Work

The Prediction Analysis of Peer-to-Peer Lending Platforms Default Risk Based on Comparative Models

Year: 2020

Type: article

Abstract: This paper examines the determinants of platform default risk using machine learning methods, including comprehensive models, and thus compares these models’ predictive abilities. To test platform’s default risk, this paper constructs three types of variables, which reflect a platform’s operating characteristics, customer feedback, and compliance capability. We find that the abnormal return tends to trigger default risk significantly. However, the default risk can be minimized if a platform has positive recommendations from customers and more transparent information disclosure or is affiliated as the member of the National Internet Finance Association of China. Empirical results indicate that the CART model outperforms the Random Forests model and Logit regression in predicting platform default risk. Our study sheds light on default risk prediction and thus can improve the government regulation ability.

Source: Scientific Programming

DOI: 10.1155/2020/8816419

Authors: Haifeng Guo, Ke Peng, Xiaolei Xu, Shuai Tao, Zhen Wu

Institutions: Harbin Institute of Technology, National Computer Network Emergency Response Technical Team/Coordination Center of Chinar

Language: English

Topic: FinTech, Crowdfunding, Digital Finance

Subfield: Management Information Systems

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $2,000

Funder: National Natural Science Foundation of China