AI and machine learning against money launderers

Australia’s banks process more than 13 billion cashless transactions over every year. This indicates that, more than 35 million transactions every single day is being made. Within this the criminals hide illegal transactions and launder money by concealing their activities. Similar to how cyber attackers, money launderers are nowadays exploiting the sheer volume of transactions with the hope that they will not get detected.

Traditional processes can’t keep up with these many transactions. Australia’s largest financial institutions continually fail to identify illegal transactions. Last year, Westpac admitted to 23 million breaches of anti-money laundering (AML) laws. $1.3 billion was fined for its failure to report international transactions and insufficient monitoring. In 2018, the Commonwealth Bank agreed to pay $700 million in a settlement with AUSTRAC. The Anti-Money Laundering and Counter-Terrorism Financing and Other Legislation Amendment Act was passed by the Federal government.

In the cyber security world, artificial intelligence (AI) and machine learning have been deployed to weed out attackers. They will automatically scan every email and also serves as an indicator of suspicious activity. The scourge of money laundering can be battled by the same technologies. RegTech start-ups are using the latest advances in AI and Machine Learning to help even the playing field. There are many challenges that must be overcome to analyse all the data. In other industries, applications like RegTech software could run on any of the major hyperscale public cloud providers.

In the anti-money laundering use case, the need for privacy, security, sovereignty, and governance controls, helps in the risk of handing transaction data. Australian enterprises are investing mutlicloud architectures. Hyperconverged infrastructure (HCI) provides all the performance and management associated with public cloud. The ease of deployment means such solutions can be easily configured and integrated into a bank’s environment with minimal impact on the IT team. This ensures that they can continue driving the core business. When anti-money laundering protections fail to the board, the business, and the victims of crime – AI can finally help to even the ledger.