role of veri cation in peer-to-peer lending

Role of Verification in Peer-to-Peer Lending∗

Oleksandr Talavera†

School of Management

Swansea University

Haofeng Xu‡

School of Management

Swansea University

Abstract

Using data from a leading Chinese Peer-to-Peer (P2P) lending platform from 2012 to 2015, we

investigate the role of verification in the P2P lending market. We find that borrowers with thor-

ough and complete verification are more likely to obtain funding and also less likely to default on

loans. We also find that borrowers that have incomplete verification are more likely to upwardly

misrepresent their income. This leads to higher default rates for this group when compared to the

default rates of more thoroughly verified borrowers. The further analysis documents that returning

borrowers are more likely to maintain a good credit record. We discuss the implications of our

findings for the role of verification in the growing P2P lending sector and the design of a stable

financial system.

JEL classification: G21, G23

Keywords: Information asymmetry, verification, P2P, income exaggeration

∗Oleksandr Talavera thanks ESRC-NSFC (Grant ES/P004741/1, Peer-to-peer lending market in China: pricing,risk management and regulation) for financial support.†Corresponding author. Address: School of Management, Swansea University, Bay Campus, Fabian Way, Swansea

SA1 8EN, UK. Tel.: +44 (01792) 602719 E-mail address: [email protected] (O. Talavera).‡E-mail address: [email protected] (H. Xu).

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1 Introduction

Peer-to-peer (P2P) lending is a rapidly growing branch of Fintech that has attracted significant

debate from both practitioners and academics. The emergence of P2P lending strengthens the

consumer lending sector regarding allocating financial resources efficiently. P2P proponents claim

that P2P lending provides loans to those borrowers who have difficulties accessing the traditional

banking system (Milne and Parboteeah, 2016), and lowers the cost of debt financing. P2P lenders

not only achieve higher returns on their invested capital but also enhance consumer access to

financing, which contributes to financial stability and economic growth. However, information

asymmetry between borrowers and lenders still exists, resulting in the misallocation of financial

resources such as the over-financing for risky borrowers (Demyanyk et al., 2017). An explanation is

that the authenticity of information about a borrower1 is fully/partially unknown (Freedman and

Jin, 2008). Recent studies document that obtaining more borrower information and market context

(e.g., soft information such as friendship) can mitigate the effect of asymmetric information in the

P2P market (Miller, 2015; Lin et al., 2013), but little is known about the properties of verification

on loan outcomes.

The objective of this paper is to investigate the role of verification in the P2P market by analyz-

ing data from a leading Chinese platform (Renrendai.com) between July 2012 and October 2015.

To this end, we first evaluate the research question: how the number and types of verification

for a borrower affect the borrower’s funding success rate, cost of finance, and ex post delinquency

rate? We further examine whether borrowers with fewer verification types (low levels of documen-

tation) have more incentives to overstate their income. We then analyze the effects of this income

exaggeration on loan outcomes.

By comparison existing P2P studies are solely based on application related information (e.g., US

platform: Prosper.com and Chinese platform: Ppdai.com). The Renrendai marketplace provides

an excellent source of verification status data for borrowers. Borrowers initiate loan requests on the

platform and a crowd of peer lenders can bid the loan (fully or partially) at a certain interest rate.

1Prosper.com (one of largest P2P platform in US) reports verified employment and/or income on approximately59% of the borrower loans originated through the marketplace on a unit basis (227,419 out of 388,617) and approx-imately 73% of such loans on a dollar basis ($3.531 billion out of $4.856 billion) between July 2009 and September2015 (based on start time of the applicable bidding period).

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Lenders can view loan information and the borrower’s characteristics. In addition, the verification

status is displayed by a table with various verification levels (Figure 1). Figure 2 shows that the

average number of verification for all listings and funded loans over the month. Borrowers raise

the loan requests with average 1.6 types of verification and 3.8 for funded loans2 (12 in total). To

explore income misrepresentation, we match a representative sample with similar characteristics

but more reliable verification methods (high cost) to calculate the extent of income exaggeration.

Beginning with the introduction of the first P2P platform in 2007, the Chinese P2P lending

has been growing rapidly reaching 600 billion RMB ($91 billion) in total outstanding loans in July

2016 (Figure 3). Despite this, in China, there is no fully developed system of credit referencing

an individual borrower (Milne and Parboteeah, 2016). Accordingly, the potential for loans fraud

and adverse selection has always been present, resulting in a severe hazard of information asym-

metry. The verification system offers an alternative way to fill this gap by validating borrower’s

documentation. The optimal number and types of verification can facilitate debt financing and

reduce ex-post default rates.

We evaluate the hypotheses for the role of verification stemming from two folders. First, we

examine whether thorough and complete verification of borrowers can facilitate funding success

rates and lower default rates. Although the P2P platform discourses various personal information

regarding borrowers, lenders are unable to distinguish quality borrowers from bad ones if the infor-

mation is not feasible. Akerlof (1970) points out that lack of credible information about borrowers

and using that information to screen applicants could result in financial underdevelopment. To

demonstrate their creditworthiness, borrowers can provide proof of documentation (e.g., income)

to show the capability of repaying the debt. Increasing the number of verification means that

borrowers attempt to indicate better qualities than those borrowers who are relatively less veri-

fied. Thus complete verification of borrowers represents an effective signal that leads to better loan

performance.

The informational value is different for each type of verification. Lenders cannot directly perceive

trustworthiness itself but inferred from signals (Bacharach and Gambetta, 2001). Borrowers show

mixed signals for P2P lenders to proof their quality and reduce the adverse selection. We then

2Renrendai.com change the verification policies in October 2015 for the regulatory requirement. Borrowers mustverify their income, employment, identity and providing credit report issued by the People’s Bank of China. Othertypes of verification are not needed.

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assess which verification can more intuitively signal the borrower’s creditworthiness in the P2P

lending market. A useful verification item can help the platform to improve its credit scoring

system and benefit to lenders screening device. In other words, the highly qualified signal means

that the inherent risk of moral hazard could be identified in the ex ante process.

Second, to better understand the consequences of incomplete verification, we further examine

the implication of income exaggeration by considering the effects of verification. In consumer

lending market, lenders heavily rely on borrowers income to achieve the expected return, but income

misrepresentation of borrowers is a potential risk for lenders. Jiang et al. (2014) find that borrowers

who provide a lower level of documentation3 resulting in borrower information misrepresentation

and elevated delinquency rates in the mortgage market. Ambrose et al. (2016) confirm the findings

and further evaluate the effects of the borrower heterogeneity with respect to employment status.

The consequences of misrepresentation drive the expansion of mortgage market between 2002 and

2006 and inflate house price (Mian and Sufi, 2009, 2017), and as one of the possible causes of the

Great Recession (Ambrose et al., 2016).

Our empirical analysis uncovers the substantial problem in P2P lending. P2P loans are based

on Internet and borrowers can raise multiple loan requests without cost. If there is no verification

process, P2P borrowers may have incentives to access financing through misrepresentation of their

income. The borrowers have higher chance to obtain loans as well gain lower interest rates compared

with other similar characteristic borrowers. The cost of default would be relatively lower. Because

the P2P lenders are unable to commence legal action for enforcement of the defaulted loans. In

particular, the P2P platform in China cannot access to the credit system of People’s Bank of China,

this leads to defaulted borrowers will not be recorded in the formal banking system. In other words,

the fraudulent P2P borrowers still can obtain financing from other financial institutions, which

threatens financial stability and other negative social effects4.

Our results show that loans with increased verification are more likely to obtain funding. Lenders

may use this complete verification as a signal because it is a strong indicator regarding endorsement

which distinguishes it from low information loans. Specifically, social media verification and edu-

3A low doc (or low documentation) loan is a type of mortgage that can be approved without the normal incomeverification requirements. See http://www.investopedia.com/

4For instance, Ezubo scam: Popular online peer-to-peer lender leaves 900,000 Chinese investors $7.69 billion outof pocket

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cation credentials of borrowers are associated with lower interest rates and ex post default rates.

