TITLE:
Research on P2P Credit Risk Assessment Model Based on RBM Feature Extraction—Take SME Customers as an Example
AUTHORS:
Jianhui Yang, Qiman Li, Dongsheng Luo
KEYWORDS:
P2P, Credit Evaluation Model, RBM Algorithm, Credit Risk
JOURNAL NAME:
Open Journal of Business and Management,
Vol.7 No.4,
August
7,
2019
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.