(Image source: Cape Analytics.)
Western Mutual Insurance Group, an Irvine, Calif.-based property/casualty insurer serving homeowners in Arizona, California, Nevada, New Mexico, Texas, and Utah, is now a fully-deployed Cape Analytics (Mountain View, Calif.) customer, according to the vendor.
Western Mutual is using Cape Analytics property intelligence in its new business underwriting and renewal processes, leading to innovative uses across the insurance workflow, according to a vendor statement. The insurer is using Cape’s imagery-derived attributes, such as roof condition and solar panels, to provide more accurate quotes, improve customer experience, accelerate underwriting decision making, and gain access to risk-relevant property changes at renewal.
“Western Mutual is excited to be working with Cape Analytics, incorporating the most inclusive and timely information into our pricing and underwriting,” comments Kelly Walker, VP, Marketing at Western Mutual. “This aligns with our continuous focus on improving the overall experience for our policyholders as company stakeholders.”
“We are thrilled to announce our partnership with Western Mutual, an insurer leading the charge in adopting new forms of geospatial property intelligence and improving the speed and performance of its insurance policies,” said Busy Cummings, VP of Sales at Cape Analytics. “From identifying solar panels at time of quote to using roof condition to inform underwriting decisions and inspections, our highly relevant property information is empowering Western Mutual to institute innovative practices that benefit their business and their insureds.”
Cape Analytics provides what it characterizes as comprehensive and accurate geospatial intelligence at time of quote, with the speed and breadth necessary to fundamentally improve underwriting processes. With its technology, Cape says Western Mutual can now instantly access contextualized intelligence across its portfolio. Cape Analytics describes its property intelligence as derived by applying cutting-edge computer vision and deep learning algorithms to geospatial imagery and including a targeted selection of rigorously-developed property attributes and loss-predictive signals.