Social
lending, also known as peer-to-peer lending, provides customers with a platform
to borrow and lend money online. It is now rapidly gaining its popularity for
its superior monetary advantage comparing to banks for both borrowers and
lenders. Thus, choosing a reliable is very important, whereas the only method
most of the platforms use now is a grading system. In order to better prevent
the risks, we propose a method of combining Random Forests and Neural Network
for predicting the borrowers’ status. Our data are from Lending Club, a popular social lending
platform, and our results indicate that our method outperforms the lending Club
good borrower grades.
References
[1]
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[2]
Emekter, R., Tu, Y., Jirasakuldech, B., & Lud, M. (2015). Evaluating Credit Risk and Loan Performance in Online Peer-to-Peer (p2p) Lending. Applied Economics, 47, 54-70. https://doi.org/10.1080/00036846.2014.962222
[3]
Klafft, M. (2008). Online Peer-to-Peer Lending: A Lender’s Perspective. In H. R. Arabnia & A. Bahrami (eds.), Proceedings of the International Conference on E-Learning, E-Business, Enterprise Information Systems, and E-Government (pp. 371-375). Las Vegas: CSREA Press. https://doi.org/10.2139/ssrn.1352352
[4]
LendingClub.com, 2015. Accessed January 27th. http://www.lendingclub.com/public/about-us.action
[5]
Lopez, S. H. (2009). Social Interactions in p2p Lending. In Proceedings of the 3rd Workshop on Social Network Mining and Analysis (pp. 1-8). ACM.
[6]
Malekipirbazari, M., & Aksakalli, V. (2015). Risk Assessment in Social Lending via Random Forests. Expert Systems with Applications, 42, 4621-4631. https://doi.org/10.1016/j.eswa.2015.02.001
[7]
Zang, D. G., Qi, M. Y., & Fu, Y. M. (2015). The Credit Risk Assessment of P2P Lending Based on BP Neural Network. In Industrial Engineering and Management Science: Proceedings of the 2014 International Conference on Industrial Engineering and Management Science (IEMS 2014) (Vol. 2, p. 91). 8-9 August 2014. Hong Kong: CRC Press.
