Google Cloud Launches AI-Driven Anti-Money-Laundering Tool

Google Cloud Launches AI-Driven Anti-Money-Laundering Tool

Google Cloud has released an anti-money laundering solution that uses artificial intelligence to aid

The startup hopes to distinguish itself from a crowded market of monitoring systems by lowering the human input required in the screening process for money-laundering threats. Financial firms have long used human judgment to calibrate technologies that detect potentially dangerous transactions and consumers. Google Cloud now wants them to give its artificial intelligence technology more influence over the process.

Alphabet's cloud division announced the introduction of a new AI-driven anti-money-laundering tool on Wednesday. Like many other tools already on the market, the company's technology uses machine learning to assist customers in the financial industry in complying with rules that require them to screen for and report potentially suspicious activities. Google Cloud intends to distinguish itself by removing the rules-based programming generally needed to develop and operate an anti-money-laundering monitoring program as a design decision. That runs counter to the industry's current approach to such tools and may be met with skepticism from some quarters.

Anti Money Laundering AI, an application programming interface, already has several famous users, including London-based HSBC, Brazil's Banco Bradesco, and Lunar, a Danish digital bank. Its introduction coincides with prominent US technology firms exercising their artificial intelligence skills in the aftermath of the success of the generative AI program ChatGPT and a rush by many in the corporate sector to incorporate such technology into a variety of businesses and industries.

For years, financial institutions have depended on more traditional kinds of artificial intelligence to help them filter through the billions of transactions that some of them conduct every day. Typically, the process begins with a succession of human judgment calls, followed by machine learning technology to create a system that helps banks detect and prevent fraud. Activities that may need to be highlighted to regulators for further examination.

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