From Transactions to Intelligence: How is AI Transforming Working Capital Finance for Global Trade

Authored by Andrew Betts: Chief Growth Officer, CredAble

Global trade has always adapted to disruption, and the current phase of geopolitical realignments, tariff shifts, and supply chain reconfigurations are no exception. While volumes may face near-term pressure, global trade is being redirected across new corridors, partnerships, and production networks, reinforcing a system that continues to evolve in the face of persistent uncertainty.

Yet amidst these changes, systems that finance these ecosystems have typically lagged in timing. Nearly 30% of time is spent on manual document processing. Reliance on physical, document-heavy workflows, unstructured and fragmented data environments due to geopolitical realignments and manual processes persists, limiting visibility and delaying decisions. As a result, $150 billion is estimated to be lost annually to the manual activities of trade finance operations.

Artificial Intelligence (AI) is now influencing how global commerce is being financed. AI enables continuous interpretation of liquidity, risk and transaction behaviour across the trade cycle while also reducing dependency on manual, time-intensive and error-prone processes. This shift is prompting banks to move towards dynamic models of trade finance intelligence, where financing decisions are shaped by real‑time signals embedded in supply chain activity.

Why the AI Shift in Trade Finance Begins with Working Capital

Traditional trade finance systems are primarily designed to process and validate transactions. However, visibility into payment cycles, supplier dependencies and financing utilisation remains fragmented and often assessed independently rather than

as part of a unified view of trade activity. Risk assessment and management sit at the core of trade and its financing, and AI is beginning to enhance how these functions are carried out.

AI is already improving how trade data is processed and interpreted. Models can extract and standardise information from trade documents across formats and sources in real time, bringing greater speed and consistency to workflows that were previously manual. When combined with policy-aligned decision engines and enriched with historical and real-time data, this allows banks to evaluate transactions with greater precision and contextual awareness. AI could boost the value of cross-border trade in goods and services by nearly 40% by 2040, with WTO simulations putting the increase in global trade at 34% to 37% depending on policy and technology catch-up scenarios.

The impact is particularly relevant for SME financing. Much of the information required to assess smaller borrowers remains fragmented across invoices, payment records and operational data. AI enables these signals to be aggregated and interpreted within a structured risk framework, improving both access to financing and the quality of credit assessment at scale. This becomes critical as the global trade finance gap continues to persist at $2.5 trillion.

This is the direction we are building towards at CredAble, where multi-source working capital data is continuously analysed to support more informed financing decisions across risk, liquidity and growth.

The objective is not to compress every workflow from hours into seconds, but to remove the highest-friction steps that slow capital deployment without improving risk outcomes.

Trade Digitisation: From Process Efficiency to Decision Infrastructure

Alongside these shifts, trade’s broader digitisation agenda is reaching a more institutional phase. The adoption of the UNCITRAL Model Law on Electronic Transferable Records (MLETR) is accelerating, with the International Chamber of Commerce targeting alignment across 100 jurisdictions by 2026. While formal adoption continues to build, the volume of trade supported by digital frameworks has already reached meaningful scale, improving the reliability and accessibility of trade data.

This is where the shift becomes more tangible. As trade data becomes more structured and accessible, its value lies in how it is interpreted. Banks can begin to identify emerging liquidity stress across client ecosystems, detect supplier concentration risks earlier, and uncover gaps in programme penetration despite strong transaction activity.

The impact is increasingly visible in credit processes. What previously required analysts to gather, reconcile and summarise information across multiple systems can now be assembled in minutes, allowing human judgement to focus on risk assessment rather than process overhead.

Institutions deploying AI-enabled systems are already reporting improved banking efficiency by up to 46% with nearly 20% reduction in credit risk

At a more granular level, this intelligence extends to the vendor ecosystem. Transaction-level data enables banks to identify suppliers with high financing potential, prioritise under-penetrated segments, and expand programmes based on actual working capital behaviour rather than assumptions.

From our perspective at CredAble, this is reflected in the development of an AI-driven SCF Intelligence, where multi-source data is interpreted to generate contextual, decision-ready insights across risk, liquidity and growth. This allows supply chain finance programmes to scale with greater precision, aligning capital deployment more closely with real economic activity.

Extending Intelligence Across the Trade Finance Lifecycle

Trade finance is a natural domain for the AI progression because it sits at the intersection of documents, decisions and timing. Onboarding remains data-intense, vulnerable to manual bottlenecks. Credit evaluation depends on stitching together structured and unstructured inputs and risk surveillance often remains episodic.

AI can improve each of these layers individually, but its larger value comes when they are linked. That is when trade finance starts to move away from isolated transaction processing and closer to an adaptive financing system.

AI-assisted software development is accelerating the pace at which these systems can be designed, tested and refined. GitHub research indicates that developers using AI tools can complete tasks up to 55% faster, while studies from Anthropic suggest that a significant share of coding interactions are already automation-led rather than assistive.

The implication for banking is more structural. As development cycles compress, trade finance platforms can evolve more quickly to reflect changes in risk frameworks, regulatory requirements and client needs. AI is not only reshaping how trade finance operates, but also how rapidly the systems underpinning it can adapt.

The Rise of Agentic Systems in Banking Workflows

As trade data becomes more structured and accessible, the focus is shifting from how workflows are executed to how they are orchestrated. AI in trade finance is moving beyond isolated use cases into systems that can manage sequences of tasks within defined policy and control frameworks. Industry evidence reflects this shift. Accenture estimates that up to 70% of banking tasks can be augmented or automated through AI, pointing to a broader transition in how operational processes are designed and delivered.

This is where agentic systems are beginning to take shape. These systems are designed to coordinate multi-step processes across functions, retrieving and reconciling

transaction and borrower data, validating documents against policy rules, applying credit logic, flagging exceptions and routing them through controlled escalation paths.

Institutions are increasingly exploring such models across front, middle and back-office environments, signalling a structural shift in how banking operations are organised and executed.

This progression also introduces new governance considerations. While AI can assimilate data, monitor activity, flag anomalies and generate recommendations, decision-making continues to operate within a human-in-the-loop framework. Finance providers will need to evolve risk frameworks to ensure these systems function within defined controls, with outputs that are explainable, auditable and integrated into established credit and compliance processes.

From Transactions to Systems of Liquidity Intelligence

Institutions that emerge ahead in this AI transition will be ones who use it to redesign how signals are captured, how decisions are sequenced and how capital is deployed across the trade cycle, over those who merely layer new capabilities onto legacy processes.

The next chapter of AI in trade finance will be by banks and non-bank lenders who can turn fragmented operating data into a coherent working capital intelligence layer. As that takes hold, trade finance moves beyond process execution and becomes a real-time system for understanding how liquidity flows, where risk is building and where capital can be deployed with greater precision.