The hottest buzz in international finance is that some of the world’s largest banks are ready to use artificial intelligence to replace human surveillance staff in a desperate effort to reduce criminal activities like money laundering and theft.
But delays in establishing a reliable manpower system mean human surveillance will prevail – at least for now – but changes may not be far away.
HSBC is already applying AI to screen transactions and the two biggest Nordic banks say they will replace compliance staff with algorithms.
So far, machines are confined to simple know-your-customer (KYC) applications and are far from ready to replace humans, Tom Kirchmaier, a visiting fellow at the London School of Economics’ Centre for Economic Performance, told Bloomberg.
And he’s not optimistic that changes are imminent either.
“There’s a lot of talk but no action,” he told Bloomberg.
The uncertainty in banking circles about compliance follows major scandals and corruption that somehow slipped past the sentries, human and artificial.
Bloomberg says Take ING Groep NV last year paid $US900m to settle an investigation by a Dutch prosecutor into alleged money laundering and other corrupt practices.
Even though the bank uses machine learning to filter out false alerts on potential problems it has increased the number of individuals handling KYC procedures.
Human intelligence sometimes triumphs over artificial intelligence.
When HSBC Holdings Plc thwarted a $US500m central-bank scam computer software did not raise the alarm as the money flowed undetected from Angola’s reserves to a dormant company’s account in London.
It was a teller at a suburban bank branch who became suspicious, declined a request to transfer $2m and triggered a review that uncovered the scam, according to one account of the episode.
That was two years ago, and the finance industry’s battle to stop the illicit transfer of as much as $2 trillion a year around the globe has not become any easier.
At least a half-dozen lenders in Europe have been embroiled in fresh allegations of dirty money schemes in the past year.
The scandals at Denmark’s Danske Bank A/S, Deutsche Bank AG, and others is undermining overall confidence in the industry as well as damaging the individual banks involved.
Financial-services executives have had little choice but to significantly step up regulatory efforts. Banks and tech companies need to overcome a number of obstacles for AI to succeed in tackling money laundering.
They need more detailed customer data, which is often neither current nor consistent, especially when a bank spans multiple jurisdictions. Enhancing the quality and frequency of data gathering is a crucial first step.
Banks are also constrained in their ability to detect bad behaviour, with or without computers, because competitors and national law enforcement agencies won’t share data.
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.
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.