Detail publikace

Best Classification Algorithms in Peer-to-Peer Lending

Autor: prof. PhDr. Petr Teplý Ph.D., Mgr. Michal Polena
Typ: Články v impaktovaných časopisech
Rok: 2020
Číslo: 0
ISSN / ISBN: ISSN: 1062-9408
Publikováno v: The North American Journal of Economics and Finance, USA
Místo vydání: https://doi.org/10.1016/j.najef.2019.01.001
Klíčová slova: klasifikace, klasifikační hodnocení, kreditní skóring, Lending Club, P2P půjčování
JEL kódy:
Citace: Teplý, P., Polena, M. (2020). Best Classification Algorithms in Peer-to-Peer Lending. North American Journal of Economics and Finance. https://doi.org/10.1016/j.najef.2019.01.001
Granty: GAČR 18-05244S - Inovativní přístupy k řízení úvěrových rizik VŠE IP100040
Abstrakt: 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 crossvalidation 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.
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