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Proposing a new loan recommendation framework for loan allocation strategies in online P2P lending

Yuting Rong (School of Management, Xi'an Jiaotong University, Xi'an, China)
Shan Liu (School of Management, Xi'an Jiaotong University, Xi'an, China)
Shuo Yan (Business School, Southern University of Science and Technology, Shenzhen, China)
Wei Wayne Huang (Business School, Southern University of Science and Technology, Shenzhen, China)
Yanxia Chen (School of Economics, University of Chinese Academy of Social Sciences, Beijing, China)

Industrial Management & Data Systems

ISSN: 0263-5577

Article publication date: 26 January 2023

Issue publication date: 9 March 2023

471

Abstract

Purpose

Lenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns with risk limitations or lowering risks with expected returns for P2P lenders.

Design/methodology/approach

This paper used data from a leading online P2P lending platform in America. First, the authors constructed a logistic regression-based credit scoring model and a linear regression-based profit scoring model to predict the default probabilities and profitability of loans. Second, based on the predictions of loan risk and loan return, the authors constructed linear programming model to form the optimal loan portfolio for lenders.

Findings

The research results show that compared to a logistic regression-based credit scoring method, the proposed new framework could make more returns for lenders with risks unchanged. Furthermore, compared to a linear regression-based profit scoring method, the proposed new framework could lower risks for lenders without lowering returns. In addition, comparisons with advanced machine learning techniques further validate its superiority.

Originality/value

Unlike previous studies that focus solely on predicting the default probability or profitability of loans, this study considers loan allocation in online P2P lending as an optimization research problem using a new framework based upon modern portfolio theory (MPT). This study may contribute theoretically to the extension of MPT in the specific context of online P2P lending and benefit lenders and platforms to develop more efficient investment tools.

Keywords

Acknowledgements

The first author would like to acknowledge the partial grants support to the research (71731009, 72061127002, 2018WZDXM020). The second author would like to acknowledge the support from the followings: the Intelligent Management & Innovation Research Center (IMIRC) of Shenzhen Research Base in Arts & Social Sciences (RBASS) and the National Laboratory of Mechanical Manufacture System, XJTU, China.

Citation

Rong, Y., Liu, S., Yan, S., Huang, W.W. and Chen, Y. (2023), "Proposing a new loan recommendation framework for loan allocation strategies in online P2P lending", Industrial Management & Data Systems, Vol. 123 No. 3, pp. 910-930. https://doi.org/10.1108/IMDS-07-2022-0399

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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