Maureen Sullivan: One certitude in supply chain finance [SCF] is an ever-evolving landscape, and banks are always looking for ways to help clients manage the financing of their supply chains, whether it be through continued product development or innovation.

One trend is looking to embed supply chain financing earlier in the cycle—while the product is in transit, or even during the preproduction phase. For example, inventory finance has been a hot topic lately. One of the lessons companies learned during the pandemic was the need to have the right product in the right place, but that meant deploying multiple sourcing venues or having buffer stock. Words like “nearsourcing” and “nearshoring” entered the mainstream vernacular. Companies now facing an impact of inventory buildup on their balance sheet are looking for relief through inventory financing techniques.

Another example might be purchase order financing, but for a product like that to be really scalable, predictability is key. And this is where I think artificial intelligence and machine learning could add value in its ability to analyze vast amounts of data and make more accurate risk assessments. Regulated differently than fintechs, banks need transparency, performance and predictability. So, if a buyer works with five suppliers, for example, and AI could predict that 99.999% of the time whenever this company places a purchase order, it is paid, then perhaps banks might be open to providing a solution.