See what the claim file can't tell you.
Tell us a bit about your claims operation and we'll set up a demo tailored to your program.
Challenge: The costliest claims can be the hardest to catch
IRMA is a member-owned, nonprofit risk pool providing workers’ compensation, auto, and general liability coverage to more than 70 municipalities across northeastern Illinois. Missing even a small number of high-risk workers compensation cases can drive significant costs.
The problem is that those claims rarely announce themselves. Critical indicators are buried in adjuster notes, case manager records, and unstructured text that no manual review process could consistently surface in time.
IRMA needed a systematic way to identify which claims demanded immediate attention while there was still time to intervene.
Solution: See what others miss. And know what it means.
Nodal Claims Triage was developed specifically for early claims intervention, using AI-powered daily risk scoring and natural language processing to identify which claims are likely to incur high costs shortly after they are reported. Claims with the lowest Nodal scores end up costing just 4% of the overall average. Without identification and intervention, claims that Nodal scores highest end up costing 490%.
For IRMA, that changed how the team worked. Claims that previously would have gone undetected now surface within days of being reported. Supervisors are alerted, and the right adjusters are assigned. What was once a calendar-driven, manual process is now a data-driven workflow that gives the team the opportunity to act before costs escalate.
Results: While industry costs climbed, IRMA saved millions
Over eight years, IRMA has consistently identified high-severity claims earlier, responded faster, and reduced costs in a market where costs trend upward annually.
Early identification
- 85% of the most severe claims flagged within the first 10 days
- 96% flagged within the first 30 days
- 94% identified before incurred costs reach $50,000
Financial impact
- 14% reduction in claim costs in year one ($1.7 million saved), exceeding the projected 3–10% range
- 5% sustained reduction across usage, totaling $4.1 million in savings
