• The Journal of Industrial Distribution & Business
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• v.8 no.4
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• pp.15-21
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• 2017

Purpose - In this work, we examined the causal relationship between credit loans from households (CLH), loan collateralized with housing (LCH) and an interest of certificate of deposit (ICD) among others in South Korea. Furthermore, the optimal forecasts on the underlying model will be obtained and have the potential for applications in the economic field. Research design, data, and methodology - A total of 31 realizations sampled from the 4th quarter in 2008 to the 4th quarter in 2016 was chosen for this research. To achieve the purpose of this study, a regression model with correlated errors was exploited. Furthermore, goodness-of-fit measures was used as tools of optimal model-construction. Results - We found that by applying the regression model with errors component ARMA(1,5) to CLH, the steep and lasting rise can be expected over the next year, with moderate increase of LCH and ICD. Conclusions - Based on 2017-2018 forecasts for CLH, the precipitous and lasting increase can be expected over the next two years, with gradual rise of two major explanatory variables. By affording the assumption that the feedback among variables can exist, we can, in the future, consider more generalized models such as vector autoregressive model and structural equation model, to name a few.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.213-226
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• 2005

Adverse selection is a heavily scrutinized subject within the financial intermediary industry. Consensus is reached regarding its effect on the loan interest rate. Despite the similar features of financial service offered by the credit card, we still have controversy regarding credit card interest rate on how is adverse selection incurred with the change of interest rate. Thus, this paper explores how does the adverse selection, if ever, take place and affect the credit card interest rate. Information asymmetry regarding the credit card users' type represented by the default probability is assumed. The users are assumed to be rational in that they want to minimize the per unit dollar expense associated with the commercial transaction and financing between the two typical payment methods, cash and credit card. Suppliers, i.e. credit card companies, would like to maximize their profit and would be better off with more pervasive use of credit cards over the cash. Then we could show that the increasing credit card interest rate is subject to the adverse selection, sharing the same tenet with that of the bank loan interest rate proposed by Stiglitz and Weiss. Hence the current theory predicts that credit card market also suffers from adverse selection with increasing interest rate.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.2
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• pp.119-122
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• 2019

The study aims to investigate factors influencing business households' decision for borrowing credit: the case of commercial banks in Tra Vinh Province, VietNam. The study was conducted by collecting data from 300 business households traded at four commercial banks in Tra Vinh province (Viet Nam bank for agriculture and rural development, Tra Vinh Branch; Viet Nam jointstock commercial bank industry and trade, Tra Vinh Branch; Asia joinstock commercial bank, Tra Vinh Branch; Viet Nam jointstock commercial bank for foreign trade, Tra Vinh Branch). By the use of the Binary Logistic regression method, the research found out that the factors influencing to borrow c redit of household business's decision including: banks brand names, loan interest rates, service attitude, and loan procedures. Of those, the banks brand names and lending interest rates have the strongest impacts on borrow credit decision of business households at commerc ials banks in Tra Vinh province. Since then, the study has proposed solutions to improve access to credit of business households in commercial banks in Tra Vinh province in the coming time, such as: developing a bank brand; the development of flexible lending interest rate policies; improve service style of bank staff; at the same time, simplifying lending procedures.

• The Journal of Fisheries Business Administration
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• v.16 no.1
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• pp.31-54
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• 1985

The mutual credit of Fisheries Cooperatives is reciprocal financing bring overs and shorts to settlement themselves by filling each other's needs among feeble fishermen economically. The spread of mutual credit through Fisheries Cooperatives reduces private loan dependence and private loan interest rate at fishery village, and that fills up policy financing being restricted by working scale. And seeing movement side of Fisheries Cooperatives, it has done an under board to settle self-supporting foundation of primary fisheries cooperatives early. The mutual credit deposit shows about 53 times increase past an interval of a ten years. This increase rate is an epoch-making record being unparalleled in other banking facilities except Fisheries Cooperatives. Then being unparalleled increase rate, time and savings deposits increase has been contributed a great deal than demand deposits. Thinking important function factors as mutual credit growth, we can classify interior and exterior factors. The exterior factor is income of fishery household in some measure, interior factors are the high deposits interest rate and the enlargement of facilities organization. As these, they have been in a better factors, also have been a restriction factors. The restriction factors are conflict cancellation between mutual credit and them bring into existence a village vault, mutual savings and finance companies, private finance. For the sake of continuance growth rate in mutual credit as past, we should eliminate restricted factors in growth. On the other hand the better factors in growth should be act upon affirmation side continually. Consequently under circumstances not to an amicable settlement bring the fisheries fund demand as policy financing, we should do continuous and sound development of fisheries financing by means of putting in good order of fisheries cooperatives mutual credit. Surveying a problem from these viewpoints, when we study more deep and a full into a subject about growth project of mutual credit, we think to expect continuous growth in mutual credit of Fisheries Cooperatives.

