P2P Lending Market Research Articles

Estimating probability of default via delinquencies? Evidence from European P2P lending market

The unprecedented growth of the financial sector's digital transformation opens wide areas to the scaling up of finance in innovative and knowledge-based projects. Improving risk management takes centre stage in the acceleration of this process. This study uses loan-book data from the peer-to-peer (P2P) lending market to empirically investigate the determinants of default risk. Using the loan-book database covering the period from 2014 to 2020, we examine multiple factors related to the default risk of loans issued by P2P lending platforms. The results indicate that a higher interest rate and higher stock market returns increase the probability of default in the P2P lending market. Results are robust to additional tests based on endogeneity correction, the LASSO method and sampling bias. The severity of the impact of market returns and interest rates is found to be significantly different based on the levels of financial technology (FinTech) adoption and banking sector distress. Increases in the market interest rate are found to boost the sensitivity of P2P loan defaults to stock market volatility. This study contributes to existing literature on risk management models with its consideration of country-specific factors, paving the way to future best practices in the market.

Role transition, investment practice and risk Management of Peer-to-peer Lending Investors: Based on the perspective of investor learning

Online peer-to-peer (P2P) lending has emerged as a result of the integration of finance and technology. Within the P2P lending market, individual investors have the opportunity to make repeated investments and engage in two-way transactions involving loans and investments. This setting provides an ideal scenario for studying the impact of different learning types on investors' risk management. Drawing on transaction data from the online lending platform Renrendai.com, we empirically examine the influence of role transition and investment practice on investors' risk identification, risk preference, and investment performance. We introduce variables related to loans and investment portfolios to analyze the effects. Our findings reveal that investment practice has a greater effect than role transition in reducing the investment default of online lending investors. Additionally, role transition is associated with a decrease in the investment amount set by investors and a reduced reliance on borrowers' credit ratings. Historical investment experience also leads to a decrease in the investment amount, while experiences of past investment failures improve investors' reference to borrowers' credit ratings. Furthermore, we observe that borrowing experience and previous investment failures contribute to a lower probability and proportion of high-risk loans in the portfolio. We shed light on the “black box” of investor learning and expand the research methodology for understanding investor behavior characteristics in the Fintech era.

Why individuals invested in China’s P2P lending market: an explanation based on categorization theory

ABSTRACT This paper examines why individuals invested in the nascent and underregulated Chinese P2P lending market based on the categorization theory in marketing studies. Using a unique account-level database of investors from a large P2P platform, we find that investors from regions with better-developed FinTech products that involve financial intermediaries invested more and took more risks in P2P lending. The effect is stronger among investors with a lower level of generalized trust or less investment experience in P2P lending. Our results suggest that category-based evaluation may occur when individuals decide whether to invest in a new risky financial market.

Peer effects in the online peer-to-peer lending market: Ex-ante selection and ex-post learning

This study investigates peer effects in the online peer-to-peer (P2P) lending market using data from a Chinese online lending platform, Renrendai. The empirical results indicate that both the borrowers' success rate in obtaining loans and the default rate after loans are deemed non-coercive among their peers, referred to as the peer effects of lending and peer effects of default, respectively. The peer effect of lending is more pronounced in high-risk cities, whereas the peer effect of defaulting is more pronounced for borrowers with more difficulty obtaining loans, indicating ex-ante selection and ex-post learning mechanisms, respectively. The peer effects of lending promote P2P lending market efficiency, and the peer effects of defaulting inhibit market efficiency. Collectively, our results suggest that both lenders and borrowers follow peer effects to reduce information asymmetry in P2P lending markets.

The impact of conventional and unconventional monetary policies on loan default risk—Evidence from UK peer‐to‐peer lending platforms

AbstractThis study investigates the effect of both conventional and unconventional monetary policies on loan default of UK personal and business peer‐to‐peer (P2P) loans. I employ loan book data of Zopa, Lending Works, and MarketFinance, which are three of the most popular UK P2P lending platforms. Survival analysis reveals consistent evidence for the existence of the risk‐taking channel of monetary policy. Monetary easing, be it conventional or unconventional, reduces loan survival in the P2P lending market. This finding delivers useful information for policymakers to rebalance the countervailing effects of expansionary monetary policies on financial stability during the era of alternative financing.

