Original scientific paper

https://doi.org/10.17559/TV-20200210110508

Credit Risk Management of P2P Network Lending

Dongmei Li ; College of Business Administration, Wonkwang University, No. 460, Iksandae-ro, Iksan, Jeonbuk 54538, Korea
Sanggyun Na* ; College of Business Administration, Wonkwang University, No. 460, Iksandae-ro, Iksan, Jeonbuk 54538, Korea
Tao Ding ; College of Business Administration, Wonkwang University, No. 460, Iksandae-ro, Iksan, Jeonbuk 54538, Korea
Congchong Liu ; College of Business Administration, Wonkwang University, No. 460, Iksandae-ro, Iksan, Jeonbuk 54538, Korea

Abstract

This article first studies the literature of P2P online loans, including online loans, credit risk factors and models, and summarizes the current status of P2P and credit risk assessment management in China. Based on the loan data of domestic P2P lending platforms, this paper conducts an empirical study on credit risk assessment. This study uses random forest importance assessment and logistic regression classification for credit risk assessment to identify loan targets with higher probability of default and improve overall loan quality. This research used 10,930 loan data, based on 26 fields, and finally selected 20 model variables to participate in credit risk quantification through feature structure and feature analysis. The final modelling test results show that the model screening accuracy rate is 73.3%, indicating that this model has a good performance in the credit risk quantification of borrowers.

Keywords

credit risk management; importance assessment; logistic regression; P2P network lending

Hrčak ID:

260778

URI

https://hrcak.srce.hr/260778

Publication date:

22.7.2021.

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