• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.63-77
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• 2020

Purpose - The purpose of this study is to examine the effects of personal characteristics, loan characteristics, and interest rate characteristics of 2,653 borrowers on the delinquency possibility. In doing so, this study applies both multiple regression and logistic regression models to the data of credit unions in the city of Daegu. Design/Methodology/Approach - The major results of multiple regression analysis using SPSS are as follows. Findings - As for the results of testing the significance of the regression coefficients, it has been found that among the personal characteristics variables membership, credit rating, credit rating changes, and LTV have significant positive (+) effects on the delinquency possibility. Also it has been shown that among the loan characteristics variables loan amount, loan balance, total debt amount, collateral type, collateral amount, and repayment method have significant positive (+) effects on the delinquency possibility. Furthermore it has been found that among the interest rate characteristics variables both overdue interest rate and interest rate spread have positive (+) effects on the delinquency possibility. However, it has been shown that among the personal characteristics variables equity and membership do not have significant effects on the delinquency possibility, and that normal interest rate among the interest rate characteristics variables also do not have a significant effect on the delinquency possibility. Research Implications - By systematically analyzing the variables affecting delinquency possibility based on the results of this study, credit unions might get positive help in improving the system of managing receivables. Furthermore, the results of this study could be extended and applied to other types of financial institutions, so that financial institutions in general will also get some help to systematically manage the delinquency possibility.

• Journal of Science and Technology Studies
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• v.19 no.1
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• pp.91-135
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• 2019

As we live a life increasingly mediated by computers, we often outsource our critical judgments to artificial intelligence(AI)-based algorithms. Most of us have become quite dependent upon algorithms: computers are now recommending what we see, what we buy, and who we befriend with. What happens to our lives and identities when we use statistical models, algorithms, AI, to make a decision for us? This paper is a preliminary attempt to chronicle a historical trajectory of judging people's economic and moral worth, namely the history of credit-rating within the context of the history of capitalism. More importantly this paper will critically review the history of credit-rating from its earlier conception to the age of big data and algorithmic evaluation, in order to ask questions about what the political implications of outsourcing our judgments to computer models and artificial intelligence would be. Some of the questions I would like to ask in this paper are: by whom and for what purposes is the computer and artificial intelligence encroached into the area of judging people's economic and moral worth? In what ways does the evolution of capitalism constitute a new mode of judging people's financial and personal identity, namely the rated self? What happens in our self-conception and identity when we are increasingly classified, evaluated, and judged by computer models and artificial intelligence? This paper ends with a brief discussion on the political implications of the outsourcing of human judgment to artificial intelligence, and some of the analytic frameworks for further political actions.

• 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.

• Korean Journal of Construction Engineering and Management
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• v.16 no.6
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• pp.84-91
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• 2015

Project Financing (PF) is a financing method, executed based upon the projected profitability from a project itself instead of relying on the credit rating of project sponsors or any type of collateral. However, most financial institutions of Korea lacks the long term profitability assessment capability, and they prefer to acquire credit reinforcement from the construction companies in the form of the guarantor or debt argument commitments. As a result, PF contingent liabilities as an indirect debt, are burdened to the construction companies. Even though the PF contingent liabilities are not supposed to be part of the financial statements, they became a mandatory disclosure items since 2009. In this study, PF contingent liabilities were studied to indicate how they were correlated with construction firms' financial ratios. Construction firms were grouped by their credit rating and each group was compared in order to analyze PF contingent liabilities' impact on the financial condition of the company in terms of liquidity, liability, and stability.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.123-136
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• 2019

This study is to provide a method to increase the credit rating of the company by examining and managing the executive competency of the CEO of SMEs and Venture Firms. We analyzed the importance and priority of AHP by surveying bank loan staff and CEOs. According to the analysis results, ethics management, strategic thinking, and expertise level were the highest in the relative importance of bank loan staff. The relative importance of CEOs was higher in order of marketing, bank transaction reliability, and financing. Result of this study is similar to the relative importance of the previous research. This study suggests to disclosing credit rating system and reflect the opinions of the CEO in order to protect financial consumers. The significance of this study is to present the factors and the importance that can help to develop advanced models.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.7
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• pp.185-196
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• 2022

The purpose of this study is to examine the effects of split ratings on earnings management through cost adjustments based on asymmetric cost behaviors. Using a sample of 2,027 Korean firm-year observations over the 2002-2019 period, we analyze whether a firm deliberately reduces discretionary costs, such as selling, general, and administrative (SG&A) expenses, to improve profits when it receives multiple ratings from credit rating agencies (CRAs). While examining earnings management incentives in the presence of split ratings, we also investigate the moderating effects of Chaebols, Korea's unique corporate governance structure. We find that split-rating firms show less stickiness in SG&A costs compared to non-split-rating firms when sales decrease. This result implies the deliberate reduction of discretionary costs to improve earnings in the presence of split ratings, which are more likely to change in future credit assessments. We also find that the incentives for earnings management of split-rating firms are limited in Chaebol firms, which have high levels of socio-economic surveillance and support affiliated firms through the internal market of corporate groups. This study contributes to existing research by identifying new determinants of cost behavior by using the framework of asymmetric cost behavior in relation to earnings management incentives.

• Korean Business Review
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• v.10
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• pp.69-83
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• 1997

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.173-176
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• 1997

In this paper, the classification performances of inductive learning methods are investigated using the credit rating data. The adopted classifiers are Multiple Discriminant Analysis (MDA), C4.5 of Quilan, Multi-Layer Perceptron (MLP) and Cascade Correlation Network (CCN). The data used in this analysis is obtained using the publicly announced rating reports from the three korean rating agencies. The performances of 4 classifiers are analyzed in term of prediction accuracy. The results show that no classifier is dominated by the other classifiers.

• Journal of the Korean Home Economics Association
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• v.44 no.3 s.217
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• pp.1-11
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• 2006

Although the personal credit rating has become more important than ever before in our era, a significant number of social problems have occurred due to the rising number of individuals and households with low credit ratings. The main objectives of this research are to determine effective policies of social remedies through an investigation of recognition, expectation, and utilization levels of relevant public policies available to assist individuals with low credit ratings. The sample population was taken from the credit defaulters who had visited the Credit Recovery Commission. The research was undertaken from April 28 to May 4, 2004. This study focused on the related variables concerning the degree of utilization of remedial public policies. The results showed that females, less educated individuals, and those with higher levels of expectation and recognition were more likely to utilize remedial policies. Based on the research, conclusions regarding the usage of public remedial policies for credit defaulters are as stated below. Education for households should be conducted in order to increase the expectation and recognition levels of relevant policies.