An Innovation of the Zero-Inflated Binary Classification in Credit Scoring Using Two-Stage Algorithms

Zero-inflated and class-imbalanced data present significant challenges in credit scoring. Zero-Inflated Bernoulli Distribution (ZIBD) models help handle excess zeros. However, the S-shaped function and the neglect of misclassification costs may degrade the ZIBD model’s classification performance. To address these challenges, this paper proposes a novel two-stage algorithm that integrates an optimized ZIBD model with Random Forest, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), respectively. Specifically, we develop a new loss function that incorporates cross-entropy and example-dependent cost-sensitive to optimize the ZIBD model, thereby minimizing cost risks. Subsequently, we suggest integrating baseline models to compensate for the ZIBD model’s classification deficiencies. This hybrid approach effectively mitigates the impact of structural zeros in imbalanced data while enhancing model robustness. The performance of the proposed method is validated using two real-world banking datasets. Experimental results demonstrate that the proposed two-stage algorithm significantly outperforms its competitors across both machine-learning metrics and savings. Hence, the proposed novel two-stage algorithm offers a more effective solution for zero-inflated banking data.