An AI system may classify incoming claims faster than a manual queue. That result matters, but it leaves the insurer with a larger question: did the process become better for the people submitting claims and the teams responsible for resolving them?
Triage decides where work goes next. It is different from establishing coverage, detecting proven fraud or authorising a final settlement. Buyers should define the exact decision the system supports before accepting a vendor’s claim that “claims automation” has improved performance.
Establish the unit of work
Begin with a defined population and period. Are the figures based on submitted claims, complete claims, individual documents or reviewed recommendations? Does the test include complex cases, or only those selected as suitable for automation?
Track the original intake population as cases move through the process. Otherwise, a pilot can appear faster simply because difficult cases are excluded from the reported result.
An illustrative pilot might measure document classification for one product line. Its findings should remain attached to that scope. They do not establish performance across all policies, languages or claim types.
Measure the work transferred to people
Record escalations, reviewer handling time, repeated document requests and reopened cases. An automated first step may save time while creating extra work downstream. Both effects belong in the assessment.
For example, a tool that flags an unreadable attachment can help a handler act sooner. If it incorrectly requests documents already supplied, it can increase customer effort. The metric should distinguish useful escalation from avoidable repetition.
Singapore’s FEAT principles provide foundational guidance on fairness, ethics, accountability and transparency in financial-sector AI and data analytics. They are a relevant governance reference, not a claim that a particular triage tool has passed a regulatory assessment.
Review error types separately
A misplaced low-complexity case and an incorrectly escalated urgent case can have different consequences. Define the error categories that matter to the workflow, and agree how reviewers will assess them.
Do not rely only on one accuracy percentage. Examine false escalations, missed escalations and the reasons behind each. Consider whether language, document format or channel creates systematic differences, using an appropriate and lawfully handled evaluation dataset.
The assessment needs operational and compliance judgment. Statistical comparisons can help identify a problem; they do not by themselves determine which differences are justified or what action is legally required.
Keep the historical examples in their proper scope
In its February 2022 Veritas announcement, MAS described assessment work involving insurance predictive underwriting and insurance fraud detection, among other use cases. Those examples show efforts to operationalise responsible-AI principles. They are not reported results from a claims-triage deployment and should not be presented as such.
For a new purchase, require evidence from the proposed workflow. Establish who approved the evaluation data, who reviewed the results and how the insurer will monitor performance after launch.
Give the pilot a balanced decision rule
A useful decision record brings together cycle time, review effort, error consequences, customer contacts and complaints or corrections. Compare like-for-like cases and explain changes in workload or staffing during the test.
Human oversight must also be practical. A handler needs enough information and authority to challenge the system’s recommendation. An override button adds little if staff cannot understand the reason for a referral or lack time to investigate it.
GAT’s pilot-to-revenue guide argues for connecting pilot evidence to a purchase decision. For insurance triage, the evidence must include the work and customer impact left outside the automated step. Faster routing earns its value when the whole process supports a defensible outcome.
Published by the Global Apex Tech Editorial Desk. Partner involvement, when applicable, is disclosed above the headline. For editorial questions or source material, contact editor@globalapextech.org.