How Aerial Imagery and Machine Learning Are Reshaping Property Claims
Tuesday, March 3rd, 2026 Catastrophe Property Risk Management TechnologyArtificial intelligence is rapidly expanding the volume of property-level data available to insurers, but more data does not automatically lead to better decisions. In a recent interview, Dr. Michael Bewley of Nearmap explained how supervised machine learning applied to aerial imagery is now a mature tool for identifying roof conditions, prior wear, and property features. For claims adjusters, this historical imagery provides a defensible baseline when evaluating damage, reserving losses, and addressing coverage disputes.
In catastrophe events such as Hurricane Milton, rapid image capture and AI processing help carriers triage claims and deploy field resources more efficiently. Still, AI outputs carry uncertainty and must be treated as decision-support tools rather than final judgments. Customer pushback over remote property assessments highlights the need for transparency, especially when underwriting or premium decisions are tied to aerial findings.
The larger shift is toward Predict and Prevent, using longitudinal imagery and data modeling to reduce risk before losses occur. For insurers, the challenge is clear: integrate high-quality AI-driven data into existing workflows without overwhelming adjusters or compromising trust, compliance, and claim defensibility.
