• 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.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.77-90
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• 2023

This study aims to investigate the potential of alternative credit assessment through Social Networking Sites (SNS) as a complementary tool to conventional loan review processes. It seeks to discern the impact of SNS usage characteristics and loan product attributes on credit loan repayment. To achieve this objective, we conducted a binomial logistic regression analysis examining the influence of SNS usage patterns, loan characteristics, and personal attributes on credit loan conditions, utilizing data from Company A's credit loan program, which integrates SNS data into its actual loan review processes. Our findings reveal several noteworthy insights. Firstly, with respect to profile photos that reflect users' personalities and individual characteristics, individuals who choose to upload photos directly connected to their personal lives, such as images of themselves, their private circles (e.g., family and friends), and photos depicting social activities like hobbies, which tend to be favored by individuals with extroverted tendencies, as well as character and humor-themed photos, which are typically favored by individuals with conscientious traits, demonstrate a higher propensity for diligently repaying credit loans. Conversely, the utilization of photos like landscapes or images concealing one's identity did not exhibit a statistically significant causal relationship with loan repayment. Furthermore, a positive correlation was observed between the extent of SNS usage and the likelihood of loan repayment. However, the level of SNS interaction did not exert a significant effect on the probability of loan repayment. This observation may be attributed to the passive nature of the interaction variable, which primarily involves expressing sympathy for other users' comments rather than generating original content. The study also unveiled the statistical significance of loan duration and the number of loans, representing key characteristics of loan portfolios, in influencing credit loan repayment. This underscores the importance of considering loan duration and the quantity of loans as crucial determinants in the design of microcredit products. Among the personal characteristic variables examined, only gender emerged as a significant factor. This implies that the loan program scrutinized in this analysis does not exhibit substantial discrimination based on age and credit scores, as its customer base predominantly consists of individuals in their twenties and thirties with low credit scores, who encounter challenges in securing loans from traditional financial institutions. This research stands out from prior studies by empirically exploring the relationship between SNS usage and credit loan repayment while incorporating variables not typically addressed in existing credit rating research, such as profile pictures. It underscores the significance of harnessing subjective, unstructured information from SNS for loan screening, offering the potential to mitigate the financial disadvantages faced by borrowers with low credit scores or those ensnared in short-term liquidity constraints due to limited credit history a group often referred to as "thin filers." By utilizing such information, these individuals can potentially reduce their credit costs, whereas they are supposed to accrue a more substantial financial history through credit transactions under conventional credit assessment system.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.303-309
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• 2022

The goal of this research is to examine how credit (loan) can help rural households in Tra Vinh province create jobs in the wake of the COVID-19 outbreak in Vietnam over the last two years. The authors performed a direct survey, using a questionnaire, with 300 customers who had loan records at the Tra Vinh VBSP branch under the loan programs. From January to April 2021, jobs will be available at 07 transaction offices in districts and cities (Cau Ngang; Duyen Hai, Tieu Can, Cang Long, Tra Vinh City, Chau Thanh, Tra Cu). Using the multivariate regression method, the research has found 12 factors affecting the ability to access the employment loan program: Age, Educational Level, Occupation of households, Income, Household land area, Asset, Loan, Interest rates, Loan procedures, Loan purpose, Credit relations. From the above research results, the authors have proposed solutions to improve the ability to access credit to create jobs for each subject group at Viet Nam Bank for Social Policies, Tra Vinh Province in the future.

• International Commerce and Information Review
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• v.7 no.3
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• pp.247-268
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• 2005

In this study, I analysed the influence of the Korean Officially Supported Export Credit System upon Improvement upon Korean Export, investigated Korean Officially Supported Export Credit System focussing on the Short-term Export Loan System & Medium and Long-term Export Loan System in Korea Eximbank and sought for the measures overcoming problems therewith. In my research I started from a basic model analyzing Short-term Export Loan & Medium and Long-term Export Loan using added several variables that have effect on export such export relative price, world income and the amount of post-shipment credit bank guarantee. As a result, amount of Short-term Export Loan & Medium and Long-term Export Loan, world income and export relative price showed expected responses and Short-term Export Loan was significant. In case of amount of Short-term Export Loan System was statistical significance.

