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Online peer-to-peer lending (P2P lending) has emerged as an appealing new channel of financing in recent years. A fundamental but largely unanswered question in this nascent industry is the choice of market mechanisms, i.e., how the supply and demand of funds are matched, and the terms (price) at which transactions will occur. Two of the most popular mechanisms are auctions (where the “crowd” determines the price of the transaction through an auction process) and posted prices (where the platform determines the price). While P2P lending platforms typically use one or the other, there is little systematic research on the implications of such choices for market participants, transaction outcomes, and social welfare. We address this question both theoretically and empirically. We first develop a game-theoretic model that yields empirically testable hypotheses, taking into account the incentive of the platform. We then test these hypotheses by exploiting a regime change from auctions to posted prices on one of the largest P2P lending platforms. Consistent with our hypotheses, we find that under platform-mandated posted prices, loans are funded with higher probability, but the preset interest rates are higher than borrowers’ starting interest rates and contract interest rates in auctions. More important, all else equal, loans funded under posted prices are more likely to default, thereby undermining lenders’ returns on investment and their surplus. Although platform-mandated posted prices may be faster in originating loans, auctions that rely on the crowd to discover prices are not necessarily inferior in terms of overall social welfare. This paper was accepted by Chris Forman, information systems .

There have been concerns about the use of alternative data sources by fintech lenders. We compare loans made by LendingClub and similar loans that were originated by banks. The correlations between the rating grades (assigned by LendingClub) and the borrowers' FICO scores declined from about 80% (for loans originated in 2007) to about 35% for recent vintages (originated in 2014-2015), indicating that nontraditional data (not already accounted for in the FICO scores) have been increasingly used by fintech lenders. The rating grades perform well in predicting loan default. The use of alternative data has allowed some borrowers who would have been classified as subprime by traditional criteria to be slotted into \"better\" loan grades, allowing them to obtain lower priced credit.

In this paper, we examine the role of peer-to-peer (P2P) platforms in enhancing financial inclusion from the borrowers' point of view across the rural-urban dimension. We show that when number of bank branches decrease in a rural community, the P2P loan requests increase if there is at least one bank branch in the community allowing people to participate in the P2P market. We also find that the number of P2P loan requests from urban areas is higher when such areas have fewer pawnshops per capita. Our results suggest that P2P enhances financial inclusion of those lacking traditional institutions in rural communities and offers an alternative to those with fewer fringe banks in urban communities.

Online peer-to-peer (P2P) lending platform is an emerging FinTech business model that establishes a link between investors and recipients of capital in supply chains (SCs). Businesses face capital constraints impacting directly on their final product price and demand. This article studies optimal decisions and operational strategies in a logistics network considering two capital-constrained manufacturers who produce products of different qualities and sell them to a retailer having deterministic demand over a specific period. The high quality product manufacturer borrows capital through an online P2P lending platform with a service fee, while the low quality product manufacturer pre-sells products for competing with the high quality product manufacturer. In this study, we find optimal prices of the SC participants, service rate of the online P2P platform and percentage of the pre-ordering quantity of the retailer. We analyse optimal Stackelberg and Nash equilibrium of the SC participants. We find that an increase in the amount of opportunity cost will cause a decrease in the pre-ordering quantity of the retailer affecting the SC profit in numerous ways. The online P2P lending platform should consider the amount of the retailer’s target profit in determining the platform’s service rate. We posit some practical insights based on our numerical study and observations for SC managers enabling them to take appropriate measures about their optimal strategies according to the networks’ existing economic conditions.

We study the online market for peer-to-peer (P2P) lending, in which individuals bid on unsecured microloans sought by other individual borrowers. Using a large sample of consummated and failed listings from the largest online P2P lending marketplace, Prosper.com, we find that the online friendships of borrowers act as signals of credit quality. Friendships increase the probability of successful funding, lower interest rates on funded loans, and are associated with lower ex post default rates. The economic effects of friendships show a striking gradation based on the roles and identities of the friends. We discuss the implications of our findings for the disintermediation of financial markets and the design of decentralized electronic markets. This paper was accepted by Sandra Slaughter, information systems.

