ENHANCING THE TRANSPARENCY OF FINANCIAL REPORTING IN THE PUBLIC SECTOR THROUGH ADVANCED DIGITAL TECHNOLOGIES

Abstract

The article examines institutional mechanisms for managing corruption risks in the field of financial monitoring under conditions of financial market globalization, economic digitalization, and the growing scale of cross-border financial threats. It is substantiated that corruption and money laundering are interconnected phenomena, since proceeds derived from corruption require legalization through financial channels, thereby creating additional risks to the economic security of the state and the stability of the financial system. It is determined that the effectiveness of anti-money laundering and counter-terrorist financing (AML/CFT) systems largely depends on the level of institutional coordination, the quality of regulatory and legal frameworks, and the integration of modern digital technologies into financial monitoring processes. The study analyzes international standards and approaches to combating corruption risks, including the FATF Recommendations, the United Nations Convention against Corruption (UNCAC), the OECD Anti-Bribery Convention, EU AML/CFT Directives, as well as methodological approaches developed by the World Bank and the International Monetary Fund. It has been established that the risk-based approach, Know Your Customer (KYC) procedures, compliance control, and international information exchange constitute key elements for enhancing the transparency of financial flows and preventing the legalization of illicit proceeds. An institutional coordination mechanism among financial monitoring entities has been developed, providing for interaction between international organizations, state regulators, financial institutions, and law enforcement agencies in the process of identifying and minimizing corruption risks. The study demonstrates that insufficient integration of information systems, limited interagency data exchange, and uneven implementation of international standards create “institutional gaps” that reduce the effectiveness of AML/CFT systems. Particular attention is paid to the role of modern artificial intelligence and machine learning technologies in transforming financial monitoring systems. It is determined that the application of AI/ML solutions, deep learning, graph-based models, and automated big data analytics enhances the accuracy of detecting suspicious transactions, reduces the number of false positives, and enables real-time monitoring of financial operations.