A loan model can be 95% accurate and still systematically exclude millions of Nigerians.
That sounds like a contradiction. It isn’t.
Most AI lending systems are trained on historical financial data:
- transaction history
- location
- spending patterns
- digital activity
On paper, this works.
But here’s what the model doesn’t see:
The informal economy.
A skilled carpenter paid in cash.
A trader with no formal credit trail.
A business owner operating outside digital systems.
To the model, that’s “high risk.”
In reality, it’s missing data.
Now scale that across millions of people.
This is how bias actually shows up in AI systems not as obvious discrimination, but as systematic exclusion baked into the data itself.
It gets more subtle.
Location can quietly influence outcomes.
Someone in Lagos Island may be scored differently from someone on the mainland, not because they are more creditworthy, but because of patterns the model has learned.
So yes, the model can be highly accurate overall…
While consistently failing specific groups.
That’s the real issue.
Not accuracy.
Representation.
This is something I’ve seen repeatedly while working on AI governance systems in Nigeria.
If we don’t design systems that account for data gaps and informal economies, AI won’t just reflect inequality, it will scale it.
To better understand how prepared African countries are for these challenges, I built a live tool tracking AI governance readiness across the continent:
👉 https://www.datawrapper.de/_/wB8Iz/?v=5
We don’t just need better models.
We need better governance.
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