Liquidity risk in FinTech lending: Early impact of the COVID-19 pandemic on the P2P lending market

This study empirically investigates the impact of the COVID-19 pandemic on the liquidity risk incurred by the peer-to-peer (P2P) lending market. As the pandemic adversely affects financial markets globally, a better understanding of the dynamics of successful P2P lending is necessary under the conditions of financial distress. By using the cross-country database of secondary market listing outcomes at Bondora (Estonia) and employing probit, ordered probit and tobit regression methods, we provide evidence of the pandemic-induced exposure to liquidity risk in the P2P lending market. Despite increased volatility in the financial markets, results show that COVID-19 risk increases the probability of successful listing during the pandemic. However, this outcome comes at significant liquidation costs for investors in the form of higher premiums. Further analysis based on listing outcomes and loan characteristics shows a negative association between COVID-19 risk and share of overdue loans and average overdue days in secondary market listings. Simultaneously, more experienced investors dominate the market as COVID-19 risk increases, a trend that is reflected in shorter listing times. The findings of this study imply certain early tendencies in financial markets during pandemic-induced turmoil and open new avenues for further research.

Interdependence between online peer‐to‐peer lending and cryptocurrency markets and its effects on financial inclusion

Online peer‐to‐peer (P2P) lending has emerged as an innovative financial technology (FinTech) platform that renders financial services that are potentially more inclusive and affordable than those offered by traditional financial institutions. A similar purpose is served by cryptocurrency markets, where transaction costs are reduced and financial accessibility is improved based on disruptive technologies such as blockchain and distributed ledgers. Despite these developments, however, in the operations management literature limited attention has been devoted to the contribution of online P2P lending to the promotion of financial inclusion (i.e., the availability and usage of financial services for all groups of people) and its dynamic interplay with cryptocurrency markets. The rise of cryptocurrency markets affects the composition and activity of borrowers and investors in P2P lending markets and hence the capacity of the latter to support financial inclusion, leading to an operations management challenge in online P2P lending. We examine how cryptocurrency markets influence P2P lending markets' democratization of access to financial services, particularly P2P borrowing. To investigate these effects in depth, we develop a simple theoretical model to derive testable propositions, which are then empirically validated on the basis of unique data sets. We find that the growth in cryptocurrency markets is associated with increased loan requests and larger loan amounts in P2P markets, especially from borrowers who maintain good credit ratings, possess technical knowledge about cryptocurrencies, and intend to borrow for investment purposes. Our results suggest that cryptocurrency markets bring economic gains to the P2P lending market, at least in the short term. Nonetheless, the transfer of funds from P2P lending to cryptocurrency markets, particularly by highly creditworthy and tech‐savvy investors, may provoke increased inequality in access to P2P lending markets. By scrutinizing the interdependence between two representative FinTech markets we uncover important operations management implications for theory and practice regarding the healthy growth and effective governance of crowdfunding platforms and the corresponding sustainability of their role in upholding financial inclusion.

Social identity of civil servants and online peer-to-peer lending: Evidence from China

Using a novel dataset of 1,777,847 peer-to-peer (P2P) transactions between 2010 and 2017, we investigate how the social identity of Chinese civil servants affects P2P lending. The empirical results indicate that the social identity of civil servants leads to a higher probability of obtaining loans and a lower interest rate in P2P lending. Our results thus reveal that Chinese civil servants obtain considerable benefits in the P2P lending market compared with non-civil servants; these empirical results remain robust after considering sample selection bias.

Media News and Social Media Information in the Chinese Peer-to-Peer Lending Market

This paper uses supervised machine learning (sentiment analysis) to analyze the sentiments of social media information in the P2P lending market. After segmentation, filtering, feature word extraction, and model training of the text information captured by Python, the sentiments of media and social media information were calculated to examine the effect of media and social media sentiments on default probability and cost of capital of peer-to-peer (P2P) lending platforms in China (2015–2019). We find that only positive changes in media and social media sentiment have significantly negative effects on the platform’s default probability and cost of capital, while negative changes in sentiment do not have any effects. We conclude the existence of an asymmetric effect of media and social media sentiments in the Chinese peer-to-peer lending market.

Global peer-to-peer lending market

Purpose- The purpose of this study is to elaborate the current impressive standing of the market in order to pave the way for future academic work. Methodology- The study employs primary data analysis of the P2P lending market. Also a detailed update literature analysis is realized. Findings- The analysis reveals that the financial conditions prevailing in global markets also contributed to the growth of P2P lending sector in the past. The low interest rate environment since the global financial crisis of 2007-2008 made P2P lending as a better alternative for small investors looking for higher yields. Conclusion- It t may be concluded that the P2P market lending will be a fruitful area of financial research in the future especially in the fiesld of the credit assessment procedures, default tendency analysis and regulatory struturings of the regional markets. Keywords: Fintech, crowd lending, P2P, Peer-to-peer lending, platforms JEL Codes: G01, G15

Underdog mentality, identity discrimination and access to peer-to-peer lending market: Exploring effects of digital authentication

Vulnerable social groups are often the recipients of statistical discrimination by financial institutions. They therefor try to access the P2P lending market through voluntary digital authentication, but the digital authentication requirements in P2P lending increase taste-based discrimination against these vulnerable groups. Empirical results in this study confirm the existence of education-based and gender-based statistical discrimination even in the Chinese P2P lending market. Digital authentication might alleviate statistical discrimination and increase financial inclusion for these vulnerable groups, but this authentication might also lead to members of vulnerable groups having to pay higher interest rates when seeking loans. The “underdog mentality” concept explains why vulnerable groups voluntarily accept digital authentication, and why they tolerate having to pay higher interest rates for loans P2P they receive.

