Despite long-term efforts by banks and governments to embrace digital transformation, trade finance remains stubbornly mired in a paper-heavy past. With the rapid innovation and adoption of AI across the financial services industry, this reality is beginning to change.
A glimpse of what the future may hold can be seen in a collaborative effort involving leading global banks and Microsoft. Specifically, a new prototype is demonstrating how agentic AI can solve longstanding problems while enabling more seamless, embedded client experiences.
An average international trade shipment can involve up to 50 separate documents exchanged between as many as 30 different stakeholders.1 The result is an avalanche of paper. More than 4 billion documents are estimated to move through the global trade system every day,2 and only 1-2% of these are handled digitally.1
Complicating matters, disconnected trade platforms and fragmented workflows often suffer from a reliance on paper and a slow adoption of data standards. Critical data, even when digitized, often needs to be manually rekeyed into disparate supply chain systems and bank platforms. This leads to delays, discrepancies, and persistent inefficiencies, creates a drag on financing, and introduces a broad range of risks.
Simply converting paper to image or text is not enough to modernize trade finance processes. True transformation requires that data be structured, understood, and actionable. This is where the latest advancements in generative AI and large language models (LLMs—systems trained on vast datasets to understand meaning and generate content that feels human) are poised to fundamentally change the game for global trade.
A collaborative proof of concept to streamline data exchange
Working in partnership with ANZ, HSBC, and Lloyds, Microsoft has built a technology proof-of-concept (POC) solution that demonstrates how AI agents, powered by LLMs, hold the potential to transform trade workflows. The prototype demonstrates how AI can be embedded directly into ERP systems to extract, validate, and digitally transmit structured trade data to banks, enabling seamless, standards-based integration.
Demonstrated at the Sibos 2025 conference in Frankfurt, Germany, the POC uses advanced AI and API technologies, together with the Key Trade Documents and Data Elements (KTDDE) framework developed by the International Chamber of Commerce’s (ICC) Digital Standards Initiative (DSI), to help enable a decentralized, end-to-end data exchange based on standardized core data elements used across trade and trade finance documents.
The demo illustrates what “agentic AI in the trade finance workflow” can look like. The POC simulated a corporate seller receiving an MT700 Letter of Credit (LC) message. An AI agent built on a generative AI model automatically parsed the LC, identified the key data elements (such as buyer and seller information, credit amount, shipment terms, and dates), and cross-checked them against the invoice and shipping data in the ERP. In the demo, the AI agent quickly detected data discrepancies across documents such as currency and amount and suggested a correction in natural language. Once verified, the data was transmitted securely to the bank.
Crucially, the POC also illustrated how treasury users can interact with the data in trade documents through a conversational AI interface. For example, a treasury manager could ask the AI agent questions like “Is this letter of credit compliant with the agreed terms?” and receive instant answers grounded in both ERP data and third-party trade documents. Data sources can extend to real-time market data such as foreign exchange (FX) and risk ratings to enable more complex treasury questions such as FX hedging and LC discounting.
This kind of agent-based interaction with enterprise data marks a breakthrough in usability. Instead of poring over documents or portal screens, stakeholders can simply ask questions and get AI-generated insights, dramatically speeding up decision-making in the trade process.
Because LLMs interpret documents with contextual understanding—not just spotting keywords but grasping meaning and relationships—AI agents can help surface subtle red flags, such as references to sanctioned entities or ambiguous descriptions of dual-use goods. By referencing regulatory frameworks such as EU dual-use export control laws, these agents can flag potential compliance risks early, enabling proactive intervention before a transaction proceeds.
By enabling direct, standards-aligned data exchange between corporates and banks, this kind of solution potentially helps to:
- Reduce document discrepancies by validating data at the source.
- Improve accuracy and auditability through structured, machine-readable data and end-to-end traceability.
- Support standards-driven interoperability across ERP systems, bank platforms, and logistics networks.
- Shorten time to funding by eliminating paper dependencies and courier delays.
- Strengthen risk management and compliance by automatically checking trade data against rules and watchlists.
The potential benefits of such a solution extend beyond banks and trading companies. Governments and customs authorities can use ERP-aligned data to potentially streamline filings and improve tax collection. Shipping and logistics providers can potentially gain earlier access to accurate data, ultimately improving planning and reducing delays. By emphasizing data interoperability and AI-powered insights, the POC offers a repeatable model that can extend beyond trade finance to other complex, document-intensive processes.
Leading banks and Microsoft: A shared vision on digital trade finance
This successful proof of concept was built on a strong collaboration between Microsoft and three leading global banks, uniting Microsoft’s AI and enterprise technology expertise with the banks’ deep trade finance experience. Together, we are shaping a new model for intelligent, data-driven trade finance.
ANZ
ANZ is exploring opportunities to apply AI in ways that can support business processes and enhance customer experience. Where appropriate, we aim to move beyond the role of a back-end transaction processor to deliver trade finance as a seamless part of client existing workflows. By safely and responsibly integrating AI into corporate ERP systems, our goal is to offer a more intuitive, built-in trade finance experience.
Hari Janakiraman, Head of Industry and Innovation, Transaction Banking, Institutional
HSBC
Trade finance is still overwhelmingly document-driven, which is why the industry needs practical interoperability: common data standards and consistent, bank-defined data sets that exporters—from small businesses to large multinationals—can exchange electronically. This proof of concept shows how aligning to frameworks like the ICC’s Key Trade Documents and Data Elements can reduce discrepancies and help move validated data securely from ERP to bank platforms, making trade more accessible and efficient for companies of all sizes.
Bhriguraj Singh, Chief Product Officer, Global Trade Solutions
Lloyds
This new development creates a strong opportunity to improve the trade finance ecosystem by moving away from paper being transferred between parties to simply exchanging data. By using open standards (aligned, structured data for key trade documents), we can integrate more easily with clients’ technology and logistics partners. Combined with AI-driven data exchange through the Microsoft connector, this allows information to flow securely and accurately between platforms. We are committed to building a more connected and collaborative digital trade environment, and this approach is an important step forward.
Surath Sengupta, Head of Transaction Banking Products
Opening the benefits of AI and interoperability
Under the hood, the prototype featured a modern decentralized architecture designed to integrate with multiple ERP systems (Microsoft Dynamics 365 and others), bank platforms, and third-party supply chain applications.
The solution was built on Microsoft Foundry, a unified Azure platform for developing, deploying, and governing AI applications and agents. Foundry brings together models, tools, governance, and observability under a single control plane, which is critical for handling sensitive trade data and ensuring enterprise-grade security.
LLMs power deep document understanding, data extraction, validation, and conversational interactions. In contrast to traditional Optical Character Recognition (OCR) or template-based systems, which can struggle when layouts change or data is missing, LLMs can adapt to varied document formats and more robustly extract and cross-check information. These capabilities are increasingly being employed across the financial services industry for innovations in payments, risk, and compliance.
Putting AI to work in trade finance
We invite organizations across the trade ecosystem—banks, corporates, fintech, and governments—to co-innovate with us on the future of international trade.
To learn more about this collaborative initiative and explore how generative AI can transform your trade and international banking operations, contact your Microsoft representative.
For more information on Microsoft’s approach to building AI agents and industry solutions, visit Microsoft for Financial Services.
1 ICC United Kingdom, “‘The Roadmap to Digitalise UK Trade,” June 16, 2025 (https://www.tradeforprosperity.co.uk/the-roadmap-to-digitalise-uk-trade/)
2 Fortune, “Global trade still depends on 4 billion paper documents daily,” October 2023
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