• IE interfaces
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• v.20 no.4
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• pp.448-457
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• 2007

The small & micro business has the characteristics of both consumer credit risk and business credit risk. In predicting the bankruptcy for small-micro businesses, the problem is that in most cases, the financial data for evaluating business credit risks of small & micro businesses are not available. To alleviate such problem, we propose a bankruptcy prediction mechanism using the credit card sales information available, because most small businesses are member store of some credit card issuers, which is the main purpose of this study. In order to perform this study, we derive some variables and analyze the relationship between good and bad signs. We employ the new statistical learning technique, support vector machines (SVM) as a classifier. We use grid search technique to find out better parameter for SVM. The experimental result shows that credit card sales information could be a good substitute for the financial data for evaluating business credit risk in predicting the bankruptcy for small-micro businesses. In addition, we also find out that SVM performs best, when compared with other classifiers such as neural networks, CART, C5.0 multivariate discriminant analysis (MDA), and logistic regression.

• The Journal of Information Systems
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• v.16 no.3
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• pp.109-132
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• 2007

In the paper, we builds an advanced new credit evaluation system for Micro-small sized individual firms through appropriate evaluation factors derived by logistic regression analysis for credit evaluation model using in Korean Federation of Credit Guarantee Foundations, and the weights of factors computed by analytic hierarchy process(AHP). Industry characteristics are more applied to previous credit model with the additional the financial fact-information and non-financial judgement-information. Our results show that the financial factors have become more important than three years ago. Moreover, in the non-financial factors, the fact-information factors consider more important then the judgement-information factors. A new credit evaluation system is developed based on this credit evaluation model.

• International Commerce and Information Review
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• v.15 no.2
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• pp.429-455
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• 2013

This paper investigates the extent and scope of electronization of Letter of Credit and examines the obstacles in the course of electronization of Letter of Credit. For this purpose, this paper adopts two track methodologies. First, we study the electronization of Letter of Credit in the macro perspective. It means that we analyze the scope and the extent of electronization of electronic Letter of Credit in the national economy as a whole. Second, we utilize the case study approach to examine the electronization of Letter of Credit in micro perspective. In other words we examine individual commercial banks in Korea. For this purpose, we analyze the data from two commercial banks out of four largest domestic banks in Korea. For example, we select two commercial banks in Korea and ask current status of electronization of Letter of Credit, i.e, the utilization of EDI L/C, e-L/C, and e-B/L. A major finding of our study is that the extent of electronization is not negligible in micro perspective as well as in macro perspective. In micro perspective, two commercial banks utilize EDI letter of credit in a critical sense. On the other hand, they seldom uses e-L/C and e-B/L in international transaction. This study shows that major bottlenecks for the electronization of Letter of Credit are pessimistic perception of SME entrepreneurs toward the electronization of Letter of Credit and the high costs of system building. We conclude that unlike superficial perception, there is a considerable progress toward the electronization of Letter of Credit in micro perspective.

• 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.

• KDI Journal of Economic Policy
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• v.44 no.3
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• pp.1-26
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• 2022

This paper analyzes the effects of the cut in the legal maximum interest rate (from 27.4% to 24%) that occurred in February of 2018 on loan interest rates, the default rates, and the loan approval rate of borrowers in the non-banking sector. We use the difference-in-difference identification strategy to estimate the effect of the cut in the legal maximum interest rate using micro-level data from a major credit-rating company. The legal maximum rate cut significantly lowers the loan interest rate and default rate of low-credit borrowers (i.e., high-credit-risk borrowers) in the non-banking sector. However, this effect is limited to borrowers who have not been excluded from the market despite the legal maximum interest rate cut. The loan approval rate of low-credit borrowers decreased significantly after the legal maximum interest rate cut. Meanwhile, the loan approval rate of high-credit and medium-credit (i.e., low credit risk and medium credit risk) borrowers increased. This implies that financial institutions in the non-banking sector should reduce the loan supply to low-credit borrowers who are no longer profitable while increasing the loan supply to high- and medium-credit borrowers.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.7
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• pp.81-91
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• 2022

