Searching through millions of financial transactions in order to discover evidence of money laundering is a growing challenge for the Australian Transaction Reports and Analysis Centre (AUSTRAC), the intelligence agency in charge of keeping Australia’s financial system free from criminal abuse.

Supported by ARC Linkage Projects scheme funding, researchers at RMIT University, led by Professor Xinghuo Yu, are helping AUSTRAC by bringing machine learning and artificial intelligence tools to the task of detecting and deterring suspicious activity. Together they have developed new software that can detect unknown money laundering networks faster and more accurately than ever before.

Their system has demonstrated its ability to detect with efficiency and precision transactions that meet criteria for further investigation by analysts, freeing analysts from manually examining large volumes of raw financial data and enabling complex financial crimes to be more easily detected.

In the 2015–16 financial year, financial intelligence derived from AUSTRAC data contributed to government partner organisations saving Australia millions of dollars, including $8.3 million in welfare fraud, $130 million in liabilities from Serious Financial Crime Taskforce activities, and $152 million in income tax assessments.

Working with RMIT University’s researchers, AUSTRAC’s continuous innovation has enabled the agency to remain effective in deterring and detecting money laundering and terrorist financing and related criminal activities.

Stock image: Money laundering.
Image courtesy: ©iStockphoto.com/AlexSava.