SME Lending

Even before the impact of the global pandemic, financial institutions and lenders were being asked to help bridge an estimated US$3.4 trillion SME funding gap in unmet trade finance, with demand for financing of small businesses rapidly becoming an acute challenge.

It’s a mind-boggling number largely driven by demand for unfilled or rejected loan, mortgage, and trade finance applications tabled by small businesses and SMEs in emerging markets. The UN in particular was aware of the challenge. Prior to the pandemic, SME lending was estimated to equate to up to 10% of global merchandise trade.

But now that figure is likely to be far higher. As a result, banks and lenders are acutely reliant on access to the very best data, automation, and technology to perform proper credit risk analysis and help bridge the gap for SMEs, sole traders, and small businesses.

Among the most promising routes is transactional analytics for SME lending.

Loan origination rejection rates have always been higher among micro-businesses, and small and medium-sized enterprise borrowers, due to the increased costs of serving customers, high-risk financial profiles, and a lack of traditional data to enable accurate loan assessment. As a result, there’s a potential to over-compensate when it comes to credit risk management.

But with access to the broadest datasets available, investment in the right technology, and application of AI and automation, SME lending can be a win-win for financial institutions and small businesses.