We examine the relation between the verification and income exaggeration; the results show that

borrowers who provide less verified documents have a greater extent of income exaggeration. Ad-

ditionally, we provide novel evidence that borrowers with incomplete verification are more likely

to have increased probability of default, but if these borrowers that are concerning about future

credit availability through the platform can mitigate the effects of adverse selection and reduce the

incidence of delinquency. This suggests that returning borrowers are willing to maintain their good

record, which can compensate for the effects of lacking verification on the P2P platform.

Our study is the first to discuss the impact of verification on loan outcomes and income exagger-

ation in P2P lending. This paper contributes to the literature on the growing P2P lending sector.

Recent studies include Pope and Sydnor (2011); Zhang and Liu (2012); Duarte et al. (2012); Lin

et al. (2013); Burtch et al. (2015); Miller (2015); Iyer et al. (2015); Wei and Lin (2016); Tao et al.

(2017). Furthermore, our study contributes to the established literature on information asymme-

try, credit rationing and signalling theory (Akerlof, 1970; Stiglitz and Weiss, 1981; Spence, 1973,

2002). This paper also contributes literature about misrepresentation and income overstatement.

Ambrose et al. (2016); Garmaise (2015); Mian and Sufi (2017) illustrate how misreporting and

falsification are associated with adverse loan outcomes in the mortgage market. Our study pro-

vides novel evidence to confirm income overstatement does occur in the P2P lending market and

thorough verification can mitigate the extent of exaggeration.

The rest of this paper proceeds as follows. Section 2 reviews the literature and summarizes our

research. Section 3 describes the institutional background in China and the operation of the P2P

platform. Section 4 describes our data set. Section 5 and section 6 contain the empirical strategy

and discuss the results of the study. Section 7 concludes this paper.

2 Research Context

2.1 Information Asymmetry and P2P Lending

Akerlof (1970) proposed the information asymmetry framework that sellers of used cars know more

about car quality than buyers. The sellers are unable to present quality resulting in high-quality

sellers withdraw from the market, leading to a market failure. Spence (1973) argues that the

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adverse selection problem can be mitigated if high-quality types use “signals” to communicate

quality. Low-quality types cannot deliver same information due to the costs of acquiring signals.

In the credit market, asymmetric information between borrowers and lenders leads to a variety

of consequence, e.g. adverse selection and moral hazard. In the framework, lenders cannot identify

ex-ante which type of borrower is “good”, lenders are unable to distinguish quality borrowers from

bad ones if the information is not feasible even borrowers offered to pay higher interest rates (Stiglitz

and Weiss, 1981). Borrowers are characterized by their profiles, which are assumed to have the

same expected returns but differ from one another in their risk. Traditionally, lenders overcome the

adverse selection and moral hazard problems by requiring collateral from borrowers that signal their

high-quality (Bester, 1985). However, The use of risk-based pricing in consumer loans, including

credit card loans and mortgages, has become widespread, reflecting the increased ability of lenders

to distinguish between borrowers with different risk profiles (Edelberg, 2003; Chomsisengphet and

Pennington-Cross, 2006). Blackwell et al. (1998) provide evidence that borrowers voluntarily submit

verified financial statements with their application leads to reduce interest rate in private business

lending.

The P2P lending is mainly serving on unsecured consumer loans for those who have difficulties

accessing the traditional banking system (Milne and Parboteeah, 2016). P2P platforms claim that

they have lower screening costs and better quality in underwriting borrowers. Demyanyk et al.

(2017) argue that P2P lenders provide loans to the riskiest clients who often continue accessing

other financing channels, resulting in leveraged growth of borrowers after P2P loan origination.

The P2P market is new, and P2P lenders face severe information problems relative to offline credit

markets due to the authenticity of information is unknown (Freedman and Jin, 2011). Lenders are

difficult to know what type of borrower is risky or not. To increase the probability of funding, P2P

borrowers provide proof of documentation (e.g., income) to demonstrate the capability of repaying

the debt.

In recent years, many of empirical studies have been made focusing on P2P lending, and most of

them have used data from the USA, e.g. Prosper and Lending Club. Puro et al. (2010) found that

smaller loan amounts can help increase the success rate and decrease the interest rate. Also, Lin

et al. (2009) showed that it is difficult for loans over a longer period to be funded because sufficient

liquidity for the lenders would not be provided. Freedman and Jin (2008) found credit rating to be

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the most crucial factor affecting the interest rates. Debt-to-income ratio takes second place, and if

a borrower has bank accounts, then his/her loan requests are more likely to be successful. Research

on soft information shows evidence of the impact of friends, borrower narratives, and photos. Lin

et al. (2013) distinguish different types of friendship and found that friendships of borrowers increase

the probability of successful funding and reducing ex-post default rates. Pope and Sydnor (2011)

showed that discrimination exists due to borrowers photos in the P2P market. Duarte et al. (2012)

find that borrowers with trustworthy appearance are more likely obtain loans and less likely to

default. These hard-to-quantify factors influence users experiences with the platforms and serve as

the indirect evidence of a users trustworthiness (Collier and Hampshire, 2010). For the regulation

change, Wei and Lin (2016) tested two market mechanism (Auction price and Post price) and found

that loans are funded with higher probability under post price, but interest rates are higher than

auction model. Miller (2015) found that exposing more information of borrowers can significantly

improve the screening performed by existing lenders and attract new lenders who were better at

screening loan applicants and earned higher returns. Zhang and Liu (2012) show that psychological

factor affects behaviours of borrowers and lenders. Rational herding behaviour does exist in P2P

lending, well-funded loans are more likely to attract more funding. But little is to study the role

of verification in P2P lending. Kumar (2007) find that completing bank account verification was

related to a lower probability of loan default, while loan size was positively correlated with the

default rates.

With respect to Chinese P2P market, Chen and Han (2012) compared online P2P lending

between the USA and China. Feng et al. (2015) carried out an empirical study of lenders and bor-

rowers strategies in which a larger loan amount increased the probability of funding and attracted

more lenders by using a small amount of data (1057 listings) from Ppdai.com. Using data from

Renrendai.com, Tao et al. (2017) find that borrowers with higher income or own a car are more

likely to obtain a loan, pay lower interest rates, and default less often. They also indicate the role

of offline verification in the lending process. Our study aims to investigate the role of verification in

the P2P lending market. We evaluate the number and each type of verification and their outcomes.

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2.2 Income Exaggeration

The overstatement of income on traditional loan application has been well documented in various

commission reports and the existing literature (Garmaise, 2015; Ambrose et al., 2016; Mian and

Sufi, 2017). The role of borrower income misrepresentation leading up to the ex-post loan default

is a source of considerable debate. For example, Jiang et al. (2014) show that borrowers who

provide a lower level of documentation resulting in borrower information misrepresentation and

elevated delinquency rates in the mortgage market, and particularly for those borrowers from

brokers. Supporting this argument, Ambrose et al. (2016) provide new evidence to indicate that

borrowers with “low-doc” mortgages originated were more likely to exaggerate their income and

cause a higher risk of default.

However, to my best knowledge, few papers studied the income misreporting in the P2P lending

market. Eid et al. (2016) found that rounding of income by a borrower is more likely to default

and less likely to repay than borrowers with more accurate income reporting. In contrast to the

study, our analysis measures income exaggeration by comparing audited income through an offline

source of a higher level of verification. Specifically, we examine the relation between the number

and each type of verification and income exaggeration. Whether a borrower inflates his/her income

has a higher probability to obtain a loan and resulting in elevated defaults. We then evaluate how

the behaviour of repeating borrowing and misreporting affect loan performance.

3 Institutional Background

3.1 P2P Lending Market in China

P2P online lending was first established in the UK and rapidly developed in other countries such

as the USA, German, and China. Beginning with the introduction of the first P2P platform in

2007, Chinese P2P lending has been growing rapidly. The growth of online P2P lending platforms

in China has been considered to challenge the market share of the bank in consumer lending.

This because the current bank system could not locate the financial resources efficiently for the

private sector- the main driver of economic growth (Allen et al., 2005). However, Chinese P2P

platforms operated in an unregulated environment until July 2015. As a result, many platforms

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have operational difficulties, and investors would be facing potential loss. Despite the negative

effect, Chinese P2P platforms realised total outstanding balances of $91 billion in July 2016. But

expected annual return has been declined from 20% to 10% over the 2014 to 2016 time period

(Figure 3). The nascent industry is changing very quickly. Up to now, the P2P lending in China

has been experiencing three evolution stages.

