Good practice Imported

Canada Revenue Agency — AI-driven detection of unwarranted GST/HST refund claims

Canada · Ottawa · See the Canada profile · See the Ottawa profile

Evidence: Observational / pre–post Top 24% 73/100 · Ask Evidence Copilot about this practice

Canada Revenue Agency uses ML and AI-powered analytics to detect unwarranted GST/HST refund claims. Since 2017–18, over C$1.1 billion in carousel-scheme fraud identified; compliance activities now flag over C$1.5 billion in suspicious refund claims annually.

C$1.1 billion+

Unwarranted carousel-scheme refunds identified since 2017–18 (2017–18 to present)

C$1.5 billion+

Suspicious refund claims flagged annually (2023–24)

C$42 million

Amount exceeding Budget 2021 target (2023–24)

C$425.7 million

Total compliance investment (Budgets 2016/2017/2018/2021)

Details

Maturity: Established
Promoter: Canada Revenue Agency (CRA) / Agence du revenu du Canada
Period: 2018-present
Keywords: tax compliance, machine learning, GST/HST fraud, carousel fraud, risk scoring, data analytics

Context

The Canada Revenue Agency uses machine learning and AI-driven risk-scoring models — cross-referencing GST/HST returns against banking, payroll, supplier-network and import/export data — to flag suspicious refund claims, particularly 'carousel' fraud, before refunds are paid; human auditors review all AI-flagged returns, and the system operates under Treasury Board's Directive on Automated Decision-Making.

Results

Since 2017–18, AI-assisted compliance activities have identified more than C$1.1 billion in unwarranted carousel-scheme refunds; by 2023–24 the system flags over C$1.5 billion annually in suspicious claims, and the CRA's 2023–24 Departmental Results Report confirms it exceeded its Budget 2021 target of identifying C$250 million in unwarranted refunds by C$42 million that fiscal year.

Implementation

Implementation detail (cost, timeline, staffing, conditions for success) is not yet available for this practice.

Commonly funded by

National / regional programmes

Indicative funding routes for practices of this type — always check each programme's current calls and eligibility rules.

Do you run this practice? Claim it — verified implementers get a public contact pathway and can propose corrections.

Data sources

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

C-NAPSE web research — accessed 2026-06-20
Canada Revenue Agency (CRA) / Agence du revenu du Canada — accessed 2026-06-20
CRA — Aggressive GST/HST tax schemes (official) — accessed 2026-06-20
Canadian Accountant — How the CRA uses AI in tax audits — accessed 2026-06-20
Canadian Tax Foundation Perspectives — Emerging technologies and tax compliance (2024) — accessed 2026-06-20

Quality score: 73/100

How is this calculated?

Evidence: Observational / pre–post

Top 24% in this catalogue (794 scored practices)

Scores cluster high, so position within the catalogue is often more telling than the number alone.

Evaluation

Evaluated by C-NAPSE

Score profile — the dimensions behind the score — how it's computed

Evidence of impact / measured public value3/3

Official Departmental Results Reports confirm C$1.1 billion identified since 2017–18 and C$1.5 billion+ in suspicious claims flagged annually, providing large-scale, verifiable public-finance impact documented in government accountability reports.

Transparency, fairness & accountability2/3

Aggregate outcomes published in annual Departmental Results Reports and on official campaign pages; CRA is subject to the Treasury Board's Directive on Automated Decision-Making; however, model features, thresholds, and algorithmic logic are not publicly disclosed.

Transferability / demonstrated replication1/3

No documented formal replication of CRA's specific deployment; other tax authorities (ATO Australia, HMRC UK) use analogous AI approaches independently — demonstrating the concept is replicable but not that CRA's model has been directly transferred.

Scalability beyond pilot3/3

System has operated at national scale since 2018, assessing GST/HST returns across all Canadian registrants before human review; expanded through successive budgets to 2021 with C$425.7 million in technology and compliance investments.

Governance, capability & sustainability2/3

Subject to Directive on Automated Decision-Making (DAMA) including impact-assessment requirements; CRA internal audit function and Parliament's Public Accounts Committee provide oversight; no independent external algorithmic audit has been published.

Cite this

Evidoria. Canada Revenue Agency — AI-driven detection of unwarranted GST/HST refund claims. Persistent ID: 8b7f24fc-37eb-4aeb-a29b-f42af9d122d6.

Export as decision brief (PDF)

Similar practices you may find useful

★ 87

Revenue REAP — Ireland's AI risk engine for human-supervised tax-compliance targeting

Ireland

Since 2011, Ireland's Revenue Commissioners have used AI risk scoring (REAP) to target tax-compliance interventions; 290,000 data-analytics-driven interventions in 2023 …

★ 80

ESCORT — Latvia's State Revenue Service ML risk-scoring system for tax compliance

Latvia

Latvia's SRS uses ESCORT ML to score every registered company for fraud risk across tax and labour domains. Fraud detection …

★ 80

VeRa — Italy's AI risk-scoring algorithm for tax-evasion detection

Italy

Italy's Agenzia delle Entrate deploys VeRa, an AI algorithm cross-referencing declarations with bank data, property records and electronic payments. In …

★ 60

Skattestyrelsen Transaction Network Analysis — Denmark’s machine-learning VAT carousel-fraud detector

Denmark

Since 2014 Denmark’s Skattestyrelsen has run a real-time ML system flagging suspicious VAT refund claims; it reports ~70% accuracy, is …