• Journal of Families and Better Life
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• v.10 no.2
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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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• v.15 no.4
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• pp.615-631
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• 2008

Current credit evaluation models based on only financial data except non-financial data are used continuous data and produce credit scores for the ranking. In this work, some problems of the credit evaluation models based on transformed continuous financial data are discussed and we propose improved credit evaluation models based on categorized financial data. After analyzing and comparing goodness-of-fit tests of two models, the availability of the credit evaluation models for categorized financial data is explained.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.85-94
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• 2013

As the financial market becomes larger, the loss increases due to the failure of the credit risk managements from the poor management of the customer information or poor decision-making. Thus, the credit risk management also becomes more important and it is essential to develop a credit scoring model, which is a fundamental tool used to minimize the credit risk. Credit scoring models have been studied and developed only for binary target variables. In this paper, we consider other types of target variables such as ordinal multinomial data or longitudinal binary data and suggest credit scoring models. We then apply our developed models to real data and random data, and investigate their performance through Kolmogorov-Smirnov statistic.

• Journal of Information Technology Applications and Management
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• v.13 no.4
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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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• v.19 no.4
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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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• v.42 no.1
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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.

• Journal of the Korean Home Economics Association
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• v.38 no.2
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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.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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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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• v.19 no.3
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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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• v.8 no.8
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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.