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Good practice Imported

Operation Protego — AI-assisted detection of GST refund fraud (Australia)

Australia · Canberra · See the Australia profile · See the Canberra profile

Evidence: Observational / pre–post Top 50% 60/100 · Ask Evidence Copilot about this practice

From 2022, Australia's ATO deployed ML to detect a large-scale GST refund fraud scheme: 57,000+ alleged offenders identified, A$2.7 billion in additional suspect refunds prevented, A$123 million recovered, and 126 convictions — with ANAO identifying significant governance gaps.

2.7 A$ billion
Additional suspect GST refunds prevented (2022-2025)
123 A$ million
Funds recovered
57,000+
Alleged offenders subject to enforcement action
126
Criminal convictions
~300 A$ million
Penalties and interest levied by July 2025

Details

Maturity
Established
Promoter
Australian Taxation Office (ATO)
Period
2022–present
Keywords
tax fraud detection, GST, machine learning, risk scoring, criminal enforcement

Context

In May 2022, Australia's Taxation Office (ATO) launched Operation Protego after a rapid surge in fraudulent GST refund claims promoted on social media, in which people set up Australian Business Numbers for non-existent businesses and lodged false Business Activity Statements to claim refunds, with an estimated A$2 billion fraudulently obtained.

Activities

The ATO deployed machine learning models to flag suspicious refund claims in near real time and identify networks of related fraudsters, enabling detection at a scale impossible through manual audit sampling, with AI-driven risk scoring used to prioritise cases for enforcement.

Results

Documented outcomes confirmed by the Australian National Audit Office include prevention of an additional A$2.7 billion in suspect GST refunds, recovery of A$123 million, enforcement action against more than 57,000 alleged offenders, 126 criminal convictions with sentences up to 7 years 6 months, and around A$300 million in penalties and interest levied by July 2025.

Conclusions

The ANAO's 2024-25 performance audit rated fraud detection 'largely effective' in one area but 'partly effective' in three others, finding no fit-for-purpose AI implementation strategy, unclear enterprise-wide AI roles, and no AI-specific risk management arrangements — illustrating both AI's capability to detect large-scale fraud and the governance gaps that can emerge without commensurate oversight.

Implementation
Indicative cost
High (€500k–€5M)
Time to results
Long (> 3 years)
Staffing & skills
ATO investigators using AI-driven risk scoring to prioritise cases, ANAO auditors performing independent governance review

Conditions for success

  • Real-time or near-real-time fraud flagging at scale
  • Clear AI governance framework with defined roles and risk management (identified as missing)

Common failure modes

  • No fit-for-purpose AI implementation strategy
  • Unclear enterprise-wide AI roles and responsibilities
  • No AI-specific risk management arrangements (per ANAO audit)

Where it fits

Governance type
national tax administration
Scale
national
Income level
high-income

Commonly funded by

National / regional programmes

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Data sources

Where this practice's information was retrieved from, and when.

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