Interpretive Ensemble Framework for Credit Default Risk Forecasting - Pavitha N (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In the realm of financial decision-making, particularly within the banking and investment sectors, credit default risk forecasting holds immense significance. The evolving landscape of financial markets has introduced heightened intricacies and interconnections among financial instruments, rendering the task of accurate credit risk assessment increasingly daunting. Conventional statistical models often fall short in capturing the dynamic essence of credit risk, thereby fueling a surge in interest towards more sophisticated methodologies such as ensemble learning frameworks. This paper presents a meticulous review and analysis of interpretive ensemble frameworks tailored for credit default risk forecasting. Introducing a multistage ensemble framework, augmented with dependency-driven explainable techniques, this research offers a novel approach. Evaluation outcomes underscore the superiority of the Proposed Algorithm, surpassing both individual base classifiers and other ensemble models across various metrics. Noteworthy is its exceptional precision, recall, F1-score, and accuracy, positioning it as a standout choice for credit risk prediction. The heightened precision underscores its capacity for accurate positive instance predictions, while the robust recall emphasizes its ability to capture nearly all positive instances. Additionally, this research introduces a Dependency-based Explainable Model meticulously crafted to enhance the interpretability of machine learning models, particularly focusing on multistage heterogeneous stacked ensembles. Despite the significant enhancements in predictive capabilities brought about by these ensemble models, their complexity often presents challenges in understanding individual contributions to final predictions. The Dependency-based Explainable Model addresses this interpretability gap by systematically identifying and elucidating dependencies between input variables, stages, and models within the ensemble.