Leveraging AI to Plug the Expanding $2.5 Trillion Trade Finance Gap

According to the OECD, the legacy banking system denies over 45% of trade finance requests by SMEs each year, with a typical application comprising up to 100 documents. The limited access to financing for SMEs has contributed to the widening trade finance deficit, now standing at $2.5 trillion, up substantially from $1.7 trillion in 2020.

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This deficit is especially concerning for smaller businesses in emerging economies that need sufficient levels of trade financing to compete in highly competitive markets. The risk averse tendencies of legacy institutions mean that SMEs often struggle to secure funding due to their size and lack of collateral. SMEs are also finding it difficult to shake off their high risk image, which undermines their creditworthiness in the eyes of financial institutions. Rising interest rates, inflation, negative economic forecasts, and geopolitical uncertainty further deter banks from providing essential trade financing to the perceived ‘higher risk’ SME clients.

Even during economic upswings, legacy trade financing processes remain slow and bureaucratic, forcing SMEs through endless red tape and delaying access to critical funding.  Lacking established credit histories, SMEs are viewed less favourably by traditional banks, which demand extensive documentation, collateral, and a proven track record before extending credit to SME clients. Consequently, SMEs are often denied essential financing or face delays that hinder their global competitiveness. A recent report on Bangladesh SMEs showed high rejection rates for trade finance due to lack of collateral (36%), high interest rates (18%), insufficient transaction information (17%), high risk (11%), and inadequate documentation (10%). Sadly for SMEs, many of these factors are outside of their control when they are starting out.

For SMEs navigating today’s unstable and fragmented supply chain environment, having rapid and reliable access to financing is crucial for survival and growth. Unfortunately, outdated payment technology and legacy trade financing options are complicating life for SME importers and exporters, exacerbating global supply chain fragmentation. Despite representing 43% of global cross-border trade volume, SMEs are seven times more likely to be denied trade financing than multinational companies, according to the WTO. This imbalance is particularly harmful to a business segment that already faces significant challenges to reach the two-year mark.

The limitations of legacy institutions are contributing to the ongoing credit crisis which is disproportionately affecting SMEs. Traditional banks often lack the bandwidth to assess the creditworthiness of SMEs at a granular level due to resource constraints and outdated evaluation methods. They can easily assess risk when it’s one large transaction within a single jurisdiction, but it’s considerably more difficult when lots of smaller amounts of credit need to be processed across borders – as is usually the case with SMEs. They rely on broad criteria such as extensive collateral, detailed credit histories, and comprehensive financial documentation, which many SMEs cannot provide. This one-size-fits-all approach fails to capture the nuanced risk profiles of smaller businesses. Additionally, the high cost and time required for thorough SME assessments make it impractical for banks to allocate sufficient resources. As a result, many creditworthy SMEs are overlooked, hindering their access to necessary financing and growth opportunities.

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How AI Can Accelerate Trade Finance Applications

Against a backdrop of ongoing macroeconomic uncertainties and rising capital costs, AI can offer a lifeline for SMEs overlooked by traditional financial institutions. Unlike the methods deployed by conventional banks, AI-powered risk assessment tools and big data analytics can accurately evaluate SMEs’ creditworthiness, increasing their chances of obtaining financing for international trade pursuits in a timely fashion. By automating repetitive tasks, AI can streamline the application process, minimizing manual errors and accelerating approval times.

AI-powered data analytics swiftly analyze various data points, such as transaction histories, cash flow patterns, and market trends, enabling more sophisticated creditworthiness assessments. This can help deliver tailored financing solutions for newer SMEs, even those lacking extensive credit histories, by leveraging real-time, comprehensive data insights for informed lending decisions. Automated underwriting can also provide financing within hours, allowing SMEs to seize time-sensitive opportunities – a sea change from the slow, cumbersome processes long associated with traditional trade financing.

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Machine Learning models can also be put to work to predict future demand by identifying patterns and trends. This can alleviate some of the baked in apprehension of legacy institutions who may not have the market-specific knowledge required for niche credit evaluations, facilitating quicker and more scalable lending solutions. As a result, AI can enhance access to trade finance for SMEs, empowering them to capitalize on global market opportunities. This new era of AI-led trade financing can provide much-needed clarity on capital availability for SMEs, enabling them to make informed inventory management decisions and plan for the future with confidence.

40Seas is a dedicated platform to finance, manage and automate B2B payments worldwide.

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