Applying hybrid machine learning algorithms to assess customer risk-adjusted revenue in the financial industry

A Peer-to-Peer (P2P) service is a decentralized platform that directly connects individuals, buyers (lenders) and sellers (investors) without the intermediation of a third party. In the P2P lending market, customer cash flows are undeniably linked to their financial risk of default. Thus, forecasting customers’ Risk-Adjusted Revenue (RAR) value is one of the most critical issues in financial decision-making. With the emergence of big data, traditional forecasting methods cannot provide the high predictive power needed for such metrics. We propose a hybrid method by integrating the use of supervised and unsupervised Machine Learning (ML) algorithms to enhance the accuracy of predicting customer-adjusted risk metrics. Using a real P2P dataset from the Lending Club, containing over two million cases, we forecast customers’ risk-adjusted revenue by applying ML algorithms for the first time. These include individual methods such as gradient boosting and decision trees, and hybrid frameworks that group customers using a clustering algorithm (k-Means or Density-Based Spatial Clustering of Applications with Noise (DBSCAN)) prior to implementing the individual methods. We compare the efficiency (processing time and accuracy) of this hybrid approach with the performance of individual regressor-based models to predict RAR. Our results indicate high predictive power for many individual ML algorithms (R2 score over 90%). Further, in most cases, hybrid models outperform the individual ones in both predictive performance and processing time. Finally, the feature importance analysis in the best predictive frameworks helps identify the most influential factors in predicting customers’ RAR in the P2P lending market.

The role of social and psychological related soft information in credit analysis: Evidence from a Fintech Company

Improvements in the quality of information in credit appraisal are paramount to the greater efficiency of credit markets. The existing research to assess the role of soft information in credit markets has so far been very limited and inconclusive due to differences in approaches and methodological limitations. The aim of this paper is to discuss the role of social and psychological related soft information in predicting defaults in the P2P lending market and to assess the importance of such information in Fintech credit analysis. Using a unique dataset from the pioneer P2P lending platform RRDai.com and alternative models of testing, we compared the predictive performance of soft information, hard information and combined hard and soft information on defaults. The results show that soft information can provide valuable input into credit appraisals. Soft information shows high predictive power in our test, and combined with hard information, it increases the power of our model to predict defaults.

Incentive and Coordination in the Two-Sided Market: Evidence from the P2P Lending Market

Two-sided markets serve as information intermediaries by connecting participants on both sides. In this study, we focus on the coordination of participants in the P2P lending market using a coupon strategy as an incentive to attract investment. Using a two-sided market model, we find that when a platform adopts the coupon strategy, (i) the platform utility and participants’ utility are both greater and (ii) the number of participants is greater. In addition, as most research on two-sided markets and coupon strategy focuses on theoretical models, our study provides empirical support using data from Renrendai.com over 2018 to 2019.

Can investors’ collective decision-making evolve? Evidence from peer-to-peer lending markets

This study tries to identify the accuracy of individual investors’ capability to predict a borrower’s creditworthiness in peer-to-peer lending markets and examine whether their ability is likely to evolve over time. The results of this study show that there is no significant difference between the predictive power of investors’ ex-ante funding decision model and that of the ex-post repayment model over a borrower’s repayment performance. Furthermore, the predictive power of investors’ ex-ante funding decision over a borrower’s repayment performance is shown to improve over time. It is also found that the main reason why investors’ predictive power improves over time is because investors can assess more accurately the information provided by the platform operator and describe the borrower's characteristics. The results of this study are important as they confirm the possibility of optimizing and streamlining the P2P lending market, through the evolution of investors’ decision making.

Risk-return modelling in the p2p lending market: Trends, gaps, recommendations and future directions

• This study reviews the literature on risk-return modeling in p2p lending market. • Our research identifies the most influential works and critical references. • Our paper shows the research trends through bibliometric visualization tools. • ML support most new approaches and improve the performance of traditional models. • Several works lack explainability and a genuine understanding of business use. Peer-to-peer (P2P) lending is a market with significant growth in recent years. We review the academic literature published during the last decade on P2P lending to identify the main research trends and find potential gaps that limit stakeholders' use of research proposals. We perform both a bibliometric and systematic analysis. The bibliometric analysis will identify the most influential papers and the relationship and evolution of the main topics. In the systematic analysis, we categorized the documents according to methodological elements and business aspects. Remarkably, many proposals include artificial intelligence or machine learning algorithms. However, many of them lack a proper understanding of the application context, the definition of potential variables in a business framework, explainability, etc. Such elements should be recognized as essential elements to exploit their benefits. In this respect, we provide some recommendations and show future research directions.