Inspired by Zopa.com in the UK and Prosper.com in the US, Ppdai.com went live online in

June 2007 as the first P2P lending website in China. It was followed by the appearance of some

major online lending platforms of a similar type, containing Renrendai.com and Lufax.com. At

this stage, most of the transactions came from personal loans that occurred between lenders and

borrowers directly. The platforms only play a role of exchanging information. Because of lacking

official credit information on the borrower, the practitioners have to find ways reducing information

asymmetry and providing integrated services to increase the stickiness of clients.

The early strategy of P2P platforms particularly intended to attract borrowers who can not ac-

cess to traditional financial institutions. The risk control for borrowers is of lower quality compared

to traditional banks. P2P platforms somehow provide multiple resources regarding the partnerships

with external guarantee institutions. For instance, Renrendai.com can provide the guarantee for

investors, which means that the platform repays the investment to lenders in advance if a loan

defaulted. However, many platforms fail to control risk for both internal operation and overseeing

borrowers. The P2P lenders are unable to commence legal action for enforcement of the defaulted

loans. In 2012, the average bad loan over $1.5 million for each platform.

From 2012 onward, P2P industry in China has been growing dramatically including the number

of investors, borrowers, platforms and business turnover. Some P2P platforms experiment addi-

tional business model for market share expansion (e.g., offline channels). CreditEase and Renrendai

are representative examples of this marketing model. Also, P2P platforms usually provide wealth

management products offering categorised investment modules based on aggregated P2P loans as

a certain interest rate with a fixed term. P2P companies sometimes collect money from investors

building a funding pool if there are no enough loans to invest, which means that these institutions

can invest money for any purpose without regulation. Some platforms also develop the secondary

market which is designed for those investors who want to release liquidity.

The government issued a guideline including ten regulatory authorities in July 2015 requiring

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that online P2P lending platforms must separate their own money from investors money by opening

a third-party depository account in a bank which is subject to the supervision of the People’s

Bank of China and other financial regulatory authorities. This regulatory framework completely

changes the industry and results in a lot of consequences. According to wdzj.com, there are 3701

problematic platforms out of total 5890 platforms by the end of April 2017, which account for 63%

overall historical platforms.

After the policy intervention, a large number of P2P platforms were closed or changed their

major business type. The regulated platforms must register themselves as “information agency”

firms with authorities. In the present, a significant number of platforms switch to sell wealth

products, which is less transparency for the information about borrowers. As a result, just a few

online platforms remain the pure P2P function. Many others intend to diversify their business

model to subject to regulation.

3.2 The Operation of Renrendai.com

Renrendai.com is a leading P2P platform in China which was established in October 2010. By

the end of March 2017, it had confirmed 385,696 loans with a total lending amount of over $3.95

billion, and 2.66 million registered users for lending and 384,288 lenders invest in loans. Figure 4

shows that the total and average principal amount are increasing over the year, and dramatically

boosting for both newly registered borrowers and lenders. There are over 1 million registered users

for borrowing and 367,413 borrowers successfully obtain loans up to now. The aggregated statistics

shows that the platform exists $1.94 billion outstanding amount in total, and the reported default

rate is 1.73% over the time period.

3.2.1 Loan Application. On the platform, lenders easily search for information about prospec-

tive funding opportunities provided by each borrower, then invest, and complete transactions with

capital gain (although sometimes, loss). The operation of Chinese P2P platforms has evolved from

original P2P type into several derivatives. The operation of Renrendai.com is illustrated in Fig-

ure 5. Potential borrowers post loan requests on an online platform directly, or via a third-party

offline institution partnered with the platform. A standard loan request contains two sections (See

Appendix Figure 6). One section consists of the loan amount, maturity and maximum interest rate

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they can accept. The other sections are information on borrowers demographic factor and sup-

porting documents. Some supporting documents are compulsory such as identity card, payslips,

mobile number, address, employment status, and credit report5. Other documents that may be

helpful in obtaining funding (for example, their asset information and education certificate) could

be uploaded voluntarily by borrowers. The borrower must fill in an application form with loan

purpose description and other information based on above two sections. However, online platforms

in China are unable to access this report from People’s Bank of China, so a borrower has to provide

the personal credit report with his/her application. To offset the lack of a national credit scoring

system, platforms usually evaluate the loan request and assign a credit grade6 (e.g. from AA to

HR-high to low) for the borrower after verifying the documents provided by borrowers.

3.2.2 Verification and Offline Audited. The credit profile can be verified by the staff of

Renrendai.com. For instance, the staff will make an essential phone call to verify the employment

of a borrower. Once the information is validated, the verification status in the loan listing will

show pass for the validation (See Figure 1). There are many types of verification which include

employment, income, credit report, identity, mobile phone number, address, homeowner, car owner,

social media, video interview, marital status, and education.

To mitigate the effect of lacking reliable credit system, the platform introduced the third-

party offline system in July 2012. The offline institution provided loan requests (borrowers don’t

directly register in Renrendai.com) to the online platform where lists the same types of information

as borrowers from online. The verification types are same as online, but field audited for the

authenticity of borrowers’ profiles. For example, the adviser in the offline office can request an

interview in the home of a potential borrower or visit his/her employer. The adviser can verify the

income a potential borrower via various sources (check payslip, accompany the customer to print

bank statement in a branch and require an employer reference). Note that loan requests from a

third-party offline institution have to be verified by the institution before listing to Renrendai.com.

5It has to be noticed that the personal credit report is a credit evaluation document issued by People’s Bank ofChina. It only reports the number of defaults of loan or credit card repayments from Chinese banks or institutions(and the records of lawsuits).

6The credit grade will be updated by any action of borrowers. We collected the sample in February 2017. Thegrade for borrowers is not same as the origination date. This indicator will be excluded in our analysis. Tao et al.(2017) point out that the credit grade assigned by the lending platform may not represent the creditworthiness ofpotential borrowers.

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Since December 2012, Renrendai.com launched their own offline institution (Ucredit.com) starting

from Beijing. Until now, it has been over 200 offline offices spreading into the whole country. Once

loan requests are pre-approved by Ucredit.com, the requests will be posted on Renrendai.com. Then

lenders can bid these loan requests like investing normal online ones.

3.2.3 Listing, Bidding, Funding, and Repayment. As with borrowers, registered lenders

are also subject to verification of mobile phone number, identity card, and bank account number.

The true identities of lenders (including all personal information) are not publicly revealed on

the website. Lenders can invest their capital through two different channels. The first channel is a

purely intermediary P2P lending type, which provides each loan listing with borrowers’ information

and verification status (See Figure 1). Lenders examine listings and screening which listings to invest

in and how much to invest. They are not required to invest in the full amount. The minimum

amount of each bid is 50 RMB. The platform also provides an automatic bid instrument. Borrowers

can easily use the instrument by setting their investment criteria (e.g., credit grade, interest rate)

to bid automatically. Lenders can diversify their investment risk by multiple bids into different

borrowers. Once the amount of loan request has been met by the aggregate amounts of lenders

bids, the loan will be fully funded and unlisted from the website. Then, loan proceeds are credited

to the borrowers’ bank account from which repayments are automatically withdrawn. The second

channel is called “investment plan.” The investment plan is a package of loan requests created by

the platform. It contains requests with specified interest rate and maturity. Lenders who invest

the packages are only able to know the fixed interest rate and term of the package, but unable to

access the information of the components in the package individually.

4 Data and Summary Statistics

4.1 Data

By using the Python program, we scraped data from the online P2P lending website, Renrendai.com.

We collected 945,089 loan listings, including each loan transaction from October 2010 to January

2017. As we introduced in the previous section, the listings contain online P2P loans and the

loans from the offline institution (Ucredit.com). Our analysis focuses on online loans because it is

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a costless way for facilitating financing. Although the offline institution can verify the borrowers’

profile more accurately and may have better risk management. The cost of debt (the actual annual

interest rate) is from 15.36% to 30.96% if a borrower raises a loan request via Ucredit.com, which is

much higher than online borrowing cost. But listed interest rate of offline loans on the P2P platform

is much lower than the rate charged by Ucredit.com. Because the local offices are established across

the country, but the huge cost finally transfers to borrowers.

