Boosting ensembles and deep vision networks optimized by forensic-based investigation algorithm for financial distress prediction in construction firms

Abstract Effective risk management is crucial in the construction industry, which has a substantial economic impact but is vulnerable to high financial risks due to volatile material costs and complex project-based financial structures. This study presents a new hybrid model to improve the prediction of financial distress for Taiwanese-listed construction companies. The research compares four boosting-based ensemble learning models, advanced deep learning models, and improved ensemble models that incorporate a novel approach using the Multi-Criteria Decision-Making (MCDM) technique, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to enhance feature selection. Experimental results show that while TOPSIS-eXtreme Gradient Boosting (TOPSIS-XGBoost) is highly effective at managing imbalanced financial datasets, Light Gradient Boosting Machine (LightGBM) performs better in balanced environments. Both models exhibit substantial performance gains when integrated with the Forensic-Based Investigation (FBI) optimization algorithm, resulting in the optimized hybrids—FBI-TOPSIS-XGBoost and FBI-LightGBM—which achieve marked improvements in predictive accuracy. These optimized models consistently outperform benchmark approaches, including the Altman Z-score, Zmijewski X-score, Logistic Regression, and Random Forest, across multiple evaluation metrics. To enhance transparency and interpretability, a global SHapley Additive exPlanations (SHAP) analysis was conducted, revealing that profitability and per-share index indicators are the primary determinants driving model predictions. Additionally, an expert system interface has been developed to enhance the practical usability of these models. These findings strengthen the methodological foundation for predicting financial distress and provide stakeholders with valuable tools for mitigating risk in Taiwan’s construction industry.