• KDI Journal of Economic Policy
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• v.38 no.3
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• pp.79-106
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• 2016

This paper investigates the effectiveness of public credit guarantee programs and interest-support programs for SMEs (small and medium enterprises). First, assuming that there is an imperfect information structure in the SME loan market, we analyze how SME support financial programs affect the corporate decisions made by SMEs with regard to default or loan sizes. In addition, this paper theoretically computes the optimal levels of credit guarantee amounts and the interest-support spread under equilibrium with imperfect information in a competitive loan market. Second, the paper empirically analyzes the continuous policy-treatment effect with the GPS (generalized propensity score) method. In particular, we consider the ratio of guaranteed debt to the total debt as a continuous policy treatment. The empirical results show that marginal effects of a credit guarantee on SMEs' productivity, profitability, and growth potential decrease with the ratio of guaranteed debt to the total debt. In addition, the average effect of a credit guarantee is maximized when this ratio is at 50% to 60%.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.2
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• pp.17-29
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• 2012

We examine a mortgage bank's incentive distortion problem when the bank sells its existing loan through MBS(Mortgage-Backed Security), considering the mortgage market structure and varying investors' risk attitude. Main findings in our comparative statics are the followings. The bank's monitoring incentive on the loan sold is distorted downwards when the deposit interest rate is lower than the coupon rate of MBS. Credit enhancement associated with the loan sale may mitigate the incentive distortion problem. However, the downward distortion of monitoring incentive does not disappear unless the credit enhancement, a loan guarantee, is provided up to 100%. Finally as the investors' risk preference changes from risk-neutral to risk-averse type, the incentive distortion problem becomes more severe. At the end, we recommend the introduction of covered bond in order to mitigate the incentive distortion problem, which is inevitable to current pass-through MBS.

• The Journal of Information Technology
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• v.8 no.3
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• pp.11-23
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• 2005

This paper study the way of increasing the credit loan for the Venture Companies. They are very important in national economics. But they are short of financial and mortgage although they have many kinds of superb ideas and technologies. Korean government has tried to financially support them. But the support has had its own limit. Banks and other financial institutions have not been in a positive position to loan money out to venture companies because their businesses are highly risky. The credit evaluation system of medium and small business are need to improve in order to increase the Venture Companies. loan. So, it is necessary to find new measures to activate credit loans to them.

• The Journal of Fisheries Business Administration
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• v.37 no.3 s.72
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• pp.45-63
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• 2006

A number of public loans with lower interests and other tax benefits have been provided for farmers and fishermen. However, much of those loans have been accumulated as non-performing. The result is that a large part of fisheries debts are now on the verge of default, Those loans, that fail to pay interests, keep rapidly growing like a time bomb. Now something has to be done before it burst. Firstly, the government must clean up the debts caused by government's mismanagement in the past. The past debt must be repaid or written off by the government since its guarantee was committed several times in guidelines regarding public loans. As such a measure, the government can greatly enlarge its capital contribution to the Credit Guarantee Fund for Farmers and Fishermen and Loss Guarantee fund for Policy Loan. It would greatly help to compensate local branches of fisheries cooperatives for their loss incurred from carrying public loans. In the past, the government used to roll over old debts of fishermen with new debts whenever maturity came. It ends up growing the size of non - performing loans. For this reason, it is not delay of the debt payment, but its write - off that fishery society needs a lot. Secondly, the loan authorities must lower overall risk in providing public loans for fishermen in the future. The whole process must be thoroughly reviewed and changed to provide and manage government loans. To facilitate this, fisheries cooperative must stop being just a public agent, rather take a bigger responsibility in selecting, and checking loan beneficiaries, and securing debt repayment. Incentives must be arranged properly enough to induce fisheries cooperatives to treat public loans just like their own business. Finally, the so - called 'special account of policy loan in fisheries industry' must be set up to enhance the transparency and to check the performance of public loans programs.

