• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.165-170
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• 2021

This paper examines the long-run relationship between financial development and economic growth. The effective function of financial development is crucial to promote the economic development of the country. To achieve the objective, this study used Gross Domestic Product as a dependent variable and Credit to The Private Sector, Ratio of the Gross Fixed Capital Formation to GDP, Trade, Consumer Price Index and Labour Force as an independent variable. Augmented Dickey-Fuller test statistic (ADF) to check the stationary. Bounds test for cointegration and Auto-Regressive Distributed Lag Models (ARDL) are used to check cointegrating relationship amongst the variables and causality between financial development and economic growth. Moreover, the Model selection method is Akaike Info Criterion (AIC). This result demonstrates that the labor force and trade hold a significantly negative relationship with economic growth. Nevertheless, inflation, Credit to The Private Sector, and Ratio of the Gross Fixed Capital Formation to GDP show a significantly positive relationship with economic growth. Therefore, there is a statistically significant relationship between Financial Development and Economic growth in Sri Lanka and the Sri Lankan government should reform its trade policies.

• Human Ecology Research
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• v.61 no.3
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• pp.297-318
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• 2023

The purpose of this study was to develop a high school home economics financial consumer education program based on backward design and validation by experts. The program was designed and developed by first selecting learning content elements through a review of existing research and an analysis of relevant literature. The next step was to categorize these elements into seven themes and apply the backward design instructional design model 2.0. The program was prepared in the form of a 21st teaching-learning course plan and workbook and was verified by nine home economics teachers with working experience in high school. The evaluation revealed that the average value for all questions was 3.81 (out of 4 points) and the CVR was .99, indicating that the program was valid. In addition, positive evaluations were received in terms of learning goals, content level, and learner participation by class. This study has significance in that a systematic financial consumer education program was developed by Education of Home Economics to improve the financial literacy of high school students. It can therefore be used as an elective course (mini-course) in Home Economics in the high school credit system. A follow-up study will be required to assess the improvement in financial literacy after implementing this program.

• 한국디지털정책학회:학술대회논문집
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• 2005.06a
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• pp.145-175
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• 2005

In 2001, e-Credit guarantee system was introduced by the Korea Credit Guarantee Fund(KCGF) to extends the credit guarantee services for the purchasing amount on credit in the B2B e-Marketplace. It combines both the merits of bill of exchange and credit card and eliminates the uneasiness related to the unpaid accounts due to the non-facial e-Marketplace. The purpose of this paper is to empirically analyze both the causal relationship and influences among theoretical variables by the structural equation model(SEM). e-Credit guarantee system can be a good strategy for promoting B2R e-Marketplace. The level of application of e-Credit guarantee system is attributable to both the characteristics of B2B e-Marketplace and products. The cost reduction or saving in purchasing procedures, production and stockpiling will be possible through e-Credit guarantee system. The close linkage of financial institutions with the enterprises based on e-Credit guarantee system will further promote B2B e-Marketplace in Korea.

• Journal of Digital Convergence
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• v.3 no.1
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• pp.45-73
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• 2005

In 2001, e-Credit guarantee system was introduced by the Korea Credit Guarantee Fund(KCGF) to extends the credit guarantee services for the purchasing amount on credit in the B2B e-Marketplace. It combines both the merits of bill of exchange and credit card and eliminates the uneasiness related to the unpaid accounts due to the non-facial e - Marketplace. The purpose of this paper is to empirically analyze both the causal relationship and influences among theoretical variables by the structural equation model(SEM). e-Credit guarantee system can be a good strategy for promoting B2B e-Marketplace. The level of application of e-Credit guarantee system is attributable to both the characteristics of B2B e-Marketplace and products. The cost reduction or saving in purchasing procedures, production and stockpiling will be possible through e-Credit guarantee system. The close linkage of financial institutions with the enterprises based on e-Credit guarantee system will further promote B2B e-Marketplace in Korea.

