Google Makes Another Big Splash; Introduces Anti-Money Laundering AI Tool
- AI will be deployed to fight money laundering
- Google's new AI product was trialed by HSBC
- AI helps Financial Organizations to Meet AML Obligations
Over the past month, we've covered numerous topics related to artificial intelligence and its importance to the banking industry, specifically in the fight against fraud. We've talked about generative AI vs. traditional AI, and how Apple's Vision Pro could usher in a new immersive environment for fraud analysts in the near future.
However, Google is emerging as perhaps the biggest player in the banking industry, recently announcing that several major companies including Uber Technologies, Victoria's Secret & Co, and Deutsche Bank AG are putting Googles AI to the test -- with Deutsche Bank looking for lower costs in call centers via Google's generative AI.
Additionally, Google introduced a new security feature to inform you if your email address has been published on the dark web -- another tool Google is deploying in the fight against fraud.
Now, after a successful trial launch with HSBC, Google Cloud has announced AML AI, an AI-powered product designed to help global financial institutions detect and target instances of money laundering.
Google Technology Showing Strong Results
As with any new technology, "the proof is in the pudding." Google is no different, and it appears that they have proven their technology is ready for deployment.
Money laundering has been one of the biggest—and costliest—challenges for the financial services sector. Through this new effort, Google Cloud is betting on AI to monitor money laundering activity and, overall, improve financial crime risk detection.
HSBC, a Google Cloud customer, found success after trialing the product. The company detected two to four times more true positive risk, and saw alert volumes decrease by more than 60%.
Jennifer Calvery, Group Head of Financial Crime Risk and Compliance at HSBC, said in a prepared statement:
“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. 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 analyze billions of transactions across millions of accounts from several weeks to a few days.”
This tool has appeared at a fortuitous time: Anti-Money Laundering (AML) regulations used to apply only to financial institutions and banks, but now extend to businesses as well so they can protect themselves and their consumers. New -- and accessible -- instruments like AML AI will be great aids in bolstering collaborative protection against fraud.
In addition to leveraging the right tech tools, collaboration is also important. In the U.S., the Anti-Money Laundering Act of 2020, the Patriot Act, and the Bank Secrecy Act, advocates for collaboration, as well as the use of advanced technology to fight financial crimes and the financing of terrorism.
AI in Fraud: The Present and the Future
It's clear that artificial intelligence will play a key role in fighting fraud -- particularly when it comes to banking. Google's entrance into the AML space is just the start as technologies become more sophisticated. It remains to be seen where AI will make its next impact in the fight against fraud.
Banks are now tasked with selecting the right mix of technologies to best protect themselves and their customers. As we've noted in the past, it's important to select complementary technologies that provide full-scope fraud detection. This includes behavioral analysis, data analytics, image forensic AI, and now, AML.
These technologies must work in harmony with each other, while also integrating into a single platform with which fraud analysts can review all the information and data available to spot fraud being perpetrated.