After dropping missing values, our sample covers loans originated from 2012 to 2015, with per-

formance data ending in October 2015, which includes 738,411 listings for both online and offline

loans. Because after October 2015 Renrendai.com changed the types of verification and business

model for regulating purpose. There are 567,955 online requests and 170,456 offline requests; of

these, 21,549 online requests and 169,960 offline requests had been successfully funded respectively.

We use 58,866 of offline loans to be our matching sample. Because the borrowers in the sample have

a higher level of income verification. The offline requests have high acceptance rate because the

platform only publishes the pre-approved requests for listings. Each listing contains the loan with

the specific conditions of the annual interest rate, the amount of loan, the period of repayment, the

guarantee type, a credit score issued by Renrendai.com, and various pieces of personal characteristic

information (age, income, location, occupation, employer’s information, education, marital status,

homeowner,car owner and borrowing histories in the platform). Furthermore, Renrendai.com pro-

vides verification status, and the lenders can see which profile of a borrower has been validated.

The variable definitions can be seen on Table 1.

4.2 Summary Statistics

Table 2 provides summary statistics for key variables of all listings and funded loans through online

requests respectively. We find that only 3.8% of the borrowers can successfully obtain requested

loans. The default rate is calculated by the completed loans because the loan can default at any

time between origination and maturity, it would be biased for loan performance if the loan is still

repaying. The default rate reaches to 17.3% in 20,191 completed loans. Comparing with funded

loans, the average interest rate that borrowers are willing to pay is 13.24% with minimum rate

7% and maximum rate 24.4%, while the average contract interest rate is slightly lower as 12.39%.

The average loan amount requested for listings is 60,721 Chinese RMB, while the average loan size

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is as small as 24269 RMB for funded loans. The average maturity for all listings is 13 months

compared with 16.2 months, which indicates that lenders prefer to fund loans with a shorter term.

Lenders also can see the borrower’s histories in the platform, that is, the average loan requests and

borrowers have been successfully funded and pay off in the platform. The loan listing report times

of borrowers’ arrears in the platform (up to 30 days and up to 90 days). Note that the loan is

described as default if the borrower doesn’t keep up repaying over 90 days.

Turning to observable borrower characteristics, Table 2 shows that the average borrower is 32

years old with reported monthly income 10,649 RMB for all listings, while the funded average

borrower is slightly turning to 34 years old with much higher reported monthly income 14,984

RMB. Borrowers can provide their asset information such as property and car. We can see that

30% of loan requests report property and only 1% with the mortgage, while 54.2% of funded loans

claim homeowner and 22.5% with the mortgage. 17.4% of requests declare car owner and 4%

existing car finance, while 37.7% of funded loans state car and 8.4% with the car loan. Borrowers

can write a self-description of loan purpose to show more information regarding the requests. We

count the length of words because Dorfleitner et al. (2016) find that investors react more strongly

to soft information and text information can significantly affect loan outcome and performance in

P2P platforms. In consistence with (Dorfleitner et al., 2016), the average length of self-disclosure

in all listings is 130 words which are lower than 150 words for funded loans.

Regarding verification, Renrendai.com provides the unique way for lenders to screen loan re-

quests. Borrowers can choose a different combination of verification types to facilitate finance.

Table 2 shows small proportion for all types of verification in all loan listings of online requests,

while borrowers who get funded have much more percentage of credit validation. We can see that

the identity verification account on 32.4% for all requests and 99.4% for funded loans, which means

for those borrowers who want to obtain loans must verify their identity. The first evidence pro-

vides a snapshot that the average number of verification for a funded loan is 3.84, which is higher

than all listings with average 1.61 verification. Figure 7 shows average verification percentage for

funded loans over the month. The monthly percentage of income, employment and credit report

verification fluctuate around 15% over the period, increasing rapidly from 2015 and reach to almost

100% in October 2015 (policy change). The identity verification is almost a horizontal line at 100%.

Because the verification is compulsory for borrowers. The monthly percentage of other types of

14

verification maintain a significant level until 2015 and reduce to 0 in October 2015.

5 Verification and Loan Outcomes

We begin our analysis by examining the relationship between the number of verification and prob-

ability of a loan successfully funded, the interest rate, loan size and maturity of funded loans. We

then conduct statistical analysis in which we examine the relation between the number of verifica-

tion and ex-post default rates for completed loans. In the second part of the section, we study the

effect of each verification on loan performance.

5.1 Number of Verification and Loan Outcomes

This study employs regression analysis to find out the number of verification that influences the

probability of loan funded, the interest rate charged, loan size, loan maturity, and ex-post default

rate. Following the univariate analysis in the previous section, we estimate the following regressions

of loan status and characteristics:

Probability(LoanStatusi = 1) = Φ(α+ βNumV erificationi + δXi + εi), (1)

LoanInformationi = α+ βNumV erificationi + δXi + εi, (2)

Where the dependant variable LoanStatusi in equation 1 measures whether a loan i had been

funded (Successi) and an indicator of measuring default of on loan i (Defaulti) and Φ is the

cumulative distribution function of the standard normal. NumV erificationi is the number of

verification submitted by a borrower. In equation 2, LoanInformationi contain contract interest

rate, loan size, and maturity. The vectorXi includes set of controls, for instance, loan characteristics

(interest rate, loan amount, maturity and the length of self-disclosure) and borrower characteristics

(the borrower’s histories in the platform, borrower age, borrower income, whether the borrower is

a homeowner, and with or without mortgage, car owner, and with or without car finance, borrower

location, occupation, employment sector, employment length, the firm size for a borrower employed,

education and marital status). We also include the variable indicating loan origination year to

15

control the cohort effects.

The parameter β is the primary coefficients of the differential effect on the probability of suc-

cessfully funded rate and default, and interest rate, loan size, and maturity for the number of

verification. Table 3 shows the relationship between the number of verification and loan perfor-

mance. Borrowers with thorough and complete verification can significantly increase the probability

of loan funded and mitigate the effect of ex-post default and are more likely to request the shorter

period of loans. The number of verification has the negative relation with interest rate and amount

but statistically significant. In consistence with credit rationing theory, our empirical results show

that increasing interest rate can result in lower funding probability, which means that interest rate

cannot be used to clear excess loan demand in the P2P market (Jaffee and Stiglitz, 1990). Hence,

the adverse selection occurs in the P2P market. Also, loans with higher interest rate are more like

to default. As expected, other loan characteristics such as loan size, maturity have a significant

impact on funding probability and default rate. Borrowers who exist mortgage are more likely to

obtain the loans and have a lower probability of default. It is an indicator to show potential lenders

that the borrower had already accessed to finance. The results of other borrower characteristics

are consistent with recent P2P lending papers (Feng et al., 2015; Freedman and Jin, 2014; Miller,

2015; Serrano-Cinca et al., 2015), particularly, Tao et al. (2017) study that borrower characteristics

and loan characteristics impact on loan funding outcomes by using data from Renrendai.com.

5.2 Verification Types and Loan Outcomes

We further discuss the relation between each verification type and probability of funding, default

rates and interest rates. We estimate each verification type by the following specifications:

Probability(LoanStatusi = 1) = Φ(γ + λV erificationi + δXi + εi), (3)

InterestRatei = γ + λV erificationi + δXi + εi, (4)

Where V erificationi represent each verification type, which includes validation status for em-

ployment, income, credit report, identity, mobile phone number, address, homeowner, car owner,

social media, video interview, marital status, and education. The dependent variable LoanStatusi,

16

InterestRatei and control variables Xi are defined as in equation 1 and equation 2. Equation 3

estimates whether the funding probability of loan requests are correlated with each verification

type, and examines the relation between the probability of default and each verification type. We

also test that borrower’s debt cost by verifying the different type of documentation in equation 4.

Table 4 presents regression results for the probability of successfully funded across each verifica-

tion type. We find that most of the verification type can increase funding probability. Specifically,

borrowers who verify income, employment status and providing credit report particularly have

higher chance to obtain loans. The verification of mobile number is negatively related to suc-

cessfully funded of requests. That is because the mobile number was a compulsory process when

borrowers had registered as members in the platform. Thus, the mobile number verification is no

longer as an effective signal for screening loan requests.