• 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 Small Business Innovation
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• v.20 no.2
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• pp.35-50
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• 2017

The aims of this study is to analyze the effectiveness of firms financial status quo and the scale of financial support on SMEs overall performance. We have gathered the financial guarantee data from 1998 to 2013, provided by Korea Credit Guarantee Fund (KODIT), to analyze the effectiveness of Financial policy. To classify both financial status quo and scale of financial support, we utilized the following variables; Interest Coverage Ratio (ICR) and newly guaranteed amount ratio. To take the measurement of the overall performance, we employed profitability, growth ratio and activity index. To minimize the effect of repeated financial support (redundancy benefits), firms were selected based on the following criteria: firms that receive no financial support prior to implementing such policy over the last 3 years and no new financial support over the last 2 years. Results suggest that firms with higher ICR and large newly guaranteed amount influence on financial performance in terms of profitability index. Firms with lower ICR and large scale financial support showed a better performance compare to firms with small-scale financial support. Firms with large-scale financial support, irrespective of ICR inclined to have better performance to those of small-scale financial support in terms of growth index. For activity index, however, firms with large scale support led to higher performance in the short term. In turn, our analysis presents objective perspective with respect to the effectiveness of financial policy through credit guarantee on overall performance of SMEs. This study, therefore, implies that well-balanced SMEs supporting policy may lead to better directions.

• Asia pacific journal of information systems
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• v.31 no.2
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• pp.236-256
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• 2021

Online financial technology products are an important consumer finance innovation. While a large body of previous research has focused on initial adoption and consumer willingness to use these products, little research explores the continued use of these products beyond the initial adoption phase. In particular, special attention should be paid to how users' trust and perceptions of privacy and security affect continued use behavior. This paper integrates the expectation confirmation model of information system continuance (ECM-ISC), the information system success model (ISSM) and the security and trust literatures to investigate continued use of online financial technology. To test the research model, we collected 398 valid questionnaires from Ant Credit Pay users. The research results show that system and service quality positively impact users' expectation confirmation, while information quality has no significant impact. Expectation confirmation and perceived usefulness positively affect user satisfaction. Moreover, the user's perception of privacy and security plays a vital role in user satisfaction. Satisfaction and perceived trust jointly promote users' continuance behaviors. Findings of this study indicates the importance of the information system success factors and security factors due to their influence on the continued use of Fintech products. This conclusion has implications for enterprises in improving the product qualities and enhancing the degree of security to meet user needs.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.751-756
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• 2024

The accuracy of the model and the explanation of the results are important factors that should be considered simultaneously Recently, applications of explainable artificial intelligence are increasing, and it is especially widely applied in the financial field where interpretation of results is important. In this paper, we compare the performance of open API credit evaluation data using various machine learning techniques. In addition, existing financial logic is verified through explainable artificial intelligence technologies, SHAP and LIME. Accordingly, it is expected to demonstrate the applicability of machine learning in the financial market.

• Journal of the Korean Regional Science Association
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• v.32 no.4
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• pp.75-90
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• 2016

It is unique from other financial institutions that credit unions in Korea have been developed as voluntary activity for enhancing the financial access of the poor in 1960s. However, currently some raise criticism that the cooperative identity as voluntary movement has been weaker. This paper endeavor to analyze the growth of credit unions during 2000s and explain what the implications of their growth are, using data from National Credit Union Federation of Korea. Our findings are as follows; firstly, the development of credit unions in 2000s are able to be regarded as a reflection of the rationale of advocate for quantitative growth. Secondly, the growth of credit unions are mostly dependant on non-taxable deposit, large loan, and collateralized loan which can lead to weaken the identity as voluntary cooperatives. Thirdly, the strategy of quantitative growth cannot be helpful for soundness of asset and profitability, eventually weakening their sustainability.

• Journal of Industrial Convergence
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• v.16 no.4
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• pp.33-46
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• 2018

Personal credit scoring is an effective tool for banks to properly guide decision profitably on granting loans. Recently, many classification algorithms and models are used in personal credit scoring. Personal credit scoring technology is usually divided into statistical method and non-statistical method. Statistical method includes linear regression, discriminate analysis, logistic regression, and decision tree, etc. Non-statistical method includes linear programming, neural network, genetic algorithm and support vector machine, etc. But for the development of the credit scoring model, there is no consistent conclusion to be drawn regarding which method is the best. In this paper, we will compare the performance of the most common scoring techniques such as logistic regression, neural network, and support vector machines using personal credit data of the financial institution in China. Specifically, we build three models respectively, classify the customers and compare analysis results. According to the results, support vector machine has better performance than logistic regression and neural networks.