MINIMIZING CORRUPTION RISKS IN TAX ADMINISTRATION THROUGH DIGITAL CRIMINOLOGY AND ARTIFICIAL INTELLIGENCE

This thesis examines the mechanisms for solving corruption crimes occurring in tax authorities not at the stage of punishment (repressive), but at the stage of detection and elimination (preventive) before their commission. The work analyzes the issues of limiting the human factor by abandoning traditional approaches and introducing digital criminology, algorithmic control based on artificial intelligence (AI), and automated "compliance" systems. Also, specific practical proposals are given on the implementation of the "Red Flags" indicator in the activities of the state tax service.