• Journal of the Korean Society for information Management
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• v.32 no.3
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• pp.199-219
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• 2015

This study aims to illustrate the current status and changes of interlibrary loan service in Korea. Transaction data of KERIS Interlibrary Loan (ILL) Service from 2004 to 2014 were analyzed and key findings include the following: 1) In case 4 year college libraries, there is a close correlation between requests and responses in the interlibrary loan, but there is none for other type of libraries. 2) Social science and literature were the most responded subject area of interlibrary loan materials. In the aspect of language, responses for English materials occupied almost half of all responses. 3) 60 percent of libraries, the number of outgoing requests exceeded the number of their responses to incoming requests. 4) After 2012, KERIS ILL service showed a steady progress in all aspects.

• Journal of Information Management
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• v.40 no.4
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• pp.73-92
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• 2009

Generally, we used to provide ILL to users for overcoming the limited resource and satisfying user's desires for information. ILL makes library service's paradigm change from ownership to sharing. K-university Library has been providing Campus inter-library loan as ILL to users for long time and continually trying to improve the service's quality. This case study is to analysis the performance of campus inter-library loan in the past 9 years. As the related automation system was developed and upgraded, the service requests have been increasing annually. Campus inter-library loan requests were 12% of total book requests in 2001, and then were 75% of total book requests in 2008. The transaction time was reduced until 17% of 2004.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1417-1426
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• 2015

We develop the high risk group predictive model for loan default by utilizing the direct loan data from 2012 to 2014 of the Korea Student Aid Foundation. We perform the decision tree analysis using the data mining methodology and use SAS Enterprise Miner 13.2. As a result of this model, subject types were classified into 25 types. This study shows that the major influencing factors for the loan default are household income, national grant, age, overdue record, level of schooling, field of study, monthly repayment. The high risk group predictive model in this study will be the basis for segmented management service for preventing loan default.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.1
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• pp.469-490
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• 2018

The purpose of this study is to analyze the status of interlibrary loan (ILL) services using data from its transection. While analyzing the ILL data, agenda to improve the quality of services was identified, and suggestions were made to address them. Three data sets including National Inter-Libary Loan data, National Library Statistics System data, and local inter-library loan system analysis data were collected and analyzed. The results indicate that the size of transaction in ILL is getting bigger. The local ILL, particularly, was expanded and actively used by people. Additionally, the type of library participating in ILL networks was diverse and the number of library was increasing. Finally, this study discussed the tool to measure the contribution of each library in ILL. The collection uniqueness and collaboration index of library as well as the ILL statistics should be considered in the process of the tool development.

• 한국벤처창업학회:학술대회논문집
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• 2007.11a
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• pp.327-352
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• 2007

The loan rate of the government loan program offered by the Small Business Corporation(SBC) can be determined as a sum of three factors such as a reference interest rate, a policy aim spread, and a credit risk spread. However the loan rate has been lower than the loan rate in the banking sector. The profit has continually run in the red figures and hence the stability the fund managed by the SBC has been damaged. Even though a policy aim spread could be emphasized, the stability and profitability of the fund should be prioritized. This means that the loan rate of the SBC should be determined such that the loss might not be occurred. This requires the policy aim spread to change from relatively large negative to near zero.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.207-224
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• 2018

In this article, an empirical study was conducted by using public dataset from Lending Club Corporation, the largest online peer-to-peer (P2P) lending in the world. We explore significant predictor variables related to P2P lending default that housing situation, length of employment, average current balance, debt-to-income ratio, loan amount, loan purpose, interest rate, public records, number of finance trades, total credit/credit limit, number of delinquent accounts, number of mortgage accounts, and number of bank card accounts are significant factors to loan funded successful on Lending Club platform. We developed online P2P lending default prediction models using discriminant analysis, logistic regression, neural networks, and decision trees (i.e., CART and C5.0) in order to predict P2P loan default. To verify the feasibility and effectiveness of P2P lending default prediction models, borrower loan data and credit data used in this study. Empirical results indicated that neural networks outperforms other classifiers such as discriminant analysis, logistic regression, CART, and C5.0. Neural networks always outperforms other classifiers in P2P loan default prediction.