Abstract
This research explores the integration of artificial intelligence (AI) in customs clearance systems to enhance trade compliance and border management. By leveraging machine learning (ML), natural language processing (NLP), and predictive analytics, the study demonstrates how AI-driven systems can optimize risk assessment, automate document processing, and detect trade anomalies. Case studies illustrate the successful implementation of AI-based solutions in enhancing customs efficiency, reducing processing times, and improving decision-making accuracy. The research identifies key challenges, including data quality, interoperability, and ethical considerations, and proposes future directions, such as integrating blockchain with AI and developing explainable AI models.
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