Smallholder farmers are often excluded from formal credit not because they are poor risks, but because lenders lack a low-cost way to verify farm performance. Conventional credit scoring relies on demographic proxies – age, years farming, location – that only weakly capture a farmer's actual productive capacity. This information gap forces lenders to ration credit conservatively, excluding many creditworthy farmers along with genuinely risky ones.

GeoCredit closes that gap by integrating satellite-derived vegetation and rainfall indicators into standard credit-scoring models, giving lenders an objective, tamper-proof record of a farmer's productive capacity – without requiring a field visit. Vegetation health metrics derived from freely available satellite imagery serve as a direct signal of a farm's agronomic condition: how well the crop is growing, how it withstood dry spells during the season, how consistent performance has been over time. These indicators are combined with a borrower's financial and demographic data using standard, transparent credit-scoring methods, producing a score that is directly interpretable by loan officers without specialized technical training.

Results from the Kenya pilot

First piloted in Kenya in partnership with MkulimaScore360, a Kenyan credit intelligence platform, GeoCredit demonstrated strong results:

• Improved discrimination – reaching an AUC of 0.846, into the "excellent" range by industry standards, and consistently outperforming a financial-data-only model across the full range of lending thresholds.
• Greater model stability – cutting cross-validation variability by 36%, making the model substantially more reliable across diverse farming conditions.
• A clean, actionable risk gradient – a monotonic default-risk gradient across credit-score bands, giving lenders a directly usable threshold for lending decisions.
• The "rescued borrower" effect – creditworthy farmers previously under-scored by demographic data alone – often younger farmers penalized by age-based scoring – see the largest gains in assessed creditworthiness when satellite evidence of their farm performance is factored in. Vegetation health indicators, in fact, ranked among the strongest predictors of loan performance identified in the pilot – outperforming loan amount, crop type, and geographic location.

Scaling up

Building on these results, GeoCredit is now scaling up to a larger number of farmers in Kenya, while extending to additional countries across Africa and Latin America and the Caribbean. The approach is designed to connect to national farmer registries, so that satellite-based credit scoring can be applied cost-effectively at national scale – turning a one-off pilot into lasting, low-cost lending infrastructure.