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RoZetta Technology
Contextual hero: government service delivery or integrity analysis theme

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A data intelligence and analytical system that turns reactive case handling into proactive integrity monitoring

8 wks to 3Case cycle, modeled
2.7xCases closed per year, modeled
Government Anomaly detection, Data analytics, AI and data intelligence

Industry

Government

Use case

Anomaly detection, Data analytics, AI and data intelligence

Product deployed

DataHex Data Analytics

Challenge

Every investigation started by rebuilding the picture

The National Disability Insurance Agency (NDIA) administers Australia’s national disability support scheme.

Administration had grown complex and the relationships between providers and beneficiaries were unclear. Reports used inconsistent entity definitions, so every investigation started by rebuilding the picture by hand while unnecessary payments grew through error, waste, and possible misconduct. The agency needed a systematic way to organize its integrity data, investigate faster, and direct funding where it counts.

Solution

A curated data asset, and the Scheme Network Map Platform

RoZetta built a robust, curated data asset and, on top of it, the Scheme Network Map Platform.

What DataHex delivered

  • Standardized entity definitions Rectified the reports used for management decisions.
  • A knowledge graph of the service value chain Every entity linked across internal, government, and public data through shared ABNs, contact details, domains, directors, and payment flows.
  • Scored associations, then anomaly detection Each association scored by volume, value, and type, with machine learning and AI anomaly detection surfacing behavior and costs inconsistent with benchmarks.
  • Ranked cases, assembled case files Cases ranked by likelihood and consequence, with the complete case file assembled in one reporting tool.

Results

Investigators investigating, not reconstructing

  • Case preparation that once took weeks of manual data wrangling is now assembled by the platform, shortening the path from trigger to investigation and action
  • Investigators spend their time investigating rather than reconstructing networks, lifting the volume of cases the same team can close
  • Cases are ranked by likelihood and consequence, so the highest-impact work is picked up first
  • Known network structures identify cases for review proactively, rather than waiting on a tip-off
  • Managers and the board have greater confidence in their reports, with transparency over how funds are distributed and clearer evidence that benefits are reaching beneficiaries

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