Přehled o publikaci
2022
Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets
LYÓCSA, Štefan; Petra VAŠANIČOVÁ; Branka HADJI MISHEVA a Marko Dávid VATEHAZákladní údaje
Originální název
Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets
Autoři
LYÓCSA, Štefan (703 Slovensko, garant, domácí); Petra VAŠANIČOVÁ (703 Slovensko); Branka HADJI MISHEVA a Marko Dávid VATEHA (703 Slovensko)
Vydání
Financial Innovation, New York, Springer, 2022, 2199-4730
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Kód RIV
RIV/00216224:14560/22:00127161
Organizace
Ekonomicko-správní fakulta – Masarykova univerzita – Repozitář
UT WoS
000780912200001
EID Scopus
2-s2.0-85128162625
Klíčová slova anglicky
profit scoring; credit scoring; financial intermediation; p2p; fintech
Změněno: 9. 3. 2024 03:34, RNDr. Daniel Jakubík
Anotace
V originále
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.