A Credit Rating Model for Online P2P Lending Based on Analytic Hierarchy Process

Abstract

The risk caused by the rapid development of online P2P lending attracts more and more attention from the public. This paper mainly establishes a credit rating model for online P2P lending based on Analytic Hierarchy Process. Among lots of qualitative and quantitative indices focused by lenders, we screened out 39 indices and then established a model for calculating the weight of each index group based on Analytic Hierarchy Process. Finally we got a comprehensive score for each online lending project, and investors can evaluate the advantages and disadvantages of each P2P platform based on this model. At the same time, the model can help investors to make a rational choice considering the balance between risk and return according to their personal preferences.

Appendix

• 1. According to the nature of the parameters, theses indices are all Qualitative variables which are divided into the two cases as below.This paper does no statistical analysis more about them (Tables 46.8 and 46.9).

  • Generally quality variable describes the existence of some feature, according to the presence or absence, a dummy variable is set to be 0 or 1. If it exists, there is a score of 1 (meaning in this model, the score is 100), or 0 (meaning in this model, the score is 0).

  • If there exists a narration, we can make assessments depending on its completion and authenticity, giving a score between 0–100.

• 2.
  $$\text {Regional leverage}=\frac{\text {Amount to be collected per month}}{\text {City integral}}.$$

  City integral can be calculated out through the following formula

  $$\begin{aligned} y={\left\{ \begin{array}{ll} 100 &{} x\leqslant \mu , \\ 100-\frac{8}{10}(x-\mu ) &{} \mu<x<10^5, \\ 20-\ln (\frac{x}{10^5}) &{}x>10^5,\\ \end{array}\right. } \end{aligned}$$

  (46.3)

  where $\mu =\text {per capita monthly salary}\times \text {city integral}\times \text {line of credit}(\text {make it 10})$, assuming that $\mu =6000$.

Table 46.8 $x_{6}\sim x_{18}$

Table 46.9 $x_{30}\sim x_{39}$