AI Credit Scoring for Informal Economy Workers

70% of Africans are unbanked. Traditional credit scoring fails for informal workers who lack formal financial history.

Evidence

Mobile money, airtime, merchant, and device-payment histories create usable risk signals for customers excluded from formal credit files.

First customer

Asset-financing lenders, microfinance institutions, and SME payment providers serving informal merchants.

Market signal

$150B credit gap

Timeline

18-24 months

Difficulty

Advanced

Build path

1. Choose one lending product with clear repayment data.
2. Secure consented mobile-money or merchant transaction data.
3. Run a challenger score beside human underwriting before automating decisions.

Risks

Alternative data can encode bias or become predatory without strong affordability checks.
Payment-provider partnerships can become the real bottleneck.

Next action

Map three lenders with default data and one mobile-money partner with consented transaction access.

Source: M-KOPA public operating metrics; World Bank financial inclusion contextOpen source

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Opportunity teardowns are editorial analysis, not investment, financial, or business advice. Market sizes and projections are estimates — do your own diligence. Read the full disclaimer.