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出处：Emekter R, Tu Y, Jirasakuldech B, et al. Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending[J]. Applied Economics, 2015, 47(1):54-70. 原文
Evaluating credit risk and loan performance in online Peer-to-Peer
(P2P) lending
RizaEmekter, YanbinTu, BenjamasJirasakuldech & Min Lu
Introduction
With the advent of Web 2.0, it has become easy to create online markets and virtual communities with convenient accessibility and strong collaboration. One of the emerging Web 2.0 applications is the online Peer-to-Peer (P2P) lending marketplaces, where both lenders and borrowers can virtually meet for loan transactions. Such marketplaces provide a platform service of introducing borrowers to lenders, which can offer some advantages for both borrowers and lenders. Borrowers can get micro loans directly from lenders, and might pay lower rates than commercial credit alternatives. On the other hand, lenders can earn higher rates of return compared to any other type of lending such as corporate bonds, bank deposits or certificate of deposits.
One of the problems in online P2P lending is information asymmetry between the borrower and the lender. That is, the lender does not know the borrower’s credibility as well as the borrower does. Such information asymmetry might result in adverse selection (Akerlof, 1970) and moral hazard (Stiglitz and Weiss, 1981). Theoretically, some of these problems can be alleviated by regular monitoring, but this approach poses a challenge in the online environment because the borrowers and the buyers do not physically meet. Fostering and enhancing the lender’s trust in the borrower can also be implemented to mitigate adverse selection and moral hazard problems. In the traditional bank-lending markets, banks can use collateral, certified accounts, regular reporting, and even presence of the board of directors to enhance the trust in the borrower. However, such mechanisms are difficult to implement in the online environment which will incur a significant transaction cost.
To reduce lending risks associated with information asymmetry, current online P2P lending has the following arrangements. First, the Lending Club screens out any potential high-risk borrowers based on the FICO score. The minimum FICO score to be able to participate is 640.2 Second, the typical size of the loans produced in this market is small, which is under $35 000 at the Lending Club. Therefore, these loans are essentially microloans which pose a relatively small loss in case of default. Third, the market maker offers matchmaking systems which can be used to generate portfolio recommendations and minimize lending risks. Fourth, if a borrower fails to pay, the market maker will report the case to a credit agency and hire a collection agency to collect the funds on behalf of the lender. Although there are certain structures imposed in the online P2P that help to minimize the risk, this form of lending is inherently associated with greater amount of risk compared to the traditional lending.
The purpose of this article is to evaluate the credit risk of borrowers from one of the largest P2P platforms in the United States provided by the Lending Club, which help lenders to make more informed decisions about the risk and return efficiency of loans based on the borrowers’
grade. There are two related research questions this article will address: (1) What are some of the borrowers’ characteristics that help determine the default risk? and (2) Is the higher return generated from the riskier borrower large enough to compensate for the incremental risk? Lenders can allocate their investments more efficiently if they know what characteristics of the borrower affect the default risk. Each borrower is classified by credit grade with corresponding borrowing rate assigned by the Lending Club. To make an efficient allocation, a lender should know whether the higher interest rates set for high-risk borrowers are sufficient to compensate the lenders for the higher probabilities of a potential loss.
This study contributes to the literature in this new and fast growing P2P marketplace in many ways. While there are few studies which explored credit screening problem in the P2P lending platforms, this research differs from the prior research in various aspects (see, for example, Iyer et al., 2009 and Lin et al., 2013). First, this research extends risk analysis research in the online P2P lending by utilizing the new data from the Lending Club, which is contrast to many prior studies which utilize the data from one of the biggest P2P platform (Prosper). Second, this study estimates the default risk of loan applicants based on their significant demographic and characteristic factors, which enables the potential lenders to determine an optimal allocation strategy. Third, this research addresses the issue of selection bias by examining whether there is a significant difference in the default risk of the borrowers from the whole US population and the Lending Club, which yields an important implication for risk minimization for the Lending Club. Finally, this research relates the default risk of borrowers with the returns generated by the lenders by comparing the calculated theoretical interest rate with the actual interest rate charged by the Lending Club for each credit grade category. This provides important information regarding the risk and return efficiency of the Lending Club.
