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Under the background of the increasing demand for credit evaluation and risk prediction, the establishment of an effective credit evaluation model for small- and medium-sized enterprises has become a research hotspot. Based on previous studies, this paper proposes a two-layer feature extraction method based on Gradient Boosting Decision Tree (GBDT) and Convolutional Neural Network (CNN). First, based on the original features, GBDT is used to combine and automatically screen them, the missing values in the feature are processed, and the transformed high-dimensional sparse features are obtained. Then, CNN is used to extract features further, and finally, the logistic regression (LR) model is used to predict. In the simulation experiment, this paper takes a dataset of 14,366 small- and medium-sized enterprise credit evaluations as the analysis samples to verify the results. The results show that the GBDT-CNN-LR model has the best performance. The model also shows good generalization ability and stability in the reliability test.

Credit Evaluation of SMEs Based on GBDT-CNN-LR Hybrid Integrated Model