The results in Table 5 provides an analysis of the relationship between verification type and the

default for completed loans. Borrowers who verify credit report, social media account, marriage

status, car ownership and education level are less likely to default on their loans. Verifying income,

employment and home ownership are not correlated with ex-post default rate, which raises the

concern of income misrepresentation and effectiveness of online verification. The mobile number

and address are additional verification which inflates the probability of default. Table 6 shows that

borrowers who verify their social media account, education and car ownership can obtain loans

with lower interest rate.

To summarize, borrowers are more likely obtain loans if they choose to verify more documents

whichever type of document with the application. Lenders catch up the signal for bidding. However,

it cannot be used to a prediction of ex-post default. Such important verification types (income,

employment) are not correlated with default. Although credit report issued by People’s Bank of

China only provides limited information about credit history, it still shows a signal to lenders for

the creditworthiness in past banking accessing for the borrower. The lower probability of default

for verifying credit report illustrates that borrowers who expose their credit histories can indicate

ex-post loan performance. It is not surprising that verifying education level and car ownership can

relief the effect of default. Education level and asset are significant characteristics dividing social

class which can determine the borrower’s risk level (Hollingshead et al., 1975).

17

6 Income Exaggeration and Loan Performance

In the previous section, we evaluate the impact of verification on loan performance. Borrowers with

complete verification facilitate the debt financing and mitigate default rates. Having considered

the problem that the particular set of borrowers have the incentive to inflate or falsify their income.

We now can examine the consequence of incomplete verification by exploring income exaggeration

and its effect.

6.1 Measure Income Exaggeration

The common method of measuring income exaggeration is to find a baseline group such as World

Values Survey compared with the income of treatment group. However, the data of survey is col-

lected from a general source of the whole population. It could be a selection bias in our data because

for those borrowers who seek finance in P2P platforms is a certain group in China. Fortunately,

Renrendai.com provides an ideal matching sample (offline audited loans) to examine income exag-

geration. As we mentioned in section 3, Renrendai.com launched a new financing channel through

offline institution from 2012. The loan requests from offline are strictly audited (higher level of

verification compared with the original sample). The offline audit only includes four types of ver-

ification: income, employment, identity and credit report (verify all four types together). Other

information is consistent with the online application. Table 7 shows the summary statistics for

funded loans from the online and offline channel. We can see that borrowers who verify their doc-

umentation via offline channel have lower average income7 than the online group. It shows initial

evidence that borrowers could exaggerate their income if incomplete verification provision is carried

out.

To formally measure income exaggeration, we employed nearest-neighbour matching with bias

correction to obtain the matching estimators for average treatment effects (Abadie and Imbens,

2006, 2011). It provides a consistent estimator for the large sample variance. The bias-corrected

matching estimators have the advantage of correcting for a large-sample bias that exists when

7Note that the original income reported by a range, e.g. 5,000 RMB to 10,000 RMB. We take the average valuefor the income (7,500 RMB for the range 5,000 RMB to 10,000 RMB). For the reported income below 500 RMB, wetake the value as 500 RMB. The reported income above 50,000 RMB, we take the value as 50,000 RMB. It won’taffect the results because both online and offline group use the same method of record.

18

matching on more than one continuous covariate. Our matching includes several continuous co-

variates (loan amount and age). We can get more accurate outcome using bias-corrected matching

estimators. Other studies to measure income exaggeration such as Ambrose et al. (2016); Jiang

et al. (2014) estimate income by employing semi-log model from borrower characteristics, loan

characteristics, area characteristics and other covariates. Our matching covariates contain borrower

characteristics (age, education, marital status, province dummies, employment sector, employment

length, firm size, industry sector, home ownership and car ownership) and loan characteristics (loan

size and loan origination year dummies). We calculate the measure of income exaggeration by sub-

tracting the estimated logarithm income from the reported logarithm income. The measure means

the percentage difference between estimated income and reported income.

Table 8 presents the average treatment effect on logarithmic income compared with offline

audited loans. The results show that the matching estimate of income reported by online applicants

is negatively related to the offline audited group, which indicates the evidence of inflating income by

online borrowers. The result is consistent with the matching estimate of propensity score matching

method.

6.2 Number and Type of Verification against Income Exaggeration

To further examine the extent of income misrepresentation, we employ the following specification:

IncExgi = γ + λV erReleatedi + δXi + εi, (5)

Where IncExgi is the measure of income exaggeration, V erReleatedi representsNumV erificationi

and V erificationi defined as in equation 1. Xi are defined as in equation 1. The model examines

the number or type of verification impact on the extent of income exaggeration. Table 9 reports

the results of the measure of income exaggeration and the number of verification for funded online

loans. We find that borrowers with incomplete verification are more likely to upwardly misrepre-

sent their income. In other words, borrowers with thorough verification can mitigate the extent

of income exaggeration. Table 10 presents that the relationship between income exaggeration and

each type of verification. Carrying out verification on income, employment, credit reports and

video interview can reduce the level of income overstatement. Other types of verification are no

19

statistically significance with income misrepresentation, suggesting no matter with higher cost but

lower effect documents. For instance, borrowers have less incentive to falsify education certificate,

homeowner certification, and car owner certificate. The income verification should be an effective

way to obtain the loans. That could be one of the causes that the platform introduces offline

audited loans. The results are consistent with other studies such as (Jiang et al., 2014; Ambrose

et al., 2016; Mian and Sufi, 2017).

We then test the effect of income exaggeration on the loans for the probability of default. The

model are specified as follows:

Probability(Defaulti = 1) =Φ(γ + νIncExg + λV erReleatedi

+ ηIncExg × V erReleatedi + δXi + εi),

(6)

Where Defaulti indicate bad debt that a loan has not been kept up repaying over 90 days.

Other independent variables IncExgi and V erReleatedi and Xi are described as in previous sec-

tions. Table 9 illustrate that income overstatement results in higher probability of delinquency, but

borrowers with complete verification can mitigate the bad effects of the default. Table 11 shows

that borrowers who validate their income and employment can reduce default rates against income

exaggeration. There is no evidence that other types of verification have the same effect on loan

default.

Also, Figure 8 shows the average marginal effects of income verification at different levels of

estimated income exaggeration. Note that 2.3 and 1.86 are the 5th and 95th percentiles of income

exaggeration, respectively. The marginal effects are derived from the probit model of loan default

described in equation 6. The default rates slightly decrease if increasing the extent of income

exaggeration with income verification, while the default rates go high when inflating the income

without the verification.

6.3 Low-doc Verification, Returning Borrower and Income Exaggeration

In the previous section, we examined the relationship between income exaggeration and the number

and type of verification. A borrower normally provides a different combination of credential files

during the verification process. We found that borrowers who verify thorough and complete verifi-

cation can reduce the probability of default. To better explain the combination of verification, we

20

classify the verification as two types: incomplete verification (low-doc) and complete verification

(high-doc). To classify these two levels of verification, we employ Latent class analysis (LCA) to

find groups or subtypes of cases in the multivariate categorical verification.The LCA will attempt

to detect the presence of latent classes (the disease entities), creating patterns of association in the

verification. As in factor analysis, the LCA can also be used to classify case according to their

maximum likelihood class for the number of verification. Followed Lanza et al. (2003), the model

can be described as below

pi1,i2,...,iN ≈T∑t

pt

N∏n

pnin,t (7)

Where T is the number of latent classes and equals 2 (two latent classes: low-doc and high-doc). pt

are the unconditional probabilities and pnin,t are the conditional probabilities. The output includes

the probability of a response to number of verification for each latent class. In other words, we can

obtain the probability that members of each class had of engaging in each verification type. We

then examine whether low-doc verification with overstated income is associated with ex-post loan

performance, and we discuss a situation if the borrowers consider future credit availability. The

models are shown as follows:

Probability(Defaulti = 1) =Φ(α+ β1Lowdoci + β2Returningi

+ β3Lowdoci ×Returningi

+ λ1IncExgi + λ2Lowdoci × IncExgi

+ λ3IncExgi ×Returningi

+ λ4Lowdoci ×Returningi × IncExgi

+ δXi + εi)

(8)

Where Lowdoci means borrower with less verified credential files measured by LCA. Returningi

means returning borrower in the platform, i.e. borrowers successfully obtained loans at least one

time and requested loans for more times. The explanation of other variables is described as in

previous sections. Table 12 illustrate that inflating income is associated with higher probability

to predict the loan defaulted. Low-doc borrowers are more likely to default. However, we find

21

that low-doc borrowers with relatively low ex-ante concerns about future credit access would raise

their income are less likely to default. Figure 9 shows the average marginal effects of Low-doc

verification by returning borrowers. The positive sloping of marginal effect for first time borrower

means that amplifies the impact on income exaggeration and is associated with a significantly

higher probability of delinquency than returning borrowers. However, the low-doc borrower has

a larger impact on the probability of default, while the slope of the marginal effect for returning

borrowers is negative but not significantly different from 0.2. It indicates that these borrowers are

more likely to maintain their credit records.

