At its core, crop insurance is the business of pricing uncertainty. Weather, disease, frost, drought — none of it is guaranteed, all of it must be measured. But the most common tool an insurer has to measure still amounts to three things: the declaration, the field visit, and historical averages. At the scale of Turkish agriculture this trio is not enough; it neither scales nor keeps decision time reasonable.
Where Does Information Asymmetry Leak?
When an underwriter accepting a policy suspects the declaration is incomplete, overstated, or wrong, there are two options: trust it and write the policy, or send an adjuster. The first choice carries moral hazard risk to the pool; the second creates time and cost. At claim time there is a similar bind: a field visit is not needed for every file, but the decision of which file to prioritize must itself rest on data. The “scarce resource” here is neither the satellite nor the AI — it is the evidence ready in front of the decision-maker.
What Do Satellites Change?
The quietest revolution of the past decade is that satellite imagery became operational. Thanks to Sentinel-2 and similar sources, there is a ten-day-cadence, 10-meter-resolution image archive for any parcel in Turkey. This archive is not a decision on its own; it is evidence. That is: that a parcel was planted, what crop was planted, the in-season health trajectory, and when an anomaly began — all of it verifiable remotely and in an archivable way.
To turn this evidence into a decision engine, three layers are needed: phenology-aware crop classification, a health indicator benchmarked against neighboring parcels, and anomaly detection. When all three run together, a verifiable version of the declaration opens in front of the underwriter; a pre-classified triage of the damage stands in front of the adjuster.
What Does This Gain for Which Insurer?
The first benefit is speed: an evidence pack that opens in seconds delivers the same level of assurance as a days-long field process. The second benefit is coverage: sample-based auditing limited by human capacity gives way to remote control that scans the entire portfolio. The third benefit — perhaps the most critical — is auditability: the justification of the decision is tied to a formula, a reference, an open dataset; the internal risk team, the regulator, or the reinsurer can each inspect that justification separately.
Does Information Asymmetry Close Completely?
It does not — but the ratio shifts. The satellite stays blind to some things (micro-topography, storage loss at harvest time, the selection quality of the crop). Being aware of these blind spots is itself part of the decision engine. Every output is delivered with a confidence band; “high confidence” files are processed with one click, “low confidence” files are prioritized to a field expert. The adjuster’s workload has not dropped; the question of “which file first” has been answered.
The health of an insurance pool is measured not by how fast it sells but by how accurately it decides. The satellite — combined with AI — is becoming the single largest input to that accuracy curve. When a decision rests not only on a declaration but also on evidence, the chain becomes credible to all stakeholders at once.
