• 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.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.73-83
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• 2013

Among class quantitative tests for the credit rating systems, the credit rating tests for calibration are to test the class homogeneous differences between observed and predicted probabilities. For one time period, binomial test and chi-square test are included, and normal test and extended traffic lights test are also contained for several time peroids. In this work, we consider real data in which there exists correlation among variables, so that these test methods could be applied to the credit rating systems as well as various kinds of the class data such as BWT data and FSI data.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.543-553
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• 2005

Linear logistic regression is one of the most widely used method for credit scoring in credit risk management. This paper deals with credit scoring using splines based on Logistic regression. Linear splines and an automatic basis selection algorithm are adopted. The final model is an example of the generalized additive model. A simulation using a real data set is used to illustrate the performance of the spline method.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.75-88
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• 2021

Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.51-60
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• 2023

This study focuses on the design of a GPT-based system for relatively rapid technology credit assessment of SMEs. This system addresses the limitations of traditional time-consuming evaluation methods and proposes a GPT-based model to comprehensively evaluate the technological capabilities of SMEs. This model fine-tunes the GPT model to perform fast technical credit assessment on SME-specific text data. Also, It presents a system that automates technical credit evaluation of SMEs using GPT and LLM-based chatbot technology. This system relatively shortens the time required for technology credit evaluation of small and medium-sized enterprises compared to existing methods. This model quickly assesses the reliability of the technology in terms of usability of the base model.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.2
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• pp.35-54
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• 2009

The recent economic crisis not only reduces the profit of department stores but also incurs the significance losses caused by the increasing late-payment rate of credit cards. Under this pressure, the scope of credit prediction needs to be broadened from the simple prediction of whether this customer has a good credit or not to the accurate prediction of how much profit can be gained from this customer. This study classifies the delinquent customers of credit card in a Korean department store into homogeneous clusters. Using this information, this study analyzes the repayment patterns for each cluster and develops the credit prediction system to manage the delinquent customers. The model presented by this study uses Kohonen network, which is one of artificial neural networks of data mining technique, to cluster the credit delinquent customers into clusters. Cox proportional hazard model is also used, which is one of survival analysis used in medical statistics, to analyze the repayment patterns of the delinquent customers in each cluster. The presented model estimates the repayment period of delinquent customers for each cluster and introduces the influencing variables on the repayment pattern prediction. Although there are some differences among clusters, the variables about the purchasing frequency in a month and the average number of installment repayment are the most predictive variables for the repayment pattern. The accuracy of the presented system leaches 97.5%.

• Journal of Families and Better Life
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• v.30 no.3
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• pp.105-118
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• 2012

This study aims to recognize cognition of credit card and shopping value in contemporary society, cognition of credit card and shopping value according to types of consumption orientation, and factors that influence cognition of credit card and shopping value, and ultimately provide a foundation for establishing proper shopping value. A total of 453 women and men residing in Seoul have been set as research object in order to achieve the purpose of the study. The data was analyzed by Cronbach' alpha, frequencies, percentile, mean, factor analysis, K-mean cluster analysis, t-test, ANOVA and Duncan's multiple range test, multiple linear regression. All analysis progress was done by spsswin12.0 statistics program. A summary of this research goes as follows: First, categorization of consumption orientation lead to two clusters of rational and symbolic & conspicuity consumption patterns and cognition of credit card was categorized into positive and negative cognition and shopping value was categorized into hedonic shopping value, utilitarian shopping value and time-save shopping value. Second, rational shoppers had high utilitarian shopping values and symbolic & conspicuity shoppers had high both hedonic shopping values and utilitarian shopping values. Third, the most influential factor in hedonic shopping value and utilitarian was consumption orientation. In conclusion, this research has showed that cognition of credit card and shopping value according to types of consumption orientation patterns varied, and that consumption orientation was an influential factor on cognition of credit card and shopping value.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.59-72
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• 2020

This paper examines whether lending structure can lower credit risk by employing econometric techniques of panel data for the Vietnamese banking system at the bank level used by economic sectors from 2011 to 2016. New light is being shed on assessing the impact of each industry's debt outstanding on credit risk. Adopting findings from previous studies, we assess credit risk from two different sources, including loan loss provision and non-performing loan. Moreover, we also focus on observing lending structure in many different aspects, from concentrative levels to the short-term and long-term stability levels of lending structure. The Generalized Method of Moments (GMM) estimator was applied to analyze the relationship between concentration and banking risks. In general, the results show that lending concentration may decrease credit risk. It is interesting to observe that the Vietnamese commercial bank lending portfolios have, on average, higher levels of diversity across different sectors. In particular, the increase in hotel and restaurant lending contributes to decrease credit risk while the lending portfolios of banks in agriculture, electricity, gas and water increase credit risk. This study suggests the need for further analysis and research about portfolio risks in lending activities for maintaining efficiency and stability in the commercial banking system.

• The Journal of Asian Finance, Economics and Business
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• v.10 no.2
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• pp.257-265
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• 2023

This paper investigates the determinants of credit accessibility and the effect of credit on the income of farm households borrowing from Vietnam Bank for Agriculture and Rural Development, Giong Rieng District Branch, Kien Giang Province. Based on the primary data of 200 farming households who are the customer of the bank, the study applied the Probit regression model to examine determinant factors of credit accessibility of farm households and employed the Propensity score matching method to investigate the impact of credit on households' income. The findings of the Probit regression shown that three independent variables that significantly influence the access to credit of households are household size, income source, and farm size. Besides that, the Propensity score matching method results showed a difference of 23.799 million VND/year between the income of borrowing households and that of non-borrowing households at the significance level of 1%. The difference in the imcome from the interval and central matching methods are VND 24.700 million VND/year and VND 24.633 million VND/year, respectively. Given empirical findings suggetsted that several recommendations to increase the credit accessibility of farm households, thereby creating favorable conditions for improving their income.

• 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.