CORRUPTION RISKS IN THE CONTEXT OF REGIONAL FINANCIAL SECURITY: ANALYSIS BASED ON KOHONEN NEURAL NETWORKS
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During the war, threats to national security related to illegal financial transactions with the aggressor country, circumvention of sanctions, fraud in the distribution of investment and humanitarian aid, earmarked funds for the reconstruction of destroyed infrastructure, and new schemes for legalizing dirty money are becoming more acute. Therefore, the study of the risks of illegal financial transactions at the regional level in Ukraine is a primary step towards understanding the specifics of corruption, identifying the main factors that shape them, and developing targeted measures to overcome them. To analyze corruption risks in the regions of Ukraine and the city of Kyiv, this article uses a base of 10 indicators covering key aspects of financial support, economic activity, public opinion, trust in government, and digital transformation to provide a holistic picture of regional security and development. Clustering by the level of corruption risk, which was implemented using Kohonen's self-organizing maps, allowed to identify 4 groups of Ukrainian regions with similar characteristics: Cluster 1 (Vinnytsia, Lviv and Ternopil regions) – regions with a high level of development and medium corruption risks; Cluster 2 (Volyn, Dnipropetrovs'k, Donetsk, Zhytomyr, Ivano-Frankivsk, Kharkiv, Khmelnytskyi and Chernihiv regions) – regions with an average level of development and moderate corruption risks; Cluster 3 (Zakarpattia, Kirovohrad, and Rivne regions) - regions with an average level of development and problems in governance; Cluster 4 (Zaporizhzhia, Kyiv, Luhansk, Mykolaiv, Odesa, Poltava, Sumy, Kherson, Cherkasy, Chernivtsi regions and the city of Kyiv) – regions with high corruption risks. The implemented clustering facilitates the development of individualized and targeted anti-corruption strategies for each group of regions. The results of the study allow us to focus anti-corruption efforts on the most problematic areas and develop targeted programs to effectively reduce corruption risks.
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