Our findings suggest that borrowers with high FICO score, high credit grade, low revolving line utilization and low debt-to-income ratio are associated with low default risk. This finding is consistent with the studies by Duarte et al. (2012) who report that borrowers with a trustworthy characteristic will have better credit scores but low probability of default. This result also suggests that besides the loan applicants’ social ties and friendship as reported by Freedman and Jin (2014) and Lin et al. (2013), the four factors discussed above are also important in explaining the default risk. When comparing with US national borrowers, the results show that the Lending Club should continue to screen out the borrowers with lower FICO score and attract the highest FICO score borrowers in order to significantly reduce the default risk. In relating the risk to the return, it shows that higher interest rate charged for the riskier borrower is not significant enough to justify the higher default probability. Our finding here is consistent with the study by Berkovich (2011) who reports that high quality loans offer excess return.
The remainder of the article is organized as follows. In the next section, we review the literature for online P2P lending. Section IIIdescribes our data and summarizes the descriptive statistics of online P2P from the Lending Club. In Section IV, we present the descriptions of methodologies and empirical results for evaluating the credit risk and measuring the risk and return efficiency for the Lending Club. The issue of selection bias is also addressed in this section. Section V offers some concluding remarks. Data
In this section, the loan applicants’ data is first described, followed by loan distribution based on loan purposes, credit grade and loan status and it ends with the detailed descriptive statistics of
the loan applicants. This study uses 61 451 loan applications in the Lending Club from May 2007 to June 2012 obtained from www.lendingclub.com. Over the study period, the Lending Club lent about $713 million to borrowers. To address the borrowers’behaviour in online P2P lending, we first examine the main reasons for borrowing money from others. Table 1 lists the borrowers’ self-claimed reasons summarized in the Lending Club. Almost 70% of loan requested are related to debt consolidation or credit card debts with a total loan amount requested of approximately $387 million and $108 million, respectively. The number of loan applications for education, renewable energy and vacation contribute less than 1% of total loans with the total loan requested ranging from 1 to 3 million. The borrowers state that their preferences to borrow from the Lending Club are lower borrowing rate and inability to borrow enough money from credit cards. The second purpose for borrowing is to pay home mortgage or to re-model home.
The Lending Club uses the borrower’s FICO credit scores along with other information to assign a loan credit grade ranging from A1 to G5 in descending credit ranks to each loan. The detailed procedure is as follows: after assigning a base score based on FICO ratings, the Lending Club makes some adjustments depending on requested loan amount, number of recent credit inquiries, credit history length, total open credit account, currently open credit accounts and revolving line utilization to determine the final grade, which in turn determines the interest rate on the loan.
Table 2 reports the loan distribution by credit grade. The majority of borrowing requests have grades between A1 and E5. The Highest loan amounts requested are from borrowers with ‘B’ credit grade, which contribute 29.56% of total amount of loans requested. The total number of applicants for this ‘B’ credit grade group is 18 707, which represents total loans of approximately $210 million. The lowest loan amounts requested are from borrowers with the lowest ‘G’ credit grade which accounts for 1.53% of total loans. There are only 608 loan applicants for this lowest credit rating ‘G’ group and it represents approximately $11 million in total loan value. According to the Lending Club’s policy, a loan credit grade is used to determine the interest rate and the maximum amount of money that a borrower can request. The higher the loan grade, the lower the interest rate. A borrowing request with a low grade renders a higher interest rate as a compensation for a high risk held by lenders.
Finally, Panel A of Table 3 shows the loan status for all the loan requests on 20 July 2012. Overall, the default rate is 4.60% with total losses of approximately $29 million.4 Another 2.45% of total loan requests which constitute $18.6 million could be potentially lost because the borrowers are late in making payment within 30 days or 120 days and not paying the normal installments. 17.98% of the loans are fully paid with an approximate value of $108 million. The $557 million loans are in current status account for 74.91% of total loans. Naturally, loans with a lower grade demonstrate a higher default rate. Therefore, study on risk management on P2P lending is relevant for the lenders to optimize their investment portfolios. Panel B of Table 3 reports the loan status for the matured loans. The overall loss rate is much higher for matured loans. Among 4904 matured loans, 914 loans are charged-off, which represent 18.6%. The total loss is $5.5 million which represents 13% of all matured loans amount. Less than 1% of the matured loans are late in terms of making payment with the unpaid balance of approximately $27 000. 80.77% or $33 million of matured loans are fully paid. Empirical Results
Evaluation of credit risks