PREDICTIVE ARCHITECTURE FOR RISKS MANAGEMENT OF money laundering IN CUSTOMS CONTROL

Abstract

This article develops a comprehensive predictive architecture for managing money‑laundering risks in customs control, grounded in the integration of artificial intelligence (AI), machine learning, and advanced data‑driven methodologies. The study addresses the systemic limitations of traditional rule‑based anti‑money laundering  approaches, which remain reactive, fragmented, and inefficient in the context of increasing digitalization, global trade expansion, and the growing complexity of illicit financial schemes, including trade‑based money laundering and cross‑border shadow flows. The research proposes a multi‑layered, cloud‑native architectural framework that combines federated deep learning, explainable artificial intelligence, and predictive analytics to enable real‑time anomaly detection, probabilistic risk scoring, and proactive identification of suspicious transactions and trade operations. The architecture incorporates key functional modules, including AI‑driven single‑window systems, anomaly detection engines, behavioral profiling tools, and automated cargo‑screening mechanisms, ensuring comprehensive coverage of financial, customs, and logistics data streams. The integration of explainable AI ensures transparency, interpretability, and auditability of algorithmic decisions, supporting regulatory compliance and strengthening trust in AI‑driven supervision systems. The study further formalizes key implementation parameters, including data quality and interoperability, model performance and adaptability, cloud‑native infrastructure, cybersecurity resilience, and governance frameworks that define accountability and institutional coordination. The findings confirm that the integration of predictive analytics, federated intelligence, and explainable compliance systems enables a transition from reactive rule‑matching to proactive risk intelligence, strengthening customs oversight, improving detection of shadow financial flows, and supporting the resilience and transparency of global trade and financial systems.