Research ArticleOpen AccessGoogle Scholar indexed
Combination of Random Forests and Neural Networks in Social Lending
Shanghai Foreign Language School, Shanghai, China
- 1 Shanghai Foreign Language School, Shanghai, China
Journal of Financial Risk Management·Volume 06 (2017)·Pages 418–426·Published 2 November 2017·DOI10.4236/jfrm.2017.64030
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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.
KeywordsPeer-to-Peer LendingMachine Learning MethodsRandom ForestsNeural Networks
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