Commercial insurance carriers are struggling with a significant data problem, threatening profitability and AI adoption. Insurers worldwide are burdened by fragmented, siloed data across numerous legacy systems.
The complexity of global commercial risk makes data consistency crucial for profitability. Arunima Gautam, head of global financial services and insurance at Quantiphi Inc., stated that the inability to provide underwriters with trustworthy data directly impacts revenue-generating decisions.
Submissions from brokers are often unstructured, arriving via emails, PDFs, and unique loss run formats. This inherent complexity presents an immediate challenge upon receiving new submissions.
Gautam, alongside Gaganpreet Randhawa of CNA Insurance, discussed how a unified enterprise AI foundation is transforming underwriting at CNA. Randhawa highlighted that CNA, operating in 11 countries, faces an acute data-quality challenge across diverse businesses, from construction to healthcare.
"It’s not that we don’t have data. We have data," Randhawa explained. "Our biggest challenge is how we make it consistent, trusted and available... so that they can make informed decisions." The goal is to ensure underwriters globally have access to the same reliable information for consistent decision-making.
Quantiphi addressed this by mapping CNA’s data landscape and designing a layered architecture to centralize and structure data for AI consumption. Data previously taking weeks to surface is now available in minutes and hours. Randhawa noted that when underwriters and compliance teams trust the data from a single source, AI can accelerate operations rather than posing a risk.
This unified approach is essential for commercial insurers to leverage AI effectively and maintain competitive advantage in a data-driven market.