{"id":13723,"date":"2026-04-20T06:00:00","date_gmt":"2026-04-20T13:00:00","guid":{"rendered":""},"modified":"2026-04-17T13:28:00","modified_gmt":"2026-04-17T20:28:00","slug":"reimagining-trade-finance-with-ai-a-collaborative-proof-of-concept-from-microsoft-anz-hsbc-and-lloyds","status":"publish","type":"ms-industry","link":"https:\/\/www.microsoft.com\/en-us\/microsoft-cloud\/blog\/financial-services\/2026\/04\/20\/reimagining-trade-finance-with-ai-a-collaborative-proof-of-concept-from-microsoft-anz-hsbc-and-lloyds\/","title":{"rendered":"Reimagining trade finance with AI: A collaborative proof of concept from Microsoft, ANZ, HSBC, and Lloyds"},"content":{"rendered":"\n

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<\/a>, this reality is beginning to change.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

An average\u00a0international trade shipment can involve up to 50 separate documents exchanged between as many as 30 different\u00a0stakeholders.1<\/sup> The\u00a0result is an avalanche of paper. More than 4 billion documents\u202fare estimated to\u00a0move through the global trade system every\u00a0day,2<\/sup>\u00a0and only\u00a01-2% of these are handled digitally.1<\/sup><\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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\u2014systems trained on vast datasets to understand meaning and generate content that feels human) are poised to fundamentally change the game for global trade.<\/p>\n\n\n\n

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Reimagine trade finance with GenAI<\/h2>\n\n\n\n

See how leading institutions are using GenAI to streamline trade finance and drive the shift from paper-based to digital platforms.<\/p>\n\n\n\n

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A collaborative\u00a0proof of concept\u00a0to streamline data exchange\u00a0<\/h2>\n\n\n\n

Working in partnership with ANZ<\/a>, HSBC<\/a>, and Lloyds<\/a>, 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. <\/p>\n\n\n\n

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\u2019s (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. <\/p>\n\n\n\n

The demo illustrates what \u201cagentic AI in the trade finance workflow\u201d 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.<\/p>\n\n\n\n

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 \u201cIs this letter of credit compliant with the agreed terms?\u201d<\/em> 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.<\/p>\n\n\n\n

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. <\/p>\n\n\n\n

Because LLMs interpret documents with contextual understanding\u2014not just spotting keywords but grasping meaning and relationships\u2014AI 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. <\/p>\n\n\n\n

By enabling direct, standards-aligned data exchange between corporates and banks, this kind of solution potentially helps to: <\/p>\n\n\n\n