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- W4288434603 abstract "Along with the new consumption form and the advanced consumption concept is generally accepted, the loan becomes the new consumption means under the new consumption form. The loan business not only brings profits to the institution but also makes the institution face a greater risk of default. Although there are relatively mature forecasting methods in the field of credit default forecasting, credit data is characterized by complex feature relationships and a large number of missing values, and credit default forecasting still faces certain challenges. In this paper, the random forest and XGBoost models are used to evaluate the default risk of personal financial loans. The results show that these two models have high AUC values, so they have good learning and prediction capabilities. In addition, comparing the two, the predictive ability of the XGBoost model is better than that of the random forest." @default.
- W4288434603 created "2022-07-29" @default.
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- W4288434603 date "2022-07-28" @default.
- W4288434603 modified "2023-10-18" @default.
- W4288434603 title "Research on financial loan default risk prediction based on integrated model" @default.
- W4288434603 cites W2070236178 @default.
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- W4288434603 doi "https://doi.org/10.1117/12.2642663" @default.
- W4288434603 hasPublicationYear "2022" @default.
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