rules generated by association rule mining could act as an estimator of good or bad credit status classifier and basic component of early warning system.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.149-154
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• 2003

Credit scoring system (CSS) starts from an analysis of delinquency trend of each individual or industry. This paper conducts a research on credit card delinquency of bank customers as a preliminary step for building effective credit scoring system to prevent excess loan or bad credit status. To serve this purpose, we use association rules that ore generating method. Specifically, we generate sets of rules of customers who are in bad credit status because of delinquency by using association rules. We expect that the sets of rules generated by association rules could act as an estimator of good or bad credit status classifier.

• 한국IT서비스학회지
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• 제11권4호
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• pp.233-250
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• 2012

In this paper, credit card delinquency means the possibility of occurring bad debt within the certain near future from the normal accounts that have no debt and the problem is to predict, on the monthly basis, the occurrence of delinquency 3 months in advance. This prediction is typical binary classification problem but suffers from the issue of data imbalance that means the instances of target class is very few. For the effective prediction of bad debt occurrence, Support Vector Machine (SVM) with kernel trick is adopted using credit card usage and payment patterns as its inputs. SVM is widely accepted in the data mining society because of its prediction accuracy and no fear of overfitting. However, it is known that SVM has the limitation in its ability to processing the large-scale data. To resolve the difficulties in applying SVM to bad debt occurrence prediction, two stage clustering is suggested as an effective data reduction method and ensembles of SVM models are also adopted to mitigate the difficulty due to data imbalance intrinsic to the target problem of this paper. In the experiments with the real world data from one of the major domestic credit card companies, the suggested approach reveals the superior prediction accuracy to the traditional data mining approaches that use neural networks, decision trees or logistics regressions. SVM ensemble model learned from T2 training set shows the best prediction results among the alternatives considered and it is noteworthy that the performance of neural networks with T2 is better than that of SVM with T1. These results prove that the suggested approach is very effective for both SVM training and the classification problem of data imbalance.

• 재무관리연구
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• 제22권2호
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• pp.213-226
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• 2005

역선택은 금융 산업에서 많이 연구되는 주제이다. 은행 대출 이자율에 대해서는 이자율과 역 선택의 관계에 대해서 공감을 형성하고 있지만, 신용카드 이자율에 있어서 이자율의 변화와 역선택이 발생하는 방향에 대해서 아직 논쟁 중이다. 그러므로 이 논문에서는 신용카드 이자율에 있어서 역선택이 어떻게 발생하는 지를 밝히고, 균형 이자율이 결정되는 과정을 보이려 한다. 신용카드 이용자들과 공급자 사이의 상호 작용이 이자율을 결정하게 되는 중요한 요소이다. 신용카드 이용자들은 거래수단으로서 혹은 자금 조달 수단으로 신용카드와 현금을 이용할 수 있는데, 단위 화폐가치당 소용 비용을 최소화하려는 선택을 하는 합리적인 소비자로 가정하고 있다. 반면에 신용카드 공급자들은 그들의 이익을 최대화하려는 노력을 기울이는데, 현금보다 신용카드가 널리 쓰일수록 유리하다. 이런 가정하에서 신용카드 이자율과 역선택의 관계를 이론적 모형을 통해서 분석하였고, 은행 대출 이자율에서 발생하는 역선택과 같은 역선택이 발생함을 보였다. 즉 증가하는 신용카드 이자율은 역선택을 유발하는 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• 디지털융복합연구
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• 제13권3호
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• pp.1-10
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• 2015

기술 발달에 따라 통신과금서비스 시장이 성장하고 있다. 연구의 목적은 통신과금서비스의 한계요인을 분석한 후 이에 대한 개선사항을 제시하여 통신과금서비스 발달에 기여하고자 한다. 본 연구에서는 통신과금서비스에 대해 델파이 기법으로 한계요인을 조사하고 이에 대한 개선 방안을 제시하였다. 통신과금서비스의 개선사항은 다음과 같다. 수익을 창출하는 이통사 또는 통신과금서비스 기업은 사용한도 금액을 신용기반으로 제공하고, 결제 시 알리는 문자내용에 사용 누적액을 표시한다. 통신과금서비스 제공을 통해 소비자 피해에 대한 최소한의 책임을 진다. 미인지 결제에 대해서는 이통사와 통신과금서비스 기업이 책임지고 관리하며, 소비자가 아닌 서비스 제공자가 과실을 입증한다. 통신과금서비스 연체비율은 신용카드와 같은 비율로 적용하며, 소액결제와 정보이용료를 통합하여야 한다. 본 연구는 통신과금서비스 개선으로 시장 성장에 기여하며 새롭게 등장하는 기술과 서비스를 통한 개선요인에 대한 지속적인 연구가 필요하다.

• 한국전자거래학회지
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• 제27권2호
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• pp.105-135
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• 2022

코로나19로 인한 이커머스 거래의 급증으로 해외에서는 Buy Now Pay Later(선구매 후결제, 이하 BNPL)가 밀레니얼 세대 사이에서 차세대 결제 수단으로 부상했다. 국내에서는 네이버 쇼핑과 쿠팡이 후불결제를 제공해 고객 락인(lock-in) 효과, 카드사 수수료 절감, 소매금융사업 진출 등을 도모한다. 그러나 소비자 관점에서 후불결제의 수용에 영향을 미치는 요인에 관한 국내 연구는 부족하다. 이에 20~30대 대상으로 실증연구를 진행한 결과, 후불결제 수용에 영향을 미치는 요인은 네이버쇼핑은 호환성>혁신저항>충동구매 성향, 쿠팡은 호환성> 상대적 이점>혁신저항>추가가치 순으로 나타났다. 한편 네이버쇼핑은 충동구매 성향이 수용의도에 정(+)의 영향을 미치므로 연체율 관리가 관건이며, 쿠팡은 충동구매 성향이 지각된 위험에 정(+)의 영향을 미치므로 연체료, 신용등급 하락 등 위험 요소를 충분히 전달하고 후불결제로 유도한다는 느낌을 소비자가 받지 않도록 해야 한다. 후불결제 수용을 높이려면 타깃 고객을 세분화하고 버티컬 커머스와 제휴하는 방안을 추천한다.