Loan default prediction of Chinese P2P market: a machine learning methodology

Abstract Repayment failures of borrowers have greatly affected the sustainable development of the peer-to-peer (P2P) lending industry. The latest literature reveals that existing risk evaluation systems may ignore important signals and risk factors affecting P2P repayment. In our study, we applied f...

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Autores principales:	Junhui Xu, Zekai Lu, Ying Xie
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6a6b92e8957b4fd08d6a6e2d575335ac
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spelling	oai:doaj.org-article:6a6b92e8957b4fd08d6a6e2d575335ac2021-12-02T17:26:55ZLoan default prediction of Chinese P2P market: a machine learning methodology10.1038/s41598-021-98361-62045-2322https://doaj.org/article/6a6b92e8957b4fd08d6a6e2d575335ac2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98361-6https://doaj.org/toc/2045-2322Abstract Repayment failures of borrowers have greatly affected the sustainable development of the peer-to-peer (P2P) lending industry. The latest literature reveals that existing risk evaluation systems may ignore important signals and risk factors affecting P2P repayment. In our study, we applied four machine learning methods (random forest (RF), extreme gradient boosting tree (XGBT), gradient boosting model (GBM), and neural network (NN)) to predict important factors affecting repayment by utilizing data from Renrendai.com in China from Thursday, January 1, 2015, to Tuesday, June 30, 2015. The results showed that borrowers who have passed video, mobile phone, job, residence or education level verification are more likely to default on loan repayment, whereas those who have passed identity and asset certification are less likely to default on loans. The accuracy and kappa value of the four methods all exceed 90%, and RF is superior to the other classification models. Our findings demonstrate important techniques for borrower screening by P2P companies and risk regulation by regulatory agencies. Our methodology and findings will help regulators, banks and creditors combat current financial disasters caused by the coronavirus disease 2019 (COVID-19) pandemic by addressing various financial risks and translating credit scoring improvements.Junhui XuZekai LuYing XieNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Junhui Xu Zekai Lu Ying Xie Loan default prediction of Chinese P2P market: a machine learning methodology
description	Abstract Repayment failures of borrowers have greatly affected the sustainable development of the peer-to-peer (P2P) lending industry. The latest literature reveals that existing risk evaluation systems may ignore important signals and risk factors affecting P2P repayment. In our study, we applied four machine learning methods (random forest (RF), extreme gradient boosting tree (XGBT), gradient boosting model (GBM), and neural network (NN)) to predict important factors affecting repayment by utilizing data from Renrendai.com in China from Thursday, January 1, 2015, to Tuesday, June 30, 2015. The results showed that borrowers who have passed video, mobile phone, job, residence or education level verification are more likely to default on loan repayment, whereas those who have passed identity and asset certification are less likely to default on loans. The accuracy and kappa value of the four methods all exceed 90%, and RF is superior to the other classification models. Our findings demonstrate important techniques for borrower screening by P2P companies and risk regulation by regulatory agencies. Our methodology and findings will help regulators, banks and creditors combat current financial disasters caused by the coronavirus disease 2019 (COVID-19) pandemic by addressing various financial risks and translating credit scoring improvements.
format	article
author	Junhui Xu Zekai Lu Ying Xie
author_facet	Junhui Xu Zekai Lu Ying Xie
author_sort	Junhui Xu
title	Loan default prediction of Chinese P2P market: a machine learning methodology
title_short	Loan default prediction of Chinese P2P market: a machine learning methodology
title_full	Loan default prediction of Chinese P2P market: a machine learning methodology
title_fullStr	Loan default prediction of Chinese P2P market: a machine learning methodology
title_full_unstemmed	Loan default prediction of Chinese P2P market: a machine learning methodology
title_sort	loan default prediction of chinese p2p market: a machine learning methodology
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6a6b92e8957b4fd08d6a6e2d575335ac
work_keys_str_mv	AT junhuixu loandefaultpredictionofchinesep2pmarketamachinelearningmethodology AT zekailu loandefaultpredictionofchinesep2pmarketamachinelearningmethodology AT yingxie loandefaultpredictionofchinesep2pmarketamachinelearningmethodology
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Loan default prediction of Chinese P2P market: a machine learning methodology

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