Best classification algorithms in peer-to-peer lending

Petr Teply and Michal Polena

The North American Journal of Economics and Finance, 2020, vol. 51, issue C

Abstract: A proper credit scoring technique is vital to the long-term success of all kinds of financial institutions, including peer-to-peer (P2P) lending platforms. The main contribution of our paper is the robust ranking of 10 different classification techniques based on a real-world P2P lending data set. Our data set comes from the Lending Club covering the 2009–2013 period, which contains 212,252 records and 23 different variables. Unlike other researchers, we use a data sample which contains the final loan resolution for all loans. We built our research using a 5-fold cross-validation method and 6 different classification performance measurements. Our results show that logistic regression, artificial neural networks, and linear discriminant analysis are the three best algorithms based on the Lending Club data. Conversely, we identify k-nearest neighbors and classification and regression tree as the two worst classification methods.

Keywords: Classification classifiers; Ranking credit scoring Lending Club P2P lending (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:51:y:2020:i:c:s1062940818302262

DOI: 10.1016/j.najef.2019.01.001

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