外文翻譯—互聯(lián)網(wǎng)金融p2p網(wǎng)絡(luò)借貸（節(jié)選）

1、<p>　　字?jǐn)?shù)：英文3063單詞，15818字符；中文5110漢字</p><p>　　出處：Riza Emekter,Yanbin Tu,Benjamas Jirasakuldech,Min Lu.Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending[J]Applied Economics.2

2、015,47(1):54-70</p><p><b>　　外文文獻(xiàn)：　</b></p><p>　　Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending</p><p>　　Abstract Online Peer-to-Peer (

3、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

4、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

5、 defaults. Loans with lower c</p><p>　　Key words: Peer-to-Peer lending; credit grade; FICO score; default risk</p><p>　　I.Introduction</p><p>　　With the advent of Web 2.0, it has be

6、come easy to create online markets and virtual communities with convenient accessibility and strong collaboration.</p><p>　　One of the emerging Web 2.0 applications is the online Peer-to-Peer (P2P) lending m

7、arketplaces, 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

8、 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</p><p>　　

9、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

10、 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

11、which pose a relatively small loss in case of default. Third</p><p>　　The purpose of this article is to evaluate the credit risk of borrowers from one of the largest P2P platforms in the United States provid

12、ed 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 addre

13、ss: (1) What are some of the borrowers' characteristics that help determine the default risk? and (2) Is the higher return generated from the riski</p><p>　　Our findings suggest that borrowers with high

14、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 borrowe

15、rs 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 J

16、in (2014) and Lin </p><p>　　II.Literature Review</p><p>　　Three main streams of research have emerged in response to the growing popularity of P2P lending. The first stream of research examines

17、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 perfor

18、mance of online P2P loan for a given level of the risk.</p><p>　　Peer group lending has been emerging in local communities and has attracted the research in this area. Conlin (1999) develops a model to expla

19、in 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

20、. Ashta and Assadi (2008) investigate whether Web 2.0 techniques are integrated to support the advanced social interactions and associations w</p><p>　　There is extant literature that identifies the factors

21、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

22、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 tha

23、t str</p><p>　　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 tha

24、t 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 tru

25、stworthy have better credit score with higher probabilities of having their loans funded and def</p><p>　　Galak et al. (2011) further show that lenders tend to favour individual over group borrowers and borr

26、owers 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 Lourei

27、ro (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,</p><p><b>　　III.Data&

28、lt;/b></p><p>　　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 descript

29、ive 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 mi

30、llion to borrowers. To address the borrowers' behaviour in online P2P lending, we first examine </p><p>　　Table 1. Loan distributions by loan purpose (May 2007–June 2012)</p><p>　　Notes: The

31、 data is obtained from 61 451 loan applicants in the Lending Club, www.lendingclub.com, from May 2007 to June 2012.</p><p>　　The loan-seeking persons are asked to provide the reasons for requesting loans.<

32、;/p><p>　　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 proce

33、dure 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 cre

34、dit account, currently open credit accounts and revolving line utilization to determ</p><p>　　Table 2 reports the loan distribution by credit grade. The majority of borrowing requests have grades between A1

35、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 1

36、8 707, 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 1.5</p><p>　　Table 2. Loans dis

37、tribution by credit grades (May 2007–June 2012)</p><p>　　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 credi

38、t 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 percent

39、age 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 </p><p>　　Finally, Panel A of Table 3 shows the loan status for all the loan

40、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 borro

41、wers 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</p&

42、gt;<p>　　Table 3. Loan distribution by the loan status (May 2007–June 2012)</p><p>　　Table 4 reports the general characteristics and credit history of the online P2P loan applicants from the Lending C

43、lub. 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, whi

44、ch 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 pay</p><p>　　Examining the borrowers' cha

45、racteristics, 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

46、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 borrow

47、er is 3.48, which corresponds to</p><p>　　Table 4. Descriptive statistics (May 2007–June 2012)</p><p>　　Notes: Credit Grade is the grade assigned by the Lending Club based on the FICOrano credit

48、 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. FICOra

49、no 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</p><p>　　IV.Conc

50、lusions</p><p>　　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-

51、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 exc

52、eption of next lowest credit grade ‘F'. In general, higher credit grade loan is associated</p><p>　　The mortality risk also increases with the maturity of the loans. Loans with lower credit grade and lon

53、ger 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 in

54、terest 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</p><p>　　The Lending Club len

