{"id":463994,"date":"2026-09-03T14:00:49","date_gmt":"2026-09-03T13:00:49","guid":{"rendered":"https:\/\/www.afis.africa\/sb_session\/women-in-finance-panel-will-alternative-data-break-or-prolong-gender-bias-in-lending\/"},"modified":"2026-09-16T15:00:50","modified_gmt":"2026-09-16T14:00:50","slug":"women-in-finance-panel-will-alternative-data-break-or-prolong-gender-bias-in-lending","status":"publish","type":"sb_session","link":"https:\/\/www.afis.africa\/en\/sb_session\/women-in-finance-panel-will-alternative-data-break-or-prolong-gender-bias-in-lending\/","title":{"rendered":"Women In Finance Panel | Will alternative data break or prolong gender bias in lending?"},"content":{"rendered":"

Africa’s women-led SMEs face a $42bn annual financing gap that alternative credit scoring, increasingly adopted by commercial banks and fintechs, could help close. Yet algorithms trained on historical data risk inheriting decades of gender bias. Research suggests women assessed with alternative data receive credit scores 6\u20138 points lower than men despite equal or lower default rates. Left unchecked, these models could perpetuate exclusion behind a veneer of technological objectivity. Some fintechs are building safeguards: Tanzania’s Tausi Africa removes gender-linked metadata from its models, while India’s Lendingkart uses audited, bias-tested scoring. How can Africa\u2019s financial industry recalibrate models to avoid algorithmic bias?<\/p>\n

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