Commercial insurance has a data problem that underwriters can no longer afford to ignore

Building a robust AI foundation has become the decisive competitive advantage for insurers, yet most carriers are still wrestling with fragmented, siloed data spread across dozens of legacy systems and geographies.

Nowhere is that tension more acute than in commercial insurance, where the complexity of bespoke, multi-geography risk makes data consistency a matter of profitability, not just operational hygiene. For carriers operating at global scale, the inability to surface trustworthy data to underwriters is a direct threat to the quality of the decisions that drive revenue, according to Arunima Gautam (pictured, left), head of global financial services and insurance GTM and products at Quantiphi Inc.

“Submissions that come to the underwriters, from brokers, from relationships in the market, they’re extremely unstructured,” Gautam said. “You’re talking about emails coming in, PDFs coming in, your loss runs coming from the brokers. Every single loss run will have its own unique nature. That whole idea of complexity … comes the moment you receive a new submission from a broker.”

Gautam and Gaganpreet Randhawa (right), assistant vice president of enterprise architecture at CNA Insurance, a division of CNA Financial Corp., spoke with theCUBE’s Rebecca Knight at the Phi Moments @ Next event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how building a unified enterprise AI foundation is transforming underwriting decision-making at one of the largest commercial property and casualty carriers in the U.S. (* Disclosure below.)

A versatile AI foundation starts with consistent, trusted data

The stakes for getting data right inside a global commercial carrier are high. CNA Insurance operates across 11 countries and insures businesses ranging from construction companies in Denmark to healthcare companies in Texas, creating an acute data-quality challenge that sits at the heart of every underwriting decision, Randhawa noted.

“It’s not that we don’t have data. We have data,” Randhawa said. “Our biggest challenge is how we make it consistent, trusted and available to our business customers so that they can make informed decisions. An underwriter sitting in Chicago or an underwriter sitting in Toronto, or London or Copenhagen, they may be looking at the same risk, and we want to make sure that … they have the right information in front of them so that they all consistently can make the same decision across the globe.”

Quantiphi approached the problem by mapping CNA’s entire international data landscape — identifying all systems, data hops and user bases — before designing a layered architecture that centralizes, structures and delivers data in a format real-time enough for AI consumption, Gautam explained. The results are tangible. Data that once required days or weeks to surface to business teams now arrives in minutes and hours, according to Randhawa.

“When underwriters start believing in the data, when the compliance folks start believing in the data, when you’re making decisions from a single source rather than trying to reconcile multiple reports, then you know that your journey is real,” Randhawa said. “That’s when you can start using AI and AI can accelerate your journey and not be a risk, because you already have well-curated information that you can trust.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the Phi Moments @ Next event:

(* Disclosure: TheCUBE is a paid media partner for the Phi Moments @ Next event. Neither Quantiphi nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE