Healthcare Fraud Detection Solutions
AEGIS ISD delivers integrated healthcare fraud detection solutions that cover the full FWA investigation lifecycle — from alert triage through case resolution and recovery. Built for SIU teams, medical reviewers, and program integrity leaders at Medicaid, Medicare, and commercial health plans.
Solutions
Each solution shares a unified case record, configurable workflows, and embedded AI assistance — so investigators never switch tools or lose context.
Centralize evidence, communications, clinical findings, and final determinations in one audit-ready investigation record. Unlike legacy SIU tools that scatter information across spreadsheets and email, AEGIS ISD keeps every case artifact in a structured, searchable timeline.
Prioritize fraud, waste, and abuse alerts with a no-code rule engine backed by a healthcare plugin: bundled rule packs for upcoding, unbundling, utilization outliers, eligibility gaps, and network-integrity adjacencies. Authors version, audit-log, and roll back rule changes; AI-assisted rule generation suggests new rules from case patterns for human approval before activation.
Coordinate clinical peer reviews with structured documentation checklists, medical necessity criteria, and reviewer collaboration workflows. Multi-stage review includes a dedicated QA Analyst stage with peer-verification permissions narrowed to MR_QA_FINALIZE, so determinations are independently validated before final closure. Sample selection runs on SVRS — statistically valid random sampling with RAT-STATS-aligned methodology, Cochran's-formula sample sizing, and Mean Per Unit extrapolation feeding directly into Recovery Tracking.
Monitor provider billing patterns, utilization outliers, and network relationships with continuous surveillance. Detect upcoding, unbundling, impossible day patterns, and geographic anomalies before they become large-scale losses.
Track identified overpayments as financial actions from identification through final resolution. SVRS extrapolations and their methodology PDFs are saved to the same case, so the documentation sits in the same record as the dollars. Maintain a clear chain of custody for every dollar recovered, with outcome reporting by program and source.
Measure alert conversion rates, investigation cycle times, recovery outcomes, and investigator productivity with real-time dashboards. Give program leaders the visibility they need to justify budgets and demonstrate ROI.
Why AEGIS ISD
Legacy healthcare fraud detection platforms force teams to work across disconnected modules, rely on vendor professional services for every workflow change, and bolt on analytics as an afterthought. AEGIS ISD was built differently.
Configurable Workflows
Adapt investigation workflows to your program requirements, clinical standards, and regulatory obligations without writing code or waiting for vendor development cycles.
Use Cases
AEGIS ISD supports diverse fraud, waste, and abuse investigation scenarios across Medicaid, Medicare, and commercial health plan programs.
Solutions by team: health plan payment integrity, Medicaid program integrity, and SIU case management.
Investigate outlier billing patterns, network relationships, and provider credentialing issues with structured case workflows.
Monitor prescription refill anomalies, durable medical equipment outliers, and controlled substance dispensing patterns.
Coordinate clinical review and documentation for high-risk behavioral health services including therapy session billing and residential treatment.
Detect and investigate member eligibility fraud including identity theft, address manipulation, and coverage period abuse.
Validate prior authorization requests against clinical criteria, provider credentials, and plan coverage rules before services are rendered.
Conduct targeted and random post-payment audits with structured sampling, documentation review, and overpayment identification.
Prepay and Postpay Detection
AEGIS ISD Detect uses one governed rule library for both payment stages, so program integrity teams can move checks upstream into prepay review without maintaining a second set of rules.
For a deeper look at splitting rules between stages, read prepay vs postpay FWA detection: where each rule belongs.
Pending claims are screened against the member's claim history and CMS NCCI, MUE, and MS-DRG reference data. Each rule recommends deny, suspend, review, or flag, and reviewers make the decision.
Reviewers work assigned queues, release, suspend, or deny at claim or line level with a required reason, and export finalized decisions for handoff to claims operations.
Paid and adjusted claims are scanned when new data loads, on demand, or as a retrospective backfill, and every alert is risk-scored from 0 to 100.
Alerts and prepay escalations become Case Management leads with the evidence attached, so investigation, medical review, and recovery continue in the same record.
Platform Comparison
Unlike Cotiviti and Optum platforms that use separate modules for detection, case management, and analytics, AEGIS ISD provides a unified workspace where all FWA investigation activities happen in one place.
AI-Powered Healthcare Fraud Detection
Every AEGIS ISD solution module includes embedded AI assistance. Investigators and medical reviewers can ask contextual questions to summarize evidence, find similar cases, compare provider patterns, and get recommended next actions — all without leaving the investigation screen.
Question: Summarize this clinical attachment for the medical review determination.
FAQ
Unlike Cotiviti and Optum platforms that use separate modules for detection, case management, and analytics, AEGIS ISD provides a unified workspace where all FWA investigation activities happen in one place. AEGIS ISD also includes embedded AI assistance, no-code workflow configuration, and schema-per-tenant data isolation as standard features.
Yes. AEGIS ISD supports Medicaid managed care, Medicare Advantage, and commercial health plan fraud investigations. Workflows, evidence requirements, and reporting formats are configurable by program type.
AEGIS ISD detects provider billing fraud (upcoding, unbundling, phantom billing), pharmacy and DME fraud, member eligibility fraud, medical necessity issues, and network integrity concerns including kickback arrangements and improper referral patterns.
No. Detection rules, risk scoring models, and investigation workflows are configurable by authorized business users without custom code. New fraud schemes can be addressed by creating new alert rules, adjusting risk thresholds, and modifying investigation templates.
Implementation follows a phased approach starting with workflow discovery, data integration, and user training. Most organizations launch their first program within 90 days and expand to additional lines of business in subsequent phases.
Next Steps
Schedule a demo tailored to your program integrity goals, data sources, and investigation workflows.
