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PurposeThe research documented in this paper aims to examine multiple factors related to borrowers' default in peer-to-peer (P2P) lending in the USA. This study is motivated by the hypothesis that both P2P loan characteristics and macroeconomic variables have influence on loan performance. The authors define a set of loan characteristics, borrower characteristics and macroeconomic variables that are significant in determining the probability of default and should be taken into consideration when assessing credit risk.Design/methodology/approachThe research question in this study is to find the significant explanatory variables that are essential in determining the probability of default for LendingClub loans. The empirical study is based on a total number of 1,863,491 loan records issued through LendingClub from 2007 to 2020Q3 and a logistic regression model is developed to predict loan defaults.FindingsThe results, in line with prior research, show that a number of borrower and contractual loan characteristics predict loan defaults. The innovation of this study is the introduction of specific macroeconomic indicators. The study indicates that macroeconomic variables assessed alongside loan data can significantly improve the forecasting performance of default model. The general finding demonstrates that higher percentage change in House Price Index, Consumer Sentiment Index and S&P500 Index is associated with a lower probability of delinquency. The empirical results also exhibit significant positive effect of unemployment rate and GDP growth rate on P2P loan default rates.Practical implicationsThe results have important implications for investors for whom it is of great importance to know the determinants of borrowers' creditworthiness and loan performance when estimating the investment in a certain P2P loan. In addition, the forecasting performance of the model could be applied by authorities in order to deal with the credit risk in P2P lending and to prevent the effects of increasing defaults on the economy.Originality/valueThis paper fulfills an identified need to shed light on the association between specific macroeconomic indicators and the default risk from P2P lending within an economy, while the majority of the existing literature investigate loan and borrower information to evaluate credit risk of P2P loans and predict the likelihood of default.

AbstractIn the early days of development, the Chinese P2P lending industry grew rapidly due to financial constraints faced by small businesses and consumers and expanded under a lax regulatory environment. The industry filled a gap in demand for finance but was accompanied by increasing risks and poor business models. Regulation was put into place piecemeal, but large regulatory action was taken in 2018 to register P2P businesses and then, in 2019 to wind down the industry. The experience of China's P2P lending industry underscores the importance of assessing credit risk reducing operational risk under experienced financial management. Although China's P2P industry is dying away, lessons from the industry have helped to improve the structure of new fintech firms.

Why do retail consumers look for P2P financial intermediation? Are internetbased peer-to-peer (P2P) loans a substitute for or a complement to bank loans? In this study we answer these questions by comparing P2P lending with the nonconstruction consumer credit market in Germany. We show that P2P lending is servicing a slice of the consumer credit market neglected by banks, namely highrisk and small-sized loans. Nevertheless, when accounting for the risk differential, interest rates are very similar. Our conclusion is that P2P lending is substituting the banking sector for high-risk consumer loans since banks are unwilling or unable to supply this slice of the market. Our study serves to show where the institutionalization of credit provision has left a slice of the market unsupplied.

AbstractOnline peer‐to‐peer (P2P) platforms both disclose and conceal borrowers' information. Minimal research has been conducted on the impact of concealing such information on borrowers' funding success. We fill this research gap by exploring a natural experiment opportunity at Prosper, an online lending marketplace where the platform enacted a policy to conceal borrowers' past failure history. We first find that the platform's decision to conceal such information can be rational because when this information was publicly available, borrowers with past failures had lower success rates than borrowers without past failures. We next investigate the effect of hiding this information on lenders' lending decisions. Our empirical results from a series of analyses demonstrate that concealing such information helps some borrowers but also surprisingly leads to an overall lower success rate, which ultimately harms the platform and runs counter to the platform's intention. These effects are strongest for low‐risk borrowers and would attenuate in the long run. We further find that the number of borrowers' past failures is associated with lower repayment ability.