Abstract:Financial distress prediction remains a significant challenge in enterprise risk analysis due to the highly imbalanced nature of real-world financial datasets, where bankrupt or distressed firms typically constitute only a small minority of observations.
This paper presents a comparative evaluation of classical statistical methods, ensemble learning approaches, and exploratory neural models for minority-class financial distress prediction under class imbalance constraints.
The study incorporates structured preprocessing, imbalance mitigation using the Synthetic Minority Oversampling Technique (SMOTE), comparative evaluation across ensemble learning architectures including XGBoost, CatBoost, LightGBM, Random Forest, and explainability analysis using SHAP-based feature attribution methods.
Experimental evaluation demonstrates that gradient-boosting approaches achieved improved minority-class sensitivity relative to baseline statistical classifiers under severe imbalance conditions. The workflow additionally emphasises reproducibility, interpretability, auditability, and governance-oriented machine learning evaluation within enterprise financial risk environments.
The work is positioned as an applied engineering evaluation intended to support reproducible and interpretable machine learning workflows for financial distress prediction under severe class imbalance constraints.
| Comments: | 16 pages, 4 figures, preprint under review. Applied machine learning evaluation involving imbalance-aware financial distress prediction, ensemble learning, SMOTE, and SHAP explainability analysis |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.14067 [cs.LG] |
| (or arXiv:2605.14067v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.14067 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Karan Sehgal [view email]
[v1]
Wed, 13 May 2026 19:44:25 UTC (4,218 KB)