• The Journal of Information Systems
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• v.29 no.1
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• pp.241-265
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• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.

• East Asian Journal of Business Economics (EAJBE)
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• v.9 no.3
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• pp.15-28
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• 2021

Purpose -In the credit transaction, the issuing bank must examine the documents to pay the credit amount. In order to smoothly execute the credit transaction, document review is a key element, so the 5th revised credit unification rule specifically defines the document review procedure. Research design, data, and methodology - The document review procedure specified in the UCP Rules can be largely divided into the document review period and the rejection procedure for inconsistent documents. First of all, confusion was caused by the ambiguous regulation.. Result - With regard to the document review period, in the actual credit transaction, the issuing bank often negotiates with the issuing client about the waiver of the document inconsistency. Next, in the process of notifying the rejection of inconsistent documents, the issuing bank shall send the rejection notice. Conclusion - This study suggests that the requirement to list all inconsistencies makes it impossible for the issuing bank to further notify the refusal, thereby limiting the right to defend against inconsistencies not listed in the first refusal notice and consequently having the effect of matching them. In addition, the issuing bank's rejection notice is closely related to the beneficiary's exercise of the right to replenish documents.

• The Journal of Industrial Distribution & Business
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• v.13 no.10
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• pp.1-8
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• 2022

Purpose: Support vector machines (SVMs) ensemble has been proposed to improve classification performance of Credit risk recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. To deal with this problem, this paper designs a support vector machines (SVMs) ensemble method based on fuzzy integral, which aggregates the outputs of separate component SVMs with importance of each component SVM. Research design, data, and methodology: This paper designs a personal credit risk evaluation index system including 16 indicators and discusses a support vector machines (SVMs) ensemble method based on fuzzy integral for designing a credit risk assessment system to discriminate good creditors from bad ones. This paper randomly selects 1500 sample data of personal loan customers of a commercial bank in China 2015-2020 for simulation experiments. Results: By comparing the experimental result SVMs ensemble with the single SVM, the neural network ensemble, the proposed method outperforms the single SVM, and neural network ensemble in terms of classification accuracy. Conclusions: The results show that the method proposed in this paper has higher classification accuracy than other classification methods, which confirms the feasibility and effectiveness of this method.

• The Journal of Sustainable Design and Educational Environment Research
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• v.19 no.4
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• pp.70-77
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• 2020

This study examines many preceding studies on school space, including the high school credit system, and attempts to present the importance after deriving the most appropriate factors when constructing an educational environment of the high school credit system. Based on the derived importance level, the purpose of this study was to provide information that is helpful for the implementation of the educational environment of the high school credit system in each school and to establish and present a model from an academic perspective by deriving the importance of the educational environment according to the high school credit system. As a research method, the importance of the educational environment was analyzed through the Analytic Hierarchy Process. The result of the analysis indicated that it is necessary to consider the interrelationship between the educational curriculum and the educational environment, which selectively and intensively performs functions and maximizes the effect according to the strategic importance of the educational environment.

• Journal of Fisheries and Marine Sciences Education
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• v.27 no.6
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• pp.1546-1555
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• 2015

This study was performed to analyze the item characteristics of competency inventory for designer (CID), which Gil (2011) developed for measurement of design competency. To accomplish the purpose of this study, general partial credit (GPC) model based on polytomous item response theory was applied. The findings were as follows: First, CID is a reliable instrument for measuring design competency. Second, most items of CID have low item discrimination and average item difficulty according to the GPC model. Especially, there are some problems to have low item discrimination in view of validation. To improve the goodness of CID, we will need to examine why CID has low item discrimination.

