NEURAL NETWORKS INTEGRATION INTO LEGAL RESOURCES FOR ANTI-СORRUPTION MEASURES IN INTERNATIONAL ECONOMIC CO-OPERATION

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.