• 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.

• 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.

• 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.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.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.71-78
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• 2019

This study aims to identify good borrowers within the context of P2P lending. P2P lending is a growing platform that allows individuals to lend and borrow money from each other. Inherent in any loans is credit risk of borrowers and needs to be considered before any lending. Specifically in the context of P2P lending, traditional models fall short and thus this study aimed to rectify this as well as explore the problem of class imbalances seen within credit risk data sets. This study implemented an over-sampling technique known as Synthetic Minority Over-sampling Technique (SMOTE). To test our approach, we implemented five benchmarking classifiers such as support vector machines, logistic regression, k-nearest neighbor, random forest, and deep neural network. The data sample used was retrieved from the publicly available LendingClub dataset. The proposed SMOTE revealed significantly improved results in comparison with the benchmarking classifiers. These results should help actors engaged within P2P lending to make better informed decisions when selecting potential borrowers eliminating the higher risks present in P2P lending.

• 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 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.

• 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.

• 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.

• 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.