Finding the perpetrators of crimes is a taxing task in terms of time and money; however, regulators require banks to comply with AML and KYC regulations or pay penalties. Data is key to uncovering the criminals who exploit banks for illicit purposes, but employing data to best advantage is easier said than done. Tools such as entity resolution and network analytics make the process much more trustworthy and less costly.
The rapid adoption of artificial intelligence and machine learning in all corners of the financial sector, particularly in anti-money-laundering (AML) efforts, has excited and inspired onlookers and participants alike. But as with all innovations, there are pitfalls to unquestioning acceptance that can actually worsen the situations these technologies are meant to address. Human intelligence must work cooperatively and in the lead role alongside AI and ML to guarantee the best results.
Rarely has a technology been met with the excitement and trepidation that AI has. Because artificial intelligence not only matches but can surpass human intelligence, it is exciting as a means to improve speed, save cost and maximize accuracy—but menacing for its potential to displace human workers. Banks are embracing AI for its staggering benefits, while also acknowledging that it creates a few wrinkles that need ironing out.