• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.207-224
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• 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
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• v.8 no.5
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• pp.403-411
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• 2021

This paper discusses the relationship between debt literacy, peer-to-peer lending, and over-indebtedness in Indonesia. It is essential because the number of loans on this platform continues to increase, both legal and illegal. Data was collected online in collaboration with commercial market research firms, JajakPendapat.net. Debt literacy and over-indebtedness were measured by self-assessment with questions from Lusardi and Tufano (2009a). Questions for debt literacy are about interest compounding, debt interest, and the application of time value of money in payment options. The question for over-indebtedness is about the amount of debt and the conditions resulting from that debt. By using descriptive methods, it is clear that the majority of respondents, both borrowers and non-peer-to-peer lending borrowers are debt illiterate, and those who have poor debt literacy have huge debt. Overall, only 1.85% of the respondents were debt literate. Those who live on the island of Java have better literacy because they are the center of economic growth in Indonesia. Debt from peer-to-peer (P2P) lending also has the potential to create problems, namely over-indebtedness. P2P lending borrowers also have very poor debt literacy. However, there is no difference in debt literacy between P2P lending borrowers and non-P2P lending borrowers.

• The Journal of Information Systems
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• v.25 no.2
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• pp.79-110
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• 2016

Purpose Online Peer-to-peer lending (hereafter P2P lending), is a new method of lending money to unrelated individuals through an online financial intermediary. Usually in the online P2P transaction, individuals who would like to borrow money (hereafter borrowers) and those who would like to lend money (hereafter lenders) have no previous relationship. Based on enormous previous studies, this study develops an integrated model, particularly for the online P2P lending environment in China, to better understand the critical factors that influence lenders' intention to lend money through the online P2P lending platform. Design/methodology/approach In order to verify the hypotheses, we develop a questionnaire with 42 survey items. We measured all the items on a five-point Likert-type scale. We use Sojump.com to collect questionnaire and gather 246 valid responses from registered members of Renrendai.com. We analyzed the main survey data by using SPSS 18.0 and AMOS 20.0. We first estimated the reliability, validity, composite reliability and AVE and then conduct common method bias test. The mediating role of trust in platform and in borrower has been tested. Last we tested the hypotheses through the structural model. Findings The results reveal that service quality, information quality, structural assurance, awareness and reputation significantly impact lenders' trust in the online P2P lending platform. Second, awareness, reputation and perceived risk significantly impact lenders' trust in borrower and lending intention. Third, trust propensity has a positive effect on lenders' trust on borrower. Last, awareness, reputation, perceived risk, platform trust and borrower trust can directly impact lenders' lending intention.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.43-51
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• 2022

Platform lending or online lending, sometimes called peer-to-peer (P2P) lending, arose due to the digital revolution to meet people's requirements for simple fund borrowing. It quickly became an alternative to other traditional lending techniques, for example, loans banks. Along with the growth of P2P lending, several academics have investigated how information technology is used in financial services, emphasizing extended application methods. This study proposes an enhanced technology acceptance model (TAM) that investigates how consumers embrace P2P lending platforms by using quality of service and perceived risk as drivers of trust, relative advantage and compatibility as drivers of perceived usefulness. For the purpose of this study, we created a questionnaire, distributed it to clients of P2P lending platforms and fintech services in general in cities in Java, Indonesia. We received 290 replies to our questionnaire. The data was analyzed to test the hypotheses using structural equation modeling (SEM). The findings show that consumers' trust, relative advantage, perceived usefulness, and perceived ease of use in P2P lending platforms substantially affect their views toward adoption. The research's findings are useful for fine-tuning platform marketing strategies and putting strategic goals into action.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1105-1117
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• 2017

Financial technology, also known as FinTech, is one of the fast growing global businesses in since its inception in 2008. Fintech is a new economic industry, comprised of companies that adopted the latest technologies to provide more efficient financial services than the traditional financial services. Fintech companies are generally small to medium sized startups trying to disintermediate existing financial systems. FinTech companies can be differentiated in several areas, based on its business solutions and target customers. In Korea, the Peer-to-Peer (P2P) lending companies are the most prominent in the FinTech sector. P2P lending is a method of borrowing or lending money to individuals through online services without the use of an official financial institution as an intermediary. The P2P lending companies operate their services entirely online or mobile environment. Consequently, mobile P2P lending application users are dramatically increasing. Thus, it is worth evaluating the acceptance of the mobile apps of the P2P lending companies from a user's perspective. This paper discusses user acceptance of the mobile P2P lending apps, guided by the Technology Acceptance Model. We conclude that the users' acceptance of mobile P2P lending apps are significantly influenced by perceived ease of use, perceived usefulness, and user satisfaction. These in turn influenced their attitude towards using mobile P2P lending apps and intention to use.

• The Journal of Society for e-Business Studies
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• v.15 no.4
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• pp.285-301
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• 2010

Peer-to-peer (P2P) lending is an open marketplace for loans not from bank but from individuals online. Financial transactions are facilitated directly between individuals ("peers") without any intermediation of a traditional financial institution. A market study by renowned research company forecasts that P2P lending will grow very fast and a couple of P2P lending sites in Korea also are getting attentions by providing the alternative financial services. In P2P lending market, Lender will enjoy higher income generated from the loans in the form of interest than interest that can be earned by financial products provided by official financial institutions. Furthermore, lenders are able to decide who they would lend the money for themselves. Meanwhile, borrowers with low credit scores are able to finance their liquidity requirement with low cost and convenient access to the Internet. The objective of this paper is to introduce P2P lending and its issues of information asymmetry. We provide the insights from the case study of one of P2P lending sites in Korea and review the issues in P2P lending market as research topics. Specifically, information asymmetry issues in both traditional financial institutions and P2P lending are discussed.

• 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.