New Research Examines Insurance's Verification Gap Amid Rapid AI Adoption
New Research Examines Insurance's Verification Gap Amid Rapid AI Adoption
摘要: New Research Examines Insurance's Verification Gap Amid Rapid AI Adoption
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Clearspeed
Sep 03, 2026, 08:00 ET
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New Research Examines Insurance's Verification Gap Amid Rapid AI Adoption
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Clearspeed
Sep 03, 2026, 08:00 ET
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Analysis of 76 insurer and reinsurer financial and regulatory filings finds AI-generated and manipulated evidence absent from discussions of claims and underwriting
CHEVY CHASE, Md.
Sept. 3, 2026
/PRNewswire/ --
Clearspeed
, the global leader in voice-based risk assessment, today released new research examining the growing challenge for insurers to move faster with AI and automation while determining whether the information those systems act on can be trusted.
The Speed of Trust: Building the Trust Intelligence Layer for Insurance in the Age of Agentic AI
, commissioned by Clearspeed and independently authored by insurance innovation strategist
Sabine VanderLinden
, CEO of Alchemy Crew Ventures, draws on a review of 76 public filings from 49 insurers and reinsurers, 31 industry studies, and 16 interviews with claims and underwriting leaders at insurance companies across the United States and United Kingdom.
The research identifies a paradox emerging as insurers rapidly adopt AI and automation:
the industry is automating decisions, handoffs, evidence review, and customer interactions faster than it is building the infrastructure needed to clear those interactions confidently.
Simultaneously, AI is making it faster and easier to create convincing false or manipulated photos, documents, voices, and identities that can enter insurance workflows.
Reserving Against A Risk Not Yet Named
"Industry research published in March 2026, based on a survey of 300 U.S. insurance claims professionals, found that 98% agree AI editing tools are driving a rise in digital media
, while just 32% say they are very confident they could identify a deepfake."
"That is the verification gap: the distance between what the industry can see coming and what it can currently detect,