Claims Adjuster
Fraud Detection & SIU Referral
What You Do Today
Spot the red flags — staged accidents, inflated injuries, phantom passengers, arson indicators. Some are obvious (the car fire the night before a loan default), some are subtle (the treatment pattern that doesn't match the mechanism of injury). When you see enough red flags, you refer to SIU.
AI That Applies
ML fraud scoring models that analyze claim patterns, provider networks, claimant history, and behavioral indicators. Social media analysis that identifies inconsistencies between claimed injuries and online activity. Network analysis that detects organized rings.
Technologies
How It Works
For fraud detection & siu referral, the system analyze claim patterns. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The output is a prioritized alert queue, with the highest-confidence findings surfaced first for immediate review. The investigation instinct.
What Changes
Fraud indicators surface automatically instead of relying solely on adjuster intuition. The model says 'this claim shares 4 characteristics with confirmed fraud cases in this zip code' — an early signal you might not have caught until deeper in the file.
What Stays
The investigation instinct. The way you notice the claimant's story doesn't quite add up. The interview technique that gets someone to contradict themselves. Fraud detection starts with data but ends with human investigation.
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 fraud detection & siu 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 fraud detection & siu 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 fraud detection & siu referral?”
They're setting the automation strategy for your unit
your SIU lead
“Who on our team has the deepest experience with fraud detection & siu 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 fraud detection & siu 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.