AI-driven algorithms can be leveraged to analyse vast datasets in real-time , identifying unusual patterns and anomalies , thus mitigating the risk of fraudulent activities and bolstering the overall security of international financial transactions . AIpowered personalisation can also facilitate more tailored experiences to individual consumers based on their scope of interests , behaviour and purchase history .

In terms of critical decision making , when it comes to facilitating cross-border transactions for SMEs , traditional trade finance institutions and banks simply don ’ t have the bandwidth to analyse companies at a granular level , leaving SMEs at a significant disadvantage . This is one of the reasons why SMEs are seven times more likely to be denied trade financing than multinational companies , and a major contributor to the widening trade finance deficit , which currently stands at US $ 2.5 trillion .

AI-powered data analytics can also be utilised to verify the creditworthiness of SMEs by quickly analysing troves of data such as financial statements and transaction history , while Machine Learning algorithms can analyse historical demand patterns and market trends to forecast future demand for products . The compound effect can facilitate more accurate , streamlined and efficient credit evaluations , enabling scalable and timely decision-making for lending institutions .

So , as we can see , Big Data analytics can enable much more accurate credit assessments of SMEs , meaning that newer and smaller SMEs will have a better chance of securing the financing they need to survive , and participate in the hyper-competitive international trade arena . By quickly analysing troves of different data points , including transaction history , online presence and industry-specific metrics , FinTech platforms can offer more tailored financing solutions . This data-driven approach enhances the chances of approval for SMEs , even those without extensive credit histories .

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