Comparative Evaluation of Machine Learning Approaches for Minority-Class Financial Distress Prediction Under Class Imbalance Constraints
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, CatBoos...
| Authors | Karan Sehgal, Khawar Naveed Bhatti |
|---|---|
| Primary category | cs.LG |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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