Automated Claims Audit & Pattern Intelligence
Zentis AI transforms claims auditing into a behavioral, data-driven, and explainable intelligence process.
Traditional claims audits rely on manual sampling and spreadsheet analysis.
Subtle utilization anomalies, care fragmentation, and behavioral shifts across members often go undetected. Audit teams struggle to prioritize high-risk cases efficiently, and findings lack clear, explainable reasoning.
Zentis AI orchestrates clien-isolated
pattern intelligence.
A Data Ingestion agent validates and structures claims data
from S3 into PostgreSQL. A Pattern Analysis agent evaluates
claim frequency shifts, provider spread, diagnosis variability,
and member-level utilization changes strictly within each client’s
population to ensure fairness. A Risk Prioritization agent
categorizes members into Normal, Monitor, or Review groups. An
Explanation agent generates human-readable justifications outlining
why a pattern stands out. A Reporting agent delivers interactive
dashboards, JSON feeds, and audit-ready PDF reports.
The system supports non-intrusive review-focused auditing without making medical judgments or claim denials empowering audit teams with prioritized, explainable findings.
Expected Impact
Improved detection of unusual utilization patterns
Witness Agentic Intelligence in Action
Client-wise data isolation and fairness
Enhanced audit transparency and reporting
Deliver intelligent, explainable, and proactive claims review
Prioritized audit workload
Clear, explainable member-level findings
