Why Accurate Loan Models Can Still Be Unfair in Nigeria

An AI lending model can be 95% accurate but still systematically exclude millions of Nigerians due to biases in the training data, which often lacks information on the informal economy.

Details

The article explains how an AI lending model can be highly accurate overall, but still systematically exclude millions of Nigerians due to biases in the training data. Most AI lending systems are trained on historical financial data like transaction history, location, spending patterns, and digital activity. On paper, this seems like a reasonable approach. However, the model doesn't see the informal economy - skilled workers paid in cash, traders with no formal credit trail, and business owners operating outside digital systems. To the model, this is 'high risk', but in reality it's just missing data. When scaled across millions of people, this leads to a systematic exclusion baked into the data itself, rather than obvious discrimination. Location can also quietly influence outcomes, with someone in Lagos Island scored differently than someone on the mainland, not because of creditworthiness but due to patterns the model has learned. So while the model may be 95% accurate overall, it can consistently fail specific groups. The real issue is not accuracy, but representation - if AI systems don't account for data gaps and informal economies, they will reflect and scale existing inequalities.