The cloud computing subsidiary of the search engine giant launched the Anti Money Laundering AI (AML AI) tool, which is designed to detect illicit financial activities, last June 21. AML AI seeks to address the limitations of traditional rules-based systems that rely heavily on human monitoring. According to ZeroHedge, these traditional systems to detect money laundering have limitations – including heavy reliance on human monitoring and low rates of identifying suspicious transactions.
Current anti-money laundering products predominantly employ manually defined rules and require substantial resources to monitor the vast number of transactions within the financial industry. Google Cloud's AML AI makes use of machine learning to overcome this hurdle, enabling the tool to swiftly identify high-risk retail and commercial customers. It provides more accurate results and alleviates the workload for banking units responsible for monitoring suspicious activities.
According to Google Cloud, using AML AI gives banks several advantages over existing systems. These include increased risk detection; lower operational costs; improved governance and defensibility; and an enhanced customer experience.
HSBC is one such customer that has adopted the Google Cloud tool. The Wall Street Journal said that AML AI "cut the number of alerts HSBC received by as much as 60%, while increasing their accuracy."
Jennifer Calvery, HSBC's group head of financial crime risk and compliance, reiterated the benefits of AML AI for the banking giant. According to her, "it has significantly improved the precision of their financial crime detection, reduced alert volumes and accelerated the analysis of billions of transactions from weeks to mere days."
Back in early June, ZeroHedge's Tyler Durden noted a similar endeavor pushed by the Bank of International Settlements (BIS) called Project Aurora. The project is designed to make use of AI as a tool – in the same way as Google Cloud's AML AI – to monitor vast flows of financial transactions from all over the world in order to identify specifically flagged patterns.
According to the BIS, Project Aurora is meant to discover criminal money laundering structures protected by "money mules." However, some remain skeptical about the potential benefits of anti-money laundering tools driven by machine learning.
According to critics, machine learning algorithms may not accurately capture the nuanced contextual information needed to identify actual risks. Some even speculate that the reliance on AI and the push for a cashless society may contribute to a "Big Brother" scenario similar to George Orwell's "1984."
Tools such as AML AI would enable banks worldwide immediate access to anyone's account worldwide. In a similar way, this would enable globalists to target individuals labeled as "offenders" with impunity, as identified by the algorithm. (Related: AI spy program launched to monitor global bank transactions for "money laundering," but people know it's just an EXCUSE to surveil their private accounts.)
Moreover, there are concerns that, similar to how the War on Drugs was used as justification for governments to obtain unrestricted access to citizen finances, fear of money laundering could be used to grant governments and global banks extensive surveillance powers through AML AI. These powers have been abused in various ways, such as by freezing accounts based on mere suspicions of a crime rather than an actual conviction.
While the project emphasizes the use of AI as a means to help financial institutions detect suspicious financial activities, it is essential to remember that AI programs serve their creators. Whoever controls the AI also controls the selection of targets for surveillance.
Watch this video about Google Cloud's AML AI tool.
This video is from The Talking Hedge channel on Brighteon.com.