STRIKE: Additive Feature-Group-Aware Stacking Framework for Credit Default Prediction

TL;DR

STRIKE is a novel feature-group-aware stacking framework that improves credit default prediction by decomposing features into coherent groups and aggregating their predictions, leading to better robustness and interpretability.

Contribution

It introduces a structured feature decomposition and aggregation approach for credit risk modeling, outperforming traditional monolithic models and stacking methods.

Findings

STRIKE outperforms tree-based baselines in AUC-ROC across datasets.

Feature decomposition contributes to performance gains rather than increased complexity.

The framework is stable, scalable, and interpretable for credit risk prediction.

Abstract

Credit risk default prediction remains a cornerstone of risk management in the financial industry. The task involves estimating the likelihood that a borrower will fail to meet debt obligations, an objective critical for lending decisions, portfolio optimization, and regulatory compliance. Traditional machine learning models such as logistic regression and tree-based ensembles are widely adopted for their interpretability and strong empirical performance. However, modern credit datasets are high-dimensional, heterogeneous, and noisy, increasing overfitting risk in monolithic models and reducing robustness under distributional shift. We introduce STRIKE (Stacking via Targeted Representations of Isolated Knowledge Extractors), a feature-group-aware stacking framework for structured tabular credit risk data. Rather than training a single monolithic model on the complete dataset, STRIKE partitions the feature space into semantically coherent groups and trains independent learners within each group. This decomposition is motivated by an additive perspective on risk modeling, where distinct feature sources contribute complementary evidence that can be combined through a structured aggregation. The resulting group-specific predictions are integrated through a meta-learner that aggregates signals while maintaining robustness and modularity. We evaluate STRIKE on three real-world datasets spanning corporate bankruptcy and consumer lending scenarios. Across all settings, STRIKE consistently outperforms strong tree-based baselines and conventional stacking approaches in terms of AUC-ROC. Ablation studies confirm that performance gains stem from meaningful feature decomposition rather than increased model complexity. Our findings demonstrate that STRIKE is a stable, scalable, and interpretable framework for credit risk default prediction tasks.