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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

has been cited by the following article:

• TITLE: Combination of Random Forests and Neural Networks in Social Lending

  AUTHORS: Yijie Fu

  KEYWORDS: Peer-to-Peer Lending, Machine Learning Methods, Random Forests, Neural Networks

  JOURNAL NAME: Journal of Financial Risk Management, Vol.6 No.4, December 29, 2017

  ABSTRACT: 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.