This research examines how identity claims constructed in narratives by borrowers influence lender decisions about unsecured personal loans. Specifically, do the number of identity claims and their content influence lending decisions, and can they predict the longer-term performance of funded loans? Using data from the peer-to-peer lending website Prosper.com, the authors find that unverifiable information affects lending decisions above and beyond the influence of objective, verifiable information. As the number of identity claims in narratives increases, so does loan funding, whereas loan performance suffers, because these borrowers are less likely to pay back the loan. In addition, identity content plays an important role. Identities focused on being trustworthy or successful are associated with increased loan funding but ironically are less predictive of loan performance than other identities (i.e., moral and economic hardship). Thus, some identity claims aim to mislead lenders, whereas others provide true representations of borrowers.

This paper uses detailed data from an online peer-to-peer lending intermediary to test whether local access to finance affects consumers’ willingness to pay for loans. After controlling for local economic conditions and borrower credit quality, we find that borrowers who reside in areas with good access to bank finance request loans with lower interest rates. This effect is stronger for borrowers with poor credit and those seeking small loans, suggesting that local access to finance is more important for marginal borrowers. Overall, our findings shed light on how consumers substitute between alternative sources of finance. This paper was accepted by Wei Jiang, finance .

This paper investigates how friendship relationships act as pipes, prisms, and herding signals in a large online, peer-to-peer (P2P) lending site. By analyzing decisions of lenders, we find that friends of the borrower, especially close offline friends, act as financial pipes by lending money to the borrower. On the other hand, the prism effect of friends’ endorsements via bidding on a loan negatively affects subsequent bids by third parties. However, when offline friends of a potential lender, especially close friends, place a bid, a relational herding effect occurs as potential lenders are likely to follow their offline friends with a bid.

Sharia fintech lending grew up at the teenage stage and has successfully taken a strategic place in the Indonesian loan market. Adopting the economics of information and signaling theory, this paper investigates the probability of successful crowdfunding. Using cross-section data, this study analyzes 1,153 funded projects on Ammana.id platform, a well-known Indonesia’s sharia P2P lending. This study runs OLS regressions to examine the effect of loan information (ranking, estimated profit shares, and financing duration) on the amount of crowded funding. This finding support both theories, that the information about the loan is a signal in determining the success of project funding. Ranking and duration of financing significantly affect the success of the P2P sharia lending platform, nevertheless profit share estimation is not significant. Loans that operated in short, tend to raise more funding, and vice versa. Loan ranking can provide the lender with instant information about the borrowers’ condition. Lenders tend to avoid low rankings loans due to the potential failure of loan payments. This study also found a surprising result that the coefficient of profit sharing is positive for Islamic funding but insignificant. This result shows that material gain is not the main issue for investors, but the elements of trust and justice are nobler according to Islamic beliefs. This study proves that loan information as a low-cost signal can be used by investors to make the best decision and reduce adverse selection problems. The findings support the strategic growth of Islamic platforms to build a sustainable Islamic investment and maintain financial stability. Acknowledgments Appreciation is given to the General Directorate of Higher Education, Research and Technology, Ministry of Education, Culture, Research and Technology, and the Institute for Research and Community Service of Universitas Islam Nahdlatul Ulama (Unisnu) Jepara, Indonesia.

PurposeLenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns with risk limitations or lowering risks with expected returns for P2P lenders.Design/methodology/approachThis paper used data from a leading online P2P lending platform in America. First, the authors constructed a logistic regression-based credit scoring model and a linear regression-based profit scoring model to predict the default probabilities and profitability of loans. Second, based on the predictions of loan risk and loan return, the authors constructed linear programming model to form the optimal loan portfolio for lenders.FindingsThe research results show that compared to a logistic regression-based credit scoring method, the proposed new framework could make more returns for lenders with risks unchanged. Furthermore, compared to a linear regression-based profit scoring method, the proposed new framework could lower risks for lenders without lowering returns. In addition, comparisons with advanced machine learning techniques further validate its superiority.Originality/valueUnlike previous studies that focus solely on predicting the default probability or profitability of loans, this study considers loan allocation in online P2P lending as an optimization research problem using a new framework based upon modern portfolio theory (MPT). This study may contribute theoretically to the extension of MPT in the specific context of online P2P lending and benefit lenders and platforms to develop more efficient investment tools.