Parametric Insurance: When Satellite Data Replaces the Claims Adjuster

#africa #insurance #python #mcp

Traditional crop insurance requires a claims adjuster to visit a farm, document damage, and approve a payout. In Kenya, where farms average 1.5 hectares and are scattered across 47 counties, this is economically impossible to do at scale.

The result: 2.3% insurance penetration. One bad drought = financial ruin.

Parametric insurance solves this by replacing the claims adjuster with satellite data.

How Parametric Insurance Works

Instead of insuring against "crop loss" (which requires verification), parametric insurance insures against a trigger event — a measurable proxy for loss:

Rainfall below 60mm in a 30-day growing period → automatic payout
NDVI (normalized difference vegetation index) below 0.3 in July → payout
Temperature above 38°C for 5+ consecutive days → payout

No claims process. No adjuster. If the satellite says the trigger fired, the payout happens automatically — within 72 hours, direct to M-PESA.

The Research Basis

ACRE Africa (2023): ACRE (Agriculture and Climate Risk Enterprise) has facilitated over $200M in parametric insurance across 12 African countries. Their methodology uses NDMA (National Drought Management Authority) data as the primary trigger source for Kenya.

NDMA Kenya: The NDMA publishes monthly county drought monitoring reports with rainfall deficit data, NDVI scores, and livestock condition indices. This is the data layer that makes parametric insurance possible.

IRI (International Research Institute for Climate and Society): El Niño/La Niña forecasting with 3-month lead time has been validated as a viable basis for planting-season insurance triggers in East Africa.

bima-mcp Implementation

from bima_mcp import parametric_risk_score

result = parametric_risk_score(
    county="Nakuru",
    crop="maize",
    season="long_rains",
    coverage_kes=50000
)
# → risk_tier: "MEDIUM",
# → trigger_threshold: "rainfall < 65mm in March-May growing period",
# → recommended_premium_kes: 3750,
# → payout_probability: 0.28,
# → data_source: "DEMO — modeled on NDMA county reports"

The Community Pool Tool

One of the more interesting tools is community_pool_calculator, which models informal risk-sharing:

from bima_mcp import community_pool_calculator

result = community_pool_calculator(
    group_size=25,
    avg_farm_value_kes=120000,
    target_coverage_pct=0.6,
    drought_probability=0.25
)
# → pool_size_needed_kes: 450000,
# → monthly_contribution_kes: 1500,
# → confidence_interval: "90% probability of full coverage for any single member in any given year"

Chama groups (informal savings societies) are already the primary financial institution for millions of Kenyan women. Parametric pooling maps directly to existing behavior.