Comparative Evaluation of Machine Learning Approaches for Minority-Class Financial Distress Prediction Under Class Imbalance Constraints

TL;DR

This study compares various machine learning methods, including ensemble and neural models, for predicting financial distress in highly imbalanced datasets, emphasizing interpretability and reproducibility.

Contribution

It provides a comprehensive evaluation of classical, ensemble, and neural approaches with imbalance mitigation and explainability techniques for financial distress prediction.

Findings

Gradient-boosting methods improved minority-class sensitivity.

SMOTE effectively mitigated class imbalance.

Workflow emphasizes reproducibility and interpretability.

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.