Credit Risk Control Algorithm Based on Stacking Ensemble Learning

With the development of Internet finance, the importance of loan risk control is increasingly manifested. Risk control is the core part of traditional financial industry and Internet finance. After investigating the latest developments in credit risk control algorithms, an improved stacking integrated learning algorithm is proposed. By improving the feature selection steps, and using 5 different learners for stacking integration, the performance of the model is improved. The basic learners used include: Logistic Regression, Random Forest, GBDT, XGBoost, LightGBM, among which there are both strong learners and weak learners. Compared with traditional integrated learning methods, the accuracy of strong learners can be fully utilized, and use weak learners to reduce overfitting. Finally, the accuracy and generalization performance of the model are improved.