• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.15-26
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• 2007

In this study, a SOHO (Small Office Home Office) bankruptcy prediction model is proposed using Modified Bagging Predictors which is modification of traditional Bagging Predictors. There have been several studies on bankruptcy prediction for large and middle size companies. However, little studies have been done for SOHOs. In commercial banks, loan approval processes for SOHOs are usually less structured than those for large and middle size companies, and largely depend on partial information such as credit scores. In this study, we use a real SOHO loan approval data set of a Korean bank. First, decision tree induction techniques and artificial neural networks are applied to the data set, and the results are not satisfactory. Bagging Predictors which has been not previously applied for bankruptcy prediction and Modified Bagging Predictors which is proposed in this paper are applied to the data set. The experimental results show that Modified Bagging Predictors provides better performance than decision tree inductions techniques, artificial neural networks, and Bagging Predictors.

• 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.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.3
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• pp.55-65
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• 2019

The purpose of this study is to discover the interaction between credit growth and operational self-sustainability and to examine factors that affect credit growth and operational self-sustainability of people's credit funds (PCFs). Credit growth and operational self-sustainability are factors affecting the operations and the goals of people's credit funds (PCFs) in the Mekong Delta region of Vietnam. After regression analysis on a set of panel data from 2013 to 2018 of 24 PCFs, it appears that deposit growth and loan-to-deposit ratio have positive relationships with credit growth, while capital adequacy ratio and operational self-sustainability have negative relationships with credit growth of PCFs; capital adequacy ratio, deposit growth and income have positive relationships with operational self-sustainability, while credit growth and non-performing loan ratio have negative relationships with the operational self-sustainability of PCFs. At the same time, credit growth and operational self-sustainability have a relationship to interact with each other in a contrary trend. The results of this research are accurate according to the characteristics and development history of PCFs in the Mekong Delta region of Vietnam from 2013-2018. This study helps researchers and managers to understand the key determinants for better management of PCFs.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.229-239
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• 2020

This paper examines the impact of gender on access to debt finance among Vietnamese enterprises. The paper investigates data and variables retrieved from the World Bank Enterprise Survey dataset using five Probit models. The regression results suggest that there exist more unfavourable debt financing conditions for women-led firms (WLF), measured as a lower probability of having loan applications fully approved. Firm's age, working sector, and perception of access to finance as a difficulty are found to have explanatory power on the discrimination. More importantly, the perception of debt finance as a difficulty or firms' level of confidence significantly explains the variance of the dependent variable of probability of loan approval, or gender effect would be more pronounced if the firm already has a low level of confidence. The paper also contributes in testing for the gender effect on Vietnamese enterprises from different sectors and scale, unlike other prior research papers focusing on specific sectors and/or small and medium enterprises only. The findings are highly useful for Vietnamese credit institutions to set out a specific business policy to attract more WLFs and help promoting gender equality in the working environment, especially in debt financing, which is often neglected in existing regulation and policy frameworks.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.85-93
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• 2020

In peer-to-peer (P2P) loan markets, as most lenders are unskilled and inexperienced ordinary individuals, it is important to know the characteristics of borrowers that significantly impact their repayment performance. This study investigates the effects and importance of borrowers' past repayment performance track record within the platform to identify its predictive power. To this end, I analyze the detailed loan repayment data from two leading P2P lending platforms in Korea using a Cox proportional hazard, multiple linear regression, and logit models. Furthermore, the predictive power of the factors proxied by borrowers' track records are evaluated through the receiver operating characteristic (ROC) curves. As a result, it is found that the borrowers' past track record within the platform have the most important impact on the repayment performance of their current loans. In addition, this study also reveals that the borrowers' track record is much more predictive of their repayment performance than any other factor. The findings of this study emphasize that individual lenders must take into account the quality of borrowers' past transaction history when making a funding decision, and that platform operators should actively share the borrowers' past records within the markets with lenders.

• 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 Information Systems
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• v.16 no.3
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• pp.1-20
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• 2007

Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, this study presents a corporate credit rating method using business failure probability map(BFPM) and AHP(Analytic Hierarchy Process). The BFPM enables us to rate the credit of corporations according to business failure probability and data distribution or frequency on each credit rating level. Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the BFPM and the AHP model using both financial and non-financial information. Finally, the credit ratings of each firm are assigned by our proposed method. This method will be helpful for the loan evaluators of financial institutes to decide more objective and effective credit ratings.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.2
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• pp.119-122
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• 2019

The study aims to investigate factors influencing business households' decision for borrowing credit: the case of commercial banks in Tra Vinh Province, VietNam. The study was conducted by collecting data from 300 business households traded at four commercial banks in Tra Vinh province (Viet Nam bank for agriculture and rural development, Tra Vinh Branch; Viet Nam jointstock commercial bank industry and trade, Tra Vinh Branch; Asia joinstock commercial bank, Tra Vinh Branch; Viet Nam jointstock commercial bank for foreign trade, Tra Vinh Branch). By the use of the Binary Logistic regression method, the research found out that the factors influencing to borrow c redit of household business's decision including: banks brand names, loan interest rates, service attitude, and loan procedures. Of those, the banks brand names and lending interest rates have the strongest impacts on borrow credit decision of business households at commerc ials banks in Tra Vinh province. Since then, the study has proposed solutions to improve access to credit of business households in commercial banks in Tra Vinh province in the coming time, such as: developing a bank brand; the development of flexible lending interest rate policies; improve service style of bank staff; at the same time, simplifying lending procedures.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.317-328
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• 2021

This study examines the impact of bank competition and efficiency in the financial stability of the banking sector in Bangladesh. The study used the Lerner index and the Boone indicator to represent the bank competition, while the non-performing loan (NPL) and Z-score are used to represent financial stability. The secondary data were collected from the annual reports of 28 DSE listed commercial banks of Bangladesh over the period from 2011 to 2018. Using a dynamic panel GMM model, the study found the Lerner index is significantly negatively related with Z-score, which means that higher bank competition results in higher bank stability. It is also seen that higher cost efficiency results in higher bank stability. The Lerner index has negative, but insignificant impact on NPL. Similarly, using the Boone indicator, this study found that lower competition increases NPL. In terms of the Z-score, the Boone indicator found that 1 unit of increment results in decrease of the Z-score by 6.15 units. The study suggests that, as more competition results in more financial soundness, the banking industry competition should be ensured by policymakers or regulators. Banks could enhance financial stability by cost control to achieve cost efficiency as well as by improving loan-to-asset ratio.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.497-502
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• 2021

This study aims to investigate the determinants of default risk of rural banks in East Java, Indonesia. The method used is descriptive verification and logistic regression analysis. The data used is secondary in the form of monthly annual financial reports of rural banks in East Java during the period 2009-2018. From the results, it was shown that net interest margin (NIM) as a proxy of market risk, non-performing loan (NPL) as a proxy of credit risk, operation efficiency as a proxy of operational risk and return on assets (ROA) as a proxy of profitability have a significant influence on default risk. Meanwhile, the loan to deposit (LDR) ratio as a proxy of liquidity risk has no significant influence on default risk. Banks need to implement risk management and meet the capital adequacy requirements of regulators so that they are resistant to risk, and also, compliant with bank governance to be able to produce high returns for rural banks have an impact on sustainability and its existence. The ability to identify setbacks in bank conditions and the ability to distinguish between healthy and problematic banks will enable to anticipate default banks.