• 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.

• 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.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.

• 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.

• 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.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.3
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• pp.245-251
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• 2020

This study aims to investigate whether financial literacy, parental socialization, peer influence and self-control have a significant impact on money management among students of a Malaysian public university. In most settings, the studies on the determinants of personal saving behavior among university students focus on personality factors, attitudes toward money and possessions, and financial knowledge as predictors of money management behavior. Primary data are collected using a self-administered questionnaire with the samples comprised 186 students. The findings show that all the financial literacy and parent socialization have a positive relationship with money management; meanwhile, peer influences and self-control have a negative relationship with money management. This is because the majority of students admit they cannot control their self in managing their money. In this study, parent socialization has a significant relationship with money management which signifies that parents are playing essential roles in ensuring their children to establish money management. In practical, this study can contribute to the parents in providing their children to foster sound money management. Besides, Perbadanan Tabung Pendidikan Tinggi Nasional (National Higher Education Fund Corporation) PTPTN should monitor the loan, and management process and arrange motivational program so that students can more focus on the educational expense.