互联网金融P2P网络借贷外文翻译文献

文献信息：

文献标题：Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending（点对点(P2P)网络借贷的信用风险与贷款绩效评估）

国外作者：Riza Emekter, Yanbin Tu, Benjamas Jirasakuldech, Min Lu 文献出处：《Applied Economics》, 2015, 47(1):54-70

字数统计：英文3063单词，15818字符；中文5110汉字

外文文献：

Evaluating credit risk and loan performance in online

Peer-to-Peer (P2P) lending

Abstract Online Peer-to-Peer (P2P) lending has emerged recently. This micro loan market could offer certain benefits to both borrowers and lenders. Using data from the Lending Club, which is one of the popular online P2P lending houses, this article explores the P2P loan characteristics, evaluates their credit risk and measures loan performances. We find that credit grade, debt-to-income ratio, FICO score and revolving line utilization play an important role in loan defaults. Loans with lower credit grade and longer duration are associated with high mortality rate. The result is consistent with the Cox Proportional Hazard test which suggests that the hazard rate or the likelihood of the loan default increases with the credit risk of the borrowers. Finally, we find that higher interest rates charged on the highrisk borrowers are not enough to compensate for higher probability of the loan default. The Lending Club must find ways to attract high FICO score and high-income borrowers in order to sustain their businesses.

Key words: Peer-to-Peer lending; credit grade; FICO score; default risk

I.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. 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.

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.

II.Literature Review

Three main streams of research have emerged in response to the growing popularity of P2P lending. The first stream of research examines the reasons for the emergence of online P2P lending. The second stream of research focuses on determining the factors that explain the funding success and default risk. The last stream of research investigates the performance of online P2P loan for a given level of the risk.

Peer group lending has been emerging in local communities and has attracted the research in this area. Conlin (1999) develops a model to explain the existence of peer group micro-lending programmes in the United States and Canada. He finds that peer groups enable fixed costs to be imposed on the entrepreneurs while minimizing the programme's overhead costs. Ashta and Assadi (2008) investigate whether Web 2.0 techniques are integrated to support the advanced social interactions and associations with lower costs for P2P lending. Hulme and Wright (2006) study a case of online P2P lending house, Zopa, in the United Kingdom. They suggest that the emergence of online P2P lending is a direct response to social trends and a demand for new forms of relationship in financial sector under the new information age.

There is extant literature that identifies the factors determining the funding success and default risk. Using the Canadian micro-credit data, Gomez and Santor (2003) find that group lending offers lower default rates than conventional individual lending does. Study by Iyer et al. (2009) shows that lenders can evaluate one third of credit risk using both hard and soft data about the borrower. Lin et al. (2013) analyse the role of social connections in evaluating credit risk and discover that strong social networking relationship is an important factor that determines the borrowing success and lower default risk. Lin et al. (2013) further report that applicants' friendship could increase the probability of successful funding, lower interest rates on funded loans, and these borrowers are associated with lower ex post default rates at Prosper. The importance of social ties in determining loans funded is also examined by Freedman and Jin (2014). The result shows that borrowers with social ties are more likely to

have their loans funded and receive lower interest rates. However, they also find evidence of risks to lenders regarding borrower participation in social networks.

Several other studies examine whether certain borrowers' characteristics and personal information determine the success of loan funding and default risk. Herzenstein et al. (2008) show that borrowers' financial strength, their listing and publicizing efforts, and demographic attributes affect likelihood of funding success. Study by Duarte et al. (2012) further argues that borrowers who appear more trustworthy have better credit score with higher probabilities of having their loans funded and default less often. Larrimore et al. (2011) demonstrate that borrowers who use extended narratives, concrete descriptions and quantitative words have positive impact on funding success. However, humanizing personal details or loan justifi cations have negative in?uences on funding success. Qiu et al. (2012) further reveal that in addition to personal information and social capital, other variables, including loan amount, acceptable maximum interest rate and loan period set by borrowers, significantly in?uence the funding success or failure.

Galak et al. (2011) further show that lenders tend to favour individual over group borrowers and borrowers who are socially proximate to themselves. They also find that lenders prefer the borrowers who are more like themselves in terms of gender, occupation and first name initial. More interestingly, Gonzalez and Loureiro (2014) have similar findings: (1) when perceived age represents competence, attractiveness has no effect on loan success; (2) when lenders and borrowers are of the same gender, attractiveness might lead to a loan failure (i.e., the ‘beauty is beastly' effect) and (3) loan success is sensitive to the relative age and attractiveness of lenders and borrowers. Herzenstein et al. (2011) find that herding in the loan auction is positively related to its subsequent performance, that is whether borrowers pay the money back on time.

