Exposure to Climate Risks and Insufficient Responsive Insurance

Problem Statement

Increasing climate variability (unpredictable rainfall, floods, droughts, extreme heat) exposes farmers to severe crop losses. Existing crop insurance and disaster relief mechanisms are often slow, generic, and not responsive to hyperlocal climate events, resulting in low claim-to-premium ratios and insufficient financial safety nets.

Innovation Opportunities

• Hyperlocal Climate Risk Prediction and Advisory Systems: Advanced AI/ML models combining global weather data with local micro-climate sensor data to provide highly accurate, plot-level forecasts for farmers (e.g., hail alerts, precise rainfall windows).
• Remote Sensing-Based Automated Crop Loss Assessment: Utilising high-resolution satellite imagery, drones, and AI for automated, objective, and timely assessment of crop damage post-climate event for faster claim processing.
• Simplified, Data-Driven Parametric Insurance Products: Development of weather-index or satellite-index based insurance products where payouts are triggered automatically upon crossing a predefined threshold (e.g., rainfall deviation, temperature extremes), eliminating the need for manual loss assessment.

Use Case Example

A chili farmer enrolls in a parametric insurance scheme. If the local rainfall recorded by a geo-tagged weather station falls below a threshold for 10 consecutive days during the flowering stage, the farmer automatically receives a pre-agreed payout.