| Image courtesy of CBS8 KFMB-TV |
With insurance companies increasingly using drones and aerial imagery to inspect properties, a new wave of automated assessments is transforming the insurance industry and policy renewals—but it’s also leading to unintended consequences. As highlighted in this recent CBS8 news segment, some homeowners are seeing their policies dropped due to errors in this automated assessments. This reliance on aerial imagery technology underscores the importance of accurate data labeling in machine learning models. Without it, assessments are prone to mistakes that can severely impact policyholders. The Issue: Mislabeling and Insufficient Data Points
Data labeling errors can arise from a lack of clear, specific categories or inadequate training data, leading models to misinterpret property features. For instance, the CBS8 video reports cases where drone or satellite images flagged moss on a roof or even a drained pool as reasons to drop a policy, without accounting for context or the temporary nature of some conditions. These errors often stem from models incorrectly labeling features due to a lack of precise, well-defined data, as also noted by MSN, where a model mistook solar panels for roof damage. Such mislabeling can have significant financial and emotional impacts on homeowners. |
A lack of comprehensive labels for various property features is a main contributes to AI errors. For example, as reported by Governing, without detailed data to differentiate features like temporary debris from permanent fixtures, models may overgeneralize, resulting in costly mistakes. For instance, imagery needs to be paired with clear, accurate labels to distinguish between features like solar panels, satellite dishes, or roof repairs. Without these specific labels, models are prone to making misinformed predictions, potentially classifying these innocuous features as significant risks. Proper labeling, therefore, becomes essential to ensure fair assessments and maintain policyholder trust.
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In one case, a church's insurance policy was canceled after a satellite image mistakenly flagged what appeared to be clutter and disrepair, resulting in the insurer dropping the policy. This example underscores how vital it is for insurers to rely on well-labeled data to avoid misunderstandings that can impact organizations and their communities. The Bigger Picture: Technology in InsuranceThese examples also reveals an emerging trend: insurance companies are increasingly relying on automated, aerial assessments to find deferred maintenance issues, aiming to minimize claims by identifying problems early. As Carl Sussman, a property assessment expert, explains, this shift is happening as insurers prepare for regulatory changes that would allow more flexible options. Rather than dropping policies over minor issues, the new rules would allow insurers to provide options, such as increased premiums for deferred repairs. However, until these regulations are in place, homeowners face an “all or nothing” decision. |
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To prevent these issues, insurers must prioritize high-quality data labeling to support AI models that make high-stakes decisions. Clear, specific categories for diverse property features, combined with regular auditing, are essential to avoiding mislabeling errors. By training models to distinguish between permanent fixtures, temporary conditions, and potential hazards, insurers can ensure more accurate and fair assessments. This is not just about improving technology—it’s about creating an equitable experience for policyholders.
In the age of AI and data-driven decision-making, proper data labeling is essential for accuracy, fairness, and reliability. With better labeling practices, insurers can responsibly leverage technology, making informed decisions that benefit both the business and the policyholder.
Committed to lifelong learning, Rob holds certifications in Python programming from the University of Michigan, Satellite Navigation from Stanford University, Spatial Computing from the University of Minnesota, and Business Innovation from the University of Maryland. These academic achievements reflect his ability to stay at the forefront of technological and business trends.
Rob is also a recognized thought leader in the geospatial and remote sensing community, having presented at global conferences and published works on topics such as aerial imagery applications, property valuation techniques, hydrographic surveys, and automated change detection. His insights have been featured at key industry events across North America, Europe, and Asia, where he has discussed the use of cloud-based geospatial platforms, oblique imagery, and the role of advanced technologies in disaster response and government operations.
Known for blending technical expertise with strategic vision, Rob specializes in GIS, remote sensing, artificial intelligence, and product management. Through Tera Analytics, he continues to deliver innovative, scalable solutions that empower organizations to achieve measurable, long-term success.