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

严硕

2023-01

10.1108/IMDS-07-2022-0399

INDUSTRIAL MANAGEMENT & DATA SYSTEMS   影响因子和分区

0263-5577

1758-5783

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.

Loan allocation strategies Modern portfolio theory Online peer-to-peer lending

SCI ; EI

英语

第一

Intelligent Management & Innovation Research Center (IMIRC) of Shenzhen Research Base in Arts & Social Sciences (RBASS)["71731009","72061127002","2018WZDXM020"]

Computer Science ; Engineering

Computer Science, Interdisciplinary Applications ; Engineering, Industrial

WOS:000919734300001

EMERALD GROUP PUBLISHING LTD

20230513473821

Finance ; Learning systems ; Linear programming ; Linear regression ; Logistic regression ; Profitability

Industrial Economics:911.2 ; Mathematical Statistics:922.2

ENGINEERING

2-s2.0-85147103876

人工提交

南方科技大学

Yan S. Proposing a new loan recommendation framework for loan allocation strategies in online P2P lending[J]. INDUSTRIAL MANAGEMENT & DATA SYSTEMS,2023,123(3):910-930.

严硕.(2023).Proposing a new loan recommendation framework for loan allocation strategies in online P2P lending.INDUSTRIAL MANAGEMENT & DATA SYSTEMS,123(3),910-930.

严硕."Proposing a new loan recommendation framework for loan allocation strategies in online P2P lending".INDUSTRIAL MANAGEMENT & DATA SYSTEMS 123.3(2023):910-930.