Fraud Detection & Pattern Flagging
Insurance fraud increasingly involves provider networks and coordinated filings that rules-based systems miss.
80%
Fewer false positives
3x
Faster investigation
<4 wks
Time to deploy
01
The Problem
The Challenge
Sophisticated fraud schemes involve multiple providers, staged claims, and coordinated filings timed to evade detection. Rules catch known patterns and frequency scoring catches volume, but neither finds the relationship patterns that indicate collusion. As a result, SIU teams spend time reviewing low-confidence alerts.
02
How It Works
How ActionAI Solves It
ActionAI analyzes claim data alongside provider networks and claimant histories to identify patterns that rules alone miss. The system assigns confidence scores based on how closely observed behavior matches known fraud patterns and the supporting evidence. High-confidence alerts arrive as investigation-ready summaries, including the pattern and related claims. Lower-confidence findings are enriched with additional data to provide context for review.
03
The Outcome
Key Capabilities
Your SIU team spends time investigating real patterns instead of noise, with each alert including evidence, involved parties, and a clear case timeline. As investigators resolve cases, their findings feed back into the system to sharpen detection.
Results that speak for themselves
80%
Alert quality improvement
Higher signal-to-noise ratio
3x
Investigation speed
Pre-enriched context per alert
100%
Investigation speed
Every flag traced to specific indicators
Healthcare
Healthcare documentation is the legal record: file a prior auth against the wrong payer policy and treatment slides two weeks to the right. A procedure code that doesn't match the chart loops the claim through three appeal cycles before anyone catches it. Ground truth is the payer policy and coding guideline controlling the decision in front of you. ActionAI scores every automated claim and prior auth against those controlling rules, and high-confidence decisions release without a reviewer opening the file. Below threshold, ExEx (Explainable Exceptions) pulls the case and routes it to the reviewer with the failed rule and source chart attached, so they open the case knowing what needs attention.
Related Use Cases
Frequently asked questions
Prior authorizations, medical coding, claim denials, and scheduling — any workflow where an AI is scoring against a known truth source. Each automation deploys individually, so a health system can start with one process and expand.
Cases that score below threshold drop out of the automated flow and route to a human reviewer with full context, under the Explainable Exceptions (ExEx) protocol. Nothing below threshold gets auto-submitted.
No. ActionAI runs on top of the systems you already have, through API or data export — no replacement needed. Clinicians and administrators keep the interface they know.
SOC2 compliant with SSO and encryption, deployed in cloud, VPC, or on-premises based on what the environment requires. The deployment model gets set in the initial scoping call.
A 1-2 hour scoping call identifies the highest-ROI process, followed by a free pilot on real data — days to weeks, not months. Impact is measured from your own data during the pilot.