Research on P2P Credit Risk Assessment Model Based on RBM Feature Extraction—Take SME Customers as an Example - Open Journal of Business and Management

OJBM > Vol.7 No.4, October 2019

Open Journal of Business and Management

Volume 7, Issue 4 (October 2019)

ISSN Print: 2329-3284 ISSN Online: 2329-3292

Google-based Impact Factor: 2.35 Citations

Research on P2P Credit Risk Assessment Model Based on RBM Feature Extraction—Take SME Customers as an Example ()

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DOI: 10.4236/ojbm.2019.74107 793 Downloads 2,075 Views Citations

Author(s)

Jianhui Yang, Qiman Li^*, Dongsheng Luo

Affiliation(s)

School of Business Administration, South China University of Technology, Guangzhou, China.

ABSTRACT

This paper combines the nonlinear dimensionality reduction method, and the Restricted Boltzmann machine (RBM algorithm), to assess the credit risk of P2P borrowers. After screening and processing many big data indicators, the most representative indicators are selected to build the P2P customer credit risk assessment model. In addition, after comparing the advantages and disadvantages of linear dimensionality reduction algorithm and nonlinear dimensionality reduction algorithm, this paper establishes a P2P enterprise customer credit risk assessment model based on RBM feature extraction combined with contrast divergence theory. It is concluded that the effect of RBM is better than that of PCA when the same model is selected. The Logistic model performs best in the three models when the same data feature extraction method is selected.

KEYWORDS

P2P, Credit Evaluation Model, RBM Algorithm, Credit Risk

Share and Cite:

Yang, J. , Li, Q. and Luo, D. (2019) Research on P2P Credit Risk Assessment Model Based on RBM Feature Extraction—Take SME Customers as an Example. Open Journal of Business and Management, 7, 1553-1563. doi: 10.4236/ojbm.2019.74107.

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