• Information Systems Review
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• v.23 no.1
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• pp.69-91
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• 2021

Credit Bureaus in Korea commonly use financial transaction information of the past and present time for calculating an individual's credit scores. Compared to other rating factors, the repayment history information accounts for a larger weights on credit scores. Accordingly, despite full redemption of overdue payments, late payment history is reflected negatively for the assessment of credit scores for certain period of the time. An individual with debt delinquency can be classified into two groups; (1) the individuals who have faithfully paid off theirs overdue debts(Normal Repayment), and (2) those who have not and as differences of creditworthiness between these two groups do exist, it needs to grant relatively higher credit scores to the former individuals with normal repayment. This study is designed to analyze the factors of normal repayment of Korean financial debt delinquents based on credit information of personal loan, overdue payments, redemption from Korea Credit Information Services. As a result of the analysis, the number of overdue and the type of personal loan and delinquency were identified as significant variables affecting normal repayment and among applied methodologies, neural network models suggested the highest classification accuracy. The findings of this study are expected to improve the performance of individual credit scoring model by identifying the factors affecting normal repayment of a financial debt delinquent.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.119-131
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• 2024

In recent years, there has been a significant growth in loan comparson services offered by fintech platforms in South Korea. However, it has been reported that loan comparison platform users tend to have a higher risk of default compared to non-users. This paper investigates the difference in platform-specific credit risk factors using survival analysis models - Kaplan-Meier curves and Accelerated Failure Time (AFT) model. Our findings show that, relative to non-users, users of loan comparison platforms are characterized by elevated default rates, a greater propensity for home ownership, lower credit scores, and shorter loan durations. Furthermore, our AFT models elucidate the variance in default risk among the various loan comparison service platforms, highlighting the imperative for customized strategies that address the unique risk profiles of customers on each platform.

• 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.

• Journal of the Korea Safety Management & Science
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• v.10 no.2
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• pp.71-83
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• 2008

Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, in this study we present a medium sized corporate credit rating system by using Artificial Neural Network(ANN) and Analytical Hierarchy Process(AHP). Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the ANN and AHP model using both financial information and non-financial information. Finally, the credit ratings of each firm are assigned by the proposed method.

• International Area Studies Review
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• v.13 no.3
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• pp.29-49
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• 2009

We examine the effects of information transparency, lender identity, and credit rating on the commitment fees of syndicated loans originated in Japan, employing a sample of 331 facilities. A syndicated loan is a financing instrument offered to a single borrower by multiple lenders, and Japanese syndicated loan volume increased 36% to a record-high of $283 billion in 2008 compared to 2007. We find that the more informational opaque the borrower, the higher the commitment fees the lender charges to the Japanese borrowers. There is evidence that a syndicate involving a Japanese lead agent is able to extract rents through higher commitment fees. We document that there is a significant relation between the credit rating of the borrower and the commitment fee cost of syndicated loans originated in Japan. Most importantly, our results provide evidence that banks in Japan extract higher returns on syndicated loans through the commitment fees in addition to higher loan spreads. Using a micro-level of Japanese borrowers, we contribute to existing literature by providing our empirical evidence after controlling for borrowing spread.

• Journal of Distribution Science
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• v.22 no.8
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• pp.17-27
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• 2024

Economic growth is influenced by various factors, including support from the banking world in channeling funds ownedthrough bank credit which will be a stimulus from economic activities as a source of economic growth. Purpose: Thisstudy aims to analyze the determinants of bank lending in supporting regional economic growth in South Sulawesi Province. Research Design, Data, and Methodology: This study uses secondary data taken from banking data and analyzed using path analysis Data analysis is carried out using the help of SPSS statistical analysis tools. Results: Non-Performance Loan, Three Partied Fund, Inflation, Exchange Rate directly affect economic growth. For the analysis of the indirect effect of Non-performance loans and Three Partied Funds have an indirect effect on economic growth through lending while the Loan to deposit Ratio, Inflation and exchange rate do not indirectly affect economic growththrough lending. Credit disbursement has a positive and significant effect on economic growth Conclusion: Economicgrowth of a region is influenced by many factors and these factors are influences from the banking world, the results ofthis study show that economic growth is strongly influenced by bank support through lending to support the economy by considering other factors such as interest rates and currency exchange rates