Did you see the Employee Benefit Research Institute (EBRI) research that found 82% of retirees are optimistic about their ability to live comfortably in retirement (near all-time high), while 67% of those still working are confident in their financial prospects during retirement (near all-time high). It is just nice to see people feeling so good about things.
One area where banks may feel good is figuring out even better ways to leverage technology to help with compliance. Today, we update you on artificial intelligence (AI) and its AML uses.
Despite the hype around AI, 63% of bank executives say their institution hasn't explored using AI to better comply with the myriad of rules and regulations, according to Bank Director's 2019 Risk Survey.
This is interesting because the Treasury Department's anti-money laundering (AML) unit and federal banking regulators issued a joint statement on Dec 3rd encouraging banks and credit unions to consider "innovative approaches" in this area.
It's no secret that the process of detecting money laundering is ever more complex. This recent guidance from regulators is a help, as banks now have a green light to delve deeper into available technologies to assist. But, you will need to be very thorough and document everything very well indeed. Things like AI processes, developing pilot programs, and deploying technology that can best assist in regulatory compliance efforts can be beneficial, when done properly.
A report from IBM and Chartis Research helps explain some of the advantages AI can have when it comes to AML compliance. Notably, technologies like AI can help to standardize manual, time-consuming tasks and make them more efficient. One example of this is robotic process automation (RPA), which can speed up routine tasks and reduce human error. Consider that one commercial bank, for example, was able to significantly cut its average alert investigation time through RPA. At the same time, the bank was able to substantially boost its daily alert processing.
Another way AI can help banks is through text analytics and insights, while still another is entity resolution and network analytics. Developing use cases where AI can help establish connections between individuals and companies can save your team time when it comes to evaluating potentially risky parties and networks.
Certainly, the stakes are high when it comes to regulatory compliance so banks have to exercise caution. Indeed, regulators haven't given banks a blanket safe harbor for this type of innovation, so be especially careful when it comes to data protection laws, for example.
Even so, your bank may want to start the exploration process by defining the problems you think AI could solve (or at least help with). Then, as the IBM report notes, plan to rigorously test and discuss mathematical constraints and definitions of particular AI techniques with stakeholders or regulators.
Along the way, be sure to also document results and pay close attention to what's working and what's not. Your teams might also learn from other banks' experiences, as some have announced plans to implement AI capabilities to help with AML efforts.
