Reducing Trade Document Processing Time Between Sri Lanka and its South Asian Neighbours by Applying Blockchain and Machine Learning Solutions

International trade documentation in South Asia are complex and time-consuming to process, and they are often hindered by delays and inefficiencies. This study investigates the potential of Blockchain (BC) and Machine Learning (ML) to reduce trade document processing time and improve trade efficiency. Using an explanatory sequence design, the research combines quantitative analysis of survey data with qualitative insights from case studies. The quantitative phase revealed that BC, ML, and trade facilitation regulations are significant in reducing trade documentation processing time. Consequently, reducing trade documentation processing time requires the implementation of BC and ML with supporting new trade facilitation regulations. The qualitative phase identifies key challenges to the adoption of BC and ML, including legal and regulatory hurdles (e.g., legal recognition of smart contracts, unclear data ownership, and unclear data privacy regulations), organisational barriers (e.g., Resistance to change, lack of awareness, and lack of skilled personnel), and technological limitations (e.g., scalability issues, integration challenges, and security concerns). The study underscores the importance of addressing these challenges through collaborative efforts between governments, financial institutions, and technology companies. This research contributes to the growing body of knowledge on BC and ML applications in international trade, providing valuable information for policy makers and practitioners seeking to modernise trade processes in South Asia.