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@article{53116,
author = {Lyócsa, Štefan and Vašaničová, Petra and Hadji Misheva, Branka and Vateha, Marko Dávid},
article_location = {New York},
article_number = {1},
doi = {http://dx.doi.org/10.1186/s40854-022-00338-5},
keywords = {profit scoring; credit scoring; financial intermediation; p2p; fintech},
language = {eng},
issn = {2199-4730},
journal = {Financial Innovation},
title = {Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets},
url = {https://jfin-swufe.springeropen.com/articles/10.1186/s40854-022-00338-5},
volume = {8},
year = {2022}
}
TY - JOUR
ID - 53116
AU - Lyócsa, Štefan - Vašaničová, Petra - Hadji Misheva, Branka - Vateha, Marko Dávid
PY - 2022
TI - Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets
JF - Financial Innovation
VL - 8
IS - 1
SP - 1-21
EP - 1-21
PB - Springer
SN - 2199-4730
KW - profit scoring
KW - credit scoring
KW - financial intermediation
KW - p2p
KW - fintech
UR - https://jfin-swufe.springeropen.com/articles/10.1186/s40854-022-00338-5
N2 - For the emerging peer-to-peer (P2P) lending markets to survive, they need to employ credit-risk management practices such that an investor base is profitable in the long run. Traditionally, credit-risk management relies on credit scoring that predicts loans’ probability of default. In this paper, we use a profit scoring approach that is based on modeling the annualized adjusted internal rate of returns of loans. To validate our profit scoring models with traditional credit scoring models, we use data from a European P2P lending market, Bondora, and also a random sample of loans from the Lending Club P2P lending market. We compare the out-of-sample accuracy and profitability of the credit and profit scoring models within several classes of statistical and machine learning models including the following: logistic and linear regression, lasso, ridge, elastic net, random forest, and neural networks. We found that our approach outperforms standard credit scoring models for Lending Club and Bondora loans. More specifically, as opposed to credit scoring models, returns across all loans are 24.0% (Bondora) and 15.5% (Lending Club) higher, whereas accuracy is 6.7% (Bondora) and 3.1% (Lending Club) higher for the proposed profit scoring models. Moreover, our results are not driven by manual selection as profit scoring models suggest investing in more loans. Finally, even if we consider data sampling bias, we found that the set of superior models consists almost exclusively of profit scoring models. Thus, our results contribute to the literature by suggesting a paradigm shift in modeling credit-risk in the P2P market to prefer profit as opposed to credit-risk scoring models.
ER -
LYÓCSA, Štefan, Petra VAŠANIČOVÁ, Branka HADJI MISHEVA and Marko Dávid VATEHA. Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets. \textit{Financial Innovation}. New York: Springer, 2022, vol.~8, No~1, p.~1-21. ISSN~2199-4730. Available from: https://dx.doi.org/10.1186/s40854-022-00338-5.
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