• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.2
• /
• pp.151-156
• /
• 2020

Small and medium enterprises (SMEs) play a very important role in developing countries. In Vietnam, SMEs operating in the field of handicrafts, besides contributing to the economy, also tasked to maintain and develop traditional handicrafts. However, accessing loans from banks of SMEs faces many difficulties. This study explores the determinants of bank lending decision for SMEs, particular, in traditional handicrafts business. Using dataset based on a survey conducted in Binh Duong province, Vietnam, we investigated to what determinant effects for loan approval. The analytical methods used include descriptive statistics for overall assessment, principal component analysis and regression to examine determinants of lending decisions. The results indicate that company's collateral was the most positive determinant to bank lending decision, follow by company's business plan. The role of company's leader is very important for banks considers to approve credit because company's leaders experience and relationship with stakeholders as well as banks have positive relations with bank's lending decision. Agreed with previous studies, the company's financial statement and company's credit history with banks are also significant determinants for lending decision. Whereas, the business environment seam unaffected lending decision as their relations is not significant..

• Journal of Information Technology Services
• /
• v.14 no.3
• /
• pp.217-236
• /
• 2015

How does social interaction among investors affect decision-making in the online social lending platform? And what is the reason? In this study, in order to obtain the answer, we carried out case study research of Moneyauction and Popfunding, which are domestic online social lending platforms. We conducted interviews with managements of both social lending platforms and investors and analyzed statistical data including investment records, social interaction history between investors and lenders from both platforms. In addition, researchers performed direct participation and observation through the platforms as real investment members. As a result, we revealed that social interaction among investors has a material impact on the investment decision-making. Also we found that investors build trust by socially interacting with each other and this trust building leads to the investment decision making. Our findings confirm that social lending investors's decision-making process comply with the social embeddedness theory and imply that loan applicants must do their best efforts to display sincerity and truthfulness through their posting.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.6
• /
• pp.85-93
• /
• 2020

The paper investigates the impact of the statement of cash flows of listed companies on lending decisions of commercial banks in the context of Vietnam. Survey data for the research were collected from 160 credit officers of Vietnamese commercial banks for short-term and long-term lending decisions, whether the cash flow statement includes complete information or has a lack of information. The cash flow statement, in which the information on the cash flow is completely contrary to the profit information on the income statement is examined. This paper employed T-tests to address the research issues in a market considered to be ineffective, like Vietnam. The research results show: (1) the information on the cash flow statement affects both the short-term and long-term lending decisions of credit officers, and (2) the lack of information on the cash flow statement in both cases of positive and negative profits affects the comfort and confidence of credit officers in making decisions. The research findings also indicate that cash flow statements are important for lending decisions of credit institutions in Vietnam. Therefore, this paper provides a new insight to managers on how to improve the quality of cash flow statement to meet the needs of lenders.

• The Journal of Asian Finance, Economics and Business
• /
• v.6 no.2
• /
• pp.119-122
• /
• 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 Society for e-Business Studies
• /
• v.23 no.3
• /
• pp.207-224
• /
• 2018

In this article, an empirical study was conducted by using public dataset from Lending Club Corporation, the largest online peer-to-peer (P2P) lending in the world. We explore significant predictor variables related to P2P lending default that housing situation, length of employment, average current balance, debt-to-income ratio, loan amount, loan purpose, interest rate, public records, number of finance trades, total credit/credit limit, number of delinquent accounts, number of mortgage accounts, and number of bank card accounts are significant factors to loan funded successful on Lending Club platform. We developed online P2P lending default prediction models using discriminant analysis, logistic regression, neural networks, and decision trees (i.e., CART and C5.0) in order to predict P2P loan default. To verify the feasibility and effectiveness of P2P lending default prediction models, borrower loan data and credit data used in this study. Empirical results indicated that neural networks outperforms other classifiers such as discriminant analysis, logistic regression, CART, and C5.0. Neural networks always outperforms other classifiers in P2P loan default prediction.

