Live Risk Map — India
Real-time parametric risk scoring · NASA MODIS, IMD, Sentinel-2 & ICAR
Known Limitation — Basis Risk & Moral Hazard
Parametric insurance carries inherent basis risk: a trigger may fire when a specific farmer suffered no loss (false positive), or fail to fire when they did (false negative), because index measurements are district-level proxies rather than plot-level observations.
AgroShield mitigates this through a 4-oracle quorum — a payout requires corroborating signals from NASA MODIS (NDVI), IMD weather stations, Sentinel-2 imagery, and ICAR soil sensors simultaneously. Requiring consensus across four independent data sources eliminates single-sensor noise and reduces false triggers by design.
Risk scoring is computed at sub-district granularity(tehsil level where data permits), and trigger thresholds are calibrated per district using historical IMD normals — so a Barmer drought boundary is not the same as a Puri cyclone boundary. Residual basis risk is disclosed in every policy certificate as a known, priced parameter, consistent with IRDAI's 2023 parametric insurance guidelines.
| District | State | Risk Score | Level | Type | Farmers |
|---|---|---|---|---|---|
| Barmer | Rajasthan | 91 | Critical | Drought | 24,300 |
| Latur | Maharashtra | 88 | Critical | Heatwave | 16,700 |
| Warangal | Telangana | 82 | Critical | Drought | 18,400 |
| Puri | Odisha | 79 | Critical | Cyclone | 14,600 |
| Nashik | Maharashtra | 71 | Critical | Drought | 22,100 |
| Khammam | Telangana | 67 | High | Flood | 12,200 |
| Jodhpur | Rajasthan | 62 | High | Heatwave | 19,500 |
| Adilabad | Telangana | 55 | High | Cyclone | 9,800 |
| Bhubaneswar | Odisha | 51 | High | Flood | 10,300 |
| Surat | Gujarat | 44 | Medium | Flood | 17,800 |
| Ludhiana | Punjab | 38 | Medium | Flood | 11,200 |
| Amritsar | Punjab | 28 | Low | Drought | 8,900 |