An AI-driven virtual reality training program aims to shorten the learning curve for new claims professionals while addressing the industry’s aging workforce.
BMI warns insurers that territorial disputes, regime instability, and shifting alliances could drive claims volatility across political risk, marine, and specialty lines.
As federal agencies scale back climate and weather programs, nonprofit groups are stepping in to preserve datasets critical to catastrophe modeling, insurance claims analysis, and risk mitigation.
As insurers rush to deploy AI agents, new governance, data controls, and decision frameworks are becoming critical to claims accuracy, compliance, and trust.
As automation reduces administrative workload, claims professionals are gaining time to strengthen communication, manage outcomes, and reduce litigation risk through human connection.
As automation expands across insurance operations, claims leaders must ensure AI supports human judgment, collaboration, and professional identity rather than reducing roles to machine oversight.
NOAA’s warning underscores exposure tied to power quality, satellite services, and timing-dependent operations that can trigger complex business interruption claims without physical damage.
The insurer says role-based AI tools are accelerating engineering, analytics, and machine learning work while supporting long-term productivity and risk expertise goals.
A proposal would require advance notice to homeowners and bar insurers from relying on aerial images older than 180 days when making coverage decisions.
Heavy investment in insurance AI continues to deliver limited returns as automation accelerates workflows without improving decision quality, explainability, or claims outcomes.
Rates may be leveling off, but jury behavior, litigation funding, and documentation demands continue to drive claim severity and settlement complexity heading into 2026.
A growing flood protection gap is driving insurers and large businesses toward parametric coverage and captive risk structures as legacy models fall short.