基于分类算法的某信贷平台的贷款违约预测（外文翻译）
打女孩屁股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 rvice 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.
科目一全部试题及答案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 borrowerdoes. Such information asymmetry might result in adver lection(Akerlof,1970)and moral hazard(Stiglitz and Weiss, 1981) Theoretically,some of th
e problems can be alleviated by regular monitoring,but this approach pos a challenge in the online environment becau the borrowers and the buyers do not physically meet.1 Fostering and enhancing the lender's trust in the borrower can also be implemented to mitigate adver lection and moral hazard problems. In the traditional bank-lending markets, banks can u collateral, certified accounts,regular reporting.and even prence 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 bad on the FICO score. The minimum FiCO score to be able to participate is640.2 Second,the typical size of the loans produced in this market is small,which is under $35 000 at the Lending Club.Therefore, the loans are esntially microloans which po a relatively small loss in ca of default, Third, the market maker offers matchmaking systems which can be ud to generate portfolio recommendations a
神击的巴哈姆特nd minimize lending risks. Fourth, if a borrower fails to pay, the market maker will report the ca to a credit agency and hire a collection agency to collect the funds on behalf of the lender. Although there are certain structures impod 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 purpo 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 bad on the borrowers' grade. There are two related rearch 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 t for high-risk borrowers are sufficient to compensate the lenders for the higher probabilities of a potential loss.
软吐司>保险柜十大品牌
This study contributes to the literature in this new and fast growing P2P marketplace in many ways. While there are few studieswhich explored credit screening problem in the P2P lending platforms, this rearch differs from the prior rearch in various aspects(e,for example,lyer et al, 2009 and Lin et al, 2013).First, this rearch extends risk analysis rearch 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 bad on their significant demographic and characteristic factors, which enables the potential lenders to determine an optimal allocation strategy Third, this rearch address the issue of lection bias by examining whether there is a significant difference in the default risk of the borrowers from the whole Us population and the LendingClubwhich yields an important implication for risk minimization for the Lending Club.Finally.this rearch 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 provi
des important information regarding the risk and return efficiency of the Lending Club.
安装声卡
谈谈个人修养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 withthe 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 discusd 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 riskIn 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.
会议礼仪The remainder of the article is organized as follows. In the next ction, we review the literature for online P2P lending. Section llldescribes our data and summarizes the descriptive statistics of online P2P from the Lending Club. In Section IV,we prent 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 lection bias is also addresd in this ction. Section V offers some concluding remarks.
Data in this ction, the loan applicants data is first described followed by loan distribution bad on loan purpos, credit grade and loan status and it ends with the detailed descriptive statistics of the loan applicants. This study us 61 451 loan applications in the Lending Club from May 2007 to June 2012 obtained Over the study period,the Lending Club lent about $713 million to borrowers. To address the borrowers' behaviourin online P2P lending,we first examine the main reasons for borrowing money from others. Table 1 lists the borrowers'lf- 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 ap
proximately $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 cond purpo for borrowing is to pay home mortgage or to re-model home, The Lending Club us 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 aba score bad 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.
