{
  "id": 12184476,
  "title": "AI Targets Trade Finance’s Paperwork Bottleneck",
  "url": "https://urgent.news/2026/10/05/ai-targets-trade-finances-paperwork-bottleneck",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-05T15:43:07.000Z",
  "source": {
    "name": "PYMNTS",
    "slug": "pymnts",
    "url": "https://www.pymnts.com/news/artificial-intelligence/2026/ai-targets-trade-finance-paperwork-bottleneck/"
  },
  "original_language": "en",
  "account": "Artificial intelligence is being deployed to confront the labor-intensive document review process that determines the outcome of a transaction in trade finance, one of the most manual facets of the industry. While software has been able to scan documents and extract data for years, recent endeavors demonstrate how AI is being utilized to interpret transaction-specific terms, compare information across documents, perform checks, and decide which cases require human intervention. This occurs prior to many trade payments being cleared to proceed, placing AI at the core of the decision-making process that governs when funds are released. HSBC's Smart Checking exemplifies this trend. The bank handles over 1 million document presentations each year, encompassing documentary credits and collections. These presentations can contain hundreds of pages of structured and unstructured data, with varying conditions governing the documents according to the nature of the transaction. Smart Checking extracts and categorizes information, applies AI models to transform inconsistent documentation into structured data, and interprets transaction-specific conditions while conducting documentary credit checks. Confidence scores ultimately determine whether results progress through the workflow or are routed to a trade specialist. This scenario highlights a key question surrounding enterprise AI: what occurs after the model has interpreted the information? Despite needing sufficient confidence in the output to incorporate it into a financial process, banks must strike a balance between automation and human oversight. A PYMNTS Intelligence survey titled \" Time to Cash™: A New Measure of Business Resilience,\" released in October 2025, indicated that businesses have automated 55% of their accounts receivable processes on average, with 70% of chief financial officers employing AI to manage cash flow. According to another PYMNTS Intelligence report titled \" CFOs Push AI Forward but Keep a Hand on the Wheel,\" published in December, 45% of CFOs were utilizing AI to continuously monitor working capital and cash flows, while being more cautious about entrusting complex tasks to the technology. Trade finance combines both structured tasks, such as extracting fields and matching information, and more judgment-based tasks, like resolving exceptions or determining the implications of conflicting evidence. Banks have been exploring the delicate equilibrium between automation and human intervention in this process. Lloyds Bank's implementation of Cleareye.ai employs optical character recognition, machine learning, and natural language processing to extract data, conduct automated document examinations, and perform compliance checks. JPMorgan has also collaborated with and invested in Cleareye, leveraging its technology to extract, validate, and categorize unstructured trade data, while identifying potential sanctions and trade-based money laundering flags. Automated trade finance decisions hinge on consistent contracts, data standards, legal frameworks, and banking systems, which must sufficiently describe transactions for software to act upon them. The payoff extends beyond banks' processing costs when expedited checking accelerates transactions towards payment. PYMNTS Intelligence's report \" The Cross-Border Opportunity: What Global Sourcing by US SMBs Means for Payment Providers,\" published in May, revealed that 43% of small businesses in the United States sourcing from international suppliers prioritize faster payment processing and settlement as their top improvement. Furthermore, 57% of small to medium-sized business (SMB) receivers were willing to pay a fixed fee for instant ad hoc payments, underscoring the value they place on payment speed. Document automation initially focused on digitizing paper and extracting fields. Now, systems are being employed to interpret conditions, compare evidence, run checks, and decide which transactions necessitate intervention. Trade documents provide banks with ample information, and the emerging frontiers in this vital segment of global fund flows will be contingent on how reliably AI converts this data into decisions that propel transactions forward.",
  "summary": "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 […] The post AI Targets Trade…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}