As more financial industry and payments ecosystems adopted cloud-based systems during the pandemic to accommodate remote working, the money launderers followed.
But so have data analytics and security firms seeking to put anti-money-laundering services in the cloud to detect and thwart illegal use of cross-border transactions.
"In correspondent banking, regulators see cross-border payments as high risk because you lose so much visibility with the transactions," said Colin Whitmore, senior analyst in Aite Group's fraud and AML practice. "The bank is generally clearing dollars for other customers in other countries — sometimes customers of their own customers."
In such scenarios, money launderers are finding it even easier to move and hide money, because there may not even be a need to create a fake business as a front, Whitmore added.
"The banks are doing transactions for customers they have no sight of," Whitmore said.
This trend has opened the door for security providers to focus on halting money laundering through the analysis of massive amounts of data, seeking to take away most of the hidden spots in a cross-border payment.
It also helps banks align with the
Data analytics provider
In making its service available on Google Cloud, ThetaRay will analyze Swift transaction traffic, risk indicators and client/payer/payee data to detect anomalies. The company says its AI-driven AML solution can be integrated and deployed within days, with minimal implementation required.
ThetaRay specializes in identification of "unknown cases" of criminal activity that legacy rules-based systems cannot identify, said Shaun Smith-Taylor, senior director and global head of solutions at ThetaRay.
ThetaRay's Sonar solution uses algorithms to detect new AML patterns as they appear through an analysis of risk indicators through transaction and other data sources.
"AML typologies are difficult to detect and it's a perfect storm at the moment with current rules-based controls broken and not being fit for a purpose," Smith-Taylor said.
Calling it artificial intuition or an advanced AI, ThetaRay is operating on the premise that you can't detect new AML patterns on a human's knowledge or rules-based architecture that places controls around an activity that has already happened. It has to be able to foresee and detect criminal activity through perceptions, inferences and hypotheses.
"Artificial intelligence and unsupervised machine learning is the only way to detect new AML typologies and unknown activity," Smith-Taylor added.
ThetaRay's system consumes transactional data in raw form from Swift, SEPA, Fedwire and other networks and analyzes it in real-time against a set of risk indicators and then through the machine-learning algorithms.
The system populates data from any unknown activity into a case management system for review by analysts. Alerts can be sent to internal case management systems through existing APIs.
"Within our solution, in every 100 alerts that are populated, 95% of these are deemed detection-worthy by our customers' financial crime analysts for subsequent action," Smith-Taylor explained.
"The cloud providers are coming up with ways to provide solutions on top of their cloud, so that they can do this work with banks, especially on these security models," Aite's Whitmore said.
The cloud providers find the partnership path as a good way to enter the security landscape, while not trying to compete with or become an AML company like Oracle or Actimize, Whitmore noted.
"They are getting into financial services because it is such a big area and there is so much data security needed," he added. "The volumes of data are becoming so vast, you need a cloud environment to manage it."
