NEURAL NETWORKS INTEGRATION INTO LEGAL RESOURCES FOR ANTI-СORRUPTION MEASURES IN INTERNATIONAL ECONOMIC CO-OPERATION
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Abstract
The article under discussion highlights that appropriate evaluation procedures are of crucial importance for anti-corruption and other compliance assessments in international commercial transactions, national physical security guarantee agreements, their combinations, and other related deals. It is imperative that legal standards of higher civilisations neutralise the corruptive barriers posed by lower civilisations, which they integrate with through economic, defence, and other alliances. The primacy of natural law is emphasised as being balanced rather than opportunistic, both at the level of individual legal relations and in inter-nation interactions. The attainment of public goods by a nation is the result of human virtues being released from the pressure of human vices. This issue is further exacerbated by the capabilities of non-anthropogenic neural networks at the current stage of human development. The study establishes that interstate corruption exhibits all the characteristics of national corruption while additionally incorporating a scaling effect, which manifests in the preservation and expansion of material benefits through the economic exploitation of other nations and/or the use of their legal systems. In contrast to the typical, long-term, and easily sustained nature of national administrative corruption, the practice of abusing power within multinational organisations is neither typical nor easily sustained. The corrupt dimension of international communication is a constant variable, with a variable volume. The presence of virtuous individuals in top public positions within the world's most powerful nations has been demonstrated to reduce the level of global corruption-driven perversion and vice versa. The study concludes that transnational corruption constitutes organised crime, with both phenomena forming complex networks involving extremely high sums of criminally acquired assets. A deep neural network has been posited as a potential analytical and predictive model with the potential to empower stakeholders engaged in anti-corruption activities and enhance national security by providing accurate data for informed decision-making. The capabilities of non-anthropic neural networks have the potential to eliminate human error and bias in analysing the expenditure of public funds by organisations, both domestically and in international relations within foreign jurisdictions. In this context, the composition, adequacy, classification, and other characteristics of financial data, as well as access rights and regulations governing the use of neural network analysis results, play a crucial role. The overarching objective of digital networks is to ensure accurate assessment of all financial and accounting documents related to public funds and other national material resources in international economic transactions. The primary anti-corruption knowledge generated by digital neural networks consists of reliable insights into interconnections, patterns, and behavioural trends concerning material assets beyond national borders. The prevention of corruption and its associated forms of organised crime is achieved through the analytical capabilities of multimodal AI models such as Gemini, GPT-4o, and other deeply trained neural networks. In addressing the challenges posed by corruption in the context of international economic relations, the efficacy of any neural network with extensive training is noteworthy. This could be a LipNet neural network, which is trained for audio-visual recognition of human speech, or another recurrent neural network, as well as convolutional neural networks, deep contrastive neural networks, residual neural networks, and others.
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agreement, criminal assets, data, deep learning, finances, cronyism, kleptocracy, neural networks, cyberspace
Alhosani, K., & Alhashmi, S. M. (2024). Opportunities, challenges, and benefts of AI innovation in government services: a review. Discover Artificial Intelligence. Vol. 4. Num. 18. P. 1–20. DOI: https://doi.org/10.1007/s44163-024-00111-w
Barcelona and Madrid team up to digitise public services. 27.12.2024. Available at: https://ajuntament.barcelona.cat/digital/en/news/news/barcelona-and-madrid-team-up-to-digitise-public-services-1468189
Carcelli, S. P. (2024). Bureaucratic Structure and Compliance with International Agreements. American Journal of Political Science. Vol. 68. No. 1. P. 177–192. DOI: https://doi.org/10.1111/ajps.12811
Catalog data throughout the World Bank Group. 2025. Available at: https://datacatalog.worldbank.org/home
Chayes, S. (2015). Thieves of State: Why Corruption Threatens Global Security. New York: W.W. Norton & Company. 272 p.
