Introducing: Nearmap for Post-catastrophe Response
Post-catastrophe imagery and AI-derived property damage and condition data unite to help insurers process customer claims more efficiently.
Post-catastrophe imagery and AI-derived property damage and condition data unite to help insurers process customer claims more efficiently.
Economic and competitive pressures, such as skyrocketing inflation, labor shortages, and supply chain issues, are pushing insurance underwriters to improve their eligibility and risk review on potential and existing policies for residential and commercial properties. McKinsey observed that the highest-performing underwriters blend human judgement with data-driven analytics.1
Nearmap is helping insurance carriers transform their underwriting decisions by launching Nearmap Roof Condition. More than a score but a full transparent assessment, Nearmap Roof Condition supports a multi-lensed approach that combines data analytics and human judgement to more accurately determine policy eligibility and property risk.
Nearmap Roof Condition is updated with every standard imagery capture to ensure that the assessment provides the timeliest information. It includes an overall roof condition score, vertical and oblique imagery of the property, and a complete set of data detailing roof features and conditions, including roof materials, roof shapes, and conditions like rusting, staining, ponding, repairs, and missing tiles. With Nearmap Roof Condition, carriers can feel confident that they and their policyholders are appropriately and adequately covered.
No one knows our imagery better than we do. Owning the entire technology stack, from cameras and processing to AI modeling and feature extraction, we built a best-in-class system optimized for developing high quality roof condition assessments. Nearmap Roof Condition uses a variety of roof condition AI derived data layers, including ponding, staining, damage, repairs, and missing tiles and shingles.
Nearmap Roof Condition is not a blackbox solution. Each assessment includes vertical and oblique imagery of the property and a complete view of AI derived information, including area, percentage of affected roof, confidence level, and visual reference on property imagery. This enables a multi-lensed approach to combine quantitative analysis with qualitative judgement.
Nearmap Roof Condition supports existing algorithms and workflows by delivering content as a CSV via an API. It ensures consistent analysis and timely decisions and enables underwriters to develop a customer-centric approach to underwriting by integrating roof condition data with customer data.
While we make every effort to ensure the accuracy of the data and analysis in blog articles, this information is not to be relied on as professional advice. No endorsement or approval of any third parties or their advice, opinions, information, products or services is expressed or implied by any information in the blog. Should you seek to rely in any way whatsoever upon this content, you do so at your own risk.