55、ders 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 rela

56、tively 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 thi

57、s market.</p><p><b>　　中文譯文：</b></p><p>　　點(diǎn)對(duì)點(diǎn)（P2P）網(wǎng)絡(luò)借貸的信用風(fēng)險(xiǎn)與貸款績效評(píng)估</p><p>　　摘要 近年來點(diǎn)對(duì)點(diǎn)（P2P）網(wǎng)絡(luò)借貸開始興起。這種小微貸款市場可以為借款人和貸款人提供一定的收益。本文利用受歡迎的P2P網(wǎng)絡(luò)社交借貸平臺(tái)之一的借貸俱樂部的數(shù)據(jù)，探討了P2P貸款的特征，評(píng)

58、估了其信用風(fēng)險(xiǎn)和貸款績效。我們發(fā)現(xiàn)，信用等級(jí)、負(fù)債收入比、FICO評(píng)分和循環(huán)貸款利用率在貸款違約中起著重要的作用。信用等級(jí)較低、期限較長的貸款往往與高死亡率聯(lián)系在一起。這一結(jié)果與Cox比例風(fēng)險(xiǎn)模型測(cè)試的相一致，這表明貸款違約的風(fēng)險(xiǎn)率或可能性隨著借款人的信用風(fēng)險(xiǎn)而增加。最后，我們發(fā)現(xiàn)，對(duì)高風(fēng)險(xiǎn)借款人收取較高利率，并不能夠降低貸款的高違約率。借貸俱樂部需要找到吸引高FICO評(píng)分和高收入借款人的方法，以維持其業(yè)務(wù)。</p>&l

59、t;p>　　關(guān)鍵詞:P2P網(wǎng)絡(luò)借貸；信用等級(jí)；FICO評(píng)分；違約風(fēng)險(xiǎn)</p><p><b>　　1.引言</b></p><p>　　隨著Web 2.0時(shí)代的到來，創(chuàng)建方便快捷、協(xié)作性強(qiáng)的在線市場和虛擬社區(qū)已經(jīng)不是一件難事。</p><p>　　新興的Web 2.0應(yīng)用程序之一是點(diǎn)對(duì)點(diǎn)(P2P)網(wǎng)絡(luò)借貸市場，在那里貸款人和借款人幾乎可以

60、完成貸款交易。將借款人引薦給貸款人，這種市場提供了平臺(tái)服務(wù)，可以為借款人和貸款人提供一些優(yōu)勢(shì)。借款人可以直接從貸款人那里獲得小額貸款，并且其支付的利率比商業(yè)貸款要低。另一方面，與任何其他類型的貸款如公司債券、銀行存款或存單相比，貸款人可以賺取更高的回報(bào)率。借款人與貸款人之間的信息不對(duì)稱，是P2P網(wǎng)絡(luò)借貸的問題之一。也就是說，貸款人不了解借款人的信譽(yù)，同樣借款人也不了解貸款人的信譽(yù)。這種信息不對(duì)稱可能導(dǎo)致逆向選擇（阿克洛夫，1970）和道

61、德風(fēng)險(xiǎn)（斯蒂格里茲和溫斯，1981）。從理論上講，這些問題可以通過定期監(jiān)測(cè)來得到緩解，但這種做法在網(wǎng)絡(luò)環(huán)境下很難實(shí)施，因?yàn)榻杩钊撕唾J款人沒有實(shí)際接觸。促進(jìn)和提高貸款人對(duì)借款人的信任也可以實(shí)施，以減輕逆向選擇和道德風(fēng)險(xiǎn)問題。在傳統(tǒng)的銀行貸款市場中，銀行可以使用抵押品、認(rèn)證賬戶、定期報(bào)告，甚至出席董事會(huì)來增強(qiáng)對(duì)借款人的信任。然而，這樣的機(jī)制將產(chǎn)生巨大的交易成本，因此難以在網(wǎng)絡(luò)環(huán)境中實(shí)現(xiàn)。</p><p>　　為了減少

62、由信息不對(duì)稱所引起的貸款風(fēng)險(xiǎn)，目前的P2P網(wǎng)絡(luò)借貸平臺(tái)有以下安排。首先，借貸俱樂部根據(jù)FICO評(píng)分篩選出潛在的高風(fēng)險(xiǎn)借款人，能夠參與平臺(tái)借貸的最低FICO評(píng)分為640。第二，這個(gè)平臺(tái)產(chǎn)生的貸款規(guī)模很小，借貸俱樂部的貸款不到35000美元。因此，這些貸款基本上是小額貸款，在違約的情況下造成的損失相對(duì)較小。第三，平臺(tái)建設(shè)者提供配對(duì)體系，可以用來生成投資組合建議，并盡可能減少貸款風(fēng)險(xiǎn)。第四，如果借款人沒有付款，則平臺(tái)建設(shè)者將向信貸機(jī)構(gòu)報(bào)告情況

63、，并聘請(qǐng)收款機(jī)構(gòu)代表貸款人收取資金。雖然P2P網(wǎng)絡(luò)借貸中有一些有助于降低風(fēng)險(xiǎn)的強(qiáng)化結(jié)構(gòu)，但與傳統(tǒng)貸款相比，這種形式的貸款在本質(zhì)上與更大的風(fēng)險(xiǎn)聯(lián)系在一起。</p><p>　　本文的目的是評(píng)估美國最大的P2P網(wǎng)絡(luò)借貸平臺(tái)之一的借貸俱樂部的借款人的信用風(fēng)險(xiǎn)，這有助于貸款人根據(jù)借款人的等級(jí)對(duì)貸款的風(fēng)險(xiǎn)和回報(bào)率做出更明智的決策。本文將討論兩個(gè)相關(guān)的研究問題：（1）有助于確定違約風(fēng)險(xiǎn)的借款人的特征有哪些？（2）高風(fēng)險(xiǎn)借款人

64、的回報(bào)率是否高到能夠彌補(bǔ)增量風(fēng)險(xiǎn)？如果貸款人知道借款人的哪些特征影響到違約風(fēng)險(xiǎn)，那么貸款人可以更有效地分配他們的投資。每個(gè)借款人按照信用等級(jí)進(jìn)行分類，借貸俱樂部分配相應(yīng)的借款利率。為了進(jìn)行有效的分配，貸款人應(yīng)該知道高風(fēng)險(xiǎn)借款人的高利率是否能夠補(bǔ)償貸款人潛在損失的更高概率。</p><p>　　我們的研究結(jié)果表明，F(xiàn)ICO評(píng)分高、信用等級(jí)高、循環(huán)貸款利用率低、負(fù)債收入比低的借款人，往往違約風(fēng)險(xiǎn)偏低。這一發(fā)現(xiàn)與杜阿爾

65、特等人（2012年）的研究相一致，他們指出具有可信賴特征的借款人信用評(píng)分較好，而違約概率低。這一結(jié)果還表明，除弗里德曼和吉恩（2014）、林等人（2013）指出的貸款申請(qǐng)人的社會(huì)關(guān)系和朋友關(guān)系外，上述四個(gè)因素對(duì)解釋違約風(fēng)險(xiǎn)也很重要。與美國國家借款人相比，結(jié)果顯示，貸款俱樂部應(yīng)繼續(xù)篩選FICO評(píng)分低的借款人，吸引FICO評(píng)分高的借款人，從而大幅度降低違約風(fēng)險(xiǎn)。在將風(fēng)險(xiǎn)與回報(bào)相關(guān)聯(lián)時(shí)，本研究表明，對(duì)高風(fēng)險(xiǎn)的借款人收取高的利率并不能對(duì)高違約率

66、作出解釋。我們?cè)谶@里的發(fā)現(xiàn)與別爾科維奇（2011）的研究相一致，他指出高質(zhì)量的貸款提供超額回報(bào)。</p><p><b>　　2.文獻(xiàn)綜述</b></p><p>　　隨著P2P網(wǎng)絡(luò)借貸的日益普及，出現(xiàn)了三大研究方向。第一個(gè)研究方向是分析P2P網(wǎng)絡(luò)借貸出現(xiàn)的原因。第二個(gè)研究方向集中于確定籌資成功和違約風(fēng)險(xiǎn)的因素。第三個(gè)研究方向是調(diào)查在一定水平的風(fēng)險(xiǎn)下P2P網(wǎng)絡(luò)貸款的表

67、現(xiàn)。</p><p>　　同儕團(tuán)體貸款在當(dāng)?shù)厣鐓^(qū)涌現(xiàn)，并吸引了這一領(lǐng)域的研究。康林（1999）開發(fā)了一個(gè)模型來解釋美國和加拿大存在同儕團(tuán)體小額貸款項(xiàng)目。他發(fā)現(xiàn)，同儕團(tuán)體可以將固定成本強(qiáng)加給企業(yè)家，同時(shí)最大限度地減少項(xiàng)目的管理成本。阿什達(dá)和亞沙（2008）研究了Web 2.0技術(shù)是如何集成的，以支持高級(jí)社會(huì)互動(dòng)和交往，降低P2P網(wǎng)絡(luò)借貸成本。休姆和萊特（2006）研究了在英國的P2P網(wǎng)絡(luò)借貸平臺(tái)Zopa的案例。他們

