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

Jianhui Yang; Qiman Li; Dongsheng Luo

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

机译：基于RBM特征提取的P2P信用风险评估模型研究-以中小企业客户为例

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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 Logist i c model performs best in the three models when the same data feature extraction method is selected.

机译：本文结合非线性降维方法和受限玻尔兹曼机（RBM算法），对P2P借款人的信用风险进行评估。在筛选和处理了许多大数据指标之后，选择了最具代表性的指标来构建P2P客户信用风险评估模型。另外，在比较了线性降维算法和非线性降维算法的优缺点之后，建立了基于RBM特征提取与对比散度理论相结合的P2P企业客户信用风险评估模型。结论：选择相同模型时，RBM的效果优于PCA。所述LOGIST选择了相同数据的特征提取方法，当我C型号进行在三种模式最好。

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《Open Journal of Business and Management》 |2019年第4期|共11页
作者
Jianhui Yang; Qiman Li; Dongsheng Luo;
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中图分类贸易经济;
关键词
P2PCredit Evaluation ModelRBM AlgorithmCredit Risk;

机译：P2PC信用评估模型RBM算法信用风险;

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1. A credit risk assessment model of borrowers in P2P lending based on BP neural network [J] . Zhengwei Ma, Wenjia Hou, Dan Zhang PLoS One . 2021,第8期

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2. Multi-view ensemble learning based on distance-to-model and adaptive clustering for imbalanced credit risk assessment in P2P lending [J] . Information Sciences: An International Journal . 2020,第期

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3. 2-stage modified random forest model for credit risk assessment of P2P network lending to "Three Rurals" borrowers [J] . Rao Congjun, Liu Ming, Goh Mark, Applied Soft Computing . 2020,第1期

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

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