Research on Financial Credit Risk Prediction under the Internet Background
DOI:
https://doi.org/10.54691/bcpbm.v19i.861Keywords:
Internet finance, random forest-logistic model, algorithmAbstract
As a new type of financial business model linked to Internet technology, Internet finance can solve the financing difficulties of individuals and small and medium-sized enterprises to a certain extent. With the continuous development of Internet technology, the data sources of Internet finance are more extensive and the scale of data is getting bigger and bigger. This paper combines two two-step sub-sampling algorithms with logistic regression model, and analyzes the prediction effect of Internet financial credit risk through the results of numerical simulation and empirical analysis. The logistic regression model based on the two-step subsampling algorithm maintains high accuracy and saves time significantly. Therefore, the regression model based on the two-step subsampling algorithm can make good predictions on the credit risk of Internet finance in both high-dimensional and low-dimensional Internet financial credit data.
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