7 Conclusion

This paper studies the impact of financial innovation in the form of P2P lending on the role of ver-

ification and its consequences for income exaggeration. We present empirical evidence consistence

with borrowers with complete verification can facilitate debt financing and reduce default rate.

However, borrowers with incomplete verification lead to misrepresenting their income upwardly. In

other words, borrowers who provide less verified documents have a more significant extent of income

exaggeration. This leads to higher default rates for this group when compared to the default rates

of more thoroughly verified borrowers. Our further analysis uncovers that low-doc borrowers who

exaggerate their income are more likely to increase the probability of default, but if they concern

about future credit availability in the platform are less likely to default.

From a policy perspective, a regulation blanket mandating “adjusted verification” (i.e., only

remain income, employment, identity and credit report for verification ) is consistence with our

prediction. However, the platforms is turning to attract more offline loans subject to higher ver-

ification cost. A possible explanation is that the government unveiled a regulatory framework in

2015. The platform is accused of misappropriating operation that leads to financial instability.

The regulation may be excessively restrictive and lead to credit rationing for the subset of the

population that faces high information verification costs. In the long run, the misallocation of fi-

nancial resources may have serious unintended consequences for the economic growth. Our analysis

therefore suggests that regulators/platforms are seeking to establish a sharing verification system

with significant ex ante low information costs.

22

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A Tables

Table 1. Variable Definitions

Variables Definition

Loan informationSuccessfully funded 1 if loan is successful funded, else 0Default 1 if loan is defaulted, else 0Interest rate (%) Annual percentage rate on the loanLoan amount (RMB) loan amount on requestMaturity (Months) Period of a loanBorrower’s history in the platform

Number of loan requests How many loan requests of borrowers have been in the platformNumber of successful requests How many loan requests of borrowers have been successful in the platformNumber of repaid How many times borrowers have been repaid in the platformNumber of arrears How many times of borrowers’ arrears in the platform (up to 30 days)Number of severe arrears How many times of borrowers’ arrears in the platform (up to 90 days)Borrowers’ characteristicsMonthly income (RMB) Borrower’s monthly income in RMBOccupation Borrowers occupationEmployment length Length of employment in yearsEmployment sector The sector of Borrowers’ employment, such as government, salesFirm size Employees number of borrowers’ firmLocation Borrowers’ working citySelf-description Borrowers’ disclosure to describe loan purposeAge Borrowers’ ageEducation 0 is high school and below, 1 is college, 2 is undergraduate and 3 is postgraduateMarital status 1 is widowed, 2 is married, 3 is single and 4 is divorcedHome owner 1 means homeowner, else 0Mortgage 1 means borrowers have existing mortgage, else 0Vehicle owner 1 means borrowers have vehicle(s), else 0Vehicle loan 1 means borrowers have existing vehicle loan, else 0Verification statusIncome verification 1 means income is validated, else 0Employment verification 1 means employment is validated, else 0Credit report verification 1 means credit report is validated, else 0Identity verification 1 means identity is validated, else 0Address verification 1 means address is validated, else 0Marriage verification 1 means marital status is validated, else 0Education verification 1 means education is validated, else 0Mobile phone number verification 1 means mobile phone number is validated, else 0Homeowner verification 1 means homeowner is validated, else 0Car owner verification 1 means car owner is validated, else 0Video verification 1 means video interview is validated, else 0Social Media verification 1 means social media such as Weibo, kaixin is validated, else 0

27

Table 2. Descriptive statistics: All listings and funded loans. Online sample.

This table reports average/min/max loan characteristics, borrowers’ characteristics and verificationcarried out by Renrendai.com for the online sample for years 2012-2015. “Funded loans” meansthat all loans in this group are successfully granted.

Full sample Funded loansmean sd min max mean sd min max

Successfully funded 0.038 0.191 0.000 1.000Default 0.173 0.378 0.000 1.000 0.173 0.378 0.000 1.000Interest rate (%) 13.242 2.656 7.000 24.400 12.389 1.843 7.000 24.400Loan amount (RMB) 60,721.459 90,025.840 3,000.000 500,000.000 24,269.184 33,847.644 3,000.000 500,000.000Maturity (Months) 16.145 9.140 1.000 36.000 12.983 8.331 3.000 36.000Monthly income (RMB) 10,649.177 11,957.452 500.000 50,000.000 14,984.009 15,402.124 500.000 50,000.000Number of loan requests 2.646 3.151 1.000 73.000 3.834 5.052 1.000 73.000Number of successful requests 0.159 1.080 0.000 68.000 2.365 3.999 1.000 68.000Number of repaid 0.136 1.045 0.000 66.000 2.036 4.001 0.000 66.000Number of arrears 0.320 1.947 0.000 54.000 4.493 6.062 0.000 54.000Number of severe arrears 0.017 0.131 0.000 4.000 0.242 0.446 0.000 4.000Age 31.620 6.313 23.000 55.000 33.970 6.614 23.000 55.000Length of Self-description 130.938 108.793 0.000 1,498.000 158.686 131.745 3.000 1,414.000Home owner 0.301 0.459 0.000 1.000 0.542 0.498 0.000 1.000Mortgage 0.099 0.298 0.000 1.000 0.225 0.417 0.000 1.000Vehicle owner 0.174 0.379 0.000 1.000 0.377 0.485 0.000 1.000Vehicle loan 0.041 0.197 0.000 1.000 0.084 0.277 0.000 1.000Employment Verification 0.017 0.129 0.000 1.000 0.252 0.434 0.000 1.000Income Verification 0.015 0.123 0.000 1.000 0.226 0.418 0.000 1.000Identity Verification 0.324 0.468 0.000 1.000 0.994 0.078 0.000 1.000Credit Report Verification 0.072 0.259 0.000 1.000 0.336 0.472 0.000 1.000Homeowner Verification 0.043 0.203 0.000 1.000 0.216 0.411 0.000 1.000Car Verification 0.031 0.173 0.000 1.000 0.199 0.399 0.000 1.000Education Verification 0.035 0.183 0.000 1.000 0.151 0.358 0.000 1.000Mobile Verification 0.961 0.195 0.000 1.000 0.846 0.361 0.000 1.000Address Verification 0.040 0.195 0.000 1.000 0.143 0.350 0.000 1.000Marriage Verification 0.031 0.173 0.000 1.000 0.175 0.380 0.000 1.000Video Verification 0.017 0.128 0.000 1.000 0.208 0.406 0.000 1.000Social Media Verification 0.023 0.151 0.000 1.000 0.092 0.289 0.000 1.000Number of Verification 1.608 1.190 0.000 12.000 3.837 1.905 0.000 12.000Number of observations 567,955 21,549

28

Table 3. Relationship between the number of verification and loan performance

This table reports the results of the following analysis:

Probability(Successi = 1) = Φ(α+ βNumV erificationi + δXi + εi),

P robability(Defaulti = 1) = Φ(α+ βNumV erificationi + δXi + εi),

LoanInformationi = α+ βNumV erificationi + δXi + εi,

Where the dependant variable Successi measures whether a loan i had been funded and Defaultiis an indicator of measuring default of on loan i and Φ is the cumulative distribution function ofthe standard normal. NumV erificationi is the number of verification submitted by a borrower.LoanInformationi contain contract interest rate, loan size, and maturity. The vector Xi includesinformation loan characteristics (e.g. interest rate, loan amount, maturity and the length ofself-disclosure) and borrower characteristics (the borrower’s histories in the platform, borrowerage, borrower income, whether the borrower is a homeowner, and with or without mortgage,car owner, and with or without car finance, borrower location, occupation, employment sector,employment length, the firm size for a borrower employed, education and marital status). We alsoinclude the variable indicating loan origination year to control the cohort effects. The 2012-2015online sample is from Renrendai.com. *** p < 0.01, ** p < 0.05, * p < 0.1

