Artificial intelligence (AI) is rapidly transforming how organisations can detect, monitor and manage money laundering risks. AI-driven tools offer the potential to process vast volumes of information far more efficiently than some traditional compliance systems and are being used for transaction monitoring, suspicious matter reporting and ongoing customer due diligence.
Anti-money laundering and counter-terrorism financing (AML/CTF) procedures are being covered by specialist platforms that use machine learning and advanced analytics to enhance transaction monitoring, customer due diligence and suspicious activity detection. At the same time, organisations are increasingly relying on the use of generative AI to support investigators and compliance teams with administrative and analytical tasks.
As Australian businesses comply with the new AML/CTF regime, organisations considering the use of AI within their AML frameworks should ensure that innovation is accompanied by appropriate oversight, transparency and accountability.
AI can strengthen AML/CTF compliance by improving transaction monitoring, customer due diligence, risk identification and the detection of emerging financial crime trends. In particular, AI enables a shift from reactive monitoring to predictive, risk-based monitoring by identifying behavioural anomalies and evolving risk patterns that traditional rule-based systems may miss. The Financial Action Task Force (FATF) has recognised that, when implemented responsibly, AI can make AML measures faster, more effective and more efficient.
The use of AI does not alter an organisation’s obligations under the Anti-money Laundering and Counter-Terrorism Financing Act 2006 (Cth) (AML/CTF Act). Organisations remain responsible for complying with the AML/CTF Act and for the decisions and outcomes generated by any AI tools they use. AI should be integrated into an organisation’s existing AML/CTF framework and supported by appropriate human oversight, rather than relied upon as a substitute for compliance processes or decision-making.
AI carries a number of risks in its application as described below:
One challenge associated with AI is the “black box” problem. While AI can improve the detection of suspicious activity, some models provide limited visibility into how decisions are reached.
Organisations should be able to explain why a transaction was flagged, how the system arrived at its conclusion and what human oversight was applied before any action was taken.
AI is a powerful tool but cannot substitute professional judgement. AI lacks contextual understanding, ethical reasoning and legal judgement.
Organisations must make sure that appropriately qualified personnel, such as their compliance officer, retain oversight of AI-generated outputs, particularly where decisions may result in suspicious matter reports, account restrictions or enhanced due diligence measures.
AI systems are only as effective as the data on which they are trained. Poor quality, incomplete or outdated data collected in the due diligence or KYC process can produce inaccurate risk assessments and unreliable monitoring outcomes.
Even where data is corrected, if historical data contained deficiencies or inaccuracies, those deficiencies could become embedded into the model itself.
Organisations should not assume that an AI model that performed effectively during implementation will continue to perform effectively as methodologies evolve or as deficiencies come to light.
AI systems may inadvertently perpetuate or amplify existing biases contained within training data. Within AML programs this risk can arise during customer onboarding, risk assessment and KYC processes. Certain demographic factors, geographic indicators or proxy variables may unintentionally influence outcomes and result in unfair treatment of customers.
Compliance with the AML/CTF regime introduces new privacy risks for businesses. For more information about those risks, please see our recent article here.
AML systems and programs process significant volumes of sensitive and personal information. AI solutions will further increase privacy and cybersecurity risks because they require extensive data aggregation, sharing and storage.
The Office of the Australia Information Commissioner (OAIC) has highlighted its concerns with AI inferring and generating personal information beyond what users have provided. Organisations should carefully assess privacy obligations when deploying AI-tools.
From 10 December 2026, new Australian Privacy Principles will require covered organisations to disclose certain uses of AI. For more information about this, please see our recent article here.
While AI has the potential to strengthen AML and financial crime compliance, it is also increasingly being used by criminals to facilitate fraud, money laundering and other illicit activities. Sophisticated threat actors can leverage AI and automation to analyse transaction patterns, adapt their behaviour to avoid detection, and scale financial crime operations more efficiently than ever before.
Organisations should not view AI solely as a compliance tool, but also as an emerging financial crime risk that must be considered within their AML/CTF risk assessments, monitoring programs and governance frameworks.
For organisations seeking to deploy AI within their AML/CTF programs, several practical measures can help manage risk:
AI has the potential to significantly improve the effectiveness and efficiency of AML compliance by enhancing risk detection, reducing false positives and enabling more sophisticated analysis of financial crime risks.
This article was written by Tegan Hill, Lawyer, Corporate Commercial.