A supplier submits an invoice for financing at the end of the business day. Traditionally, the request would move through multiple stages of review. Financial statements would be examined, credit reports retrieved, documents verified, and risk assessments prepared before a financing decision could be made. Today, much of that work can happen automatically. The financing request can be evaluated using transaction data, buyer-supplier relationships, payment history, business intelligence, and supply chain information gathered from multiple sources. By the time the underwriter reviews the application, a significant portion of the analysis has already been completed.
This shift matters more than it might appear. According to UNCTAD’s Trade and Development Report 2025, more than 90% of world trade relies on trade finance. The infrastructure behind that number, the credit lines, guarantees, and payment facilities that allow goods to move across borders and between businesses, depends entirely on the quality of the risk decisions underpinning it. Better underwriting is what keeps global commerce moving, and AI-led underwriting is transforming trade finance by helping make better-informed decisions faster by augmenting human judgment.
Why Underwriting is Being Reimagined
Traditional underwriting was designed for a world where financial statements, banking records, and credit reports were the primary sources of information available to lenders. These remain essential inputs, but they often provide only a periodic view of a business.
Advances with AI in Trade Finance are making it possible to analyze trade and transaction data at scale and incorporate it into financing decisions to complement financial statements and credit records reported by companies. Indicators generated through actual commercial activity can include:
- Transaction frequency and consistency
- Invoice settlement behavior
- Buyer payment track records
- Length and stability of trading relationships
- Supply chain dependencies
- Sector and counterparty risk indicators
Analyzed together, these inputs bring greater context to the underwriting process, supporting a more comprehensive risk assessment, helping financiers evaluate risk with a wider set of reference points.
The industry has recognized this potential. The 2025 ADB’s Global Trade Finance Gap Survey found that nearly 56% of the 110 trade finance providers it surveyed are already applying AI to identify ways to increase trade financing capacity. Further, 75% were leveraging AI to assess and manage risk or otherwise improve efficiency in trade finance operations. Hence, institutions that understand the trade finance gap best see AI-driven risk intelligence as central to narrowing it.
Expanding Access to Finance for Thin-File Businesses
Assessing companies with limited credit histories has been a longstanding challenge in business lending, especially for MSMEs. Despite having strong customer relationships, recurring orders, and a proven operating track record, a business may struggle to meet conventional underwriting requirements if it lacks extensive borrowing records.
AI-powered credit assessment helps bridge this gap by incorporating alternative sources of evidence into the underwriting process. Consideration of transaction history, buyer relationships, repayment behavior, and supply chain activity contributes to a more complete view of creditworthiness and aids financiers in underwriting promising businesses that may otherwise be overlooked while maintaining appropriate risk controls.
How AI-Led Underwriting Works at Vayana
Vayana’s underwriting approach combines transaction-level information with broader risk intelligence to support financing decisions. Every financing request on the platform is linked to an underlying trade transaction, which provides visibility into the commercial activity supporting the financing requirement and enables a more contextual risk assessment.
Through its integration with Rubix Data Sciences, Vayana brings together risk intelligence from more than 120 data sources. These insights complement transaction data and help underwriters evaluate counterparties, supply chain relationships, and business conditions more effectively. By linking financing requests to real-time trade data alongside external risk intelligence, Vayana helps underwriters evaluate the commercial context behind a funding requirement instead of assessing the applicant in isolation.
What Better Underwriting Makes Possible
The benefits of digital underwriting extend well beyond faster turnaround times.
AI-led models can help financiers:
- Accelerate decision-making by reducing the time spent gathering, validating, and analyzing information.
- Improve consistency by ensuring underwriting decisions are supported by a common analytical framework rather than fragmented information sources.
- Set more informed credit limits using a broader understanding of trade activity, counterparties, and supply chain relationships.
- Expand financing access for businesses whose commercial performance may be stronger than their formal credit history suggests.
- Strengthen risk-adjusted decision-making by combining traditional financial analysis with real-time trade data and evidence drawn from actual trading activity across multiple sources, helping identify and manage credit risk more effectively.
Together, these capabilities strengthen B2B trade risk management while enabling more responsive and scalable trade finance solutions.
Also Read : Securing B2B Trade – The Imperative of Verification and Credit Risk Assessment
The Future of Trade Risk Management
Digitalization of modern supply chains is generating vast amounts of data and has effectively broken that glass ceiling. However, it has resulted in a new challenge of determining which information matters most.
This is perhaps where AI has brought in the most value by bringing structure to that complexity. It allows underwriting teams to evaluate businesses through a wider lens that considers both financial strength and evidence of how companies actually trade.
At Vayana, the goal is not to replace underwriting judgment but to equip it with better evidence. When financing decisions are informed by both financial records and real-world trading activity, underwriters gain a clearer basis for assessing risk, extending credit, and supporting business growth.
