Abstract:

To handle the issues of some high-accuracy models neglecting data imbalance and other studies effectively handling imbalanced data but not achieving satisfactory accuracy， a novel ensemble learning model is constructed for credit risk assessment under imbalanced data categories—a class-imbalanced credit scoring model based on the SMOTE-BO-XGBoost ensemble algorithm. Firstly， the model tackles the sample imbalance problem through the Synthetic Minority Over-sampling Technique （SMOTE）. Secondly， Bayesian optimization （BO） is employed to obtain optimal model parameters. Finally， an optimized XGBoost ensemble classification model is constructed. Based on the home-credit-default-risk dataset， four different ensemble models are sequentially trained， and the SMOTE-BO-XGBoost model is compared with traditional models. The experimental results demonstrate that： ① Ensembling is effective， and the multi-angle fused SMOTE-BO-XGBoost model exhibits the best model performance， outperforming general ensemble learning models and traditional classification algorithms； ② It can effectively overcome the problem of imbalanced samples. This model addresses individual credit risk issues of financial institution customers from two perspectives： data augmentation and model performance enhancement， providing an effective personal credit risk assessment method for banking sectors.

Key words: credit scoring, ensemble learning, class-imbalance, XGBoost