68、認(rèn)為，P2P網(wǎng)絡(luò)借貸的出現(xiàn)是新信息時(shí)代的金融業(yè)對(duì)社會(huì)趨勢(shì)的直接反應(yīng)，以及對(duì)新形式關(guān)系的需求。</p><p>　　現(xiàn)有的文獻(xiàn)中，有的確定了籌資成功和違約風(fēng)險(xiǎn)的決定因素。利用加拿大小額信貸數(shù)據(jù)，戈麥斯和桑托爾（2003）發(fā)現(xiàn)，團(tuán)體貸款的違約率比傳統(tǒng)的個(gè)人貸款要低。伊耶等人（2009）的研究顯示，通過分析借款人的硬數(shù)據(jù)和軟數(shù)據(jù)，貸款人可以評(píng)估出三分之一的信貸風(fēng)險(xiǎn)。林等人（2013）分析了社會(huì)關(guān)系在評(píng)估信用風(fēng)險(xiǎn)中的作用

69、，發(fā)現(xiàn)強(qiáng)大的社交網(wǎng)絡(luò)關(guān)系是決定借款成功和降低違約風(fēng)險(xiǎn)的重要因素。林等人（2013年）進(jìn)一步指出，申請(qǐng)人的朋友關(guān)系可能會(huì)增加籌資成功的可能性，降低貸款利率，而且在Prosper上，這些借款人的事后違約率較低。弗里德曼和吉恩（2014）也研究了社會(huì)關(guān)系在確定貸款資金中的重要性。結(jié)果表明，具有社會(huì)關(guān)系的借款人更有可能獲得貸款資金，且利率較低。然而，他們也發(fā)現(xiàn)這一跡象，即借款人參與社交網(wǎng)絡(luò)，對(duì)貸款人也有風(fēng)險(xiǎn)。</p><p&

70、gt;　　其他一些研究探討了借款人的某些特征和個(gè)人信息是否決定了籌資成功和違約風(fēng)險(xiǎn)。赫斯坦恩等人（2008）表示，借款人的財(cái)務(wù)實(shí)力、上市和宣傳工作以及個(gè)人特征，影響到籌資成功的可能性。杜阿爾特等人（2012）的研究進(jìn)一步認(rèn)為，看起來更值得信賴的借款人信用評(píng)分較高，往往籌資成功可能性更高，違約率更低。拉里莫爾等人（2011）表明，借款人使用擴(kuò)展敘述、具體描述和定量單詞，對(duì)籌資成功有積極的影響。然而，個(gè)性化的個(gè)人資料或貸款理由對(duì)籌資成功有負(fù)

71、面影響。邱等人（2012）進(jìn)一步揭示，除了個(gè)人信息和社會(huì)資本外，借款人設(shè)定的其他變量，包括貸款金額、可接受的最高利率和貸款期限，都顯著影響了籌資的成敗。</p><p>　　加拉克等人（2011）進(jìn)一步表明，與團(tuán)體借款人相比，貸款人更傾向于個(gè)人借款人，以及社交上接近自己的借款人。他們還發(fā)現(xiàn)貸款人傾向于在性別、職業(yè)和名字的首字母上更像自己的借款人。更有趣的是，岡薩雷斯和洛雷羅（2014）也有類似的發(fā)現(xiàn)：（1）當(dāng)把外

72、表年齡視為其能力的體現(xiàn)時(shí)，吸引力對(duì)貸款成功沒有影響；（2）當(dāng)貸款人和借款人的性別相同時(shí)，吸引力可能導(dǎo)致貸款失?。础凹t顏禍水”效應(yīng)）；（3）貸款成功對(duì)貸款人和借款人的相對(duì)年齡與吸引力很敏感。赫斯坦恩等人（2011）發(fā)現(xiàn)，貸款拍賣中的羊群效應(yīng)與其后續(xù)表現(xiàn)呈正相關(guān)，即借款人是否按時(shí)還錢。</p><p><b>　　3.數(shù)據(jù)</b></p><p>　　本部分首先對(duì)貸款申

