In short, the historical dataset available to train the machines is misleading, complicating their ability to learn detection.
Criminals, by contrast, are constantly adapting their ways, finding new routes for their cash when existing ones are blocked off. Catching tomorrow’s money launderers requires anticipating where they’ll move next. Will they trade gold or crypto assets? When parameters change even slightly, AI struggles to stay ahead of the criminals.
Trust in financial services after the 2008 crisis is taking a very long time to rebuild. Banks are wary that they risk teaching machines to stereotype customers based on where they come from or where they do business. “Ethical concerns associated with AI are rightfully restraining banks’ full embrace of machine learning,” says Alexon Bell, chief product officer at Quantexa, a London-based data analytics company that counts HSBC among its customers.
Regulators, frustrated with the slow speed of change, have encouraged banks to deploy more technology. In December the U.S. Treasury Department’s Financial Crimes Enforcement Network, jointly with the Federal Reserve and other U.S. agencies, called on banks to try new approaches to meet anti-money-laundering requirements, including AI, and have offered leniency if the tools uncover deficiencies in existing systems.
One thing seems clear: Compliance spending at banks may be shifting away from employing humans to adopting new software. But for now, those living and breathing internal cops are here to stay.
This article was provided by Bloomberg News.