Artificial intelligence in insurance is entering a second phase. After years of automating claims-first notice of loss, fraud detection, damage assessment-carriers are now directing AI toward underwriting and continuous risk evaluation before losses happen, according to analysis from Finextra. The shift carries measurable stakes: insurers using advanced analytics achieved combined ratios roughly six points lower than slower adopters between 2022 and 2024.
A 2025 National Association of Insurance Commissioners survey of 93 health insurers found that 84% already use AI or machine learning in their operations, with fraud detection among the most common applications. But the technology's footprint is expanding well beyond post-loss triage.
Claims automation built the foundation
Claims became AI's first beachhead because the work involved visible inefficiencies and structured data tied to discrete events. Computer vision now analyzes damage photos in place of in-person inspections. Fraud detection systems scan thousands of claims for suspicious patterns. Digital first notice of loss lets policyholders report incidents through mobile apps at any hour.
The operational gains have been stark. Insurers that invested heavily in claims automation increased straight-through processing rates from roughly 10% to 15% up to 70% to 90%. What once required manual review now moves through systems with minimal human touch.
Risk automation changes the timeline
Risk automation tackles a different problem: evaluating and monitoring exposure continuously, not just at policy issuance. Vehicle condition degrades, building maintenance lapses, driver behavior shifts. Agentic underwriting systems now ingest data from IoT sensors, satellite imagery, telematics, and third-party databases to maintain updated risk profiles throughout a policy's life.
The article cites research showing AI-driven underwriting improvements contributed to an approximately 3-percentage-point reduction in loss ratios for business lines that incorporated unstructured vehicle condition data into pricing. Computer vision converts pre-policy vehicle inspections, roof scans, and fleet images into structured condition reports within minutes, eliminating the need for manual site visits.
Multiple technologies feed continuous assessment
Predictive analytics combine historical loss data with current conditions to generate forward-looking risk scores. Industry case studies report underwriting accuracy improvements of up to 54% in certain applications. Telematics and IoT sensors supply ongoing streams of driving behavior and building condition data, enabling monitoring that doesn't depend on policy renewal cycles.
AI improved the accuracy of underwriting data intake from roughly 75% to more than 90% in certain workflows. Underwriting cycle times dropped by 31%, and risk assessment accuracy for complex policies improved by 43%. In some automated workflows, underwriting timelines shrank from three days to three minutes. Insurance AI deployments increased 87% year over year, with agentic AI systems accounting for roughly one in five public deployments.
For professionals tracking how AI for insurance is reshaping the industry, the data suggests the center of gravity is moving. Claims automation solved a post-loss efficiency problem. Risk automation aims to prevent losses from occurring in the first place-or at minimum, price them more accurately from the start.
Why this matters for insurance professionals
Underwriters and claims adjusters are working alongside systems that don't just process paperwork faster-they actively surface risk signals that human review might miss. The 87% year-over-year jump in AI deployments means the competitive gap between early adopters and holdouts is widening, with a six-point combined ratio advantage already visible in the data. For carriers still limiting AI to claims, the underwriting use cases now have performance numbers attached. A 3-percentage-point loss ratio reduction tied specifically to unstructured condition data is a signal that's hard to ignore.
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