The aim of this paper was to analyze the determinants of the extent of individual credit rationing at Can Tho Military Commercial Joint Stock Bank (MB). The data was collected from 150 customers according to the systematic random sampling method listed in the bank. This study employed quantitative analysis methods, and Tobit regression model, to test the proposed hypotheses. The results showed that the average loan amount of an individual customer was 1,181.3 million VND, the average credit limit was 48.6%, and the average interest rate was 10.9% per year. Most of the individual customers borrowed money to buy properties. In addition, the analysis results also indicated that individual borrowers still faced some difficulties in accessing bank credit, such as cumbersome procedures, long waiting times, insufficient collateral assets, and loan documents. The results of the Tobit model pointed out that there were five factors affecting the degree of credit rationing to individual customers at the bank, including (1) Collateral, (2) Income, (3) Credit history, (4) Loan purpose, (5) Relationship between borrower and bank. Based on the empirical findings, the possible solutions for the bank and individual borrowers to improve credit efficiency for individual customers at commercial banks are obtained.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2153-2160
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• 2008

This paper develops an algorithm for probabilistic production cost credit evaluation of wind turbine generators(WTG) with multi-state. Renewable energy resources such as wind, wave, solar, micro hydro, tidal and biomass etc. are becoming importance stage by stage because of considering effect of the environment. Wind energy is one of the most successful sources of renewable energy for the production of electrical energy. Case study demonstrates that the wind speed credit in view point of economics can be assessed by using the proposed methodology.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.75-85
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• 2021

In this paper, we present a deep neural network-based prediction model that processes and analyzes the corporate credit and personal credit information of individual business owners as a new method to predict the default rate of individual business more accurately. In modeling research in various fields, feature selection techniques have been actively studied as a method for improving performance, especially in predictive models including many features. In this paper, after statistical verification of macroeconomic indicators (macro variables) and credit information (micro variables), which are input variables used in the default rate prediction model, additionally, through the credit information feature selection method, the final feature set that improves prediction performance was identified. The proposed credit information feature selection method as an iterative & hybrid method that combines the filter-based and wrapper-based method builds submodels, constructs subsets by extracting important variables of the maximum performance submodels, and determines the final feature set through prediction performance analysis of the subset and the subset combined set.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.6
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• pp.13-24
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• 2017

The purpose of this study is to examine the effect of the management of receivables on the management performance of micro manufacturing industries. The results of the survey are as follows. First, among the factors of management of pre- and post-trade receivables in the micro manufacturing industries, management organization and regulations, contract execution management, bad debt control, which are the subordinate factors of credit control, are positive (+) significant effect on stability. In terms of profitability, management organizations and regulations, which are subordinate factors of credit control management, have a positive (+) significant effect on profitability. The recovery management, which is a factor of management of post - receivable receivables, did not have a significant effect on the stability and profitability of financial management performance. Second, the effect of financial performance on organizational satisfaction is positively related to stability, while profitability has no significant effect on organizational satisfaction. The implication of this study is that pre - trade receivables management is more important than post - trade receivables management in the management of accounts receivables of micro manufacturing industries. Proactive credit management refers to the procedure of establishing and managing personal guarantees and physical guarantees in order to smooth the execution of the obligations at the same time as the contract is concluded through processes such as credit investigation, analysis and evaluation, and sales decision before the contract is concluded. Post receivables management based on the assumption of default is a receivables management procedure from receipt of receivables that are already defaulted to bad debts to bad debt processing. If the collection of receivables is delayed or bad debt is increased, Furthermore, a corporation may be subject to bankruptcy risk (insolvency by paper profits). Therefore, it is meaningful that this study suggests direction to induce change of contract type in advance by understanding the possibility of settlement of accounts receivable and recovery of bad debts within the day of transition in case of contract of micro manufacturing industries.

• Proceedings of the KIEE Conference
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• 2008.11a
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• pp.312-314
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• 2008

This paper develops an algorithm for probabilistic production cost credit evaluation of wind turbine generators (WTG) with multi-state. Renewable energy resources such as wind, wave, solar, micro hydro, tidal and biomass etc. are becoming importance stage by stage because of considering effect of the environment. Wind energy is one of the most successful sources of renewable energy for the production of electrical energy. Case study demonstrates that the wind speed credit in view point of economics can be assessed by using the proposed methodology.