Trade finance operations have traditionally been performed by manual processes, document-intensive workflows, and a high reliance on human judgment. For example, letters of credit require careful examination of documents to determine whether a complying presentation has been made in accordance with the terms and conditions of the credit, applicable rules, and international standard banking practice. In recent years, advanced technology has increasingly been introduced to support these processes, and more recently, Generative Artificial Intelligence (Gen AI) has emerged as a new tool for trade operations.
While Gen AI has the potential to promote efficiency and consistency, its application in trade finance must be approached with care. Trade operations encounter the intersections of commercial risk, compliance, sanctions, and anti-money laundering (AML) controls, making governance and oversight essential. The future of Gen AI in trade finance operations is therefore not about replacing human decision-making and expertise, but supporting it within a well-defined control framework.
The Evolution of Technology in Trade Finance Operations
Before considering Gen AI, it is important to recognise that trade finance operations have already undergone significant technological change. Many banks have implemented systems to support document digitisation, workflow automation, sanctions screening, and transaction monitoring. Optical character recognition (OCR), rules-based discrepancy checking, and automated message generation have helped reduce manual processing and improve turnaround times.
Potential Applications of Gen AI in Trade Finance Operations
Gen AI offers several potential use cases within trade finance operations, particularly as a supporting tool rather than a final decision-maker.
One area is document examination and data extraction. Gen AI may assist in the examination of full document sets against applicable international rules and standard banking practices, identifying discrepancies and highlighting inconsistencies across documents. This could support operational scalability, lower operational risk, and enhance customer servicing.
Another application is knowledge support. Gen AI could be used internally to help staff quickly reference relevant rule provisions such as UCP 600, URC 522, and ISP98 or summarise procedural guidance, particularly for less experienced team members. This does not replace training or expertise, but may enhance accessibility to knowledge.
Gen AI could also support compliance-related checks, such as identifying potential trade-based money laundering indicators, sanctions concerns, vessel tracking anomalies, or dual-use goods detection. This may elevate compliance consistency and efficiency while still serving a complementary role to human review and approval.
Importantly, these applications should remain assistive in nature. Decisions on document compliance, sanctions mandates, or AML escalation must ultimately continue to rest with qualified professionals.
Risks and Limitations of Gen AI in Trade Finance
Despite its potential benefits, Gen AI presents several risk considerations that are particularly relevant in trade finance operations, such as data accuracy and “hallucination” risk; confidentiality and data privacy concerns; sanctions and AML false negatives; and regulatory uncertainty.
Governance and Control Considerations
For Gen AI to be responsibly integrated into trade finance operations, strong governance is essential. Banks should clearly define when and how AI tools may be used and be prepared to demonstrate how these tools are governed, monitored, and controlled. Additionally, AI outputs should be subject to human oversight, with clear accountability for decisions resting with operational and compliance staff.
Training is also critical. Staff must understand both the capabilities and limitations of AI tools and be encouraged to validate outputs rather than blindly accepting them, escalating where anomalies are identified.
Impact on Trade Operations Roles and Skills
The introduction of Gen AI is likely to change the nature of trade finance operations roles, but not eliminate their importance. Rather than reducing the need for skilled professionals, Gen AI is more likely to shift operational focus from manual processing to risk-based decision-making. Trade operations professionals will therefore need solid analytical skills and the ability to interpret and challenge both documents and system-generated outputs.
Current Adoption and Future Direction
Gen AI is already being deployed across multiple industries, including healthcare for clinical documentation support and financial services for customer interaction and real-time fraud monitoring. Within banking, many institutions are conducting structured pilots and experimentation with large language models (LLMs), particularly in operational support and compliance.
In trade finance specifically, adoption remains measured. Many banks are currently testing AI-enabled document checking solutions in collaboration with fintech providers, rather than full production deployment. Regionally, discussions around Gen AI have increased across banking and regulatory forums, reflecting growing strategic interest. In Egypt, the focus remains primarily on broader digital transformation and automation initiatives, with Gen AI considered part of longer-term capability development rather than immediate large-scale implementation within trade operations.
Conclusion
The future of advanced technology and Gen AI in trade finance operations lies not only in automation, but also in carefully governed, human-led implementation through a human-in-the-loop (HITL) approach. Successful adoption depends on balancing innovation with robust controls, clear accountability, and continued investment in skilled professionals. In this context, ongoing training and upskilling will therefore be essential for trade finance professionals, ensuring that technology supports sound banking practice rather than undermining it.
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Doaa El Atawy is Head of Trade Finance Operations at Egyptian Gulf Bank and a Chartered Associate of the London Foundation for Banking & Finance. She holds many LIBF qualifications, including CITF, CDCSAdv, CSCF, QTFE, CTFC, PDipB&F, and AdvDipB&F.
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