Credit scoring prediction leveraging interpretable ensemble learning

Credit scoring models based on machine learning often need to work on accuracy and interpretability in practical applications. Original KCDWU has a more prominent adaptive property but ignores intra‐class and inter‐class distances in the clustering process, resulting in the possibility of inaccurate identification of class features and cluster structure of data, which compromises the clustering effect. Therefore, we improve the automatic K‐means clustering based on the Calinski–Harabasz index, thus achieving a clustering output for improved results. We also scrutinize representative five single classification models and six ensemble learning models for credit scoring prediction. We empirically test the superior performance of ensemble learning models and identify the best model CatBoost by comparing them based on multiple evaluation indicators. Empirical results reveal that the SHAP method conforms well to CatBoost and delivers a global and local interpretation of the predictions. This work provides financial institutions with a promising candidate for interpretable credit scoring models.