Research on the Identification and Prediction of Default Risk of Online Lending Platform Customers

ShuaiQi Liu () and Sen Wu ()
Additional contact information
ShuaiQi Liu: University of Science and Technology Beijing
Sen Wu: University of Science and Technology Beijing

A chapter in LISS 2020, 2021, pp 823-835 from Springer

Abstract: Abstract P2P lending is a new market-oriented and networked financial service platform, which has attracted the attention of researchers in different fields since its establishment. The P2P lending platforms have operation mechanism design and fund management issues. Due to the problem of information asymmetry in the credit information of the P2P platform and the lack of a physical mortgage mechanism, the user’s default identification and the risk management and control of the platform have become the core problems of P2P operation, and data mining has become the main method to study this problem. This paper studies the default problem of P2P platform customers, uses the random forest method to establish a classification model on Lending Club data to predict the P2P platform customer default status, and analyzes the importance of the attributes that affect whether or not to default. The research shows that the random forest model can effectively predict the customer’s default behavior, and the repaid amount has become an important factor for default.

Keywords: The random forest; Attribute importance; P2P online lending; Default prediction (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-33-4359-7_57

Ordering information: This item can be ordered from
http://www.springer.com/9789813343597

DOI: 10.1007/978-981-33-4359-7_57

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().