Claims Manager
Investigate potential fraud referral
What You Do Today
Review a claim flagged for fraud indicators — staged accident, inflated damages, suspicious medical treatment patterns. Decide whether to refer to SIU or continue normal handling.
AI That Applies
Fraud detection — AI scores claims for fraud likelihood using network analysis, behavioral patterns, and provider/claimant history to surface the claims that deserve investigation.
Technologies
How It Works
The system ingests network analysis 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 output — claims that deserve investigation — surfaces in the existing workflow where the practitioner can review and act on it.
What Changes
Your team investigates the right claims. Instead of relying on adjuster gut feel, the AI identifies fraud rings, suspicious provider networks, and staged patterns across thousands of claims.
What Stays
The investigation itself — interviewing claimants, gathering evidence, making the fraud determination — requires experienced investigators.
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 investigate potential fraud referral, 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 investigate potential fraud referral 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 investigate potential fraud referral?”
They're setting the automation strategy for your unit
your SIU lead
“Who on our team has the deepest experience with investigate potential fraud referral, 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 investigate potential fraud referral, 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.