III.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 https://www.360docs.net/doc/6811162667.html,. 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.

Table 1. Loan distributions by loan purpose (May 2007–June 2012)

Notes: The data is obtained from 61 451 loan applicants in the Lending Club, https://www.360docs.net/doc/6811162667.html,, from May 2007 to June 2012.

The loan-seeking persons are asked to provide the reasons for requesting loans.

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. Table 2. Loans distribution by credit grades (May 2007–June 2012)

Notes: The Lending Club uses the borrowers’ FICO credit scores along with other information to classify a loan from Grade A1 to G5 in descending credit risk. Therefore, A1 credit grade represents the highest credit quality/low-risk borrowers, whereas G5 credit grade represents the lowest credit quality/ high-risk borrowers. Total amount of loans requested as a percentage of total loan is 19.35% for credit grade group ‘A’, 29.56% for ‘B’, 19.94% for ‘C’, 14.84% for ‘D’, 10.15% for ‘E’, 4.59% for ‘F’ and 1.53% for ‘G’.

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. 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 instalments. 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.

Table 3. Loan distribution by the loan status (May 2007–June 2012)

Table 4 reports the general characteristics and credit history of the online P2P loan applicants from the Lending Club. Based on our sample of 61 451 loan

applicants, the average monthly interest charged on a loan is 12.34%. On average, 471 days passed from the issue date of the loan. The average credit grade of a borrower is 25, which corresponds to credit category between B and C. The average size of a typical loan is $11 604 and the average monthly payment is $351. The borrower in general pays back $4384 a month and has $7873 left to be paid. The average ratio of the remaining balance to total loans is 63%.

Examining the borrowers' characteristics, it shows that the mean income of a borrower from the Lending Club is $5796 with the debts to income ratio of 0.1381. On average, a borrower has 9.56 open credit lines and 22 total credit lines, carries $14 315 average revolving credit balance and almost half (51.6%) of his or her credit limit. In the last six months, there is 1 credit inquiry requested by an average borrower. Average FICO score category of a typical borrower is 3.48, which corresponds to a FICO score between 680 and 750.

Table 4. Descriptive statistics (May 2007–June 2012)

Notes: Credit Grade is the grade assigned by the Lending Club based on the FICOrano credit rating information along with other information. Credit Grade ‘1’ is the loan category of ‘G’ which is the riskiest class of loans. Credit Grade ‘7’ is the loan category of ‘A’ which is the lowest risk borrowers. FICOrano is the credit rating of the borrowers rated by credit card companies. FICO 6 corresponds to borrowers with the FICO score above 780, FICO 5 corresponds to FICO score between 750–779, FICO 4 = 714–749, FICO 3 = 679–713, FICO 2 = 660–678 and FICO 1 = 640–659, respectively.

IV.Conclusions

Credit risk is an important concern for the P2P loans. This study employs the data from the Lending Club to evaluate the credit risk of the P2P online loans. We find

that credit score, debt-to-income ratio, FICO score and revolving line utilization play an important role in determining loan default. The credit categorization used by the Lending Club successfully predicts the default probability with one exception of next lowest credit grade ‘F'. In general, higher credit grade loan is associated with lower default risk.

The mortality risk also increases with the maturity of the loans. Loans with lower credit grade and longer duration are associated with high mortality rate. The Cox Proportional Hazard Test results show that as the credit risk of the borrowers increases, so does the likelihood of loan being default. However, the higher interest rate currently charged for the riskier borrower is not significant enough to justify the higher default probability. This suggests that the lenders would be better off to lend only to the safest borrowers in the highest grade category of 7 or Grade A. Increasing spreads on riskier borrower may lead to a more severe adverse selection resulting in higher default risk.

The Lending Club lenders should either extend credits only to the highest grade borrower or try to find more creative ways to lower the default rate among current borrowers. When comparing with the US national consumers, borrowers with relatively higher income and potentially higher FICO scores do not participate in the P2P market. Creating incentives to attract these types of borrowers would have a significant potential to decrease the default risk in this market.

