arbiter
JournalsPricingAccount

AI driven transformation in trade finance: A roadmap for automating letter of credit document examination

Mounaf Asaad Khalil et al.

Digital Business2025https://doi.org/10.1016/j.digbus.2025.100130article
ABDC C
Weight
0.52

What the paper says

International trade with unfamiliar stakeholders poses challenges of trust, payment security, and regulatory compliance. Letters of Credit (LC) help mitigate these risks but rely on manual, error prone processes that lead to delays, high costs, and limited scalability. This study proposes a roadmap for AI adoption in trade finance, specifically targeting the automation of LC document examination. Guided by the Technology-Organization-Environment (TOE) framework and complemented by individual-level insights from the Technology Acceptance Model (TAM), the research integrates organizational, technological, and behavioral factors to frame the adoption process. A literature-driven approach, supported by expert insights and case study analysis, was used to identify trade finance bottlenecks and the role of AI in addressing them. The findings highlight AI's potential in discrepancy detection, workflow optimization, and compliance improvement. However, full automation remains impractical due to regulatory and trust concerns. A hybrid AI-human approach is proposed as a practical and effective solution. This study contributes to bridging research gaps in AI-driven trade finance and provides strategic insights for implementing intelligent document examination systems.

7 citations

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1016/j.digbus.2025.100130

Or copy a formatted citation

@article{mounaf2025,
  title        = {{AI driven transformation in trade finance: A roadmap for automating letter of credit document examination}},
  author       = {Mounaf Asaad Khalil et al.},
  journal      = {Digital Business},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1016/j.digbus.2025.100130},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

AI driven transformation in trade finance: A roadmap for automating letter of credit document examination

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.52

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.47 × 0.4 = 0.19
M · momentum0.68 × 0.15 = 0.10
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.