Corruption Perceptions Index 2012-2024. Available at: https://www.transparency.org/en/cpi/2024
Cybersecurity market analysis – 2032. February 03, 2025. Available at: https://www.fortunebusinessinsights.com/industry-reports/cyber-security-market-101165
Dang, M. T., & Vu, T. L. (2024). Good Governance and Anti-Corruption: advantages and challenges in the era of digital technology. Revista Gestão & Tecnologia. Vol. 24. n. 3. P. 128–154. DOI: https://doi.org/10.20397/2177-6652/2024.v24i3.2899
Djenna, A., Barka, E., Benchikh, A., & Khadir, K. (2023). Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics. Sensors. Vol. 23. Iss. 14. Num. 6302. P. 1–21. DOI: https://doi.org/10.3390/s23146302
Dong, W., & Sun, S. (2023). Multi-View Deep Gaussian Processes for Supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 45. No. 12. P. 15137–15153. DOI: https://doi.org/10.1109/TPAMI.2023.3316671
Draghi, M. (2024). The future of European competitiveness. Part A – A Competitiveness strategy for Europe. Report. 69 p. Part B – In-depth analysis and recommendations. Brussels. 328 p.
Grigore, S.-A. (2025). The Narrow Definition of Genocide & Its Role in Investigating and Prosecuting Charges of Genocide in Romania. Problems of post-communism. Vol. 72. No. 1. P. 97–111. DOI: https://doi.org/10.1080/10758216.2024.2337944
Echenausía-Monroy, J. L., Magallón-García, D. A., Ontañón-García, L. J., Rodriguez, R. R., Ramirez, J. P., & Álvarez, J. (2024). Does a Fractional-Order Recurrent Neural Network Improve the Identification of Chaotic Dynamics? Fractal and Fractional. Vol. 8. Iss. 11. Num. 632. DOI: https://doi.org/10.3390/fractalfract8110632
Hoeffler, C., & Hofmann, S. C. (2024). Organizational Overlap and Bureaucratic Actors: How EU–NATO Relations Empower the European Commission. JCMS. Vol. 62. Num. 5. P. 1260–1277. DOI: https://doi.org/10.1111/jcms.13571
Ivaskevych, Kh. І. (2019). Internal financial control of expenditures of budgetary institutions in Ukraine. Dissertation of candidate of economic sciences. Spec. 08.00.08 – money, finance and credit. I. Franko NU of Lviv. Lviv. 234 p.
Jiao, J., Zhang, X., Liu, Z., Zhang, L., Wu, H., Gao, M., Li, T., & Wu, J. (2024). A deep contrastive framework for unsupervised temporal link prediction in dynamic networks. Information Sciences. Vol. 667. P. 1–14. DOI: https://doi.org/10.1016/j.ins.2024.120499
Keatinge, T. (2024). Combating Kleptocracy Lessons from the Response to Russia’s War in Ukraine. London: Royal United Services Institute. 31 p.
Khaldi, A. (2024). The Role of Digital Administration in Enhancing Political Rationality and Good Governance: A Comparative Analysis of Different Political Systems for Successful Implementation. Afak of Science Journal. Vol. 09. N 04. P. 114–134.
Lee, H., Jang, C., Bok Lee, D., & Lee, J. Dimension agnostic neural processes. Inter. Conf. on Learning Representations. 28.02.2025. P. 1-34. DOI: https://doi.org/10.48550/arXiv.2502.20661
Lizundia, E., Djuric, M., Lillehaugen, M., Shakaryan, A., Yanguas, P. and others (2024). Identifying Successful Collective Action Approaches Against Kleptocracy. Consortium for Elections and Political Process Strengthening. 49 р.
Makarenkov, O. (2025). AI-driven forensic excellence in evidence collection: public spending non-compliance cases. Congresso Internacional “Direito Probatório – Perspetiva Interdisciplinar e Comparada”. 3 e 4 de Abril. Porto. Portugal. Available at: https://ijp.upt.pt/congresso-internacional-direito-probatorio-perspetiva
Makarenkov, O. L. (2016). Definition of "constitutional crowdsourcing". Inter. scient. pract. conf. "Information Technology, Economics and Law". April 14-15, 2016. Chernivtsi: Bukovynian University. P. 38–39.
