For more than a decade, the global trade finance gap has been treated almost like a fixed cost of doing international business — a persistent shortfall that everyone acknowledges and few expect to meaningfully shrink. The Asian Development Bank’s latest Global Trade Finance Gap Survey puts the figure at $2.5 trillion, unchanged from 2023, and warns that ongoing U.S. tariff volatility will only push demand for trade finance higher as companies diversify suppliers and reroute supply chains (East & Partners).

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But buried inside that survey is a smaller, more interesting number. The share of SME trade finance applications rejected by banks fell to 41%, down from 45% in 2023 — and for the first time, that figure is closing in on the rejection rate for large corporates, which sits at roughly 40% (Global Trade Review). That is a meaningful shift. As recently as last year, the World Trade Organization estimated that banks rejected around half of all SME trade finance applications, compared to a 7% rejection rate for multinational corporations (Global Trade Review). The gap between how small and large exporters are treated by lenders hasn’t closed — but it is narrowing, and the reason is increasingly the underwriting technology banks and fintechs are putting behind their decisions.

Why Traditional Underwriting Has Always Failed Smaller Exporters

The structural problem with SME trade finance has never really been that small exporters are bad credit risks. It’s that the traditional underwriting model wasn’t built to see them clearly. ADB’s own trade finance team has been blunt about this: too many SME applications are still considered unbankable, not because the businesses are fundamentally weak, but because traditional credit models struggle to properly assess their risk in the first place (Global Trade Review).

That’s a structural critique, not a risk assessment. A conventional bank underwriting process leans heavily on financial statement history, formal credit bureau records, and collateral — all of which tend to be thin or simply unavailable for a five-year-old exporter in Lagos or Ho Chi Minh City, even one with strong, consistent revenue. The business gets rejected not because the data says no, but because the bank never had the right data to ask the question properly.

What’s Actually Changed: Cash Flow Data Replaces the Credit File

The shift now underway in SME lending — and increasingly in trade finance specifically — is the move from static credit-bureau scoring toward real-time cash flow underwriting. Instead of asking “what does this company’s credit history look like,” AI-driven models ask “what does this company’s actual money movement look like, right now.” Bank transaction data — incoming payments, payment timing, recurring obligations, seasonal patterns — has emerged as the highest-signal category for predicting repayment behavior in alternative lending, ahead of almost any other data source available (BuildMVPFast).

This isn’t a marginal improvement. Research from FinRegLab published in 2025 found that machine learning models combining cash flow data with traditional credit bureau data delivered the strongest predictive performance of any model type tested — outperforming bureau data alone (BuildMVPFast). For an exporter who has been operating successfully but has limited formal credit history, that combination is the difference between an automatic rejection and an approved facility.

The speed difference is just as significant as the accuracy difference. Mercado Libre’s introduction of AI-powered credit lines for its marketplace sellers cut approval times from roughly a week down to two days (Acropolium). For an exporter waiting on financing to fulfill a confirmed order, that compression from days to hours can be the difference between fulfilling a contract and losing it.

Where This Is Already Operating in Trade Finance Specifically

This isn’t a theoretical trend — it’s already built into platforms exporters can access today, particularly in markets where SME trade finance gaps have historically been worst.

India’s Trade Receivables Discounting System (TReDS) is the clearest large-scale example. It’s a Reserve Bank of India–regulated electronic marketplace purpose-built for MSME invoice discounting, and it has scaled dramatically: the total TReDS market is projected to exceed ₹2 lakh crore in FY2024–25, with platforms like M1xchange leading by network size and KredX and others operating alongside it (Decentro). For Indian MSME exporters specifically, platforms like Drip Capital and CredAble have positioned themselves around exactly this gap — financing built around receivables and trade flow rather than a traditional balance sheet review (Decentro).

On the enterprise infrastructure side, the SemFi joint venture between Tradeshift and HSBC, launched in October 2024, embeds HSBC’s balance sheet directly into Tradeshift’s global B2B trade network, allowing invoice financing and dynamic discounting to be offered within the procurement workflow itself rather than as a separate loan application process (Supply Chain Digital). That embedded model matters for smaller suppliers in particular — it removes the friction of a standalone bank application and replaces it with financing that’s simply available at the point where the trade transaction is already happening.

The investment numbers reflect how seriously the market is taking this shift: spending on trade finance technology platforms reached $6.3 billion in 2024, up 17% year-on-year (HES FinTech). That capital is flowing specifically into the financing layer — invoice financing, supply chain lending, working capital facilities — where AI-assisted scoring and automated pre-approval workflows are now standard features rather than experimental add-ons (HES FinTech).

A Necessary Caveat

None of this means the trade finance gap is solved, and it’s worth being precise about what the data does and doesn’t show. ADB itself cautions that its 2025 survey data was collected before the most recent wave of U.S. tariff escalation, and that demand for trade finance is likely to rise faster than supply as companies scramble to diversify supply chains in response (East & Partners). The ADB has also noted that the narrowing SME-versus-corporate rejection gap may partly reflect cyclical factors — falling commodity and energy prices since 2023 reducing working capital needs — rather than underwriting technology alone, and has said this needs further study before anyone declares victory (Reuters, via AOL).

It’s also true that a 41% rejection rate, even improved from 45%, is still a high bar for any exporter trying to fulfill a contract on a deadline. As one analysis bluntly put it: for an SME, a single rejected financing request can mean a missed contract or a cashflow crisis that takes months to recover from (Highways Today).

What This Means for Exporters Right Now

The practical takeaway for smaller exporters isn’t that financing has become easy — it’s that the criteria for qualifying have genuinely shifted, and it’s worth re-testing assumptions that may be a few years out of date. An exporter who was rejected by a traditional bank facility eighteen months ago, purely on the basis of a thin credit file, may now qualify through a cash-flow-based platform that’s looking at actual transaction history rather than bureau records.

ADB’s own head of trade and supply chain finance, Steven Beck, has pointed to fintech platforms that emerged out of an earlier funding boom as a meaningful part of what’s helping fill the gap — though he’s also said the industry needs deeper study into exactly how much impact they’re having (Reuters, via AOL). For exporters, that ambiguity is less important than the practical reality: a wider set of financing options now exists than existed even two years ago, evaluated through underwriting models that are, for the first time, built to actually see businesses like theirs.

The trade finance gap isn’t closing because banks have suddenly become more generous. It’s narrowing because the tools for measuring creditworthiness finally caught up to how smaller exporters actually operate.