We isolated the exact moment climate volatility triggers agricultural loan defaults. 14 years of satellite data. One million synthetic records. One undeniable signal.
Why do some agricultural loans fail while others succeed under identical financial conditions? We spent months digging through data before the pattern emerged — it wasn't the borrower's credit history. It was what the weather did in the 90 days after they received the loan.
FinFix Labs was born from that discovery. We're a Nairobi-based team combining climate science, financial modeling, and machine learning to solve a problem that costs African agriculture billions annually: unpredictable climate risk in lending.
We don't just analyze weather data. We bridge the temporal gap between climate events and credit outcomes — and we've built the datasets to prove it works.
We focus on validated signals, not big data for its own sake.
Synthetic data that preserves patterns without exposing individuals.
Built for East African agriculture, by people who understand it.
Our approach is documented. We show our work.
Three types of climate shocks drive agricultural loan defaults. We quantified each one.
Heavy rainfall (>10mm/day) shows strongest correlation with defaults.
Physical crop damage creates immediate cash flow gaps.
Temperature drops below 10°C damage tea leaf quality.
Shock-exposed loans are 63% more likely to default.
Critical post-disbursement period for shock exposure.
Of historical defaults had climate exposure at origination.
We connect NASA satellite weather data to loan performance through precise temporal windows.
14 years of daily weather from NASA POWER API. Temperature, precipitation, and derived stress indicators across 25 regions.
For each loan, we scan the 90-day post-disbursement window and count rain, hail, and cold shock events.
Rain >10mm, cold <10°C, hail proxy (>15mm + cold). Each threshold validated against agronomic research.
Gaussian Copula preserves the climate-credit signal while ensuring complete privacy protection.
Rain, hail, or cold stress here → Harvest disruption → Cash flow gap → Default
Comprehensive climate-credit data across Kenya's major agricultural regions.
Each with 14 years of daily satellite weather data
Deep dives into climate risk, agricultural finance, and the data behind our methodology.
The technical journey from raw NASA data to privacy-preserving synthetic twins with 98.99% correlation fidelity.
Analysis of why post-disbursement climate exposure is more predictive than total loan-period weather.
A landscape view of how climate volatility is reshaping agricultural lending across the region.
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