Google Cloud enters anti-money laundering space with AI product

The newly launched AML AI solution from Google Cloud aims to help global financial institutions more effectively detect money laundering

Levaraging proprietary machine learning along with the wider Google Cloud ecosystem, the new AML AI product looks to mitigate a market challenge of manually defined rules yielding low threat identification rates, by delivering an ML-generated generated customer risk score.

Using the solution, financial service companies will be able to better identify instances and groups of high-risk retail and commercial customers, with said risk score being based on transactional patterns, network behaviour, Know Your Customer (KYC) and other data.

Reduced alert volumes and explainable outputs brought by cloud-based AML capabilities can cut time spent on investigations, and in turn costs, while supporting internal risk management for compliance with data regulations.

Full confidence in anti-money laundering procedures yet to be reachedResearch reveals that over half of UK financial services professionals are only ‘somewhat confident’ in their anti-money laundering procedures.

“Google is a pioneer in AI, and now we’re making our tools, technologies, and expertise available to solve one of the biggest and most costly challenges in the financial services industry,” said Thomas Kurian, CEO of Google Cloud.

“Building on our commitment to bring AI-powered innovation to the financial services industry, we are launching Google Cloud’s AML AI to help financial institutions more accurately and efficiently identify AML risk while enhancing business operations and governance.”

Going forward, Google Cloud plans to provide generative AI foundations for the financial services sector with the goal of boosting employee productivity.

It is predicted that over 95 per cent of system-generated alerts turn out to be ‘false positives’ in the first phase of review, with around 98 per cent never culminating in a suspicious activity report (SAR) — showing the need for more detailed insights.

Meanwhile, the amount of money laundered each year, for illegal activities including drug and human trafficking to terrorist financing, is estimated to be 2-5 per cent of global GDP, or up to $2tn annually.

Early trials

Trialists of the Google Cloud’s new AML solution include financial institutions HSBC, Bradesco and Lunar.

HSBC adopted a cloud-based AI-first approach as its primary AML transaction monitoring system in its key markets, which is estimated to help detect two to four times more true positive risk, while decreasing alert volumes by over 60 per cent.

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“Google Cloud’s AML AI has significantly improved HSBC’s AML detection capability. Google’s models are already demonstrating the tremendous potential of machine learning to transform anti-financial crime efforts in the industry at large,” said Jennifer Calvery, group head of financial crime risk and compliance at HSBC.

“By enhancing our customer monitoring framework with Google Cloud’s sophisticated AI-based product, we have been able to improve the precision of our financial crime detection and reduce alert volumes, meaning less investigation time is spent chasing false leads.

“We have also reduced the processing time required to analyse billions of transactions across millions of accounts from several weeks to a few days.”

Rafael Cavalcanti, senior vice-president of data & analytics at Bradesco, commented: “As threats become more sophisticated globally and the challenges in fighting money laundering become increasingly complex, we believe in the combination of AI and decision science as the best strategy to detect suspicious activity with more accuracy and efficiency.

“We see the value of Google Cloud’s AML AI product for the financial industry, and have greatly enjoyed working with Google Cloud in advancing the industry’s approach to anti-money laundering.”


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Aaron Hurst

Aaron Hurst is Information Age's senior reporter, providing news and features around the hottest trends across the tech industry.