Comparative Analysis of Ensemble Machine Learning Models for Risk-Oriented Monitoring of Military Procurement

This study examines the application of ensemble machine learning methods for identifying and flagging potentially risky transactions in military public procurement in Ukraine, a sector characterized by elevated financial and security sensitivity and limited capacity for comprehensive ex post control. Using an integrated dataset of procurement procedures conducted between 2021 and 2025, enriched with 56 financial, economic, and behavioral indicators of suppliers, the study develops and compares standard logistic and LASSO-penalized regression as econometric benchmarks, Random Forest, XGBoost, XGBoost with SMOTE balancing, and CatBoost classification models. The target variable is defined on the basis of officially detected violations identified through state monitoring. Model performance is evaluated using standard binary classification metrics, with particular emphasis on recall. Model uncertainty and predictive robustness are addressed through partial dependence analysis, temporal stability assessment, and out-of-sample residual diagnostics. The results indicate that the CatBoost model demonstrates the most balanced performance across evaluation measures. Feature importance analysis identifies expected contract value, procurement method, CPV code, and suppliers’ financial capacity as significant determinants of procurement-related risk. The findings provide empirical evidence on the usefulness of risk-oriented machine learning tools in supporting earlier detection and monitoring of irregularities in military procurement.