CF-NN: a novel decision support model for borrower identification on the peer-to-peer lending platform

Abstract

With the prevalence of online individual micro-loans, an increasing number of peer-to-peer lending platforms have been established during the last ten years. One main problem for these platforms is to accurately identify the ‘bad’ applicants with high default risk. In this paper, we propose a CF-NN model that combines neural network and collaborative filtering for identifying high-risk borrowers. It is demonstrated in the experimental analysis that the CF-NN model significantly outperforms other widely used data mining models on the identification of bad borrowers. Moreover, the experimental results show that, to achieve the best performance in borrower identification, the CF-NN model should be equipped with parameters of intermediate values.

KEYWORDS:

• Peer-to-peer lending platform
• default risk
• bad borrowers
• neural network
• collaborative filtering

Acknowledgements

This work is supported by the Ministry of Science and Technology of China under Grant 2016YFC0503606, by the National Natural Science Foundation of China (NSFC) for Distinguished Young Scholar (Grant 71825007), by the Chinese Academy of Sciences (CAS) Frontier Scientific Research Key Project (Grant QYZDB-SSW-SYS021), and by a CAS Strategic Research and Decision Support System Development grants (Grant GHJ-ZLZX-2017-36)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the Ministry of Science and Technology of the People Republic of China [grant number 2016YFC0503606], -by the Chinese Academy of Sciences Key Project- [grant number QYZDB-SSW-SYS021].

Notes on contributors

Yuchen Pan

Yuchen Pan is an assistant professor in the School of Information Resource Management, Renmin University of China, Beijing, China. He has authored or coauthored in refereed journals such as Journal of Management Information, Decision Support Systems, Information Sciences, IEEE Systems Journal, etc. His research interests are big data analysis, big data mining and recommender systems.

Shuzhen Chen

Shuzhen Chen is an assistant professor in Business School, Central South University, Changsha, China. She has authored or coauthored in refereed journals such as Risk Analysis, IEEE SMC, IEEE Systems Journal, etc. Her research interests include operations management of banking services, risk analysis and dynamic optimisation.

Desheng Wu

Desheng Wu is with the School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China, and also with the Stockholm Business School, Stockholm University, Stockholm, Sweden. He has authored or coauthored more than 100 papers in refereed journals such as Production and Operations Management, Decision Support Systems, Decision Sciences, Risk Analysis, IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, etc. His research interests include enterprise risk management in operations, performance evaluation in financial industry, and decision sciences. Dr. Wu has been an Associate Editor/Guest Editor for the IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, Omega, etc. He is elected member of Academia Europaea and the member of European Academy of Sciences and Arts.

Alexandre Dolgui

Alexandre Dolgui is a Fellow of IISE, Distingished Professor and Head of Department at the IMT Atlantique, France. His research focuses on manufacturing line design, production planning and supply chain optimisation under uncertainty. He is the co-author of 5, co-editor of 20 books, he published 252 refereed journal papers, 30 editorials and 32 book chapters as well as over 400 papers in conference proceedings. He is the Editor in Chief of the International Journal of Production Research.