Advanced Management Accounting and Big Data Analytics for the Detection and Prevention of Illicit Trade

Abstract

Objectives: This paper explores the role of Artificial Intelligence (AI) in the ethical and anticipatory detection and prevention of illicit trade, highlighting the preventive potential of accounting techniques within digitally interconnected markets. Prior Work: Building on existing research in forensic accounting, continuous auditing, and AI-driven anomaly detection, the study positions accounting not merely as a retrospective tool but as a forward-looking governance mechanism capable of mitigating risks associated with counterfeiting, smuggling, tax evasion, and financial fraud. Approach: The paper develops an integrated conceptual framework combining anticipatory governance, ethical AI design, and accounting-based control mechanisms. It synthesizes evidence from case studies, literature, and analytical methodologies including transaction pattern analysis, ratio and variance assessments, and financial statement anomaly detection. Results: The findings indicate that AI-enabled accounting analytics can identify inconsistencies in trade flows and financial reporting at early stages, serving as reliable early-warning systems. Ethical considerations, including transparency, accountability, proportionality, and respect for fundamental rights, are crucial for effective implementation. Implications: The study provides actionable insights for academics, researchers, and institutional administrators, emphasizing how AI-supported accounting frameworks can enhance risk prevention and institutional integrity. Value: The paper demonstrates that ethically grounded AI, integrated with robust accounting practices, offers a novel approach to preventing illicit trade while maintaining financial transparency, public trust, and democratic governance.