Title: Predicting P2P lenders' decisions: the prospect theory approach

Authors: Yong Lu; Zhexiang Sheng; Kaidong Zhang; Qiang Duan

Addresses: Valor Growth Fund, Gulf Breeze, Florida, USA ' Amazon, Seattle, Washington, USA ' Stevens Capital Management LP, Radnor, Pennsylvania, 19087, USA ' Abington College, Pennsylvania State University, USA

Abstract: 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.

Keywords: prospect theory; machine learning; P2P lending.

DOI: 10.1504/IJEB.2022.119935

International Journal of Electronic Business, 2022 Vol.17 No.1, pp.1 - 12

Received: 07 Jan 2019
Accepted: 09 Oct 2019
Published online: 04 Jan 2022 *

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