Artificial intelligence is being harnessed to tackle the document review that determines whether a transaction moves forward, one of the most manual parts of trade finance.
Software has long been able to scan documents and extract data. But a spate of initiatives show how AI is being tasked with interpreting transaction-specific terms, comparing information across documents, conducting checks and deciding which cases need a person’s further attention. The work takes place before many trade payments are cleared to proceed, putting AI inside the process that determines when money gets released.
HSBC’s Smart Checking illustrates the trend. The bank processes more than 1 million documentary presentations annually, including documentary credits and collections. Individual presentations can contain hundreds of pages of structured and unstructured information, while the conditions governing the documents vary by transaction.
Smart Checking extracts and classifies information and applies AI models to turn inconsistent documentation into structured data. It also interprets transaction-specific conditions and conducts documentary credit checks. Confidence scores determine when a result moves through the workflow and when an exception goes to a trade specialist.
The use case exposes one of the central questions surrounding enterprise AI. What happens after a model has read the information? A bank still needs enough confidence in the output to use it in a financial process.
PYMNTS Intelligence data revealed why companies are concentrating AI investment on financial workflows. “Time to Cash™: A New Measure of Business Resilience” found in October 2025 that enterprises have automated 55% of accounts receivable processes on average and 70% of chief financial officers use AI to manage cash flow.
The PYMNTS Intelligence report “CFOs Push AI Forward but Keep a Hand on the Wheel” revealed in December that AI adoption by CFOs concentrated in structured work, as 45% were using AI to continuously monitor working capital and cash flows. Finance chiefs were more cautious about handing complex tasks to the technology.
Trade finance brings both categories into the same workflow. Extracting fields and matching information are structured tasks. Resolving an exception or determining what conflicting evidence means requires more judgment.
Banks have been exploring the balancing act between automation and additional eyes on the process, so to speak.
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Lloyds Bank’s deployment of Cleareye.ai uses optical character recognition, machine learning and natural language processing to extract information and conduct automated document examinations and compliance checks. JPMorgan has also worked with and invested in Cleareye, whose technology extracts, validates and classifies unstructured trade data and identifies potential sanctions and trade-based money laundering flags.
Automated trade finance decisions depend on contracts, data standards, legal regimes and banking systems describing transactions consistently enough for software to act on them, PYMNTS reported Aug. 13.
From AI Processing to Working Capital
The payoff reaches beyond a bank’s processing costs when faster checking moves a transaction toward payment sooner.
The PYMNTS Intelligence report “The Cross-Border Opportunity: What Global Sourcing by US SMBs Means for Payment Providers” found in May that 43% of small businesses in the United States sourcing from international suppliers put faster payment processing and settlement at the top of their list of desired improvements.
Businesses also assign a price to faster access to funds. The PYMNTS Intelligence report “How Instant Ad Hoc Payment Costs Impact Small SMBs” revealed that 57% of small to medium-sized business (SMB) receivers were willing to pay a fixed fee for instant ad hoc payments. That study covered instant ad hoc payments, not trade finance, but it provided a measure of how small businesses value payment speed.
Document automation once centered heavily on digitizing paper and extracting fields. Systems are now being used to interpret conditions, compare evidence, run checks and decide which transactions need intervention.
Trade documents give banks plenty of information, and the emerging frontier in this essential corner of global fund flows will be tied to how reliably AI turns it into decisions that move transactions forward.
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