Variables

Definition

Time

The time spent on the completion of the borrowing target. Considering the possible impact of borrowing scale, the full bidding time here is the real full bidding time (seconds)/borrowing scale (10,000 Yuan);

Rate

The interest rate of a loan;

Default

In this paper, overdue, advance payment and breach of contract are treated as breach of contract. The default target value is 0 and the normal repayment value is 1;

Bids

In order to eliminate the influence of time and amount, the dependent variable is measured by (the number of bidders per borrowing/the duration of borrowing)/the amount of borrowing (10,000) when the tender is completed or the total number of people applying for lending

after the expiration of the tender period is stipulated;

Lterm

The borrowing period is monthly and the minimum value is 1;

Lmoney

The value of borrowing amount after natural logarithm;

Gender

If the borrowers sex is male, it is 1, and vice versa, it is 0.

Age

Borrowers age;

Marry

If the borrowers marital status is married, divorced or widowed, take 1, and if he is unmarried, take 0;

Income

If the monthly income of the borrower is not disclosed, the value is 1; if the monthly income is below 1000 Yuan, take 2,1000-2000 Yuan and 3,2000-5000 Yuan and 4,5000-10000 Yuan and 5,10000-20000 Yuan and 6,20000-50000 Yuan and above 7,50000 Yuan and 8 Yuan.

Worktime

If the length of service of the borrower is not disclosed, the value is 1; when the length of service is less than 1 (including) years, 2 is taken in 1-3 (including) years, 3-5 (including) years, 4 is taken in 3-5 (including) years, and 5 is taken in more than 5 years;

Edu

If the borrowers educational background is not disclosed, the value is 1; if the borrower obtains a high school or lower education, the value is 2; if he obtains a college education, the value is 3; if he obtains a bachelors degree, the value is 4; if he obtains a graduate student or

above, the value is 5;

House

Whether the borrowers real estate is available or not, take 1 if there is, and vice versa, 0.

Car

Whether the borrower has a car or not, if so, it is 1, and vice versa, it is 0.

HouseD

Whether the borrower has a mortgage or not, if so, it is 0, and vice versa, it is 1;

CarD

Whether the borrower wants car loan or not, if so, it is 0, and vice versa, it is 1;

Household_register

The certificate of the borrowers household register or place of residence, if any, takes the value of 1, and vice versa, 0;

Credit_cert

If the borrowers credit report has the certificate, the value is 1, and vice versa, 0.

CG

According to the Credit Rating Table of Renrendai, when the borrowers credit rating is HR, it is 1, E is 2, D is 3, C is 4, B is 5, A is 6, AA is 7.

PR_L

The borrowers historical borrowing and repayment sta

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外文文献原文

Risk Prevention of P2P Online Loan Based on the Criteria of Selection of Commercial Bank Loan

Chenqi Jiang*

School of Economics and Management

Nanjing University of Science and Technology

Nanjing, China

1446749163@qq.com

Abstract—This paper empirically tests whether the traditional commercial bank lending selection criteria can help investors effectively reduce lending risk. Establishing Poisson and Logit regression models based on loan data of RenrenDai, the results show that although the borrower who provides proof information according to the commercial bank loan screening criteria makes the investor spend more time to judge, it significantly reduces the number of bidders required to complete the tender, and the probability of default is also lower. This reveals that the traditional commercial bank lending selection criteria still have good applicability in the network lending market, providing investors with an effective information review reference to accurately judge the situation of borrowers, lowering the investment risk as well.

Keywords—online peer-to-peer lending; commercial banks; investors; lending platforms

I. INTRODUCTION

Since 2018, runway incidents on P2P platform have occurred frequently. After choosing a platform with high reliability, investors need to choose reliable borrowers on the platform to decide whether to lend funds or not. With the strengthening of government supervision, the difficulty of risk identification of the platform will be further reduced, but the risk control of borrowers in the platform will always need to be handled by investors themselves. Different from the mode in the United States, the structure of P2P in China is a large number of individual investors responding to a large number of individual borrowers. Information asymmetry exists in the traditional commercial bank lending business and the emerging Internet finance. Borrowers are always in the dominant position. Besides of interest rate and lending period, the completion of loan depends largely on investors judgment on borrowers repayment ability and credit status. Commercial banks, as traditional financing intermediaries, have established a set of relatively perfect and effective criteria for loan selection. Thus, whether investors can use this logic system for reference has become topic in this paper.

At present, there are various studies on the information of online borrowers. Previous studies have found that borrowers age, appearance, gender [1], professional status [2], social capital, credit rating [3] have strong transmission effects. In the aspect of risk prevention of P2P lending, most of the existing

Caixia Zhou

School of Economics and Management

Nanjing University of Science and Technology

Nanjing, China

1114241428@qq.com

studies focus on the risk evaluation of online lending platform [4, 5, 6]. Although the theoretical research on online lending is getting deeper, the domestic online lending market is still not as effective as the weak market. Investors are far from fully rational [7]. Therefore, it is still important to help investors improve decision-making efficiency and reduce risk through information research on borrowers.

Based on the actual operation mode of Chinas P2P online lending platform business, this paper puts forward the hypothesis to be verified in connection with similar loan projects of commercial banks, and establishes an empirical model based on 20823 loan data extracted from Renrendai Platform. The results of empirical test show that the traditional selection criteria for commercial banksrsquo; lending have good applicability in the network lending market, and can provide information audit reference for investors, so that they can more accurately judge the situation of borrowers, thereby reducing investment risk.

II. EMPIRICAL ANALYSIS

A. Hypothesis

In the network P2P lending market, investors need to evaluate borrowers repayment willingness and repayment ability in a number of borrowing targets, and select borrowers who meet their own requirements. When a large number of borrowers and investors with different maturities and financial needs gather in an individual network lending platform, without guaranteed repayment, and investors are at their own risk, if the platform cannot take effective collection mechanism for defaulting borrowers, due to information asymmetry and other factors, only borrowers with single-term borrowing needs will have the motivation to default or not to repay their loans. For borrowers with multi-period capital needs, they are more likely to choose to build a good reputation. Reputation status affects investors judgment on borrowers credit status, and then determines borrowers borrowing results. On the other hand, a good reputation requires borrowers to pass on qualifications to investors. In other words, the final utility level of multi-period borrowers is closely related to the default probability deduced by investors based on borrowers information. The results also theoretically emphasize the importance of the quality of information disclosure of borrowers and the importance of prescreening the credit status of borrowers by investors.

For single-term borrowers, if the platform can introduce an effective collection and punishment mechanism, it can form a deterrent effect in theory. In the face of strong penalties, dishonest borrowers not only have to repay their loans, but also pay an extra heavy price. Ther

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