Extreme weather - Increase competitiveness

ClimatePulse can enable vulnerable communities to collect aid 4 days before peak flooding, providing critical support ahead of disaster impact.

In many rural economies, the financial health of individuals and businesses is closely tied to local weather patterns. ClimatePulse allows financial institutions such as banks, microfinance institutions (MFIs), and insurtechs to integrate weather data into their credit scoring models. This integration helps predict the likelihood of loan repayment by considering the impacts of climate events like droughts or floods on a borrower’s income. For example, if a flood is forecasted in a borrower’s region, this could affect their income from agriculture, making loan repayment less likely.
By adjusting credit models with this additional climate data, banks can offer better-suited loan terms and adjust repayment schedules based on expected weather patterns. It also improves portfolio resilience, as banks can better assess the risks associated with loans in climate-vulnerable areas. One program in Bangladesh saw cash delivered 4 days before peak flooding, compared to 100-day delays in traditional aid, allowing families to act in advance and reduce hardship.
Example: ClimatePulse can provide seasonal flood risk forecasts to microfinance institutions (MFIs), enabling them to adjust loan terms for vulnerable borrowers. This may help improve repayment rates, reduce defaults, and strengthen financial resilience in high-risk regions.

Use Cases:

Climate-Adjusted Credit Scoring: Banks can offer more accurate loan terms by incorporating climate data that affects borrower income. In a Kenyan pilot, weather insurance payouts compensated over 80% of the value of farmer inputs during a drought, showing how fast, automated models improve resilience.

Portfolio Heatmapping: This visual tool identifies high-risk areas based on exposure to extreme weather conditions, helping institutions better diversify their portfolios. By 2018, 1.7 million farmers were insured through mobile platforms like M-Pesa, covering ~$180 million in crops and livestock.

Early Warning for Borrower Disruption: By tracking weather forecasts, institutions can anticipate disruptions to borrower income and adjust payment schedules. In one case, 36% of families receiving forecast-based cash were less likely to go a day without food, while 17% more households evacuated livestock early, reducing long-term financial loss.