Makarenkov, O. L. (2023). Corruption of personnel policy in the sphere of public-legal relations at the level of a real threat to national security. Kyiv Law Journal. Vol. 1. P. 214–222. DOI: https://doi.org/10.32782/klj/2023.1.32
Miller, K., Hettinger, C., Humpherys, J., Jarvis, T., & Kartchner, D. Forward Thinking: Building Deep Random Forests. 31st Conference on Neural Information Processing Systems. 20.05.2017. Long Beach, CA. USA. P. 1–8. Available at: https://arxiv.org/abs/1705.07366
Misra, A., Okewu, E., Misra, S., & Fernández-Sanz, L. (2022). Deep Neural Network Model for Evaluating and Achieving the Sustainable Development Goal 16. Applied Sciences. Vol. 12. N. 9256. P. 1–15. DOI: https://doi.org/10.3390/app12189256
Naik, S., Patil, R., Agarwal, S., & Baths, V. (2022). Probing Semantic Grounding in Language Models of Code with Representational Similarity Analysis. Chapter. Cham: Springer. P. 1–15. DOI: https://doi.org/10.48550/arXiv.2207.07706
Nalepa, M. (2022). After Authoritarianism Transitional Justice and Democratic Stability. Cambridge: University Printing House. 355 p.
Nguyen, T.K., Lai, C.P., Phan, H.V, & Nguyen, T.K.T. (2024). Detecting Informal Costs in Vietnamese Firms: a Data Mining Application. Transformations in Business & Economics. Vol. 23. No 1 (61). P. 381–403.
Ogg, M., Bose, R., Scharf, J., Ratto, C., & Wolmetz, M. (2024). Turing Representational Similarity Analysis (RSA): A Flexible Method for Measuring Alignment Between Human and Artificial Intelligence. The Johns Hopkins University APL. P. 1–30. DOI: https://doi.org/10.48550/arXiv.2412.00577
Olaniyan, K. (2023). Ownership of Proceeds of Corruption in International Law Series. Oxford: OUP Oxford. 400 p.
On a mandatory transparency register: agreement between the EU Parliament, the Council and the Commission. May 20, 2021. #32021Q0611(01). Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32021Q0611(01)
Rafiei, H., & Akbarzadeh-T., M.-R. (2024). Hedge-Embedded Linguistic Fuzzy Neural Networks for Systems Identification and Control. IEEE Transactions on AI. Vol. 5. No. 10. P. 4928–4937.
Pedersen, K. H., & Johannsen, L. (2023). When corruption hits the judiciary: A global perspective on access to justice and corruption. Oñati Socio-legal Series. Vol. 13. Iss. 4. P. 1258–1280. DOI: https://doi.org/10.35295/osls.iisl/0000-0000-0000-1354
Peng, H., & Wang, J. (2024). Advanced Spiking Neural P Systems: Models and Applications. Singapore: Springer. 304 p.
Revkin, M., Alrababah, A., & Myrick, R. (2024). Evidence-Based Transitional Justice: Incorporating Public Opinion into the Field, with New Data from Iraq and Ukraine. The Yale Law Journal. Vol. 133. n. 5. P. 1582–1675.
Schwab, K. (2016). The Fourth Industrial Revolution. Geneva: World Economic Forum. 172 p.
The 2030 Agenda for Sustainable Development. UN. 25-27 September 2015. Available at: https://www.un.org/sustainabledevelopment/development-agenda/
The Kleptocrat’s Playbook: A Taxonomy of Domestic and Transnational Tactics (2021). Washington: International Republican Institute. 45 р.
Threat assessment Italian organised crime. Hague: Europol. 2013. 18 р.
Transparency Register gets a new feature. 23 January 2025. URL: https://transparency-register.europa.eu/find-out-more/news/new-transparency-register-gets-new-feature-2025-01-23_en
Yermolaev, О. А. (2021). System for determining the quality of goods in online stores based on AI. Author's abstract qualification. work. Special. 122 "Computer Science". Black Sea NU named after P. Mohyla. Mykolaiv. 15 p.
Zhang, Q., Zeng, X., Lo, W., & Fan, B. (2023). Enterprise innovation evaluation method based on swarm optimization algorithm and artificial neural network. Neural Computing and Applications. Vol. 35. Iss. 36. P. 25143–25156. DOI: https://doi.org/10.1007/s00521-023-08317-2
Zhang, X., Goel, R. K., Jiang, J., & Capasso, S. (2023). Do deep regional trade agreements strengthen anticorruption? A social network analysis. The World Economy, Wiley Blackwell. Vol. 46(8). P. 2478–2513. DOI: https://doi.org/10.1111/twec.13399
Zhu, J., & Wen, B. (2022). Trace the Money, Seize the Fugitives: China’s Other Anticorruption Battle. Journal of contemporary China. Vol. 31. No. 138. P. 993–1011. DOI: https://doi.org/10.1080/10670564.2022.2031006
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