Claims Manager
Review high-severity claims and reserve adequacy
What You Do Today
Audit claims above a threshold — check reserve accuracy, investigation completeness, coverage determination, and whether the claim is on the right resolution track.
AI That Applies
Severity prediction — AI estimates ultimate claim value at first notice using claim characteristics, claimant profile, and historical patterns to set accurate reserves early.
Technologies
How It Works
The system ingests claim characteristics as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
Reserves are accurate from Day 1 instead of developing over months. The AI predicts 'Based on injury type, attorney involvement, and jurisdiction, this claim will likely settle at $85K' within hours of assignment.
What Stays
Complex claim strategy — when to litigate versus settle, how to negotiate, and managing the claimant experience — requires adjuster and manager judgment.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for review high-severity claims and reserve adequacy, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long review high-severity claims and reserve adequacy takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your claims director or VP Claims
“What data do we already have that could improve how we handle review high-severity claims and reserve adequacy?”
They're setting the automation strategy for your unit
your SIU lead
“Who on our team has the deepest experience with review high-severity claims and reserve adequacy, and what tools are they already using?”
AI fraud detection changes how investigations are triggered and prioritized
a claims adjuster with 15+ years experience
“If we brought in AI tools for review high-severity claims and reserve adequacy, what would we measure before and after to know it actually helped?”
Their judgment sets the benchmark that AI tools are measured against
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.