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Google Cloud launches anti-money laundering tool for banks
The company seeks to differentiate itself from a crowded field of surveillance platforms by reducing the amount of human involvement in the money laundering risk detection process.
Financial institutions have long relied on human judgment to calibrate systems that help spot potentially risky customers and transactions. Now, Google Cloud wants them to let their AI technology take more control of that process.
Alphabet's cloud business announced a new AI-powered anti-money laundering product. Like many other tools already on the market, the company's technology uses machine learning to help financial sector clients comply with regulations that require them to detect and report potentially suspicious activity.
Google Cloud aims to differentiate itself by removing the rules-based programming that is typically integral to setting up and maintaining an anti-money laundering surveillance program. This design choice runs counter to the general approach of such tools and could be subject to skepticism from some sectors of the industry.
The product, an application programming interface dubbed Anti Money Laundering AI, already has some notable users, including London-based HSBC, Brazil's Banco Bradesco and Lunar, a Denmark-based digital bank.
Its launch comes as major US tech companies are flexing their AI capabilities following the success of the generative AI app ChatGPT and a race by many in the corporate world to integrate such technology into a variety of businesses and industries.
Financial institutions for years have relied on more traditional forms of artificial intelligence to help them sort through the billions of transactions that some of them facilitate daily. The process typically starts with a series of human judgment calls. Machine learning technology is layered on top of it to create a system that allows banks to spot and review activity that might need to be flagged to regulators for further investigation.
Google Cloud's decision to remove rule-based inputs to guide what its surveillance tool should look for is a bet on the power of AI to solve a problem that has dogged the financial industry for years.
Depending on how calibrated a financial institution's anti-money laundering tools can flag too little or too much activity. To few alerts can raise questions, or worse, from regulators. Too many can overwhelm a bank's compliance staff, which reviews each hit and decides whether to file a report with regulators.
