SIU Investigator
Monitor fraud trends and update detection criteria
What You Do Today
You track emerging fraud schemes — telemedicine abuse, rideshare staging, cryptocurrency laundering — and update your team's detection playbooks accordingly.
AI That Applies
AI monitors claims data for emerging patterns and can surface new scheme types before they become widespread, learning from confirmed fraud outcomes.
Technologies
How It Works
The system ingests claims data for emerging patterns and can surface new scheme types before they b as its primary data source. Machine learning establishes a baseline of normal patterns from historical data, then flags any new observation that deviates beyond the learned thresholds. The output — new scheme types before they become widespread — surfaces in the existing workflow where the practitioner can review and act on it.
What Changes
You get early warning on emerging schemes rather than discovering them after losses mount.
What Stays
Understanding the criminal mindset and adapting detection strategies to stay ahead of increasingly sophisticated fraud operations.
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 monitor fraud trends and update detection criteria, 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 monitor fraud trends and update detection criteria 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 monitor fraud trends and update detection criteria?”
They're setting the automation strategy for your unit
your SIU lead
“Who on our team has the deepest experience with monitor fraud trends and update detection criteria, 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 monitor fraud trends and update detection criteria, 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.