A new analysis of 76 public filings from 49 insurers and reinsurers found zero mentions of synthetic media, synthetic identity, or voice cloning, and only six companies referenced deepfakes - exclusively as cybersecurity threats, never in connection with claims or underwriting evidence. The research, commissioned by Clearspeed and independently authored by insurance innovation strategist Sabine VanderLinden, identifies what she calls a "verification gap" emerging as the industry automates decisions faster than it builds the infrastructure to verify the information those systems act on.
The report, The Speed of Trust: Building the Trust Intelligence Layer for Insurance in the Age of Agentic AI, draws on the regulatory filings review, 31 industry studies, and 16 interviews with claims and underwriting leaders in the U.S. and U.K. It describes a paradox: insurers are rapidly adopting AI for decisions, evidence review, and customer interactions, while AI-generated false or manipulated photos, documents, voices, and identities become easier to create and harder to detect.
The numbers behind the gap
Industry research published in March 2026 and cited in the report found that 98% of 300 U.S. insurance claims professionals agree AI editing tools are driving a rise in digital media fraud. Yet just 32% said 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," VanderLinden said. "Insurance is automating decisions faster than it can verify the information behind them."
The Alchemy Crew Ventures research team searched annual reports, 10-K filings, proxy statements, and statutory returns for a dozen terms related to AI-generated and manipulated evidence. Among the findings: five of the world's top 10 reinsurers were analyzed, and none mention deepfakes, synthetic media, or AI-generated evidence in their most recent annual reporting. "There is a striking gap between where this risk is discussed and where capital is committed," VanderLinden said. "In the filings that set reserves, uncertainty, litigation pressure, and adverse development are all named. The trust problem beneath them is not."
Genuine customers absorb the cost
The Coalition Against Insurance Fraud estimates fraud accounts for roughly 10% of property and casualty losses, draining at least $308.6 billion annually from the U.S. insurance system. But the report argues the deeper cost is structural. Until insurers can consistently determine which interactions need speed, scrutiny, or human judgment, the industry pays for trust twice: once through leakage from bad actors, and again through friction imposed on honest claimants.
"90% of customers who make a claim are honest, good people for whom we should be sorting out their service needs as quickly as possible," Ian Thompson, former Group Chief Claims Officer at Zurich Insurance, told researchers. "But how many of those genuine customers get the feeling that they're not being trusted, because we're trying to catch the other 10%?" The report frames this as a trust deficit that slows processing for the majority while failing to catch sophisticated synthetic fraud.
Making trust measurable
VanderLinden proposes a Trust Intelligence Layer: a continuous, regulator-ready risk indicator running across the policyholder journey. Rather than deploying more AI to catch more fraud, insurers would use this layer to clear genuine cases quickly and direct human judgment to exceptions. The signal informs a decision rather than making one, produces an audit trail rather than an automated denial, and requires no demographic or historical knowledge of the individual being assessed.
"In the age of agentic AI, deepfake evidence, embedded distribution, and automated workflows, insurers can no longer treat trust as a soft value or a late-stage consideration," VanderLinden said. "The opportunity for carriers is to establish trust earlier and make it a measurable operating layer across the policyholder journey." Alex Martin, co-founder and CEO of Clearspeed, added: "Today's promise of AI should yield a faster, richer experience for genuine customers, but that must begin with verifying where to extend trust." For professionals working in AI Agents & Automation, the report offers a concrete framework for operationalizing trust in automated workflows.
Vision 2030: agent-to-agent interactions
The report looks ahead to a near future where a material share of insurance interactions are agent-to-agent - a customer's AI agent transacting with an insurer's AI agent at machine speed, with no human in the loop for routine business. In that environment, the verification question does not disappear. It intensifies. When the action is always executed correctly, the remaining question is whether the interaction behind it can be trusted.
The report describes this as a shift in what gets verified, not whether verification is still needed. As more insurance interactions become AI-to-AI, the human input behind each transaction still has to be trusted before automated systems act on it. Insurers will need an auditable way to establish that trust at the moment of interaction. The organizations that build that capability while interactions remain human-led will set the standard when those interactions become machine-to-machine. The report concludes: "The arms race is symmetrical. The only durable advantage is to establish trust earlier than the adversary can manufacture doubt."
Why this matters for insurance professionals
The findings point to an immediate operational gap, not a distant threat. Claims and underwriting leaders are already automating decisions on evidence that AI can fabricate faster than their systems can detect. The report does not suggest slowing AI adoption. It argues that insurers who build verifiable trust signals into their workflows now - while human oversight is still the norm - will be positioned to scale agentic automation safely. Those who treat trust as a soft value will face mounting leakage, regulatory pressure, and customer friction as synthetic media becomes indistinguishable from genuine evidence.
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