Betterview Launches Catastrophe Response System

The new system, CAT-RS, solves many of insurers’ greatest challenges, by using property-level analytics to support a fast, accurate catastrophe response.

(Image credit: NOAA.)

Betterview (San Francisco), an InsurTech provider of actionable property intelligence to property and casualty (P&C) insurance companies, has launched its new Catastrophe Response System (CAT-RS) which the vendor says solves many of the greatest challenges insurers face when responding to natural catastrophe (CAT) events such as hurricanes, wildfires, and more.

Betterview explains the utility of the new system by noting that with existing tools that focus on regional hazard data and modeling instead of property-level analytics, claims teams often only get broad, inaccurate loss predictions on CAT events. Claims response teams also typically struggle to deploy boots-on-the-ground to damaged properties in a timely manner. These challenges naturally lead to inefficient claims processes and slow post-event response, the vendor asserts.

Using a combination of machine learning, high-quality aerial imagery, and third-party data, CAT-RS shows CAT impact in near real-time, accurately predicts which properties and structures are most likely to be impacted based on conditions and characteristics prior to the event, and allows rapid visual verification of actual property damages post-event, according to the vendor. Betterview says these capabilities empower claims teams to:

  • Allocate CAT resources more effectively: Strategically stage claims response teams in areas with a high number of predicted-damage properties, before damage happens. Then after the damages, triage the impacted policies and prioritize resources for properties with great damage or complex claims.
  • Keep good communication and help customers recover from tragedy faster and easier: Proactively jump-start the claims process even before the first notice of loss (FNOL), without the need for costly boots-on-the-ground.
  • More accurately predict claims losses in near-real-time and cut loss-adjustment expenses: Automatic damage detections of the whole book of business allow for a more accurate and immediate understanding of real damages and losses, for instant financial planning. A more accurate budget and reserve enable faster claims payments and settlements, which leads to lower LAE.

Jason Janofsky, VP, Engineering, Betterview.

“Our system tells insurers everything they need to know about CAT impact, before, during, and after the event,” comments Jason Janofsky, VP, engineering, Betterview. “We use trusted public and private data to pull in the latest projection of CAT events and to quantify the potential losses based on the estimated replacement cost of each structure. Following the event, we utilize aerial imagery that is available within 24-48 hours from the most reputable, comprehensive aerial map providers in the world, together with our proprietary computer vision and new damage classifiers to analyze damages. This allows us to more accurately and promptly predict actual losses and help insurers reduce claims cycle time.”

Betterview says the new CAT-RS empowers insurers to evolve from a slow and reactive catastrophe response approach to a fast and proactive approach with their customers at the center. The system optimizes the efficiency of claims processes, and most importantly, it will allow claims teams to proactively help homeowners, business owners, and communities who desperately need the resources to recover from devastation quickly, the vendor says.

Nationwide Extends Partnership with Betterview: ‘The Sky’s the Limit’

Anthony R. O’Donnell // Anthony O'Donnell is Executive Editor of Insurance Innovation Reporter. For nearly two decades, he has been an observer and commentator on the use of information technology in the insurance industry, following industry trends and writing about the use of IT across all sectors of the insurance industry. He can be reached at AnthODonnell@IIReporter.com or (503) 936-2803.

Leave a Comment

(required)