Claims Adjuster
Subrogation Identification & Pursuit
What You Do Today
Identify claims with subrogation potential — the other driver was at fault, a product was defective, a contractor caused the damage. Refer to subro, track recovery, respond to adverse subro demands against your insured. Money left on the subro table is money the company doesn't recover.
AI That Applies
AI-powered subrogation scoring that analyzes loss facts, liability indicators, and recovery potential at first notice. Automated adverse party identification from police reports and claim narratives. Predictive models for recovery likelihood and expected timing.
Technologies
How It Works
The system ingests police reports and claim narratives 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 pursuit strategy.
What Changes
Subro potential gets flagged at intake instead of being caught (or missed) 3 months into the claim. The AI identifies recovery opportunities you'd have found eventually — but catches them when they're still recoverable.
What Stays
The pursuit strategy. Negotiating with the adverse carrier, deciding when to arbitrate vs. settle, managing the timeline. Subrogation is claims work applied in reverse — same skills, different direction.
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 subrogation identification & pursuit, 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 subrogation identification & pursuit 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 subrogation identification & pursuit?”
They're setting the automation strategy for your unit
your SIU lead
“Who on our team has the deepest experience with subrogation identification & pursuit, 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 subrogation identification & pursuit, 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.