An AI-Enabled Automation Framework to Increase Efficiency and Accuracy in International Logistics Transit Declaration Processes

Resumo

Increasing global trade volumes and increasingly complex customs regulations have increased the need for accurate and traceable document management in international logistics. Manual preparation of transit declarations has become a significant challenge, especially after the transition to NCTS Version 5, which imposes higher data requirements and stricter validation rules. This study proposes a scalable, AI-powered automation framework that combines digital archiving, OCR-based data extraction, machine learning-assisted validation, data normalization, and automated declaration generation. The proposed system performs content analysis on multiple logistics documents, including CMRs, invoices, insurance policies, and T1/T2 forms, while conducting logical consistency checks using learning-based algorithms. Declaration packages are generated end-to-end and transmitted to customs authorities via SGS Transitnet using a REST-based interface. Real-world operational results show that processing time has been reduced from 20 minutes to approximately 3 minutes, and erroneous entries have been reduced by 84.6%. These findings demonstrate the technical and industrial applicability of the proposed model in high-volume transit operations and highlight the potential for fully autonomous, customs-compliant digital declaration execution in future developments.