Access to finance remains a persistent barrier for women-owned and informal SMEs across emerging markets. Traditional credit scoring methods often exclude first-time borrowers, informal workers, or entrepreneurs - disproportionately women - due to lack of collateral or formal financial histories. However, a new generation of fintechs is using AI-powered credit scoring models and alternative data - such as mobile money, transaction history, utility bills, and behavioral indicators - to assess creditworthiness more inclusively. This panel builds on a recent IFC scan of the global landscape of alternative credit scoring models coupled with borrower-level data analysis to explore gender-differentiated outcomes to examine how use of AI and non-traditional data can expand credit access and change how financial institutions provide credit for women-owned MSMEs.
The session will explore how these models work in practice, what challenges remain, and what policy, infrastructure, or partnerships can help scale their impact - especially to reach more women entrepreneurs and informal businesses.