A buyer can look financially strong while the invoice being financed remains disputed, duplicated or unsupported by delivery. A supplier can provide convincing documents while lacking the capacity to fulfil an order. Trade finance underwriting must assess the businesses, their relationship and the transaction connecting them.

Generative and agentic AI offer useful ways to examine this evidence more thoroughly. Their value depends on reliable information, sound processes and experienced people who understand what the technology might miss. Faster processing offers little protection if the underlying assessment is flawed.

What Generative and Agentic AI Actually Do

Generative AI can extract, compare and summarise information from financial statements, contracts, invoices and correspondence. It can help analysts navigate documents in different languages and identify questions requiring investigation.

Agentic AI can coordinate steps. Within approved systems, an agent could retrieve company records, compare transaction documents, identify missing evidence and prepare a review file. Its permissions should determine which actions require human approval.

These capabilities complement sanctions screening, credit models and calculation engines. A language model’s fluent explanation should never be mistaken for a validated credit score.

Assessing Buyers and Suppliers

Counterparty selection starts with identity, ownership, commercial activity and financial capacity. AI can assemble information from registries, credit reports, screening services and financial disclosures, highlighting contradictions.

The assessment should distinguish each party’s role:

• Buyer Due Diligence: Examine liquidity, indebtedness, cash generation and payment history. AI can highlight deteriorating payment patterns or inconsistencies between reported profits and operating cash flow.
• Supplier Due Diligence: Examine trading history, production or sourcing capacity, delivery performance and financial resilience. Growth exceeding operational capacity deserves closer scrutiny.
• Ownership and Integrity: Map directors, beneficial owners and connected entities. Potential sanctions matches, adverse reporting and unexplained ownership structures require qualified review.

Require material findings to show their sources and dates. Limited public information should trigger further enquiries, rather than an automatic rejection or assumption ofsafety.

Understanding the Buyer-Supplier Relationship

Two credible businesses do not automatically produce a sound receivable. Underwriters must understand why they trade together, their relationship’s history and whether its economics make sense.

AI can compare orders, invoices, payments and credit notes to identify changes in volumes, payment terms, goods or payment recipients. Relationship mapping can reveal shared addresses or ownership connections requiring explanation.

The FATF–Egmonttrade-based money laundering indicators provide useful reference points for suspicious structures, documents and transactions. These support investigation; they do not establish wrongdoing.

Context matters. Seasonal growth may be reasonable, while circular trading between connected companies requires deeper examination. Explanations must be tested against independent evidence.

Examining Transactions, Receivables and Documents

AI can compare purchase orders, contracts, invoices, packing lists, transport documents and delivery confirmations, flagging inconsistent quantities, dates, prices or payment instructions.

Consider an illustrative transaction: an invoice covers 1,000 units, but delivery evidence supports only 800. AI can identify the mismatch and relevant acceptance clauses. The underwriter must establish whether there was a partial shipment, an error or an unsupported claim before determining the eligible financing amount.

Three distinctions are essential:

• Consistency is not authenticity. Matching documents may still be fabricated. Verification with independently identified buyers, carriers or other relevant parties remains important.
• An invoice is not necessarily an enforceable receivable. Disputes, returns, deductions and assignment restrictions require commercial and legal assessment.
• Duplicate detection has visibility limits. Internal checks cannot establish whether another financier has funded the invoice without access to relevant external information.

AI cannot independently prove that goods exist or that a buyer will pay.

Turning Findings Into Risk Decisions

Credit risk concerns payment capacity and willingness. Operational risk includes delivery failures, documentation errors, fraud and diverted collections. Both affect repayment.

AI can help prepare scenarios involving delayed payments, falling margins or currency movements. Calculations should use verified financial tools, explicit assumptions and checked source data.

Findings should influence counterparty limits, eligible amounts, financing duration, reserves and monitoring. Unresolved delivery concerns may justify withholding financing. Weak buyer liquidity may warrant reduced exposure despite complete documentation.

Monitoring should continue after funding. Changes in overdue balances, disputes or payment instructions can trigger review, provided someone resolves each alert.

Establish Sound Rules Before Automating with AI

Before deploying AI, review the underwriting process and risk policy. Three foundations matter:

• Clear Decision and Verification Rules. AI cannot fix weak go/no-go criteria, inadequate evidence requirements or inconsistent exception handling. Automation can reproduce these mistakes faster and across more transactions.
• Discipline in Acting on Findings. Detecting red flags or policy breaches offers little protection if decision-makers proceed without resolving concerns. Permitted exceptions need documented justification, appropriate approval and credible mitigating measures.
• Controlled Deployment. Define checks, escalation rules and responsibilities first. Test a bounded task against experienced reviewers before expanding its role.

Technology cannot compensate for a willingness to disregard risk management rules.

Protect Data and Control AI’s Actions

Underwriting files contain sensitive financial, commercial and personal information. Deployment therefore requires safeguards alongside credit expertise:

• Protect Confidentiality and Privacy. Use approved systems and share only necessary data, redacting unnecessary identifiers. Check provider terms covering model training, retention, deletion, subcontractors and storage locations. Confirm that processing and cross-border transfers meet applicable legal and contractual requirements.
• Restrict Access and Authority. Apply access controls and encryption. Give agents only     necessary permissions, requiring human approval for financing commitments, payment-detail changes and external disclosures.
• Treat External Content as Untrusted. Documents can contain malicious instructions designed to redirect an agent. Separate document content from operating instructions and restrict connected tools.
• Verify Outputs and Test Performance. Require source references, check calculations and measure missed risks and false alarms across languages and markets. Retest after model updates and assess whether limited digital records unfairly disadvantage smaller businesses.
• Maintain Accountability and Continuity. Keep secure records of evidence, outputs and approvals. Train reviewers to challenge findings, designate an incident owner and retain a manual fallback.

These precautions address risks highlighted in NIST’s Generative AI Profile, including fabricated outputs, privacy, security and harmful bias. An enterprise subscription alone does not establish that a system is suitable for confidential underwriting work.

Better Evidence, Stronger Judgement

AI can make underwriting more systematic and free specialists to investigate difficult cases. Its contribution should be judged by decision quality and risk outcomes, alongside efficiency.

For businesses seeking working capital through providers such as Incomlend, verifiable trading relationships and disciplined operations remain fundamental. Sustainable financing depends on understanding the trade, structuring exposure appropriately and taking responsibility for the decision.

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