• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.451-458
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• 2021

The objective of this research is to analyze the effect of bad credit and liquidity on bank performance with the mediation of capital adequacy. Data were provided by banking institutions listed on the Indonesia Stock Exchange from the period of 2011-2019. The analysis technique was PLS-SEM supported by an application named WarpPLS 6.0. The results of the research show that the effect of bad credit and liquidity on bank performance is not significant. A high level of bad credit is associated with a low level of bank performance. Bank earnings decline along with low profitability. This relationship is not significant because banks can still cover some proportions of bad credit through capital availability. Capital adequacy as an intervening variable has mediated partially the effect of bad credit and liquidity on bank performance. Besides, capital adequacy has a strong effect on credit distribution. Agency theory says that the owner of the fund (the savers of saving account, current account, deposit account) is called principal while the bank as the trusted institution to manage the fund is called an agent. If customers fulfill their duty, then bad credit never happens.

• 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.

• 한국생활과학회지
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• 제18권1호
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• pp.123-136
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• 2009

Debt and credit problems in Korea have been escalated during the past decade. The number of people with debt and credit problems is in its historic high. In May 2008, about 2.48 million debtors are officially classified as bad debtors and 7.20 million people have low credit scores. People with low credit scores are in disadvantageous situation in the financial market thus their financial transactions and activities are limited. In 2004, Korean government introduced various credit rehabilitation programs. However, most of these problems are remedial in nature and preventive programs such as credit counseling and debtor education are lacking. The purpose of this review is to examine US credit counseling and debtor education programs to obtain insights for preventive credit program developments in Korea. The review focused on programs offered through National Foundation for Credit Counseling, Jump Start, and Cooperative Extension Services from two large land grant Universities. From the program review suggestions and recommendations for educational contents, program and educator developments, and program quality control were discussed.

• 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.

• 시큐리티연구
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• 제9호
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• pp.27-68
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• 2005

현재 개인신용정보이용 신용카드범죄는 그 특성상 고도의 전문화와 광역화로 인해 피해 발생후 원상회복이 어렵고 범죄인의 신속한 검거와 처벌 또한 쉽지 않다. 소수의 범죄인들에 의해서 발생하던 예전의 신용카드 위${\cdot}$변조 범죄들은 현재의 신용카드 관련 범죄와는 유형과 양상이 사뭇다르다. 현대신용사회에서 개인신용정보의 중요성은 굳이 강조할 필요가 없다. 따라서 개인신용정보이용 신용카드범죄가 국민 생활에 끼치는 악영향은 한 개인과 신용카드가맹점, 그리고 신용카드회사 모두에게 엄청난 경제적 손시로가 피해를 유발하는 것이다. 이 연구에서는 개인신용정보이용 신용카드범죄와 개인신용정보 부정이용범죄에 대한 효율적 대처방안들을 다음과 같이 제시하였다. 먼저, 신용카드정보 유출의 방지를 위한 대책으로서 카드사용자의 사용의식 전환, 신용카드매출전표의 인쇄내용 개선, PG(Payment Gateway) 업체 등을 통한 카드정보 암호화의 법제화를 들었다. 그리고, 비밀번호 입력에 대한 보완, 키보드 프로텍션(해킹방지) 시스템의 보급, 결제내역 즉시통보의무의 법제화를 들었다. 또한 다양한 본인 인증 방법으로서 전자인증서를 통한 인증, 생체인식 기술을 이용한 인증을 들었으며, 개인신용정보를 보호받지 못하는 나라에서의 신용카드사용을 제한하여 신용카드관련 피해 발생을 최소화하는 방안을 제시하였다. 그러므로 개인신용정보이용 신용카드범죄에 대한 효율적인 대처를 위해서는 카드사용자, 카드사, PG 업체, 정부 기관, 공인인증기관 등의 종합적인 협력과 노력이 요구되며, 경각심을 고취시키기 위한 처벌법규의 강화와 정책적 대안을 수립하여 현대사회에서의 건전한 신용문화를 형성하여야 할 것이다.

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

• 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.

• 산업공학
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• 제20권4호
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• pp.448-457
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• 2007

The small & micro business has the characteristics of both consumer credit risk and business credit risk. In predicting the bankruptcy for small-micro businesses, the problem is that in most cases, the financial data for evaluating business credit risks of small & micro businesses are not available. To alleviate such problem, we propose a bankruptcy prediction mechanism using the credit card sales information available, because most small businesses are member store of some credit card issuers, which is the main purpose of this study. In order to perform this study, we derive some variables and analyze the relationship between good and bad signs. We employ the new statistical learning technique, support vector machines (SVM) as a classifier. We use grid search technique to find out better parameter for SVM. The experimental result shows that credit card sales information could be a good substitute for the financial data for evaluating business credit risk in predicting the bankruptcy for small-micro businesses. In addition, we also find out that SVM performs best, when compared with other classifiers such as neural networks, CART, C5.0 multivariate discriminant analysis (MDA), and logistic regression.

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