中文译文：

点对点（P2P）网络借贷的信用风险与贷款绩效评估

摘要近年来点对点（P2P）网络借贷开始兴起。这种小微贷款市场可以为借款人和贷款人提供一定的收益。本文利用受欢迎的P2P网络社交借贷平台之一的借贷俱乐部的数据，探讨了P2P贷款的特征，评估了其信用风险和贷款绩效。我们发现，信用等级、负债收入比、FICO评分和循环贷款利用率在贷款违约中

起着重要的作用。信用等级较低、期限较长的贷款往往与高死亡率联系在一起。这一结果与Cox比例风险模型测试的相一致，这表明贷款违约的风险率或可能性随着借款人的信用风险而增加。最后，我们发现，对高风险借款人收取较高利率，并不能够降低贷款的高违约率。借贷俱乐部需要找到吸引高FICO评分和高收入借款人的方法，以维持其业务。

关键词:P2P网络借贷；信用等级；FICO评分；违约风险

1.引言

随着Web 2.0时代的到来，创建方便快捷、协作性强的在线市场和虚拟社区已经不是一件难事。

新兴的Web 2.0应用程序之一是点对点(P2P)网络借贷市场，在那里贷款人和借款人几乎可以完成贷款交易。将借款人引荐给贷款人，这种市场提供了平台服务，可以为借款人和贷款人提供一些优势。借款人可以直接从贷款人那里获得小额贷款，并且其支付的利率比商业贷款要低。另一方面，与任何其他类型的贷款如公司债券、银行存款或存单相比，贷款人可以赚取更高的回报率。借款人与贷款人之间的信息不对称，是P2P网络借贷的问题之一。也就是说，贷款人不了解借款人的信誉，同样借款人也不了解贷款人的信誉。这种信息不对称可能导致逆向选择（阿克洛夫，1970）和道德风险（斯蒂格里兹和温斯，1981）。从理论上讲，这些问题可以通过定期监测来得到缓解，但这种做法在网络环境下很难实施，因为借款人和贷款人没有实际接触。促进和提高贷款人对借款人的信任也可以实施，以减轻逆向选择和道德风险问题。在传统的银行贷款市场中，银行可以使用抵押品、认证账户、定期报告，甚至出席董事会来增强对借款人的信任。然而，这样的机制将产生巨大的交易成本，因此难以在网络环境中实现。

为了减少由信息不对称所引起的贷款风险，目前的P2P网络借贷平台有以下安排。首先，借贷俱乐部根据FICO评分筛选出潜在的高风险借款人，能够参与平台借贷的最低FICO评分为640。第二，这个平台产生的贷款规模很小，借贷俱乐部的贷款不到35000美元。因此，这些贷款基本上是小额贷款，在违约的情况下造成的损失相对较小。第三，平台建设者提供配对体系，可以用来生成投资组合建议，并尽可能减少贷款风险。第四，如果借款人没有付款，则平台建设者

将向信贷机构报告情况，并聘请收款机构代表贷款人收取资金。虽然P2P网络借贷中有一些有助于降低风险的强化结构，但与传统贷款相比，这种形式的贷款在本质上与更大的风险联系在一起。

本文的目的是评估美国最大的P2P网络借贷平台之一的借贷俱乐部的借款人的信用风险，这有助于贷款人根据借款人的等级对贷款的风险和回报率做出更明智的决策。本文将讨论两个相关的研究问题：（1）有助于确定违约风险的借款人的特征有哪些？（2）高风险借款人的回报率是否高到能够弥补增量风险？如果贷款人知道借款人的哪些特征影响到违约风险，那么贷款人可以更有效地分配他们的投资。每个借款人按照信用等级进行分类，借贷俱乐部分配相应的借款利率。为了进行有效的分配，贷款人应该知道高风险借款人的高利率是否能够补偿贷款人潜在损失的更高概率。

我们的研究结果表明，FICO评分高、信用等级高、循环贷款利用率低、负债收入比低的借款人，往往违约风险偏低。这一发现与杜阿尔特等人（2012年）的研究相一致，他们指出具有可信赖特征的借款人信用评分较好，而违约概率低。这一结果还表明，除弗里德曼和吉恩（2014）、林等人（2013）指出的贷款申请人的社会关系和朋友关系外，上述四个因素对解释违约风险也很重要。与美国国家借款人相比，结果显示，贷款俱乐部应继续筛选FICO评分低的借款人，吸引FICO评分高的借款人，从而大幅度降低违约风险。在将风险与回报相关联时，本研究表明，对高风险的借款人收取高的利率并不能对高违约率作出解释。我们在这里的发现与别尔科维奇（2011）的研究相一致，他指出高质量的贷款提供超额回报。

2.文献综述

随着P2P网络借贷的日益普及，出现了三大研究方向。第一个研究方向是分析P2P网络借贷出现的原因。第二个研究方向集中于确定筹资成功和违约风险的因素。第三个研究方向是调查在一定水平的风险下P2P网络贷款的表现。

同侪团体贷款在当地社区涌现，并吸引了这一领域的研究。康林（1999）开发了一个模型来解释美国和加拿大存在同侪团体小额贷款项目。他发现，同侪团体可以将固定成本强加给企业家，同时最大限度地减少项目的管理成本。阿什达

