Increased overpayment recovery
Enhanced utilization management
Optimized network efficiency
Higher savings impact
The challenge
Optimizing claims integrity
A top 5 US payer sought to enhance claims anomaly detection and tracking. The existing process relied heavily on manual, post-pay business rules and lacked a visual tracking solution, leading to limited effectiveness.
Key challenges
Incomplete billon issue resolution (e.g., unprovided services, non-covered items)
Missed high-cost out-of-network and ambulatory claim anomalies
Limited recovery/cost optimization to $1–2M annually
The solution
Enhanced claims review through predictive analytics
Anomaly detection
Identified anomalies
Improved detection
Prioritized claims
AI-powered insights
Uncovered hidden patterns
Used AI for detection
Automated tracking
Implementation approach
1
Claims prioritization
Targeted high-cost claims
Applied selection rules
Developed hypotheses
2
Data-driven analysis
Improved detection with models
Integrated data insights
Used AI for claims data
3
Automated monitoring
Enabled real-time evaluation
Automated updates
Built tracking tool
The impact
Maximizing recovery and utilization
Enhanced recovery
Addressed unauthorized out-of-network claims
Identified overpayments
Boosted recovery
Optimized utilization
Detected consecutive day ER visits
Reduced unnecessary claims
Optimized utilization
Increased savings opportunities
Found 4X more opportunities
Funded future solutions
Optimized network
Looking ahead
Enhanced anomaly detection
Refine AI and predictive analytics for deep insights
Optimized network management
Expand AI use for higher network performance, lower costs
Scalable solutions
Use robust analytics for business growth
