Credit scoring models play a significant role in risk management and decision-making processes within the credit industry. Nevertheless, these models often encounter challenges in dealing with fragmentary data characterized by missing values and varying patterns prevalent in credit datasets, which significantly impedes their predictive accuracy. This study proposed a novel two-stage credit scoring method to effectively handle this fragmentary data without resorting to deletion or imputation tactics. In the first stage, we fit candidate models and construct model groups for each missing pattern - the models within different groups are tailored according to their respective missing patterns. In the second stage, for each missing pattern, the results from the corresponding model group are incorporated into the final prediction through a flexibly chosen link function. The link function, chosen from linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), random forest (RF), and XGBoost, is adept at capturing the intricate nonlinear relationships among the models. To enhance interpretability, the influence of candidate models is elucidated using the Shapley Additive exPlanations (SHAP) method which measures the contribution of each variable. Both simulated and real-world data application results demonstrate that the proposed method surpasses alternative methods in predicting fragmentary data.
Zheng et al. (Mon,) studied this question.