Insurance Carrier American National Integrates Geospatial Data From CAPE Analytics

Proptech company and data provider CAPE Analytics just signed a deal with insurance carrier American National, which provides P&C services in 38 states, to provide geospatial property data. The carrier looks to improve risk analysis and management throughout policy lifecycles.

American National will begin by using property attributes—such as a roof condition rating—along with other underwriting data, in policy renewals “to improve profitability, mitigate property conditions that increase risk, and create additional workflow efficiencies,” according to a press release.

Eventually, American will use CAPE data “in new business underwriting workflows to assist in optimizing inspection processes and better assessing new business risks.”

CAPE’s geospatial analysis rests on two technologies. One is computer vision, which is the use of varying types of scanners—whether in normal visible light or using infrared sensors that can often show aspects of an object that would otherwise be invisible. That type of thermal imaging is already in common use with buildings.

The other foundational technology is machine learning. A form of artificial intelligence (AI), machine learning is a set of techniques for recognizing and learning patterns and then improving the performance over time with more examples and experience.

One would be to identify potential problems in advance. Are there trees that might fall onto a roof? Is there a pool that could represent a potential liability?

The other is post-damage. Software could perform an analysis on visual data to help determine what damage a structure had undergone and perhaps what repairs might be needed.

CAPE signed a similar deal last summer with property and casualty insurer UPC. The latter specializes in coastal states and must regularly navigate the aftermaths of such hurricanes, tornados, and floods. After an event, there could be many policy owners looking for compensation. Remote imagery and machine learning would at least provide an initial level of analysis to reduce the number of in-person assessments by appraisers.

Machine learning and computer vision wouldn’t necessarily be a replacement for traditional forms of inspection and adjustment. Software can make mistakes and people can misunderstand and misapply the results. However, at the least, such data can help pinpoint changes in a building’s exterior condition and help an insurer better focus its efforts on policies where claims are more likely or to better plan and control claims work that needs to be done.