HSBC and Silicon Valley-based AI start-up Ayasdi have partnered up in order to help combat money laundering through automation.
HSBC is looking to automate some of its compliance processes in order to become more efficient by working with the US-based AI start-up, Ayasdi.
HSBC’s Chief Operating Officer, Andy Maguire, said in an interview that with Ayasdi’s help, they will work to automate anti-money laundering investigations that have in the past conducted why a huge team of people, often in the thousands.
Ayasdi claims that the majority of these investigations at banks to tend to find that there’s been no suspicious activity, which actually means that the whole process can be a waist of time, money and resources.
HSBC has already tested Ayasdi’s technology in a pilot and found that the number of investigations dropped by 20 percent. Moreover, this was achieved without seeing fewer cases being referred for further scrutiny.
“It’s a win-win,” Maguire told Reuters. “We reduce risks and it costs less money.”
There’s been a marked increase in the number of banks turning to AI and automation over the last year in an effort to save money and time on mundane tasks that could be completed faster and quicker by an algorithm. This is especially the case in customer service, which is almost completely run by chatbots nowadays.
Moreover, many of these banks have been working closely with AI start-ups in an effort to reduce the amount of technology that has to be built in-house.
Back in 2012 HSBC had to pay $1.92 billion in fines to the U.S. authorities because, among other things, the bank had been used to launder drug money out of Mexico without having realised what was actually happening. HSBC will be hoping that this move towards AI will prevent something like that from happening again.
Maguire noted that anti-money laundering checks “is a thing that the whole industry has thrown a lot of bodies at it because that was the way it was being done.”
Maguire continued by claiming that this AI technology will be able to help with compliance due to its ability “to do things human beings are not typically good at like high frequency high volume data problems.”