Credit risk is one of the main financial risks which commercial banks are faced with, while traditional credit scoring methods cannot effectively make use of the existing feature learning methods, resulting in low prediction accuracy. To solve this problem, an enhanced multi-dimensional and multi-grained cascade forest method was proposed to build credit scoring model, with the use of the idea of residual learning, the multi-dimensional and multi-grained cascade residual Forest (grcForest) model was built, which greatly increased the extracted features. Besides, the multi-dimensional multi-grained scanning was used to extract features of the raw data as many as possible, which improved the efficiency of feature extraction. The proposed model was compared with the existing statistical and machine learning methods on four credit scoring datasets, and evaluated by Area Under Curve (AUC) and accuracy. The AUC of the proposed model was 1.13% and 1.44% higher then that of the Light Gradient Boosting Machine (LightGBM) and the eXtreme Gradient Boosting (XGBoost). Experimental results show that the proposed model performs best in the prediction.