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 6–8 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’s financial industry recalibrate models to avoid algorithmic bias?
Key points:
- Which forms of alternative data genuinely expand women’s access to finance, and which risk reinforcing existing biases?
- How can regulators and investors set standards for bias audits, transparency, and appeals processes for contested lending scores?
- Where should human judgement remain part of the lending process?