73、請(qǐng)人的資料進(jìn)行了描述，然后是根據(jù)貸款目的、信用等級(jí)及貸款狀況的貸款分配，最后是貸款申請(qǐng)人的詳細(xì)描述統(tǒng)計(jì)。本研究利用了借貸俱樂部上的自2007年5月至2012年6月的61451筆貸款申請(qǐng)，這些數(shù)據(jù)是從www.lendingclub.com網(wǎng)站獲取的。在研究期間，借貸俱樂部貸給借款人約7.13億美元。為了研究借款人在P2P網(wǎng)絡(luò)借貸中的行為，我們首先分析了他們向別人借款的主要原因。表1列出了借貸俱樂部的借款人自稱的原因。近70%的貸款需求與債

74、務(wù)合并或信用卡債務(wù)有關(guān)，總貸款額分別約為3.87億美元和1.08億美元。教育、可再生能源和休假貸款申請(qǐng)數(shù)量少于貸款總額的1％，所需貸款總額為1至300萬美元。借款人表示，他們喜歡從借貸俱樂部借款是由于其較低的貸款利率，以及無法從信用卡上借到足夠的錢。借款的第二個(gè)目的是支付住房抵押貸款或重新建房。</p><p>　　表1.根據(jù)貸款目的的貸款分配（2007年5月—2012年7月）</p><p&

75、gt;　　注：這些數(shù)據(jù)為借貸俱樂部上的自2007年5月至2012年6月的61451筆貸款申請(qǐng)，是從www.lendingclub.com網(wǎng)站獲取的。</p><p>　　要求貸款人提供要求貸款的理由。</p><p>　　借貸俱樂部利用借款人的FICO信用評(píng)分以及其他信息，將貸款信用等級(jí)從A1到G5由高到低分配給每個(gè)貸款的信用等級(jí)。詳細(xì)程序如下：根據(jù)FICO評(píng)分分配基準(zhǔn)分?jǐn)?shù)后，借貸俱樂部將

76、根據(jù)申請(qǐng)貸款的金額、最近幾次的信用查詢、信用歷史長度、總開立信用賬戶、目前開立的信用賬戶和循環(huán)貸款利用率進(jìn)行一些調(diào)整，來確定最終等級(jí)，從而確定貸款利率。</p><p>　　表2顯示的是根據(jù)信用等級(jí)的貸款分配。大多數(shù)借款要求等級(jí)在A1和E5之間。所要求的最高貸款金額來自信用等級(jí)“B”的借款人，占所需貸款總額的29.56％。信用等級(jí)“B”的申請(qǐng)人總數(shù)為18707人，總貸款額約為2.1億美元。所要求的最低貸款金額來自

77、信用等級(jí)“G”的借款人，其信用等級(jí)最低，占貸款總額的1.53％。這個(gè)最低信用等級(jí)“G”只有608名貸款申請(qǐng)人，總貸款額約為1100萬美元。根據(jù)借貸俱樂部的政策，貸款信用等級(jí)用于確定借款人可以要求的利率和最大金額。貸款等級(jí)越高，利率越低。一個(gè)低等級(jí)的借款申請(qǐng)會(huì)提出更高的利率，作為對(duì)貸款人持有的高風(fēng)險(xiǎn)的補(bǔ)償。</p><p>　　表2.根據(jù)信用等級(jí)的貸款分配（2007年5月—2012年7月）</p>&

78、lt;p>　　注：借貸俱樂部利用借款人的FICO信用評(píng)分以及其他信息，將貸款信用等級(jí)從A1到G5由高到低進(jìn)行分類。因此，信用等級(jí)A1代表信用質(zhì)量最低/低風(fēng)險(xiǎn)的借款人，而信用等級(jí)G5代表信用質(zhì)量最低/高風(fēng)險(xiǎn)的借款人。信用等級(jí)A的貸款總額占貸款總額的百分比為19.35％，“B”為29.56％，“C”為19.94％，“D”為14.84％，“E”為10.15％ “F”為4.59％，“G”為1.53％。</p><p&g

