Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets

Lyócsa, Štefan; Vašaničová, Petra; Hadji Misheva, Branka; Vateha, Marko Dávid (2022). Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets Financial Innovation, 8(1), pp. 1-21. Springer 10.1186/s40854-022-00338-5

Preview

Text s40854-022-00338-5.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (2MB) | Preview

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.

Item Type:	Journal Article (Original Article)
Division/Institute:	Business School > Institute for Applied Data Science & Finance Business School
Name:	Lyócsa, Štefan; Vašaničová, Petra; Hadji Misheva, Branka and Vateha, Marko Dávid
Subjects:	H Social Sciences > HB Economic Theory
ISSN:	2199-4730
Publisher:	Springer
Language:	English
Submitter:	Branka Hadji Misheva
Date Deposited:	17 Aug 2022 10:12
Last Modified:	17 Aug 2022 10:12
Publisher DOI:	10.1186/s40854-022-00338-5
Uncontrolled Keywords:	Profit scoring, Credit scoring, Financial intermediation, P2P, Fintech
ARBOR DOI:	10.24451/arbor.17290
URI:	https://arbor.bfh.ch/id/eprint/17290