• Journal of Internet Computing and Services
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• v.7 no.1
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• pp.121-130
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• 2006

In next generation wireless networks, an application must be capable of rating service information in real-time and prior to initiation of the service it is necessary to check whether the end user's account provides coverage for the requested service. However, to provide prepaid services effectively, credit-control should have minimal latency. In an endeavor to support real-time credit-control for Mobile IPv6 (MIPv6), we design an implementation architecture model of credit-control authorization. The proposed integrated model combines a typical credit-control authorization procedure into the MIPv6 authentication procedure. We implement it on a single server for minimal latency. Thus, the server can perform credit-control authorization and MIPv6 authentication simultaneously. Implementation details are described as software blocks and units. In order to verify the feasibility of the proposed model. latency of credit-control authorization is measured according to various Extensible Authentication Protocol (EAP) authentication mechanisms. The performance results indicate that the proposed approach has considerably low latency compared with the existing separated models, in which credit-control authorization is separated from the MIPv6 authentication.

• The Journal of Information Systems
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• v.30 no.4
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• pp.203-226
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• 2021

Purpose The purpose of this study is to enhance the accuracy score of personal credit scoring using the convolutional neural networks and secure the transparency of the deep learning model using eXplainalbe Artifical Inteligence(XAI) technique. Design/methodology/approach This study built a classification model by using the convolutional neural networks(CNN) and applied a methodology that is transformation of numerical data to imaged data to apply CNN on personal credit data. Then layer-wise relevance propagation(LRP) was applied to model we constructed to find what variables are more influenced to the output value. Findings According to the empirical analysis result, this study confirmed that accuracy score by model using CNN is highest among other models using logistic regression, neural networks, and support vector machines. In addition, With the LRP that is one of the technique of XAI, variables that have a great influence on calculating the output value for each observation could be found.

• Journal of Distribution Science
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• v.22 no.3
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• pp.49-58
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• 2024

Purpose: Research on financial development plays a crucial role in guiding and implementing policies for both financial development and economic growth. This study aims to evaluate the impact of financial development on the economic growth of Southeast Asian countries. Research design, data and methodology: The research utilizes data from 11 Southeast Asian countries from 2015 to 2022. Financial development data is proxied by credit distribution in private sector. Results: Based on the analysis using the FGLS model, it indicates that financial development has a positive impact on the economic growth of Southeast Asian countries. In addition, the study also examines the impact of state investment costs and FDI investment on economic growth. The results also show that foreign direct investment flows still play an important role in Southeast Asian countries (FDI has a positive impact on economic growth). State investment costs also impact economic growth, showing that the development of public investment also brings good development to countries. Conclusions: These results suggest that credit policies for financial development in general, and the development of private credit in particular, play a significant role in these countries. Building a system to promote the activities of private sector economies will help stimulate the economic development of Southeast Asian countries.

• Proceedings of the Korea Fashion and Costume Design Association Conference
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• 2005.06a
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• pp.33-34
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• 2005

• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.199-215
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• 2020

Purpose - This article tries to test if the conditional consumption capital asset pricing model (CCAPM) with bank credit for household as a conditional variable can explain the cross-sectional variation of stock returns in Korea. The performance of conditional CCAPM is compared to that of multifactor asset pricing models based on Arbitrage Pricing Theory. Design/methodology/approach - This paper extends the simple CCAPM to the conditional version of CCAPM by using bank credit for household as conditioning information. By employing KOSPI and KOSDAQ stocks as test assets from the second quarter of 2003 to the first quarter of 2018, this paper estimates risk premiums of conditional CCAPM and a variety of multifactor linear models such as Fama-French three and five-factor models. The significance of risk factors and the adjusted coefficient of determination are the basis for the comparison in models' performances. Findings - First, the paper finds that conditional CCAPM with bank credit performs as well as the multifactor linear models from Arbitrage Pricing theory on 25 test assets sorted by size and book-to-market. When using long-term consumption growth, the conditional CCAPM explains the cross-sectional variation of stock returns far better than multifactor models. Not only that, although the performances of multifactor models decrease on 75 test assets, conditional CCAPM's performance is well maintained. Research implications or Originality - This paper proposes bank credit for household as a conditional variable for CCAPM. This enables CCAPM, one of the most famous economic asset pricing models, to conform with the empirical data. In light of this, we can now explain the cross-sectional variation of stock returns from an economic perspective: Asset's riskiness is determined by its correlation with consumption growth conditional on bank credit for household.