Bankruptcy forecasting is an important function for banks and other financial institutions for determining default risk and making informed choices. However, conventional bankruptcy forecasting models are challenged by the intricate, non-linear relations among different financial metrics. These models are often unable to identify the best features and obtain high predictive accuracy, which results in poor risk estimation. For solving these issues, sensitivity analysis is used within this paper to find out which features are most contributing to bankruptcy outcomes. The predictive performance is boosted by using sophisticated optimization methods, including Ant Colony Optimization (ACO) and Cuckoo Search Optimization (CSO), for hyperparameter and feature selection. These tools facilitate improved determination of features and the accuracy of a model. The findings indicate that Competitiveness is indeed the most significant characteristic that is ranked highest by Recursive Feature Elimination (RFE) and sensitivity analysis. Moreover, ensemble-based models, including XGBC, optimized by ACO (XGAC), achieve better performance compared to standard models, including Logistic Regression Classification (LRC). The performance is tested against two scenarios, namely, bankrupt (B) and non-bankrupt (NB), with better performance by XGAC. The conclusions are of direct applicability for real-world bankruptcy forecasting, which will help banks enhance risk-handling capabilities. Further research ought to focus on combining time-series data for further model enhancement.