和亚沙（2008）研究了Web 2.0技术是如何集成的，以支持高级社会互动和交往，降低P2P网络借贷成本。休姆和莱特（2006）研究了在英国的P2P网络借贷平台Zopa的案例。他们认为，P2P网络借贷的出现是新信息时代的金融业对社会趋势的直接反应，以及对新形式关系的需求。

现有的文献中，有的确定了筹资成功和违约风险的决定因素。利用加拿大小额信贷数据，戈麦斯和桑托尔（2003）发现，团体贷款的违约率比传统的个人贷款要低。伊耶等人（2009）的研究显示，通过分析借款人的硬数据和软数据，贷款人可以评估出三分之一的信贷风险。林等人（2013）分析了社会关系在评估信用风险中的作用，发现强大的社交网络关系是决定借款成功和降低违约风险的重要因素。林等人（2013年）进一步指出，申请人的朋友关系可能会增加筹资成功的可能性，降低贷款利率，而且在Prosper上，这些借款人的事后违约率较低。弗里德曼和吉恩（2014）也研究了社会关系在确定贷款资金中的重要性。结果表明，具有社会关系的借款人更有可能获得贷款资金，且利率较低。然而，他们也发现这一迹象，即借款人参与社交网络，对贷款人也有风险。

其他一些研究探讨了借款人的某些特征和个人信息是否决定了筹资成功和违约风险。赫斯坦恩等人（2008）表示，借款人的财务实力、上市和宣传工作以及个人特征，影响到筹资成功的可能性。杜阿尔特等人（2012）的研究进一步认为，看起来更值得信赖的借款人信用评分较高，往往筹资成功可能性更高，违约率更低。拉里莫尔等人（2011）表明，借款人使用扩展叙述、具体描述和定量单词，对筹资成功有积极的影响。然而，个性化的个人资料或贷款理由对筹资成功有负面影响。邱等人（2012）进一步揭示，除了个人信息和社会资本外，借款人设定的其他变量，包括贷款金额、可接受的最高利率和贷款期限，都显著影响了筹资的成败。

加拉克等人（2011）进一步表明，与团体借款人相比，贷款人更倾向于个人借款人，以及社交上接近自己的借款人。他们还发现贷款人倾向于在性别、职业和名字的首字母上更像自己的借款人。更有趣的是，冈萨雷斯和洛雷罗（2014）也有类似的发现：（1）当把外表年龄视为其能力的体现时，吸引力对贷款成功没有影响；（2）当贷款人和借款人的性别相同时，吸引力可能导致贷款失败（即“红颜祸水”效应）；（3）贷款成功对贷款人和借款人的相对年龄与吸引力很敏感。

赫斯坦恩等人（2011）发现，贷款拍卖中的羊群效应与其后续表现呈正相关，即借款人是否按时还钱。

3.数据

本部分首先对贷款申请人的资料进行了描述，然后是根据贷款目的、信用等级及贷款状况的贷款分配，最后是贷款申请人的详细描述统计。本研究利用了借贷俱乐部上的自2007年5月至2012年6月的61451笔贷款申请，这些数据是从https://www.360docs.net/doc/6811162667.html,网站获取的。在研究期间，借贷俱乐部贷给借款人约7.13亿美元。为了研究借款人在P2P网络借贷中的行为，我们首先分析了他们向别人借款的主要原因。表1列出了借贷俱乐部的借款人自称的原因。近70%的贷款需求与债务合并或信用卡债务有关，总贷款额分别约为3.87亿美元和1.08亿美元。教育、可再生能源和休假贷款申请数量少于贷款总额的1％，所需贷款总额为1至300万美元。借款人表示，他们喜欢从借贷俱乐部借款是由于其较低的贷款利率，以及无法从信用卡上借到足够的钱。借款的第二个目的是支付住房抵押贷款或重新建房。

表1.根据贷款目的的贷款分配（2007年5月—2012年7月）

注：这些数据为借贷俱乐部上的自2007年5月至2012年6月的61451笔贷款申请，是从https://www.360docs.net/doc/6811162667.html,网站获取的。

要求贷款人提供要求贷款的理由。

借贷俱乐部利用借款人的FICO信用评分以及其他信息，将贷款信用等级从A1到G5由高到低分配给每个贷款的信用等级。详细程序如下：根据FICO评分分配基准分数后，借贷俱乐部将根据申请贷款的金额、最近几次的信用查询、信用历史长度、总开立信用账户、目前开立的信用账户和循环贷款利用率进行一些