The Role of Online Peer-to-Peer Lending in Crisis Response: Evidence from Kiva (original) (raw)
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This study examines the impact of the COVID-19 pandemic on the determinants of FinTech Peer-to-Peer (P2P) lending. The issue is significant because P2P lending platforms have attracted borrowers with little to no access to the credit facilities offered by conventional banks during the pandemic. Although many banks and financial institutions have offered online loan application services during the COVID-19 pandemic, few have developed verification of loan applications submitted online. The results of this study show that the COVID-19 has brought a drastic change in the key determinants of P2P lending. The results imply that FinTech P2P lending has become the most viable alternative credit option available to borrowers. The findings are significant and likely to be of interest to borrowers, investors, practitioners, academics, and policymakers because they highlight the usefulness of P2P lending platforms and their potential to augment or replace lending provided by traditional or conventional banking institutions.
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PLOS ONE
Peer-to-peer lending is hypothesized to help equalize economic opportunities for the world's poor. We empirically investigate the "flat-world" hypothesis, the idea that globalization eventually leads to economic equality, using crowdfinancing data for over 660,000 loans in 220 nations and territories made between 2005 and 2013. Contrary to the flat-world hypothesis, we find that peer-to-peer lending networks are moving away from flatness. Furthermore, decreasing flatness is strongly associated with multiple variables: relatively stable patterns in the difference in the per capita GDP between borrowing and lending nations, ongoing migration flows from borrowing to lending nations worldwide, and the existence of a tie as a historic colonial. Our regression analysis also indicates a spatial preference in lending for geographically proximal borrowers. To estimate the robustness for these patterns for future changes, we construct a network of borrower and lending nations based on the observed data. Then, to perturb the network, we stochastically simulate policy and event shocks (e.g., erecting walls) or regulatory shocks (e.g., Brexit). The simulations project a drift towards rather than away from flatness. However, levels of flatness persist only for randomly distributed shocks. By contrast, loss of the top borrowing nations produces more flatness, not less, indicating how the welfare of the overall system is tied to a few distinctive and critical country-pair relationships.
International Journal of Entrepreneurship, 2021
The Covid 19 Pandemic has rapidly accelerated the structural shift toward fully digital solutions, hence uplifting demand for virtual financial services such as digital lending services. This paper aims to predict the probability of loan defaults in the US Peer-to-Peer lending market through utilizing the dataset of Lending Club, the biggest US P2P lending platform, which covers 1,137,850 loans within the period from Q1 2017 to Q3 2020. A logistic regression model is developed to consolidate strong evidence of Borrowers characteristics, Loan characteristics and Credit Characteristics on the likelihood of defaulted loans. More importantly, the impact of Covid-19 pandemic presence is found visible for the performance of the defaulted loans in this P2P market.
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Banking & Insurance eJournal, 2017
Despite the lack of delegated monitor and of collateral guarantees P2P lending platforms exhibit relatively low loan and delinquency rates. The adverse selection is indeed mitigated by a new screening technology (information processing through machine learning) that provides costless public signals. Using data from Prosper and Lending Club we show that loans' spreads, proxing asymmetric information, decline with credit scores or hard information indicators and with indications from "group ties" (soft information from social networks). Also an increase in the risk of bank run in the traditional banking sector increases participation in the P2P markets and reduces their rates (substitution effect). We rationalize this evidence with a dynamic general equilibrium model where lenders and borrowers choose between traditional bank services (subject to the risk of bank runs and early liquidation) and P2P markets (which clear at a pooling price due to asymmetric information, bu...
Determinants of Default in P2P Lending
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This paper studies P2P lending and the factors explaining loan default. This is an important issue because in P2P lending individual investors bear the credit risk, instead of financial institutions, which are experts in dealing with this risk. P2P lenders suffer a severe problem of information asymmetry, because they are at a disadvantage facing the borrower. For this reason, P2P lending sites provide potential lenders with information about borrowers and their loan purpose. They also assign a grade to each loan. The empirical study is based on loans’ data collected from Lending Club (N = 24,449) from 2008 to 2014 that are first analyzed by using univariate means tests and survival analysis. Factors explaining default are loan purpose, annual income, current housing situation, credit history and indebtedness. Secondly, a logistic regression model is developed to predict defaults. The grade assigned by the P2P lending site is the most predictive factor of default, but the accuracy of the model is improved by adding other information, especially the borrower’s debt level.
Springer New York LLC, 2019
Most previous literatures focus on the micro level default risk of individual borrowers whereas the platform default risk has not been rigorously studied yet. In this paper, we investigate the factors affecting platform default risk by employing the Chinese online P2P platform data. We find significant evidence that severe competition among platforms can increase risky behaviors of platforms by allowing riskier borrowers into the system. Some of the risk management devices could alleviate the default risk of platforms; however, others are not effective at alleviating the default risks. In addition, we find evidence that macro environment such as stock market condition or increases in speculative investment opportunities plays critical roles to increase the platform default rate. Our study sheds light on the platforms' default risk issues and verifies key factors that influence their risky behaviors.
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In the current study, we examine why peer-to-peer (P2P) lending platforms play only a minor role in the finance industry in Israel, compared to the traditional banking system. We conducted two studies and attempted to discover if a discrepancy exists between the lenders' preferences and the platforms’ incentives. In the first study, we conducted a conjoint analysis to examine the impact of lenders' decisions to invest through P2P platforms. The second study examines the factors in which platforms use to determine the lending interest rate for loans. We found that although lenders wish to decrease their risk and guarantee their investment, P2P companies encourage riskier borrowers. This contradiction between the priorities of the lenders and those of the platforms may explain why the non-users consider P2P lending to be a high risk. We offer several suggestions to increase the attractiveness of the Fintech and lending platforms industry. Supplementary Information The online v...
In recent years, the importance of social lending activities and their effects on consumers have been highlighted by the widespread use of peer-to-peer lending platforms and the global race in fintech. Our study focuses on factors that affect the likelihood that European borrowers on peer-to-peer lending platforms, which are currently based in Estonia, Finland, and Spain, will default on their loans. Starting with the publicly accessible Bondora database, we examine the different economic and social characteristics of the borrowers to analyze the factors that contributed to loan default between 2013 and 2021. We use a Logit model to calculate the ex-post probability of default for factors derived from Principal Component Analysis as well as the original variables supplied by the database. The results show how crucially important education is for borrowers in lowering the risk of default, along with loan characteristics like high debt levels, long loan terms, and high interest rates. In addition, gender plays an important role in determining loan default, with a particular focus on women's conditions within the family. Regarding financial inclusion and its social implications, our findings suggest different ways to improve financial literacy and promote peer-to-peer lending. Future research could develop on the findings by applying them to other lending platforms and countries.