VP of Claims
Fraud Detection & SIU Oversight
What You Do Today
Oversee the Special Investigations Unit and fraud detection program — balancing aggressive fraud pursuit with customer experience and regulatory requirements. False accusations are as damaging as missed fraud.
AI That Applies
AI fraud detection models that score claims for fraud indicators using network analysis, behavioral patterns, and anomaly detection — identifying organized rings that individual investigation can't see.
Technologies
How It Works
The system ingests network analysis 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 is a prioritized alert queue, with the highest-confidence findings surfaced first for immediate review. The investigation and prosecution decisions.
What Changes
Fraud detection shifts from referral-based to predictive. The AI identifies fraud rings by connecting claims across time and geography that appear unrelated individually.
What Stays
The investigation and prosecution decisions. Deciding which referrals warrant investigation, how aggressively to pursue, and when the evidence supports denial requires claims expertise and legal judgment.
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 oversight, 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 oversight 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 board chair or lead independent director
“What data do we already have that could improve how we handle fraud detection & siu oversight?”
They shape expectations for how AI appears in governance
your CTO or CIO
“Who on our team has the deepest experience with fraud detection & siu oversight, and what tools are they already using?”
They own the technology infrastructure that enables AI adoption
a peer executive at a company further along on AI adoption
“If we brought in AI tools for fraud detection & siu oversight, what would we measure before and after to know it actually helped?”
Their lessons learned are worth more than any consultant's framework
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.