• 가정과삶의질연구
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• 제10권2호
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• pp.245-261
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• 1992

The Purpose of this study is to find out ;Which is the recognition of housewives abut the credit cards as a financial tool\ulcorner by what kind of motive is the use made\ulcorner How important the using credit card in the financed to household\ulcorner For this purpose, a survey was conducted by interview using questionnaire. The data were analyzed by frequency , percentage, arithmetic mean, standard deviation, x2 -test, ANOVA, correlation, multiple regression using SPSS/PC+ linear structural relationship using LISREL VI program. the conclusion deduced through result of data analysis and the discussion are as follows; First, in the respondent housekeeping, monthly average repayment of credit card is about 1/3 of the living expenses. Second, the knowledge of respondents about credit card was low generally Third, respondents use credit card by the motive of circulating money rather tan the motive of convenience. Fourth , generally respondents are not overdue the charge of credit card, but the smaller the cost of living is or the larger the motive of using credit card, the more overdue the charge of credit card. Fifth, as a result of linear structural relationship among the information credit card, motive of use and management behavior, the motive of using credit card effect on the management of credit card more directly than the knowledge of credit card. Sixth, as credit card is spread widely on the future, the information of credit card will be important variable on the personal credit and the management of credit card will be more important in the household financial management.

• Communications for Statistical Applications and Methods
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• 제15권4호
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• pp.615-631
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• 2008

재무자료에 대한 신용평가모형은 각각의 재무변수를 평활한 예측부도율로 변환하여 사용한다. 본 연구에서는 연속형 재무자료를 변환하여 설정된 신용평가모형의 문제점을 살펴보고, 연속형 재무변수를 다양한 형태로 범주화한 신용평가모형들을 제안한다. 범주형 재무자료를 사용해서 개발한 여러 종류의 신용평가모형들의 성과를 다양한 적합성 검증 방법으로 비교하고, 범주형 재무자료를 이용한 신용평가모형의 유용성을 토론한다.

• Journal of the Korean Data and Information Science Society
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• 제24권1호
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• pp.85-94
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• 2013

금융시장의 규모가 점점 더 커짐에 따라 고객정보 관리 미숙 또는 부실한 의사결정, 즉 신용 리스크 관리 실패로 인한 손실이 막대하게 증가하고 있다. 따라서 신용 리스크 관리가 점차 더 중요해지고, 이런 신용 리스크를 최소화하는 기본적인 도구인 신용 평점 모형이 절실히 요구된다. 신용평점 모형은 주로 이항형 목표변수만 이용하여 개발 연구되었다. 본 논문에서는 순서형 다항 자료 또는 경시적 이항 자료 같은 다른 형태의 목표 변수를 고려한 신용평점 모형구축 방법을 제시한다. 그 개발된 모형을 실제 자료와 랜덤화한 자료에 적용하여 Kolmogorov-Smirnov 통계량으로 비교 분석한다.

• 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 and communication convergence engineering
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• 제19권4호
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• pp.228-233
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• 2021

In this study, we analyze the credit information (loan, delinquency information, etc.) of individual business owners to generate voluminous training data to establish a bankruptcy prediction model through a partial synthetic training technique. Furthermore, we evaluate the prediction performance of the newly generated data compared to the actual data. When using conditional tabular generative adversarial networks (CTGAN)-based training data generated by the experimental results (a logistic regression task), the recall is improved by 1.75 times compared to that obtained using the actual data. The probability that both the actual and generated data are sampled over an identical distribution is verified to be much higher than 80%. Providing artificial intelligence training data through data synthesis in the fields of credit rating and default risk prediction of individual businesses, which have not been relatively active in research, promotes further in-depth research efforts focused on utilizing such methods.

• KDI Journal of Economic Policy
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• 제42권1호
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• pp.31-56
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• 2020

Does financial development contribute to economic growth? The literature finds that an expansion in financial resources is useful for economic growth if the degree of financial development is under a certain threshold; otherwise, the expansion is detrimental to growth. Almost every published study, however, considers country-panel data. Accordingly, the results are not directly applicable to the Korean economy. By examining Korean time-series data, this paper finds that there is an inverse U-shaped relationship between the per capita real GDP growth rate and private credit (as a percentage of nominal GDP)-a well-known measure of quantitative financial development, where the threshold is 171.5%. This paper also finds that private credit is positively associated with economic growth if the share of household credit out of private credit is less than 46.9%; otherwise, private credit is negatively associated with economic growth. As of 2016, the ratio of private credit to GDP and the ratio of household credit to private credit are both higher than the corresponding thresholds, which implies that policymakers should place more emphasis on qualitative financial development than on a quantitative expansion of financial resources.

• 대한가정학회지
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• 제38권2호
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• pp.79-89
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• 2000

The purpose of this study was to investigate the consumer credit use in Korea at the macro and micro level. For this purpose, various published data from the Korean Bank and other institutions were analyzed. The data showed that the total amount of consumer credit use has been rapidly increased although it decreased a little bit after the 1977 economic crisis, for a while. The influencers of consumer credit use were also investigated. Finally, implications for consumer credit use in the future were suggested.

• 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%.

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

• The Journal of Asian Finance, Economics and Business
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• 제8권8호
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• pp.325-334
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• 2021

The study investigates the effectiveness of the macroprudential policy on credit growth in Vietnam. The authors use the logic of the transmission mechanism of macroprudential policy on credit growth. Research variables include economic growth, inflation, interest rate, and quarterly bank-level data from 28 commercial banks in Vietnam during 2011-2018. The results reveal that: (i) GDP growth had a positive impact on credit growth of small banks but had no impact on large banks, (ii) Domestic Systemically Important Banks (D-SIBs) and small banks respond differently to macroprudential measures of imposing different credit growth targets for different bank groups, (iii) Restrictions on foreign currency loans are found to be effective in curbing credit growth for the full sample and small banks, (iv) Inflation and economic cycle have significantly impacted credit growth at bank-level in Vietnam and (v) Interestingly, a significant positive relationship between interest rates and credit growth is found for the full sample and D-SIBs in Vietnam. The findings suggest that a stable macroeconomic environment should be good conditions for financial stability, and monetary authority should pay more attention to small banks' behaviors than D-SIBs behavior, toward such "administration" tools since small banks tend to prefer "breaking the rules" to make profits.