The economics of insurance claims handling have not changed as fundamentally as they should have, given how much the underlying technology has changed. Most carriers still route the majority of their claims volume through a process designed for a world where every claim required a human to look at it. In a world where AI can accurately assess claim severity, flag fraud indicators, and recommend handling path within seconds of first notice of loss, that process is leaving significant efficiency gains unrealized.
The opportunity is not to eliminate adjusters. It is to redeploy them. When adjusters spend their days processing straightforward claims that AI could handle, they are not available for the complex claims where their judgment genuinely matters. AI triage fixes that allocation problem.
What Triage Actually Does
Claims triage is the process of assigning each incoming claim to the appropriate handling path based on its characteristics. A simple auto glass claim does not need the same adjuster attention as a multi-vehicle commercial accident with bodily injury. A homeowner water claim without structural damage does not need the same resources as a fire loss with displacement.
Manual triage systems rely on the first notice of loss information — whatever the claimant reported when they called in or submitted online — and route based on coverage type and stated loss amount. This is a crude approximation. Claims that look simple at first notice can turn out to be complex. Claims that look complex can resolve quickly. Manual triage cannot assess complexity signals that are not explicitly stated by the claimant.
AI triage can look at a much broader signal set: prior claims history on the policy, characteristics of the loss event relative to historical patterns, geographic and temporal factors, coverage structure, and in some cases external data sources like weather events or fraud ring databases. The result is a materially more accurate assignment of claims to handling paths at first notice.
The Straight-Through Processing Case
For a well-defined class of low-complexity, low-fraud-risk claims, straight-through processing — automated settlement without adjuster involvement — is achievable with current technology and acceptable regulatory risk. The parameters vary by line of business and carrier risk appetite, but the general pattern holds across auto, property, and specialty lines.
The key requirements for straight-through processing are: a high-confidence fraud assessment, a loss estimate within an automated settlement threshold, coverage confirmation with no material ambiguity, and claimant consent to the automated process. When all four conditions are met, human involvement in the settlement decision adds cost and delay without adding accuracy.
Carriers that have deployed straight-through processing for eligible claims typically report handling times dropping from days to hours or minutes for the automated segment, with customer satisfaction scores that are higher than the manual average — because speed of settlement is a primary driver of claims satisfaction.
The Human Escalation Path Is Not Optional
Deploying AI triage without a robust escalation path is a regulatory and reputational risk, not just an operational gap. Claimants have the right to have their claim reviewed by a person. Disputed settlements require human involvement. Claims where the AI assessment is uncertain need a clear path to escalation that does not create an adversarial process.
The escalation path also needs to be instrumented. When claims escalate from automated handling to adjuster review, that should generate data that feeds back into the triage model. Escalations are signals about where the model is uncertain or wrong. Treating them as exceptions rather than learning opportunities misses a significant improvement opportunity.
What the Regulatory Environment Requires
Insurance regulators in most US states have not issued comprehensive guidance on AI-driven claims handling, but the principles that apply are consistent with the broader direction of financial AI regulation. Automated claims decisions must be explainable, disputable, and non-discriminatory. The AI system cannot be used in ways that systematically disadvantage protected classes. And the carrier retains responsibility for the decision, regardless of whether a model made it.
Carriers deploying AI triage need documentation that demonstrates the system meets these standards: testing results for disparate impact, audit trails for automated decisions, and clear processes for human review when requested. Prism Layer's claims triage capabilities include these governance components as standard, not as add-ons. The compliance infrastructure is part of the product, because in regulated markets, it has to be.
