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.
Highlights
In the current boom of e-commerce, social networking, Internet finance, P2P consumer credit, consumer finance and other Internet platforms, the central bank’s credit reporting has become increasingly prominent in the timeliness, comprehensiveness and hierarchy of data
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 Restricted Boltzmann Machine (RBM) feature extraction combined with contrast divergence theory
After analyzing the credit risk characteristics of P2P industry, the credit risk of P2P borrowers is evaluated by using artificial intelligence method
Summary
In the current boom of e-commerce, social networking, Internet finance, P2P consumer credit, consumer finance and other Internet platforms, the central bank’s credit reporting has become increasingly prominent in the timeliness, comprehensiveness and hierarchy of data. How to dig deep into the massive information flow of the Internet, develop a big data risk control model based on massive indicators, comprehensively assess the credit risk status of enterprise customers, and provide a judgment basis for financial credit approval of P2P lending platform, have become the core of credit risk model system construc-. The operating experience and risk management capabilities of platform operators are generally insufficient, and the development situation is extremely unstable. From this perspective, credit issues remain the cause of large-scale risks in the P2P industry in the future. The credit risk is through the machine learning method, by learning the borrower’s historical data, to assess its future repayment ability and default risk, and obtain a P2P enterprise credit risk assessment model suitable for China’s current national conditions
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