Volume 11 Number 3 (Sep. 2021)
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IJEEEE 2021 Vol.11(3): 85-92 ISSN: 2010-3654
doi: 10.17706/ijeeee.2021.11.3.85-92

A DBM-Based Ensemble Method for Improving Default Risk Prediction of Peer-to-Peer (P2P) Lending

Shan Gao, Xuefeng Wang
Abstract—With the peer-to-peer lending (P2P) business growing up, the most important influencing factor for the healthy development of this industry is the default risk of borrowers. Because the behavior between lenders and borrowers is real time, naturally large amounts of transaction data are being generated all the time. However, it is difficult to extract useful representative features and choose an appropriate model to predict the default risk of the borrowing behavior. In this paper, we proposed a (Deep Boltzmann Machines) DBM-based ensemble method for the default risk prediction in p2p lending, which is based on the real data generated by Lending Club company. Experimental results on the real world data indicate that our model is more effective and powerful with a 0.9093 explanation power.

Index Terms—Peer to peer lending, default risk prediction, deep Boltzmann machines, deep learning.

The authors are with Harbin Institute of Technology, Harbin and 150001, China (email: shangao@hit.edu.cn).

Cite: Shan Gao, Xuefeng Wang, "A DBM-Based Ensemble Method for Improving Default Risk Prediction of Peer-to-Peer (P2P) Lending," International Journal of e-Education, e-Business, e-Management and e-Learning vol. 11, no. 3, pp. 85-92, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

ISSN: 2010-3654 (Online)
Abbreviated Title: Int. J. e-Educ. e-Bus. e-Manag. e-Learn.
Frequency: Quarterly
Editor-in-Chief: Prof. Kuan-Chou Chen
Executive Editor: Ms. Nancy Lau
Abstracting/ Indexing: EBSCO, Google Scholar, Electronic Journals Library, QUALIS, ProQuest, INSPEC (IET)
E-mail: ijeeee@iap.org
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