Method for identifying default risks of P2P network lending borrowers

A P2P network and borrower technology, applied in the field of information security, can solve problems such as low accuracy of default identification and difficult analysis, and achieve the effects of improving overall adaptability, high identification accuracy, and increased accuracy

Inactive Publication Date: 2018-05-08
SHANGHAI PUBLISHING & PRINTING COLLEGE
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, for the Internet lending industry where the information asymmetry problem is particularly serious, the current borrower default risk identification method based on the objective basic data of the borrower is difficult to play a good analytical role, so the general default identification accuracy is not high, about 60%

Method used

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  • Method for identifying default risks of P2P network lending borrowers
  • Method for identifying default risks of P2P network lending borrowers
  • Method for identifying default risks of P2P network lending borrowers

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Embodiment Construction

[0031] The specific implementation manners of the present invention will be described below in conjunction with the accompanying drawings.

[0032] For the Internet lending industry where the information asymmetry problem is particularly serious, the current borrower default risk identification method based on the objective basic data of the borrower is difficult to play a good analytical role. The text mining-based default risk identification method proposed by the present invention is relatively The existing default risk identification algorithm based on the objective basic data of the borrower has a higher accuracy rate. The specific borrower default risk identification process is as follows: figure 1 shown.

[0033] Step S1, using a web crawler tool to collect all loan application data from the Renrendai platform (http: / / renrendai.com) during the two-year period from January 2013 to January 2015, a total of 493,888 loan applications. The invention divides the crawled loa...

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Abstract

The invention provides a method for identifying default risks of P2P network lending borrowers. The method comprises the following steps of: 1, acquiring P2P lending application data information; 2, preprocessing data to obtain lending description text information; 3, carrying out feature extraction on the lending description text information; 4, identifying a default risk of P2P network lending through combining objective basic information features of a borrower and the lending description text information by using a support vector machine classification method; and 5, carrying out SVM kernelfunction parameter optimization by adoption of ten-fold cross validation. According to the method for identifying default risks of P2P network lending borrowers, the default identification correctness is improved by 10% or more than 10% and the highest default identification correctness is 73.42%.

Description

technical field [0001] The invention relates to a method for identifying the default risk of a P2P network lending borrower, which belongs to the field of information security. Background technique [0002] With the popularity of online virtual communities, a new lending method has appeared in the credit market: P2P network lending (peer to peer lending) (Bachmann et al., 2011). The P2P network lending model emerged in Europe and the United States, and is a new financial service model based on the Internet. Different from the traditional financial model, P2P got rid of the lending medium (Zhang & Liu, 2012), and is a direct, unsecured micro-lending model from person to person (Lin et al., 2013; Greiner and Wang, 2010). As an innovative financial model, P2P network lending has the following characteristics: (1) The transactions between borrowers and lenders are convenient. Borrowers and lenders can participate in the online lending platform with a lower threshold, and conve...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q40/02G06K9/62G06F17/30G06F17/27
CPCG06F16/355G06F16/951G06F40/284G06Q40/03G06F18/214G06F18/2411
Inventor 陈群宗利永
Owner SHANGHAI PUBLISHING & PRINTING COLLEGE
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