In recent years, the Federal Govt has invested heavily with the ATO in implementing “artificial intelligence tools” to identify tax avoidance activities within the complicated tax affairs of wealthy individuals, companies & trusts.
Two of the key areas the ATO’s “AI tools” are applied against to detect transactions of tax avoidance (Tax Evasion & Fraud Detection) are discussed below:
Type of Activity | ATO's Issue | ATO's "AI" Solution |
Tax Evasion | Humans typically struggle to see patterns in complex high-frequency transactions among multiple entities established by wealthy taxpayers in a sophisticated arrangement to avoid tax. | Computers using a graph database are trained to identify networks of relationships between related parties. This then assists the ATO to identify suspicious transactions between related parties in order to expose fraud and detect practices that avoid tax. The ATO has deployed around 150 different machine-learning and deep-learning models to identify these types of behaviour. |
Type of Activity | ATO's Issue | ATO's "AI" Solution |
Fraud Detection | Humans analysing complicated transactions typically struggle to detect fraudulent activity between taxpayers and their associated entities, that are attempting to gain an economic benefit by misrepresenting their entitlements/claims. | The ATO uses “AI” to identify potential fraudulent activities, such as false claims or identity theft. “AI” algorithms can analyse large amounts of data and identify patterns that may indicate fraudulent behaviour. The ATO recently busted a major GST fraud ring, saving over $2 billion in lost revenue using advanced analytical tools that found patterns of suspicious behaviour among false businesses claiming GST. “AI” is also being used by the ATO to “nudge” taxpayers – such as sending a query – when their deduction claims are inconsistent with claims from similar taxpayers. The smart tools analyse expense receipts from taxpayers claiming work-related deductions, helping ATO staff to focus on higher-risk claims. |
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