Real-time behavioral and network intelligence built for NDIS-scale complexity — designed to detect systemic risk across large-scale government care systems.
Technically, this is a dynamic, multi-relational graph system with temporal drift — not a flat relational dataset. Existing architecture was not designed for this complexity.
POST-EVENT ONLY — No real-time detection
STATIC CONSTRAINTS — Lacks system-wide modeling
CLAIMS-CENTRIC — Lacks relationship context
Current systems process transactions in isolation. There is no representation of:
Hidden relationships between providers, participants, and workers create risk patterns invisible at the transaction level.
Gradual shifts in billing behavior, service mix, and growth trajectories signal systemic issues before they escalate.
Collusive provider groups and synthetic behavior patterns form detectable community structures in the graph.
Physically impossible schedules — overlapping services, travel time gaps, staffing conflicts — reveal fabrication.
Multi-relational dynamic graph. Entities: Providers, Participants, Workers, Services, Locations. Edges: Service delivery, billing relationships, shared staffing, co-location. Output: continuously updated knowledge graph of the care economy.
Detects drift, not just anomalies -- modeling behavioral fingerprint evolution over time. Time-series embeddings per provider, rolling baseline comparisons, gradual billing inflation detection, service mix drift (e.g., therapy to SIL expansion).
A physics-based feasibility engine asking: "Is this care delivery physically possible?" Worker time capacity and schedule overlap detection, travel time feasibility between locations, service simultaneity constraints.
Graph Neural Networks, node embeddings (provider behavior vectors), unsupervised anomaly detection, community detection (Louvain / spectral clustering). Detects: collusive provider clusters, abnormal network centrality shifts, synthetic-looking provider behavior patterns.
0-100 composite score aggregating all detection layers into a single actionable metric.
Community-level risk assessment for provider groups with shared infrastructure signals.
Temporal deviation from established baseline patterns -- catching gradual fraud escalation.
Physically impossible schedules flagged in real time with forensic evidence.
| Capability | Legacy | CareIntegrity.ai |
|---|---|---|
| Transaction fraud detection | ✓ | ✓ |
| Network-level detection | ✕ | ✓ |
| Temporal behavior modeling | ✕ | ✓ |
| Physical feasibility validation | ✕ | ✓ |
| Graph intelligence | ✕ | ✓ |
| Real-time risk scoring | Limited | ✓ |
Aged care · Health insurance · Social welfare · Global public sector infrastructure
Live demo with 8 AI engines processing 98,966 claims across 60 providers in real-time.
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