Alternative Credit Scoring
Scores thin-file customers from behavioral cashflow and app-usage data, delivering real-time credit decisions.
Solution Overview
The Challenge
Across MENA and Africa a large share of the addressable market has no bureau file, so traditional scorecards return no decision at all and the applicant is declined by default.
- 1Thin-file and unbanked customers cannot be scored by traditional bureau models
- 2Large creditworthy segments declined by default rather than by assessment
- 3Lenders competing for the same narrow band of bureau-scored customers
Our Approach
CloudGate builds alternative scoring models on Azure Machine Learning using cashflow patterns, mobile money and wallet activity, app usage behavior, device and telco signals and repayment history where available.
- Behavioral scoring from cashflow, wallet, app usage and device signals
- Sub-200ms scoring API embedded directly in the application journey
- Probability of default plus recommended limit and pricing per applicant
Typical Outcomes
Typical outcomes for this solution pattern, not the results of a named client engagement.
Technologies Used
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