(1) (2) (3) (4) (5)Dependent Variable: Successfully funded Default Interest rate (%) Maturity (Months) Log(Amount)

Number of Verification 0.392∗∗∗ -0.026∗∗∗ -0.005 -0.139∗∗∗ -0.005(0.003) (0.008) (0.007) (0.029) (0.003)

Interest rate (%) -0.101∗∗∗ 0.081∗∗∗ 1.846∗∗∗ 0.009∗∗

(0.002) (0.008) (0.030) (0.003)Maturity (Months) 0.012∗∗∗ 0.044∗∗∗ 0.097∗∗∗ 0.024∗∗∗

(0.001) (0.002) (0.002) (0.001)log amount -0.508∗∗∗ -0.057∗∗ 0.055∗∗ 2.939∗∗∗

(0.006) (0.022) (0.019) (0.079)Borrower Characteristics Yes Yes Yes Yes YesOrigination Year Fixed Effects Yes Yes Yes Yes YesOther controls Yes Yes Yes Yes YesR2 0.311 0.368 0.444Number of observations 322,427 16,723 17,913 17,913 17,913

29

Table 4. Probability of funding and different type of verification


Probability(Successi = 1) = Φ(γ + λV erificationi + δXi + εi),

Where the dependent variable Successi and control variables Xi are defined as in Table 3.V erificationi represent the range of verification types, which contains validation status foremployment, income, credit report, identity, mobile phone number, address, homeowner, carowner, social media, video interview, marital status, and education. We estimate whether thefunding probability of loan requests are correlated with each verification type. The 2012-2015online sample is from Renrendai.com. *** p < 0.01, ** p < 0.05, * p < 0.1

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)Dependent Variable: Success Success Success Success Success Success Success Success Success Success Success

Income Verification 1.815∗∗∗

(0.017)Employment Verification 1.824∗∗∗

(0.017)Credit Report Verification 1.035∗∗∗

(0.011)Social Media Verification 0.448∗∗∗

(0.018)Marriage Verification 0.694∗∗∗

(0.015)Mobile Verification -0.704∗∗∗

(0.015)Homeowner Verification 0.684∗∗∗

(0.014)Car Verification 0.944∗∗∗

(0.016)Education Verification 0.607∗∗∗

(0.014)Address Verification 0.444∗∗∗

(0.014)Video Verification 1.439∗∗∗

(0.018)Borrower Characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesOrigination Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesOther controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesNumber of observations 322,427 322,427 322,427 322,427 322,427 322,427 322,427 322,427 322,427 322,427 322,427

30

Table 5. Probability of default and different type of verification for completed online loans


Probability(Defaulti = 1) = Φ(γ + λV erificationi + δXi + εi),

Where the dependent variable Defaulti and control variables Xi are defined as in Table 3. Thevariable V erificationi is defined as in Table 4. We examine the relation between the probabilityof default and each verification type. The 2012-2015 online sample is from Renrendai.com. ***p < 0.01, ** p < 0.05, * p < 0.1

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)Dependent Variable: Default Default Default Default Default Default Default Default Default Default Default

Income Verification -0.033(0.034)

Employment Verification -0.048(0.034)

Credit Report Verification -0.399∗∗∗

(0.033)Social Media Verification -0.258∗∗∗

(0.055)Marriage Verification -0.080∗

(0.039)Mobile Verification 0.436∗∗∗

(0.042)Homeowner Verification -0.021

(0.037)Car Verification -0.143∗∗

(0.045)Education Verification -0.292∗∗∗


(0.037)Video Verification 0.054


31

Table 6. Interest rate and different type of verification for funded online loans


InterestRatei = γ + λV erificationi + δXi + εi,

Where the dependent variable InterestRatei and control variables Xi are defined as in Table 3.The variable V erificationi is defined as in Table 4. We test that borrower’s debt cost for verifyingthe different type of documentation. The 2012-2015 online sample is from Renrendai.com. ***p < 0.01, ** p < 0.05, * p < 0.1

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)Dependent Variable: Interest Interest Interest Interest Interest Interest Interest Interest Interest Interest Interest

Income Verification 0.048(0.030)

Employment Verification 0.045(0.030)

Credit Report Verification -0.012(0.028)

Social Media Verification -0.257∗∗∗

(0.042)Marriage Verification -0.042

(0.034)Mobile Verification 0.071∗

(0.032)Homeowner Verification -0.022

(0.033)Car Verification -0.103∗∗

(0.039)Education Verification -0.242∗∗∗


(0.034)Video Verification 0.201∗∗∗

(0.043)Borrower Characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesOrigination Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesOther controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesR2 0.311 0.311 0.311 0.312 0.311 0.311 0.311 0.311 0.313 0.311 0.312Number of observations 17,913 17,913 17,913 17,913 17,913 17,913 17,913 17,913 17,913 17,913 17,913

32

Table 7. Descriptive statistics: Funded loans from online and offline. Online sample and matchedoffline sample.

This table reports average monthly income, loan characteristics and verification carried out byRenrendai.com for the online sample and matched offline sample for years 2012-2015.

(1) (2)Online loans Offline audited

mean sd mean sd

Monthly income (RMB) 14,984.009 15,402.12 13,565.291 13,649.01Interest rate (%) 12.389 1.84 11.595 0.82Loan amount (RMB) 24,269.184 33,847.64 63,762.788 30,536.50Maturity (Months) 12.983 8.33 28.683 8.13Employment Verification 0.252 0.43 1.000 0.00Income Verification 0.226 0.42 1.000 0.00Identity Verification 0.994 0.08 1.000 0.00Credit Report Verification 0.336 0.47 1.000 0.00Homeowner Verification 0.216 0.41Car Verification 0.199 0.40Education Verification 0.151 0.36Mobile Verification 0.846 0.36Address Verification 0.143 0.35Marriage Verification 0.175 0.38Video Verification 0.208 0.41Social Media Verification 0.092 0.29Number of Verification 3.837 1.91 4.000 0.00Number of observations 21,549 58,408

33

Table 8. Average treatment effect on Log(income) by offline audited group

This table presents the average treatment effect on logarithmic income compared with offlineaudited loans. The results show that the matching estimate of income reported by online applicantsis negatively related to the offline audited group, which indicates the evidence of inflating incomeby online borrowers. The result is consistent with the matching estimate of propensity scorematching method. *** p < 0.01, ** p < 0.05, * p < 0.1

ATE Log(income)Nearest-neighbor matching with bias correction Propensity score matching

Offline Audited -0.929∗∗∗ -0.470∗∗∗

(0.033) (0.046)Number of observations 72,930 72,930

34

Table 9. Income exaggeration and the number of verification for funded online loans


IncExgi = α+ βNumV erificationi + δXi + εi

Probability(Defaulti = 1) = Φ(α+ βNumV erificationi + νIncExg + δXi + εi),

Where the dependent variable IncExgi represents the extent of income exaggeration. Defaultiis defined as in Table 3, V erificationi and control variables Xi are defined as in Table 4. Weexamine the relationship between the number of verification and the extent of income exaggerationand the their impact on delinquency rates. The 2012-2015 online sample is from Renrendai.com,income exaggeration is calculated by comparing the matched offline sample. *** p < 0.01, **p < 0.05, * p < 0.1

(1) (2)Dependent variable: IncExg Default

Number of Verification -0.020∗∗∗ -0.057∗∗∗

(0.003) (0.008)IncExg 0.072∗∗

(0.027)IncExg × Number of Verification -0.012∗

(0.006)Number of observations 17913 16723

35

Table 10. Income exaggeration and different type of verification for funded online loans


IncExgi = γ + λV erificationi + δXi + εi

Where the dependent variable IncExgi is defined as in Table 9. V erificationi and controlvariables Xi are defined as in Table 4. We examine which type of verification impact on theextent of income exaggeration. The 2012-2015 online sample is from Renrendai.com, income exag-geration is calculated by comparing the matched offline sample. *** p < 0.01, ** p < 0.05, * p < 0.1