79、t;　　最后，表3的A組顯示了到2012年7月20日所有貸款申請(qǐng)的貸款狀況?？傮w而言，違約率為4.60％，總損失約為2900萬美元。另有2.45％的貸款總額為1860萬美元潛在的損失，因?yàn)榻杩钊嗽?0天或120天內(nèi)逾期付款，而沒有正常分期付款。17.98％的貸款已全額付清，約為1.08億美元。5.47億美元貸款在當(dāng)前狀態(tài)下占貸款總額的74.91%。當(dāng)然，低等級(jí)貸款表現(xiàn)出較高的違約率。因此，P2P借貸風(fēng)險(xiǎn)管理研究與貸款人的優(yōu)化投資組合有關(guān)

80、。表3的B組顯示了到期貸款的貸款狀況。到期貸款的整體損失率高得多。在4904筆到期貸款中，914筆貸款被注銷，占18.6％?？倱p失為550萬美元，占所有到期貸款金額的13％。不足1％的到期貸款逾期支付了未付余額27000美元。80.77％或3300萬美元的到期貸款已全額付清。</p><p>　　表3.根據(jù)貸款狀況的貸款分配（2007年5月—2012年7月）</p><p>　　表4列出了

81、借貸俱樂部P2P網(wǎng)絡(luò)貸款申請(qǐng)人的一般特征和信用記錄。根據(jù)我們對(duì)61451個(gè)貸款申請(qǐng)人的樣本，貸款的平均每月利息為12.34％。從貸款發(fā)行之日起平均時(shí)間為471天。借款人的平均信用等級(jí)為25，對(duì)應(yīng)于B和C之間的信用類別。一個(gè)一般貸款的平均規(guī)模是11604美元，平均每月支付351美元。借款人一般每月支付4384美元，剩下7873美元。余額與貸款總額的平均比例為63％。</p><p>　　從借款人的特點(diǎn)來看，借貸俱樂

82、部的借款人的平均收入為5796美元，負(fù)債收入比為0.1381。平均來說，借款人有9.56個(gè)開立信用額度和22個(gè)總信貸額度，平均循環(huán)信用余額為14 315美元，差額約為其信貸額度的二分之一（51.6％）。在過去的六個(gè)月中，平均借款人要求1筆信用查詢。一般借款人的平均FICO評(píng)分為3.48，對(duì)應(yīng)于680至750之間的FICO評(píng)分類別。</p><p>　　表4.描述性統(tǒng)計(jì)（2007年5月—2012年7月）</p

83、><p>　　注：信用等級(jí)是根據(jù)FICOFICO信用評(píng)分以及其他信息，由借貸俱樂部進(jìn)行分配。信用等級(jí)“1”是“G”的貸款類別，是最危險(xiǎn)的貸款類別。信用等級(jí)“7”是“A”的貸款類別，這是風(fēng)險(xiǎn)最低的貸款類別。 FICO評(píng)分是信用卡公司評(píng)級(jí)的借款人的信用評(píng)級(jí)。FICO 6對(duì)應(yīng)于FICO評(píng)分高于780的借款人，F(xiàn)ICO 5對(duì)應(yīng)于FICO評(píng)分為750-779，F(xiàn)ICO 4 = 714-749，F(xiàn)ICO 3 = 679-713，

84、FICO 2 = 660-678和FICO 1= 640-659。</p><p><b>　　4.結(jié)論</b></p><p>　　信用風(fēng)險(xiǎn)是P2P借貸的重要關(guān)注點(diǎn)。本研究采用借貸俱樂部的數(shù)據(jù)來評(píng)估P2P網(wǎng)絡(luò)借貸的信用風(fēng)險(xiǎn)。我們發(fā)現(xiàn)信用等級(jí)、負(fù)債收入比率、FICO評(píng)分和循環(huán)貸款利用率在貸款違約中起著重要的作用。除了最低信用等級(jí)“F”外，借貸俱樂部使用的信用分類成功地

85、預(yù)測(cè)了違約概率。一般來說，較高的信用等級(jí)貸款的違約風(fēng)險(xiǎn)較低。</p><p>　　死亡風(fēng)險(xiǎn)也隨貸款期限的增加而上升。信用等級(jí)較低、持續(xù)時(shí)間較長的貸款的死亡率高。Cox比例風(fēng)險(xiǎn)模型測(cè)試結(jié)果顯示，隨著借款人的信用風(fēng)險(xiǎn)增加，貸款違約的可能性也會(huì)增加。然而，目前對(duì)高風(fēng)險(xiǎn)借款人收取較高利率，并不能夠降低貸款的高違約率。這表明，貸款人最好只貸款給最安全的借款人，即在最高級(jí)別的7或A級(jí)的借款人。風(fēng)險(xiǎn)較高的借款人的收益差越來越大