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Frontiers in Signal Processing
FSP > Volume 4, Number 1, January 2020

Research on the Installment Risk of P2P Network Loan

Download PDF  (435.2 KB)PP. 1-9,  Pub. Date:October 10, 2019
DOI: 10.22606/fsp.2020.41001

Author(s)
Bo LI, Du-yu LIU
Affiliation(s)
Southwest Minzu University, Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu Sichuan, 610041 China
Southwest Minzu University, Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu Sichuan, 610041 China
Abstract
The problems about some borrowers default in the rapid development of P2P network loan
causes economic losses to online lending platforms and investors. Based on the situation of borrowers
repaying loans in installments, this paper uses automatic binning to select features, builds a risk
monitoring model, and predicts whether the borrower will perform next month. The model can discover
the signs of borrower’s default in advance, so that the platform can take preventive measures earlier
and prevent the problem of platform fund circulation caused by insufficient repayment. In addition, it
can provide reference for the platform to estimate the monthly payment amount. In this paper, the
borrower data of 2016-2018 on Lending Club is used. The risk monitoring models of borrowers are
based on CART algorithm, random forest algorithm and XGBoost algorithm respectively. The
precision accuracy of the algorithms above is above 95%. The repayment amount of borrowers at last
month, the borrower's occupation, the total amount of borrowing, the borrower's monthly repayment
amount, and whether the borrower is working with the debt settlement company which are very
effective in analyzing the willingness of the borrower to perform on time next month. Therefore, they
could be used as the main basis for analysis.

Keywords
Peer to peer lending, CART decision tree, random forest, XGBoost
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