VP of Claims
Claims Operations & Performance
What You Do Today
Oversee the claims operation — cycle times, accuracy, settlement rates, customer satisfaction, and expense ratios. Every percentage point of improvement or deterioration flows directly to the combined ratio.
AI That Applies
AI-powered claims dashboards that track operational KPIs in real time, identify bottleneck patterns, and predict emerging performance trends before they hit monthly reports.
Technologies
How It Works
The system ingests operational KPIs in real time as its primary data source. Predictive models fit to historical outcome data identify which variables are the strongest leading indicators, then apply those weights to current inputs to generate forward-looking scores. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The operational leadership.
What Changes
Performance monitoring becomes predictive. The AI flags that cycle times are increasing on a specific claim type because adjusters are waiting for a particular vendor, enabling targeted intervention.
What Stays
The operational leadership. Improving claims operations requires process redesign, adjuster coaching, vendor management, and organizational change — all fundamentally human work.
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 claims operations & performance, 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 claims operations & performance 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 board chair or lead independent director
“What data do we already have that could improve how we handle claims operations & performance?”
They shape expectations for how AI appears in governance
your CTO or CIO
“Who on our team has the deepest experience with claims operations & performance, and what tools are they already using?”
They own the technology infrastructure that enables AI adoption
a peer executive at a company further along on AI adoption
“If we brought in AI tools for claims operations & performance, what would we measure before and after to know it actually helped?”
Their lessons learned are worth more than any consultant's framework
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.