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Low rank representation and discriminant analysis-based models for peer-to-peer default risk assessment

Gui Yuan (School of E-commerce and Logistics, Changjiang Polytechnic, Wuhan, China)
Shali Huang (Hubei Urban Construction Vocational and Technological College, Wuhan, China)
Jing Fu (College of Public Administration, Huazhong Agricultural University, Wuhan, China)
Xinwei Jiang (School of Computer Science, China University of Geosciences, Wuhan, China)

Journal of Systems and Information Technology

ISSN: 1328-7265

Article publication date: 11 January 2021

Issue publication date: 11 April 2022




This study aims to assess the default risk of borrowers in peer-to-peer (P2P) online lending platforms. The authors propose a novel default risk classification model based on data cleaning and feature extraction, which increases risk assessment accuracy.


The authors use borrower data from the Lending Club and propose the risk assessment model based on low-rank representation (LRR) and discriminant analysis. Firstly, the authors use three LRR models to clean the high-dimensional borrower data by removing outliers and noise, and then the authors adopt a discriminant analysis algorithm to reduce the dimension of the cleaned data. In the dimension-reduced feature space, machine learning classifiers including the k-nearest neighbour, support vector machine and artificial neural network are used to assess and classify default risks.


The results reveal significant noise and redundancy in the borrower data. LRR models can effectively clean such data, particularly the two LRR models with local manifold regularisation. In addition, the supervised discriminant analysis model, termed the local Fisher discriminant analysis model, can extract low-dimensional and discriminative features, which further increases the accuracy of the final risk assessment models.


The originality of this study is that it proposes a novel default risk assessment model, based on data cleaning and feature extraction, for P2P online lending platforms. The proposed approach is innovative and efficient in the P2P online lending field.



Funding: This work was supported in part by the Joint Funds of the National Natural Science Foundation of China under Grant U1911205, and the Science and Technology Project of Department of Natural Resources, Hubei Province under Grant ZRZY2020KJ12, and Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing.


Yuan, G., Huang, S., Fu, J. and Jiang, X. (2022), "Low rank representation and discriminant analysis-based models for peer-to-peer default risk assessment", Journal of Systems and Information Technology, Vol. 24 No. 2, pp. 96-111.



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