In today’s fast-moving and increasingly uncertain trade environment, access to financing remains one of the most pressing challenges for SMEs, especially in the wholesale sector. Despite their critical role in global supply chains, driving economic growth and constituting 43% of global trade – SMEs often encounter difficulties when seeking financing from traditional lenders, who apply rigid eligibility criteria to funding applications. Given the current backdrop of macroeconomic and geopolitical flux, SMEs are crying out for higher levels of clarity regarding capital availability, really just to plan ahead with a sense of confidence. 

The WTO estimates that SMEs are seven times more likely to be denied trade financing than multinationals, a mammoth disparity that is largely due to outdated, manual underwriting processes that fail to accurately assess the creditworthiness of smaller, less established businesses. As we explored in a previous blog post, traditional underwriting relies heavily on static financial statements, manual checks and strict scoring models, which often overlook dynamic business factors and the nuances of SME operations. As a result, many viable businesses are unfairly categorized as high-risk, reducing their access to critical trade financing and stifling growth.

The Impact of Automated Underwriting 

Innovative deployments of AI can be used to rapidly sift through vast datasets, while sophisticated algorithms can deliver a more comprehensive and nuanced view of a company’s financial health. This is great news for SMEs, who can use this to provide a more accurate risk profile to prospective lenders. Machine learning models can also analyze real-time and historical data, including cash flow trends, transaction histories, supply chain relationships, market conditions, and even behavioral patterns. This enables lenders to make more informed, data-driven decisions about credit risk, significantly increasing the fairness and efficiency of the underwriting process.

For wholesalers, this is particularly important, as they are operating in a sector that is defined by tight margins, high volume transactions and complex supplier-buyer relationships. As delays in securing financing can disrupt operations and strain relationships across the supply chain, AI-driven credit assessments can dramatically reduce approval times, enabling wholesalers to access funds when they need them the most. Unlike static credit scores, AI systems continuously learn and adjust as new data becomes available, offering a dynamic risk profile that better reflects an SME’s current position. Many SMEs, by their upstart nature, can’t point to traditional credit histories, especially in emerging markets. Thankfully, AI can fill these gaps by tapping into alternative data sources, such as utility payments and e-commerce activity to build a more complete picture of their creditworthiness.

Market Examples of AI-led Credit Assessments

With these clear value adds in mind, it’s no wonder that AI is already being put to work by industry heavyweights to streamline credit assessments. HSBC, in partnership with IBM, has implemented AI to automate the review of trade finance documents, significantly reducing processing times and improving risk evaluation. Additionally, Standard Chartered has adopted AI-driven models to enhance SME credit assessments by incorporating alternative data sources like supply chain behavior and transactional history.

At 40Seas, we ourselves are leveraging AI and data-driven technology to offer flexible payment options that effectively increase the availability of working capital for SME importers, exporters, freight forwarders and sourcing agencies – delivering a much more efficient, simplified, and cost-effective financing framework for SMEs. Suppliers can further enhance their credit decision making using our recently launched Trade Insurance Management platform, which supports KYB (Know Your Business) checks and risk underwriting, enabling them to make informed decisions on who to extend credit to, and under what conditions. Credit limits can be set for each customer, and when invoices are issued, customers clearly see their available credit. If they've exceeded their limit, they’re required to settle outstanding invoices upfront, helping reduce the risk of delayed payments and maintaining healthier cash flow.

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