{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T11:54:41Z","timestamp":1772452481810,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:00:00Z","timestamp":1772236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JRFM"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3390\/jrfm19030170","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T10:24:34Z","timestamp":1772447074000},"page":"170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative Analysis of Ensemble Machine Learning Models for Risk-Oriented Monitoring of Military Procurement"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9197-0560","authenticated-orcid":false,"given":"Tetiana","family":"Zatonatska","sequence":"first","affiliation":[{"name":"Faculty of Economics, Taras Shevchenko National University of Kyiv, 03-022 Kyiv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2040-8762","authenticated-orcid":false,"given":"Oleksandr","family":"Dluhopolskyi","sequence":"additional","affiliation":[{"name":"Faculty of Economics and Management, West Ukrainian National University, 46-027 Ternopil, Ukraine"},{"name":"Institute of Public Administration and Business, WSEI University, 20-209 Lublin, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3638-4961","authenticated-orcid":false,"given":"Oleksandr","family":"Artiushenko","sequence":"additional","affiliation":[{"name":"Faculty of Economics, Taras Shevchenko National University of Kyiv, 03-022 Kyiv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4833-470X","authenticated-orcid":false,"given":"Isabel Cristina","family":"Lopes","sequence":"additional","affiliation":[{"name":"CEOS.PP, ISCAP, Polytechnic of Porto, 4465-004 S\u00e3o Mamede de Infesta, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9103-6943","authenticated-orcid":false,"given":"Anzhela","family":"Ignatyuk","sequence":"additional","affiliation":[{"name":"Faculty of Economics, Taras Shevchenko National University of Kyiv, 03-022 Kyiv, Ukraine"}]},{"given":"Olena","family":"Liubkina","sequence":"additional","affiliation":[{"name":"Faculty of Economics, Taras Shevchenko National University of Kyiv, 03-022 Kyiv, Ukraine"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.22495\/clgrv7i1p6","article-title":"Public procurement contracts futurity: Using of artificial intelligence in a tender process","volume":"7","author":"Aboelazm","year":"2025","journal-title":"Corporate Law & Governance Review"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1108\/JOPP-05-2024-0057","article-title":"Assessing the value of artificial intelligence (AI) in governmental public procurement","volume":"25","author":"Andersson","year":"2025","journal-title":"Journal of Public Procurement"},{"key":"ref_3","first-page":"53","article-title":"Application of multi-criteria optimisation method for decision-making in the sphere of financial support of military troops","volume":"2","author":"Artiushenko","year":"2024","journal-title":"Visnyk Taras Shevchenko National University of Kyiv. 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