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)Dependant variable: IncExg IncExg IncExg IncExg IncExg IncExg IncExg IncExg IncExg IncExg IncExg

Income Verification -0.068∗∗∗

(0.017)Employment Verification -0.080∗∗∗

(0.017)Credit Report Verification -0.036∗

(0.016)Social Media Verification -0.045

(0.023)Marriage Verification 0.003

(0.019)Mobile Verification -0.027

(0.018)Homeowner Verification 0.004

(0.018)Car Verification -0.039

(0.022)Education Verification -0.031


(0.019)Video Verification -0.054∗


36

Table 11. The impact of income exaggeration with each type verification on delinquency rates forfunded online loans


Probability(Defaulti = 1) = Φ(α+ λV erificationi + νIncExg + δXi + εi),

Where the dependent variable Defaulti is defined as in Table 3. IncExgi is defined as in Table9, V erificationi and control variables Xi are defined as in Table 4. We examine different type ofverification with income exaggeration effect on the extent of income exaggeration. The 2012-2015online sample is from Renrendai.com, income exaggeration is calculated by comparing the matchedoffline sample. *** p < 0.01, ** p < 0.05, * p < 0.1

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Dependent variable: Default Default Default Default Default Default Default Default Default Default

IncExg 0.044∗∗ 0.045∗∗ 0.039∗ 0.032∗ 0.024 0.029 0.022 0.033∗ 0.038∗ 0.039∗

(0.017) (0.017) (0.017) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016)Income Verification × IncExg -0.060∗

(0.027)Employment Verification × IncExg -0.064∗

(0.027)Credit Report Verification × IncExg -0.014

(0.025)Social Media Verification × IncExg -0.020

(0.045)Marriage Verification × IncExg 0.044

(0.030)Homeowner Verification × IncExg 0.001

(0.026)Car Verification × IncExg 0.012

(0.029)Education Verification × IncExg -0.004

(0.038)Address Verification × IncExg -0.041

(0.030)Video Verification × IncExg -0.047

(0.028)Borrower Characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes YesOrigination Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes YesOther controls Yes Yes Yes Yes Yes Yes Yes Yes Yes YesNumber of observations 16723 16723 16723 16723 16723 16723 16723 16723 16723 16723∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

37

Table 12. The impact of Low doc, returning borrowers and income exaggeration on delinquencyrates for funded online loans


Probability(Defaulti = 1) =Φ(α+ β1Lowdoci + β2Returningi

+ β3Lowdoci ×Returningi

+ λ1IncExgi + λ2Lowdoci × IncExgi

+ λ3IncExgi ×Returningi

+ λ4Lowdoci ×Returningi × IncExgi

+ δXi + εi)

Where the dependent variable Defaulti is defined as in Table 3. IncExgi is defined as in Table9, V erificationi and control variables Xi are defined as in Table 4. Lowdoci means borrowerwith incomplete verification. Returningi means returning borrower in the platform, i.e. borrowerssuccessfully obtained loans at least one time and requested loans for more times. We examinethe impact of Low doc, returning borrowers and income exaggeration on delinquency rates. The2012-2015 online sample is from Renrendai.com, income exaggeration is calculated by comparingthe matched offline sample. *** p < 0.01, ** p < 0.05, * p < 0.1

Dependent variable: Default

IncExg 0.071∗∗

(0.026)Lowdoc 0.104∗

(0.049)Lowdoc × IncExg 0.034

(0.032)Returning Borrower -0.557∗∗∗

(0.041)Returning Borrower × IncExg -0.015

(0.027)Lowdoc × Returning Borrower 0.247∗∗∗

(0.058)Lowdoc × Returning Borrower × IncExg -0.097∗

Borrower Characteristics YesOrigination Year Fixed Effects YesOther controls YesNumber of observations 16723

38

B Figures

Figure 1. Loan Listing and Verification Status

The figure shows that a loan listing contains an annual interest rate, the amount of loan, theperiod of repayment, the guarantee type, a credit score issued by Renrendai.com, and variouspieces of personal characteristic information (age, income, location, occupation, employer’sinformation, education, marital status, homeowner,car owner and borrowing histories in theplatform). Furthermore, the below table exhibits verification status which includes various type ofverification. The validate documentation is displayed by a green tick with verification time. Forexample, the below figure shows that the borrower validates his/her credit report, identity card,education, employment, income, mobile phone number and social media. Source: Renrendai.com

Loaninformation

Borrower’sinformation

39

Figure 2. Average number of verification for all listings and funded loans over the month

The figure shows the average number of verification for all listings and funded loans over the month.Source: Renrendai.com, Observation number are 567,955 for all listings and 21,549 for funded loans.

40

Figure 3. Transaction volume, outstanding debt and average interest of P2P lending in China

The figure shows the trend of total transaction volume (left axis) and outstanding loan volume ofP2P lending in China and average interest (right axis) in the marketplace between January 2014and July 2016. Source: Wind Information, http://www.wind.com.cn/en/

41

Figure 4. Total and average principal amount issued over year of Origination; Total new borrowersand lenders over Year of origination

The figure shows the total and average principal amount issued over the year of origination andtotal new borrowers and lenders over Year of origination. Source: Renrendai.com

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Total Principal Amount Issued over Year of Origination

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Total New Lenders for Funded Loans over Year of Origination

42

Figure 5. The operation of Renrendai.com

The figure shows the operation of Renrendai.com. For the online channel, potential borrowers raisea requisition on the platform. After evaluating the credentials of the borrowers, the loan requestis made open for the lenders to screening. Lenders then can bid all listed loans. The borrowerwill receive the fund if a loan request is fully funded. Borrowers can apply for loans via the offlinechannel (Ucredit.com), the documents provided are similar to the online application, but theverification provision is different (see section 3). Other processes are same as online operation.

Renrendai.comBorrowers

Offline-Ucredit.com

Lenders

43

Figure 6. Loan Application Process

The figures show the loan application form of Renrendai.com. A borrower needs to fill in theloan purpose, amount, maturity, maximum interest rate they can accept and self-description.The borrower must provide personal characteristics and related supporting documents subject toverification. Source: https://www.renrendai.com/help/borrow/borrow!detail.action?flag=txsqb

44

Figure 7. Average verification percentage for funded loans over the month

The figure shows average verification percentage for funded loans over the month. The monthlypercentage of income, employment and credit report verification fluctuate around 15% over theperiod, increasing rapidly from 2015 and reach to almost 100% in October 2015 (policy change).The identity verification is almost a horizontal line at 100%. Because the verification is compulsoryfor borrowers. The monthly percentage of other types of verification maintain a significant leveluntil 2015 and reduce to 0 in October 2015. Source: Renrendai.com

45

Figure 8. Marginal effect of income verification by income exaggeration

The figure shows the average marginal effects of income verification at different levels of estimatedincome exaggeration. Note that 2.3 and 1.86 are the 5th and 95th percentiles of income exaggera-tion, respectively. The marginal effects are derived from the Probit model of loan default describedin equation 6. The default rates slightly decrease if increasing the extent of income exaggerationwith income verification, while the default rates go high when inflating the income without theverification.

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−2.3 −1.98−1.66−1.34−1.02 −.7 −.38 −.06 .26 .58 .9 1.22 1.54 1.86Inc_Exg

Income non verified Income verified

Marginal Effect of Income Verification on Default with 95% CIs

46

Figure 9. Marginal effect of Low-doc verification by returning borrowers

The figure shows the average marginal effects of Low-doc verification by returning borrowers. Thepositive sloping of marginal effect for the first time borrower means that amplifies the impact onincome exaggeration and is associated with a significantly higher probability of delinquency thanreturning borrowers. However, the low-doc borrower has a larger impact on the probability of de-fault, while the slope of the marginal effect for returning borrowers is negative but not significantlydifferent from 0.2. It indicates that these borrowers are more likely to maintain their credit records.

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−2.3 −2 −1.7 −1.4 −1.1 −.8 −.5 −.2 .1 .4 .7 1 1.3 1.6IncExg

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First time borrower with Low−doc Returning borrower with Low−doc

Marginal Effect of Low−doc verification on Default with 95%CIs

47

role of veri cation in peer-to-peer lending

Documents