• Korean Management Science Review
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• v.33 no.3
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• pp.65-87
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• 2016

Being the FinTech technologies rapidly developed, the non face-to-face private loan market is also growing dramatically. While the real-world interests in this market are keen, the empirical studies on the issue are few compared to its prospective impact on credit loan market. This paper suggests a credit scoring model for the non face-to-face private loan employing the ratings approach (the absolute measurement method) of AHP. Analyzing a sample of data consisting of 460,000 transaction records over an 8-year period in the United States, we develop a scoring model for the non face-to-face private loan screening, and validate the model for the practical usage. Conducting sensitivity analysis, we suggest customized cut-off points for the loan execution to suit each individual loan institution's need.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.87-94
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• 2020

The paper aims to investigate the impact of presenting statements of cash flow using the direct method and the indirect method on loan decision by credit officers at Vietnamese banks. The data was collected from 150 credit officers of commercial banks in Vietnam based on the questionnaire about making loan decision when the cash flow statement is presented in different methods, namely, direct and indirect methods. This research uses T-tests to check whether using the direct or indirect method affects the accurate calculation of loan criteria, affects the loan decision by credit officers, and compare these two methods in the aspects of information provision. The research has pointed out that: 1) the direct method helps the calculation of indicators related to loans more accurately; 2) credit officers say that, while the direct method of presenting cash flow statement provides clearer information, the use of either the direct method or the indirect method does not affect the banks' loan decision. Since then, the author recommends that cash flow statements should be provided with information in a direct method to present the information needed for loan decision more accurately so as to improve the quality of cash flow statement.

• KDI Journal of Economic Policy
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• v.38 no.3
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• pp.79-106
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• 2016

This paper investigates the effectiveness of public credit guarantee programs and interest-support programs for SMEs (small and medium enterprises). First, assuming that there is an imperfect information structure in the SME loan market, we analyze how SME support financial programs affect the corporate decisions made by SMEs with regard to default or loan sizes. In addition, this paper theoretically computes the optimal levels of credit guarantee amounts and the interest-support spread under equilibrium with imperfect information in a competitive loan market. Second, the paper empirically analyzes the continuous policy-treatment effect with the GPS (generalized propensity score) method. In particular, we consider the ratio of guaranteed debt to the total debt as a continuous policy treatment. The empirical results show that marginal effects of a credit guarantee on SMEs' productivity, profitability, and growth potential decrease with the ratio of guaranteed debt to the total debt. In addition, the average effect of a credit guarantee is maximized when this ratio is at 50% to 60%.

• The Journal of Information Technology
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• v.8 no.3
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• pp.11-23
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• 2005

This paper study the way of increasing the credit loan for the Venture Companies. They are very important in national economics. But they are short of financial and mortgage although they have many kinds of superb ideas and technologies. Korean government has tried to financially support them. But the support has had its own limit. Banks and other financial institutions have not been in a positive position to loan money out to venture companies because their businesses are highly risky. The credit evaluation system of medium and small business are need to improve in order to increase the Venture Companies. loan. So, it is necessary to find new measures to activate credit loans to them.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.79-95
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• 2012

As credit loan products significantly increase in most financial institutions, the number of fraudulent transactions is also growing rapidly. Therefore, to manage the financial risks successfully, the financial institutions should reinforce the qualifications for a loan and augment the ability to detect a credit loan fraud proactively. In the process of building a classification model to detect credit loan frauds, utility from classification results (i.e., benefits from correct prediction and costs from incorrect prediction) is more important than the accuracy rate of classification. The objective of this paper is to propose a new approach to building a classification model for detecting credit loan fraud based on an individual-level utility. Experimental results show that the model comes up with higher utility than the fraud detection models which do not take into account the individual-level utility concept. Also, it is shown that the individual-level utility computed by the model is more accurate than the mean-level utility computed by other models, in both opportunity utility and cash flow perspectives. We provide diverse views on the experimental results from both perspectives.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3627-3641
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• 2021

Fintech, which stands for financial technology, is growing fast globally since the economic crisis hit the United States in 2008. Fintech companies are striving to secure a competitive advantage over existing financial services by providing efficient financial services utilizing the latest technologies. Fintech companies can be classified into several areas according to their business solutions. Among the Fintech sector, peer-to-peer (P2P) lending companies are leading the domestic Fintech industry. P2P lending is a method of lending funds directly to individuals or businesses without an official financial institution participating as an intermediary in the transaction. The rapid growth of P2P lending companies has now reached a level that threatens secondary financial markets. However, as the growth rate increases, so does the potential risk factor. In addition to government laws to protect and regulate P2P lending, further measures to reduce the risk of P2P lending accidents have yet to keep up with the pace of market growth. Since most P2P lenders do not implement their own credit rating system, they rely on personal credit scores provided by credit rating agencies such as the NICE credit information service in Korea. However, it is hard for P2P lending companies to figure out the intentional loan default of the borrower since most borrowers' credit scores are not excellent. This study analyzed the voices of telephone conversation between the loan consultant and the borrower in order to verify if it is applicable to determine the personal credit score. Experimental results show that the change in pitch frequency and change in voice pitch frequency can be reliably identified, and this difference can be used to predict the loan defaults or use it to determine the underlying default risk. It has also been shown that parameters extracted from sample voice data can be used as a determinant for classifying the level of personal credit ratings.