• Title/Summary/Keyword: P2P Lending

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Evaluation of Mobile Application in User's Perspective: Case of P2P Lending Apps in FinTech Industry

  • Lee, Sangmin
    • 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 Determinants of Potential Failure of Islamic Peer-to-Peer Lending: Perceptions of Stakeholders in Indonesia

  • MUHAMMAD, Rifqi;FAKHRUNNAS, Faaza;HANUN, Amalia Khairina
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.2
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    • pp.981-992
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    • 2021
  • This study identifies the determinants of potential failure of Islamic Peer-to-Peer (P2P) lending in Indonesia, and the mediating effect of Islamic ethics on reducing the potential for failure of Islamic P2P lending. This study uses primary data retrieved through questionnaires from the perspective of 152 stakeholders in Islamic P2P lending. Using a structural equation model (SEM), the study found that indebtedness, financing size, and governance have positive and significant relationships with the potential failure of Islamic P2P lending. This study provides evidence that the customer's internal conditions and the governance structure applied can increase the potential failure of Islamic P2P lending. Further, Islamic ethics is evidently able to partially reduce the potential failure of Islamic P2P lending by lessening risk management exposure, but it fails to address failure through Ponzi scheme exposure. As an implication, this study suggest that Islamic P2P lending must implement Islamic ethics more comprehensively by optimizing the advisory and supervisory role of the shariah board within their overall boards of directors also in their operational activities. Finally, it also adds to the existing knowledge on financial technology literature, particularly on the determinants of potential failure of financial technology from the perspective of stakeholders.

Exploring the Performance of Synthetic Minority Over-sampling Technique (SMOTE) to Predict Good Borrowers in P2P Lending (P2P 대부 우수 대출자 예측을 위한 합성 소수집단 오버샘플링 기법 성과에 관한 탐색적 연구)

  • Costello, Francis Joseph;Lee, Kun Chang
    • 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.

Factors Determining Adoption of Fintech Peer-to-Peer Lending Platform: An Empirical Study in Indonesia

  • SUNARDI, Rudy;HAMIDAH, Hamidah;BUCHDADI, Agung Dharmawan;PURWANA, Dedi
    • 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.

Determining Personal Credit Rating through Voice Analysis: Case of P2P loan borrowers

  • Lee, Sangmin
    • 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.

Information Asymmetry Issues in Online Lending : A Case Study of P2P Lending Site (인터넷 대부시장에서의 정보비대칭성 문제 : P2P 금융회사 사례를 중심으로)

  • Yoo, Byung-Joon;Jeon, Seong-Min;Do, Hyun-Myung
    • 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.

Artificial Intelligence Techniques for Predicting Online Peer-to-Peer(P2P) Loan Default (인공지능기법을 이용한 온라인 P2P 대출거래의 채무불이행 예측에 관한 실증연구)

  • Bae, Jae Kwon;Lee, Seung Yeon;Seo, Hee Jin
    • 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.

Influencing Factors on the Lending Intention of Online Peer-to-Peer Lending: Lessons from Renrendai.com (온라인 P2P 대출의도의 영향요인에 관한 연구: 런런다이 사례를 중심으로)

  • Yang, Qin;Lee, Young-Chan
    • 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 Relationship Between Debt Literacy and Peer-To-Peer Lending: A Case Study in Indonesia

  • HIDAJAT, Taofik
    • 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.

Sexism and Ageism in a P2P Lending Market: Evidence from Korea

  • KIM, Dongwoo
    • The Journal of Asian Finance, Economics and Business
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    • v.7 no.6
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    • pp.537-550
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    • 2020
  • This study attempts to identify gender and age discrimination by individual lenders in P2P lending markets by analyzing empirical transaction data from multiple platforms including Moneyauction, Popfunding, and 8percent. To do this, the study investigates the effects and importance of a borrower's gender and age on individual lenders' preferences and a borrower's actual repayment performance using multiple linear regression and relative weight analyses. As a result, no gender discrimination is found in the three Korean P2P lending markets, and such indiscrimination is rational, on the grounds that the borrower's gender does not have a statistically significant impact on the lenders' preferences as well as his/her actual repayment performance, and its relative importance is minimal. While, there marginally exists age discrimination against a borrower in the markets, and such ageism is likely to be irrational, on the grounds that the borrower's age has a partly significant and minimally important impact on the lenders' preferences, but has no significant and important impact on his/her repayment performance. For the first time, these findings help to clarify gender and age discrimination issues in the P2P lending market by identifying the rationality of individual lenders' preferences to the borrower's gender and age in the Korea.