Director of Claims
Review and authorize complex claim settlements
What You Do Today
Review settlements that exceed adjuster authority — large losses, disputed liability, coverage questions, or claims with litigation potential. Approve, modify, or redirect the handling strategy.
AI That Applies
AI-generated settlement recommendations based on comparable claims, jurisdiction-specific verdict data, and predicted outcome ranges that inform your decision.
Technologies
How It Works
The system ingests comparable claims as its primary data source. The analytics engine aggregates data across sources, applies statistical analysis to identify significant patterns and outliers, and presents the results through visualizations that highlight what needs attention. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
You review settlements with data context — what similar claims settle for, what this jurisdiction awards, what factors suggest this claim is different. Better-informed decisions.
What Stays
Settlement authority is judgment — weighing liability uncertainty, coverage defenses, litigation cost, and customer relationship in a way that models can't fully capture.
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 and authorize complex claim settlements, 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 and authorize complex claim settlements 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 and authorize complex claim settlements?”
They're setting the automation strategy for your unit
your SIU lead
“Who on our team has the deepest experience with review and authorize complex claim settlements, 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 and authorize complex claim settlements, 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.