• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.129-138
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• 2021

Several studies have revealed that information on borrower characteristics plays an important factor in approving their credit requests. Though the extent to which such characteritics are also applicable to the case of fintech lending remain uncertain. The aim of this study is, thus, to investigate the determinant factors that influence MSEs in obtaining credit through fintech lending. Here, we emphasize virtual trust in fintech lending encompasing the dimension of social network, economic attributes, and risk perception based on several indicators that are used as proxies. Primary data used in the study was gathered from an online survey to the respondents of MSEs in Java. The result of the study indicates that determinants of MSEs in obtaining credit from lender through fintech lending are statistically influenced by internet usage activities, borrowing history, loan utilization, annuity payment system, completeness of credit requirement documents and compatibility of loan size with the business need. These factors have a significant effect on credit approval because they can generate virtual trust of fintech lender to MSEs as potential borrowers. It concludes that the probability of obtaining fintech loans in accordance with their expectations are influenced by the dimensions of social network, economic attributes and risk perception.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.79-94
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• 2013

In this paper, we examine factors of success in online P2P (peer-to-peer) lending auctions. This paper finds the following empirical results. First, loan applicants with a stable employment status are more likely to succeed in the auction than loan applicants with an unstable employment status. Second, loan applicants, who actively share personal information and interact with lenders through online message boards, are likely to succeed in the auction. Third, the purpose of a loan for debt repayment has a significant impact on the success of the auction. However, the purpose of a loan for essential living expenses such as housing, living, and medical expenses has an insignificant relationship with the success of the auction. Our results imply that the characteristics of loan applicants such as employment status and social interaction are the factors of success in online P2P lending auctions.

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

• 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.

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

• Journal of Information Technology Applications and Management
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• v.21 no.4_spc
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• pp.485-499
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• 2014

Sharing Economy is the modern main item with ICT Development. Of course sharing economic item is the old and long run mainstream, but by the ICT technological development sharing economy is the fostering and affluent factors in the world economic growth. Though, in Korea, till now sharing economy is minimal, that will growth sharply. We can track various business models of sharing economy. Sharing economy is to buy use right not ownership. With the sharing economic business model wee can make also financial sharing model. In finance model we can divide two kind models. First, we can trace small size lending model with p2p type. And second, we can make financial information transaction model. But till now sharing economic system is not activated, because of many reasons. To activate, first we have to set law and various standards, and also government actively support many sharing economy firms and institutions. To catch up developed countries in the field of sharing economy we have to make aggressive and flexible rules and standards.

• Natural Product Sciences
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• v.19 no.2
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• pp.77-119
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• 2013

The genus Patrinia, belonging to the Valerianaceae family, includes ca. 20 species of herbaceous plants with yellow or white flowers, distributed in Korea, China, Siberia, and Japan. Among them, P. scabiosaefolia (yellow Patrinia), P. saniculaefolia, P. villosa (white Patrinia), and P. rupestris are found in Korea. Several members of this genus have long been used in folk medicine for the treatment of inflammation, wound healing, ascetics, and abdominal pain after childbirth. Thus far, ca. 217 constituents, namely flavonoids, iridoids, triterpenes, saponins, and others have been identified in this genus. Crude extract and isolated compounds have been found to exhibit anticancer, anti-inflammatory, antioxidant, antifungal, antibacterial, cytotoxic activities, lending support to the rationale behind several of its traditional uses. The present review compiles information concerning the phytochemistry and biological activities of Patrinia, with particular emphasis on P. villosa, as studied by our research group.

• Knowledge Management Research
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• v.21 no.3
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• pp.229-247
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• 2020

In the remarkable growth of P2P financial platform in the field of knowledge management, only companies with big data and machine learning technologies are surviving in fierce competition. The ability to analyze borrowers' credit is most important, and platform companies are also recognizing this capability as the most important business asset, so they are building a credit evaluation system based on artificial intelligence. Nonetheless, online P2P platform providers that offer related services only act as intermediaries to apply for investors and borrowers, and all the risks associated with the investments are attributable to investors. For investors, the only way to verify the safety of investment products depends on the reputation of P2P companies from newspaper and online website. Time series information such as delinquency rate is not enough to evaluate the early stage of Korean P2P makers' credit analysis capability. This study examines the credit analysis procedure of P2P loan platform using artificial intelligence through the case analysis method for well known the top three companies that are focusing on the credit lending market and the kinds of information data to use. Through this, we will improve the understanding of credit analysis techniques through artificial intelligence, and try to examine limitations of credit analysis methods through artificial intelligence.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.935-958
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• 2020

The core value of finance is credit. It can be said that without credit, there can be no finance. The distributed structure of the blockchain and the low-cost trust-building mechanism based on mathematical algorithms provide a new solution and path for solving and optimizing related problems in the financial field. The blockchain technology is applied in the development of the financial industry through consensus mechanisms, smart contracts, and distributed networks. In this research, a comprehensive survey of the blockchain technology is proposed in the development of financial services including equity crowdfunding and credit investigations in inclusive finance, cross-border remittance, Internet financial payment, P2P lending, supply chains finance, and the application of blockchain in the field of anti-money laundering. This paper discusses the role of blockchain in solutions to different issues in the financial field. It also discusses the architectures in different financial service application scenarios from the perspective of the financial trust mechanism and the perspective of the technology and rule change of blockchain participation in financial innovation. Finally, the problems and challenges of blockchain in financial services are discussed, and corresponding solutions are proposed.

• Information Systems Review
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• v.22 no.3
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• pp.15-30
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• 2020

In the real estate industry, the latest changes in the Fourth Industrial Revolution, such as big data analytics, machine learning, and VR (virtual reality), combine to bring about industry change. Proptech is a new term combining properties and technology. This study aims to derive and analyze from a comprehensive perspective the quality factors (systems, services, interfaces, information) for mobile real estate brokerage services that are well known and used in the domestic market. The surveys in this study were conducted online and offline and a total of 161 samples were used for statistical analysis. As a result, all hypotheses were approved to except system quality and service quality. The results show that the domestic proptech companies who are mostly focused on real estate brokerage services, peer-to-peer lending, advertising platforms and apartments need to grow in various fields of proptech business of other countries including Europe, USA and China.