• Journal of the Korean Data and Information Science Society
• /
• v.25 no.6
• /
• pp.1591-1598
• /
• 2014

The reviews of the balance sheet of commercial banks showed that loan item constitutes the largest portion of bank's assets. Although the sector has highest rate of profit, it possesses the greatest risk. Identifying factors that can contribute in lifting-up the loan repayment rate of customers of Hawassa district commercial bank is the major goal of this study. A sample of 183 customers who took loan from October, 2005 to April, 2012 was taken from the bank record. Kaplan-Meier estimation method and univariate Cox proportional hazard model were applied to identify factors affecting bank loan repayment rate. The result from Kaplan-Meier survival estimation revealed that the loan repayment rate is significantly related with loan type, and previous loan experience, educational level and mode of repayment. The log-rank test indicates that the survival probability of loan customers is not statistically different in repaying the loan among groups classified by sex. Moreover, the univariate Cox proportional hazard model result portrayed that educational level, having previous loan experience, mode of repayment, collateral type and purpose of loan are significantly related with loan repayment rate of customers commercial bank. Hence, banks should design loan strategies giving special emphasis on the significant factors while they are giving loans to their customers.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.6
• /
• pp.1417-1426
• /
• 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
• /
• v.52 no.1
• /
• pp.469-490
• /
• 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.

• Journal of Korean Library and Information Science Society
• /
• v.50 no.3
• /
• pp.217-238
• /
• 2019

Recently, interest in Public Lending Right has increased in Korea. This study aims to collect valuable data necessary for the discussion of the introduction of Public Lending Right by analyzing the loan statistics of 820 public libraries nationwide for five years from 2014 to 2018. In order to analyze the loan statistics of Korean public libraries, 1,178,300,000 big data provided by 'Data for Library' operated by the National Library of Korea were used. Through the analysis of loan statistics, 125 books were identified, which have been lent the most in the last five years. The study examined the 125 books to find out who are authors and Japanese authors and authors' nationality. The study also analyzed publishers and number of lending of cartoons.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.5
• /
• pp.109-115
• /
• 2021

This study aims to examine the effect of Operating Costs and Income, Loan to Deposit Ratio on the Return on Asset (ROA) of Public-Private Foreign Exchange Banks listed on the Indonesia Stock Exchange (IDX) during the 2015-2018 period. This study is a quantitative study using financial reports of Public-Private Foreign Exchange Banks listed on the IDX as a data source. This study's population is 25 Public-Private Foreign Exchange Banks listed on the IDX. This study uses purposive sampling to determine the sample to produce 21 banking companies. Data was analyzed using multiple linear regression methods and descriptive statistics. The F Test calculation results state that all the variables of free operating expenses, operating income, and the loan to deposit ratio simultaneously and significantly affect the return on assets (ROA) variable in Public-Private Foreign Exchange Banks listed on the IDX. This study's results indicate that simultaneously Operational Costs, Operational Income, and Loan to Deposit Ratio have a significant effect on ROA. Operational Costs and Operational Income have a significant negative impact on Return on Assets. The third hypothesis shows that the Loan to Deposit Ratio has a positive and insignificant effect on Return on Assets.

• Journal of the Korean Society for Library and Information Science
• /
• v.44 no.2
• /
• pp.333-356
• /
• 2010

Small libraries, with their insufficient quantities of materials and lack of diversity within the collection compared to larger general public libraries, may need to be assessed and develop collections based on empirical analysis. This study suggests a method for collection evaluation with other cases analyzing ILL (interlibrary loan) data, which is especially heavy in Small libraries in addition to the holdings and circulation data that are traditionally used in collection development. Collecting and analyzing materials proceeded from 14 Small libraries which operate ILL in a city and tried to figure out features of each library comparing collection statistics with usage statistics including circulation and interlibrary loans. It also identified subject areas heavily used in a Small library, based on the analysis of collection and usage statistics, for the purpose of formulating future policy.

• Journal of the Korean Society for Library and Information Science
• /
• v.50 no.3
• /
• pp.429-453
• /
• 2016

This study analyzes circulation patterns of books with checkout transaction count by 11 subject areas, 5 positions, and 5 divisions with a Use Factor developed by Bonn in an Academic Library. 20% of the loan books occupies more than half of circulation and these are regarded as core collection. It proposes a 'Loan books 20/50 rule' that 20% core collection accounts for 50% of its circulation. It analyzes the proportion of core collection from the aspect of each subject area with a use factor, monthly change trend and loan period. It also defines 'book usage' considering checkout frequency of each title and loan period. Circulation patterns of core collection are compared and analyzed in terms of both checkout frequency and book usage. Core collection occupies about more than half of both total checkout transactions and total book usages and they all show a Power Law distribution.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.3
• /
• pp.411-425
• /
• 2013

The importance and necessity of the credit loan are increasing over time. Also, it is a natural consequence that the increase of the risk for borrower increases the risk of non-performing loan. Thus, we need to predict accurately in order to prevent the loss of a credit loan company. Our final goal is to build reliable and accurate prediction model, so we proceed the following steps: At first, we can get an appropriate sample by using several resampling methods. Second, we can consider variety models and tools to fit our resampling data. Finally, in order to find the best model for our real data, various models were compared and assessed.

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

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.5
• /
• pp.1293-1305
• /
• 2016

In this paper, we develop debt collection predictive models for the person in arrears by utilizing the direct loan data of the Korea Student Aid Foundation. We suggest credit risk scorecards for overdue student direct loan using the developed 3 models. Model 1 is designed for 1 month overdue, Model 2 is designed for 2 months overdue, and Model 3 is designed for overdue over 2 months. Model 1 shows that the major influencing factors for the delinquency are overdue account, due data for payment, balance, household income. Model 2 shows that the major influencing factors for delinquency loan are days in arrears, balance, due date for payment, arrears. Model 3 shows that the major influencing factors for delinquency are the number of overdue in recent 3 months, due data for payment, overdue account, arrears. The debt collection predictive models and credit risk scorecards in this study will be the basis for segmented management service and the call & collection strategies for preventing delinquency.