Macroeconomic determinants of loan defaults: Evidence from the U.S. peer-to-peer lending market

The study documented in this paper utilises a probit regression analysis to empirically investigate the key macroeconomic factors that influence default risk in the peer-to-peer (P2P) lending market. By aggregating the United States (U.S.) state-level data with LendingClub's loan book covering the period from 2008 to 2019, this study examines multiple factors related to default risks of loans issued by P2P lending platforms. Our results show that a higher interest rate and inflation increase the probability of default in the P2P lending market. We also find that the impact of interest rate on the probability of default is significantly higher for loans with lower ratings. By paving the way to future market best practices, the study's outcomes apply to P2P lending platforms and investors in their default estimation of loans.

Do investors prefer borrowers from high level of trust cities? Evidence from China’s P2P market

This study explores the effects of trust on individuals’ access to the peer-to-peer (P2P) lending market. We use data collected from the P2P lending market and the China General Social Survey and find that borrowers from cities with high trust have high borrowing success rates, thereby indicating that lenders prefer high social trust. Results in the successful sample indicate that borrowers with high regional trust also receive low loan costs and large loan amounts. Regression of trust and default proves that borrowers from cities with high social trust have minimal default rates, which may be the channel of our conclusion. Results remain unchanged when using slope and river length as instrumental variables. This research further shows that personal heterogeneity, including income level, whether borrowers work in state-owned enterprises or state agencies, and whether the fund is used for development purposes, affects the impact of social trust. In addition, the conclusions continue to be robust after replacing the explanatory variable, control variable, and sample. Finally, this study determines that fairness plays a consistent role with trust, but happiness plays an opposite role.

Empirical Evidence of Faulty Credit Scoring and Business Failure in P2P Lending

Purpose: This study attempts to identify the cause of the failure of a platform operator in a P2P lending market, from the perspective of the internal credit rating system. Design/methodology/approach: Data from Moneyauction, the first P2P lending platform in Korea, which led the market for a while and then unexpectedly closed down, was analyzed using a series of binary logistic regression and relative weight analysis methods. The study concentrates on the effect and importance of the self-assessed credit score system used by Moneyauction with regard to investor funding decisions and the actual repayment performance outcomes of borrowers. The predictive power of this self-assessed credit score is also identified. Findings: The findings show that while the internal credit score is considered the most important factor in an investor s funding decision, its importance with regard to a borrower s actual repayment performance is significantly lower compared to other factors. Specifically, the predictability when using a model with an internal credit score with regard to a borrower’s repayment performance is inferior to a model without this factor. The findings therefore suggest that low-quality self-assessed credit rating systems may in fact contribute to the failure of individual platforms, even when the market is growing rapidly. Research limitations/implications: A series of business failures in a P2P market in the early stages of growth could make it difficult for the market to grow as a promising means of alternative finance due to the mounting distrust of potential participants and the exodus of existing investors. Therefore, the results of this study may present important issues to be discussed in relation to healthy market growth. The findings of this study are from the special case of Moneyauction in Korea. Thus, there is a limit to generalizing the results in this study, and further research is needed on additional platforms in similar situations in different markets. Originality/value: None of the previous studies on P2P lending markets has investigated individual platform failure cases. Existing studies mainly focus on the statistical significance of the effects of borrower characteristics on investors decisions or borrowers repayment performances. This study is also distinct in terms of its methodology in that it goes one step further and discusses the relative importance among all aspects of the borrowers characteristics.

Herd Behavior of Investors and Default Risks of P2P Online Lending

We examine the impact of investors’ herding on the default risk of P2P online loans. More specifically, we first decompose the number of investors in each loan into two components: the component caused by public information and the component reflecting factors other than public information. Then we investigate the effect of each component on the default risk of loans, using an ordered probit analysis. We use the data on 3,720 loans that were traded through 8percent, a Korean price-posted P2P platform from February 2015 to December 2017. We find the results as follows: First, the number of investors is determined by information that are provided by the platform and macro-economic variables (hereafter public information). Second, the number of investors explained by public information decreases the default risk of the loans. However, the number of investors reflecting factors other than public information increases the default risk of the loans. These results are interpreted as an evidence supporting ‘herding’ hypothesis: Investors follow intentionally other investors’ investment decision. These results suggest that the quantity increasement and quality improvement of public information provided by a P2P platform can improve the efficiency of P2P lending market reducing herding caused by factors other than public information.