Quick answer: The global trade finance gap, the difference between requests for trade financing and approvals, reached a record $2.5 trillion in 2022, and small and medium enterprises are hit hardest. Tradeteq’s argument is that traditional credit scoring rejects too many creditworthy SMEs because it leans on thin, annually-filed accounting data, and that machine learning across richer datasets can score them more accurately. Digitizing trade records is the enabling step.
Updated 07/17/2026. By Jake Claver. Educational content, not investment advice.
This is a concrete, well-documented corner of finance rather than a speculative one. The figures come from the Asian Development Bank, the credit-scoring method from Tradeteq’s own research, and the market-infrastructure context from the WTO and BIS. Below is what each source actually says.
How big the trade finance gap is
According to the ADB 2023 Trade Finance Gaps, Growth and Jobs Survey, the global trade finance gap grew to $2.5 trillion in 2022, up from $1.7 trillion two years earlier, as higher interest rates, weaker growth, and geopolitical volatility reduced banks’ capacity to extend trade financing. The survey draws on responses from 137 banks across 54 countries and reflects more than 60% of the global market for bank-intermediated trade finance. As the ADB reports, the shortfall is especially acute for SMEs, women-led businesses, and firms in fragile or remote markets.
Why SMEs get rejected
The gap is not only about bank capacity; it is also about assessment. Compliance and due-diligence requirements, including Know Your Customer checks, are repeatedly cited as obstacles to financing smaller firms. On top of that, the credit models themselves are a poor fit. In its research on machine learning for SME credit scoring, Tradeteq notes that linear models such as the Altman Z-score rely on limited accounting data filed once a year, so a company missing even one input can be rejected outright.
What Tradeteq’s approach actually is
Tradeteq’s stated mission, in its own words, is direct. From the company’s machine-learning credit analytics whitepaper:
Tradeteq’s mission is to expand access to trade finance for SMEs by making trade finance exposures investable. This requires better transparency of risks. The traditional approach to trade finance would require a lot of antiquated and labour-intensive bank processes to assess SME credit.
The method, per the whitepaper by Tradeteq’s head of AI, is to combine machine learning with more varied data: accommodating differing data availability across companies, and drawing on geographical and trade-network signals such as common clients, suppliers, and bank relationships. Tradeteq reports a neural-network model that can outperform the traditional Altman Z-score even on pure company-registration data, without full accounting inputs. That is a claim from the vendor’s own research, worth reading as a documented method rather than an independent audit.
Why digital trade records are the enabler
Better scoring depends on better data, and trade finance still runs heavily on paper. The WTO’s work on trade digitalization and financing for MSMEs and the ADB’s briefs on driving digitalization in global trade both point to the same bottleneck: moving trade documents onto interoperable digital rails is what makes richer, machine-readable credit data possible in the first place. Without digitized records, an AI model has little structured data to learn from.
The market-infrastructure angle
This connects to the broader institutional shift toward digital and tokenized market infrastructure. The Bank for International Settlements, in its work on tokenisation and the future monetary system, describes how trusted records and interoperable workflows underpin next-generation settlement. Trade finance, which needs exactly those trusted records, gives distributed-ledger systems a practical role beyond speculation. One honest caveat: do not read this as a specific XDC Network implementation of Tradeteq’s scoring. The infrastructure themes overlap, but a direct integration should not be assumed unless separately sourced.
Why this matters
Trade finance is the plumbing behind physical goods crossing borders, and a $2.5 trillion shortfall means real businesses, disproportionately small ones, cannot get the financing to ship or import. More accurate credit assessment and digitized records are practical levers on that problem. That is a technology-and-infrastructure story, separate from any claim about the price or investment merit of a token or company, and this article makes no such claim.
Common questions
What is the trade finance gap and how big is it?
The trade finance gap is the difference between requests for trade financing and approvals. According to the ADB 2023 Trade Finance Gaps, Growth and Jobs Survey, it reached a record $2.5 trillion in 2022, up from $1.7 trillion two years earlier, and it hits SMEs, women-led businesses, and firms in fragile markets hardest.
What does Tradeteq do?
Tradeteq runs a trade-asset platform and develops credit analytics aimed at making SME trade-finance exposures investable. Its stated mission is to expand SME access to trade finance by improving transparency of risk, replacing antiquated, labour-intensive bank processes for assessing SME credit.
How does machine learning improve SME credit scoring?
Traditional models like the Altman Z-score rely on limited, annually-filed accounting data and can reject firms missing a single input. Tradeteq’s research describes machine-learning models that use more varied data, including geographical and trade-network signals, and reports outperforming the Altman Z-score even on pure registration data. This is the vendor’s own documented method.
Why does digitizing trade documents matter?
Trade finance still relies heavily on paper. WTO and ADB research identify moving trade documents onto interoperable digital rails as the step that makes richer, machine-readable credit data possible. Without digitized records, AI models have little structured data to learn from.
Is XDC Network directly involved in Tradeteq’s credit scoring?
Not based on these sources. The broader themes of tokenized market infrastructure and digital trade records overlap, but a direct XDC implementation of Tradeteq’s scoring should not be assumed unless it is separately sourced.
This content is educational only. It is not tax, legal, or investment advice. Check primary sources and speak with a qualified professional before making financial decisions.
