Predicting P2P lenders’ decisions: the prospect theory approach

Yong Lu, Zhexiang Sheng, Kaidong Zhang, Qiang Duan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


This study investigates the importance predictors for lender’s decisions on peer-to-peer lending based on prospect theory. We apply two machine learning algorithms (decision trees and random forest) to identifying the important measures. We found that borrower’s default history is the most important variable, followed by the default ratio of the borrower’s other type of friend and borrower’s credit score as lender. These results prove that lenders play ‘safety rule’ in the highly risky P2P lending business. Maximum entropy algorithm verifies these results and proves the robustness of our model.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalInternational Journal of Electronic Business
Issue number1
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • General Business, Management and Accounting
  • Computer Science Applications


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