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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F22%3A00127161" target="_blank" >RIV/00216224:14560/22:00127161 - isvavai.cz</a>

Result on the web

<a href="https://jfin-swufe.springeropen.com/articles/10.1186/s40854-022-00338-5" target="_blank" >https://jfin-swufe.springeropen.com/articles/10.1186/s40854-022-00338-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s40854-022-00338-5" target="_blank" >10.1186/s40854-022-00338-5</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

50206 - Finance

Project

—

Continuities

R - Projekt Ramcoveho programu EK

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Financial Innovation

ISSN

2199-4730

e-ISSN

2199-4730

Volume of the periodical

8

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

21

Pages from-to

1-21

UT code for WoS article

000780912200001

EID of the result in the Scopus database

2-s2.0-85128162625