| 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. |
