SIU Investigator
Review new referrals for investigation
What You Do Today
Each morning you open a queue of claims flagged by adjusters or tip lines, reading through loss details and deciding which ones warrant a full investigation.
AI That Applies
AI scoring models rank incoming referrals by fraud probability, highlighting red flags like prior claim history, provider patterns, and timeline inconsistencies before you even open the file.
Technologies
How It Works
For review new referrals for investigation, the system draws on the relevant operational data and applies the appropriate analytical models. 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.
What Changes
You spend less time on low-yield referrals and focus on cases with the highest fraud indicators from day one.
What Stays
Your judgment on which cases to pursue — AI flags probability, but you decide what's worth investigating.
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 new referrals for investigation, 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 new referrals for investigation 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 new referrals for investigation?”
They're setting the automation strategy for your unit
your SIU lead
“Who on our team has the deepest experience with review new referrals for investigation, 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 new referrals for investigation, 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.