• Asia-Pacific Journal of Business
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• v.9 no.3
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• pp.37-50
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• 2018

This paper examines the relationship between diversification and financial performance of community credit unions in Korea from 2011 to 2017. To do so, I employ fixed-effects panel analyses using credit union level panel data collected from the National Credit Union Federation of Korea. This study finds evidence that business diversification is likely to lower the ratio of troubled loans, which means improving asset quality of credit unions. However, the relationship between diversification and asset quality is not linear but nonlinear, which means over-diversification would have negative effects on asset quality. Next, diversification tends to increase profitability. Specifically, although diversification results in a rise in expenditures, an increase in profits made by diversification outweighs the rise in expenditures, which contributes to profitability. Put together, diversification would be a good business strategy to improve both profitability and asset quality. Given a result that fast loan growth deteriorates asset quality, credit unions' managers might adopt the diversification strategy to enhance asset quality, and not to pursue their own objectives motivated by moral hazards.

• Smart Media Journal
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• v.5 no.4
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• pp.18-25
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• 2016

Virtualized hardware resources provides efficiency in use and easy of management. Based on the benefits, virtualization techniques are used to build large server clusters and cloud systems. The performance of a virtualized system is significantly affected by the virtual machine scheduler. However, the existing virtual machine scheduler have a problem in that the I/O response is reduced in accordance with the scheduling delay becomes longer. In this paper, we introduce the Loan/Redemption mechanism of a virtual machine scheduler in order to improve the responsiveness to I/O events. The proposed scheme gives additional credits for to virtual machines and classifies the task characteristics of each virtual machine by analyzing the credit consumption pattern. When an I/O event arrives, the scheduling priority of a virtual machine is temporally increased based on the analysis. The evaluation based on the implementation shows that the proposed scheme improves the I/O response 60% and bandwidth of virtual machines 62% compared to those of the existing virtual machine scheduler.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.771-789
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• 2021

This study aims to examine the experiences and behaviors of youth's stock investment with credit loans. Using a qualitative case study method (Creswell, 2015), we interviewed 7 young investors. As a result of the analysis, based on the research method within the case, it was possible to find out the process and reasons for how the participants had credit loan experience and invested in stocks. In addition, 19 common categories could be derived from this. Further analyses classified the process as "start of stock investment", "immersion into the investment", "stock investment through credit loans", and "consequence of stock investment with credit loans". The study concludes with several policy implications and suggestions for future studies.

• Journal of Information Processing Systems
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• v.17 no.1
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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.

• International Commerce and Information Review
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• v.11 no.4
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• pp.419-444
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• 2009

In this paper, we use case studies to analyze the supporting programs of policy funds for Korean and Japanese small and medium-sized enterprises(SMEs). It is found that supporting firms are suitable to the excluded companies from financial institutions and excellent corporate credit rating. It is also shown that subordinated loan program as well as loan limit can be enlarged policy funds with priming water of private funds. Moreover, it shows that credit guarantee funding has a positively significant influence on long-term funding facility. Therefore, this findings can improve the complementary relationship between policy funds and financial institutions.

• Information Systems Review
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• v.4 no.2
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• pp.361-376
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• 2002

This paper proposes a framework for integrated credit risk management system in domestic bank financial institutions. Credit evaluation system, loan processing system, credit monitoring system, and credit risk management system are integrated for efficient and effective risk-adjusted performance management in this framework. Risk exposures, not only for each credit, but also for bank's whole credit portfolio need to be measured and analyzed through the concept of Value-at-Risk (VaR). The effects of changes in credit ratings of individual loaners on bank's credit risk exposure are also considered. We tried to model this integrated credit risk management system by using object-oriented modeling language, UML.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.179-189
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• 2020

The paper empirically examines the bank-specific determinants of loan growth in the Vietnamese banking system for the period from 2007 to 2019. We approach the CAMELS framework and employ the dynamic panel regression to determine the effects of each CAMELS factor on bank lending. To ensure the robustness of results, we also use alternative definitions of the variables and different specifications with and without full sets of CAMELS components. With these settings, we display multiple important results. (i) We find that a large capital buffer tends to boost bank lending expansion faster. (ii) High asset quality might positively contribute to high loan growth; in other words, banks subject to high credit risk are discouraged from making loans. (iii) Less efficiently managed banks are more likely to adopt an aggressive lending strategy, highlighting the moral hazard incentives of Vietnamese banks. (iv) More profitable banks with excellent competitive advantages could expand their lending activities to a larger extent. (v) Liquidity is positively related to the loan growth of banks. (vi) Perceived interest rate risk tends to suppress loan growth since interest-rate-sensitive banks might be concerned about the adverse effects of unpredictable adverse changes in interest rates in the future.

• KDI Journal of Economic Policy
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• v.43 no.3
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• pp.1-32
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• 2021

With CB data in South Korea, this study examines whether the credit risk of borrowers changes when the regulation on bank mortgage supply is relaxed. We analyze the effect of deregulation on LTV and DTI limits in the Seoul-metropolitan area in August 2014 with a difference-in-difference approach. We find that the probability of delinquency is lower in the Seoul metropolitan area after the deregulation than in other urban areas. The effect is noticeable among low-income and low-credit borrowers. We also find that borrowers change their debt structure to reduce the interest costs utilizing their improved access to bank mortgages. The findings suggest the necessity to consider the burden of the high interest costs of unsecured loans for debtors with low incomes and low credit ratings in designing housing finance regulations.

• 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.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.195-201
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• 2021

This study aims to assess the impact of ownership structure of commercial banks on bank credit risk in Vietnam. The authors used the unbalanced table data of 28 commercial banks in the period from 2004 to 2020 with 439 observations. The ratio of loan loss provisioning to loans (CR) is selected as a dependent variable representing credit risk at commercial banks. The regression methods used include: least squares method (OLS), fixed-effect model (FEM), random-effect model (REM) and general least squares method (GLS). The results reveal that, with interaction variable between the ratio of equity to total assets and foreign ownership, the national GDP annual growth rate is negatively associated with credit risk. With the ratio of equity to total assets, the interaction variable between equity and state ownership, and bank size have a significant positive impact on credit risk. In addition, inflation has negligible impact on the credit risk of commercial banks in Vietnam over the research period. The findings of this study suggest that, if foreign-owned banks increase equity capital, there will be a stronger impact on reducing credit risk than other banks. On the other hand, when state-owned commercial banks in Vietnam increase equity, they will have higher credit risk.