Deep feature extraction for long-term delinquency transition prediction under short observation windows

Abstract

Early identification of borrowers at risk of transitioning into long-term delinquency is a critical challenge in credit risk management. In real-world financial settings, risk prediction is often constrained by short observation windows and highly aggregated tabular credit data, limiting the applicability of conventional time-series models and handcrafted feature-based approaches. To address these limitations, this study proposes a deep feature extraction framework that employs representation learning as a complementary enhancement to traditional classifiers rather than as a standalone predictive model. The proposed framework integrates self-attention-based global interaction learning with TabNet-inspired soft feature selection to construct latent representations that effectively capture delinquency-related risk signals from low-information credit data. Extensive experiments on a real-world multi-debtor credit dataset demonstrate the potential of the proposed feature extraction method to enhance long-term delinquency transition predictions. Beyond predictive accuracy, analyses of feature-space distances and model explainability reveal enhanced class separability and provide interpretable insights into the underlying drivers of delinquency transitions. Overall, the results suggest that the proposed deep representation learning offers a practical, interpretable, and effective solution for early delinquency transition detection in realistic tabular credit data environments with limited temporal information.