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

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.6
• /
• pp.676-686
• /
• 2016

This study analyzed the long term relationship between the K-REITs' lending rate and interest rate variables based on ARDL (autoregressive distributed lag) and also examined the short term relationship based on the ARDL-ECM model. In the results of the empirical test, there is a co-integration relationship among the K-REITs' lending rate, 3 year government bond (rate), 3 year government bond (rate), corporation bond (rate) (AA-, 3year) and general fund loan rate. This means that the K-REITs' lending rate is related to the long term interest rate. The corporate general fund loan rate has a significant correlation with the K-REITs' lending rate in the long term relation and short term adjustment process. The establishment of a management plan by the REITs considering the trends in the corporate general fund loan rate in the decision making process for finance sector borrowings can be practically helpful for the K-REITs.

• Knowledge Management Research
• /
• v.19 no.2
• /
• pp.109-132
• /
• 2018

Recently, new financial services related to FinTech has gained attention more and more. Online P2P financial services transactions such as FinTech require careful examination of the constituents of information systems as an investment is made based on the information presented on the online platform without direct face-to-face contact. The purpose of this study is to find out the success factors of online P2P Lending service among FinTech. To serve the purpose, we build IS (information system) success model, and then use Kano model and fuzzy analytic hierarchy process (Fuzzy-AHP) to find out factors for the success of online P2P Lending service. In particular, this study uses Kano model to classify information system satisfaction factors and to calculate the satisfaction coefficient. The Kano model, however, has a drawback of evaluating single criterion. Therefore, we use multi-criteria decision-making technique such as Fuzzy-AHP to derive the relative importance of the factors. The analysis results show different results depending on the analysis technique. In the Kano model, most of the information system factors are a one-dimensional quality attribute. The satisfaction coefficient is highest for personalized service, followed by the responsiveness of service, ease of using a system, understanding of information, usefulness of information' reliability. The service reliability is the highest in dissatisfaction coefficient, followed by system security, service responsiveness, system stability, and personalized service. The results of the Fuzzy-AHP analysis shows that the usefulness of information quality, the personalization of service quality, and the security of system quality are the significant factors and the stability of system quality was a secondary factor.

• Journal of Digital Convergence
• /
• v.20 no.4
• /
• pp.185-192
• /
• 2022

This study investigates the effect of the semi-supervised learning(SSL) method on predicting default risk of peer-to-peer(P2P) loans. Despite its proven performance, the supervised learning(SL) method requires labeled data, which may require a lot of effort and resources to collect. With the rapid growth of P2P platforms, the number of loans issued annually that have no clear final resolution is continuously increasing leading to abundance in unlabeled data. The research data of P2P loans used in this study were collected on the LendingClub platform. This is why an SSL model is needed to predict the default risk by using not only information from labeled loans(fully paid or defaulted) but also information from unlabeled loans. The results showed that in terms of default risk prediction and despite the use of a small number of labeled data, the SSL method achieved a much better default risk prediction performance than the SL method trained using a much larger set of labeled data.

• Korean Journal of Artificial Intelligence
• /
• v.9 no.1
• /
• pp.21-27
• /
• 2021

In this Paper, Since the 1990s, Korea's credit card industry has steadily developed. As a result, various problems have arisen, such as careless customer information management and loans to low-credit customers. This, in turn, had a high delinquency rate across the card industry and a negative impact on the economy. Therefore, in this paper, based on Azure, we analyze and predict the delinquency and delinquency periods of credit loans according to gender, own car, property, number of children, education level, marital status, and employment status through linear regression analysis and enhanced decision tree algorithm. These predictions can consequently reduce the likelihood of reckless credit lending and issuance of credit cards, reducing the number of bad creditors and reducing the risk of banks. In addition, after classifying and dividing the customer base based on the predicted result, it can be used as a basis for reducing the risk of credit loans by developing a credit product suitable for each customer. The predicted result through Azure showed that when predicting with Linear Regression and Boosted Decision Tree algorithm, the Boosted Decision Tree algorithm made more accurate prediction. In addition, we intend to increase the accuracy of the analysis by assigning a number to each data in the future and predicting again.