InsurTech

Insurance Claim Verification

Name: Satalyse
Author: Satalyse

Verify storm and flood damage claims in minutes, not weeks.

1–3 minutes (cached: instant)

Turnaround

~0.01 km²

Minimum detectable damage

Flooding, fire, structural, vegetation

Change types

$0.50 per verification

API cost per claim

The Problem

Property and casualty insurers process thousands of weather-related claims per event, but physical adjuster visits are slow, expensive, and create claims backlogs that frustrate policyholders. Manual verification also struggles to catch fraudulent claims for pre-existing damage presented as event-related. Existing satellite verification tools are expensive enterprise contracts with long onboarding times.

How Satalyse Solves It

Satalyse's Change Detection endpoint compares satellite imagery from before and after the claimed event date over the property polygon, returning the extent and nature of any changes detected. Integrate this directly into your claims workflow: trigger a verification call when a claim is filed, and auto-flag claims where no satellite-detectable damage matches the event footprint. Use Risk Scoring to provide underwriters with pre-event baseline risk context.

Change Detection

POST /api/v1/satellite/detect-changes

Compares before/after imagery around the event date to detect actual property damage

Risk Scoring

POST /api/v1/satellite/score-risk

Provides pre-event flood, fire, and weather risk baseline for the property

Code Example

insurance-claim-verification.py

REQUEST

1"color:#c084fc">import requests
2"color:#c084fc">from datetime "color:#c084fc">import date, timedelta
3
4claim_date = "2024-09-14"  "color:#4b5563"># Hurricane Milton landfall
5event_start = "2024-09-10"
6event_end   = "2024-09-18"
7
8"color:#4b5563"># Verify hurricane damage at claimed property
9response = requests.post(
10    "https://api.satalyse.dev/api/v1/satellite/detect-changes",
11    headers={"Authorization": "Bearer sat_sk_live_..."},
12    json={
13        "geometry": {
14            "type": "Polygon",
15            "coord">inates": [[
16                [-81.655, 28.541], [-81.641, 28.541],
17                [-81.641, 28.530], [-81.655, 28.530],
18                [-81.655, 28.541]
19            ]]
20        },
21        "start_date": event_start,
22        "end_date":   event_end,
23        "sensitivity": "medium"
24    }
25)
26
27result = response.json()
28changes = result["changes_detected"]
29damage_detected = changes["total_change_area_km2"] > 0.001
30"color:#c084fc">print(f"Damage verified: {damage_detected}")
31"color:#c084fc">print(f"Affected area: {changes['total_change_area_km2']} km²")

200 OKRESPONSE

{
  "request_id": "req_ins001",
  "status": "success",
  "timestamp": "2025-01-15T12:00:00Z",
  "location": { "area_km2": 0.08, "bbox": { "min_lon": -84.39, "min_lat": 33.74, "max_lon": -84.37, "max_lat": 33.76 } },
  "changes_detected": {
    "total_change_area_km2": 0.18,
    "percent_of_total_area": 22.5,
    "change_magnitude_avg": 31,
    "confidence_avg": 0.89,
    "change_type": "water_inundation"
  },
  "change_geometry": {
    "changed_polygons": [
      {
        "id": "poly_1",
        "type": "Polygon",
        "coordinates": [[[...]]],
        "change_type": "water_inundation",
        "change_magnitude": 31,
        "confidence": 0.89
      }
    ]
  },
  "api_metadata": { "version": "2.0", "execution_time_ms": 1870, "cost_credits": 16, "cache_hit": false }
}