Robotic process automation is accelerating productivity within the financial sector. Much of the recent progress that’s been made innovating artificial intelligence (AI) technology toward greater efficiency has been driven by the significant resource investments of fund administrators, such as U.S. Bank Global Fund Services.
“Since early 2013, our teams have worked tirelessly to be able to fully strike an automated NAV with zero human intervention,” says Christine Waldron (pictured), chief global strategy officer at U.S. Bank Global Fund Services. “We’re now able to deliver this NAV five days sooner than we were able to historically.”
Robotic intervention has produced significant time savings – enabling staff, on average, to do their work 10 to 15 percent more quickly, according to Waldron, adding the aim is to improve personnel efficiency by two to five percent per year: “Many companies focus on overall efficiency gains; we’re more granular. We want derive value from knowing how every staff member contributes to that overall efficiency improvement.”
U.S. Bank Global Fund Services first applied robotic tools to help automate the movement and management of data files. By March 2018, their teams had achieved a fully automated NAV calculation capability.
“Approximately 30 percent of our NAVs are produced autonomously,” confirms Waldron. “This has enabled clients to give their investors faster access to data and greater transparency. It’s something we’ll continue to invest in over the next couple of years – not just for hedge funds, but also for our private equity and mutual fund clients.
“Our clients are already seeing a lot of benefits: automated closing of the general ledger, the quality around corporate actions and more.”
Waldron’s assessment is supported by the fact that robotics is now moving into desktop automation. Artificial intelligence is still used for the bank’s electronic communication needs, but it has migrated to a point where U.S. Bank Global Fund Services now empowers staff to automate some of the more mundane tasks they have on their desktops. For example, their investor services group can now run a bot that instantaneously logs individuals into necessary websites and systems, whereas before, they had to do it manually.
“Likewise, with respect to machine learning tools, the capabilities have come a long way,” says Waldron. “The accuracy ratio is now to a point where machine learning technology is very effective at identifying exceptions or areas where we should look a little deeper.”
Waldron believes next generation technologies, such as natural language processing, are a real game changer. This has prompted U.S. Bank Global Fund Services to run two additional projects.
The first project focuses on exploring the potential of cognitive learning. This technology has come so far that they can take an offering document for a PE fund, scrape out all the details around the waterfall calculation and translate it into an automatic electronic waterfall. The second focuses on expenses.
“We applied an NLP tool to our clients’ invoices. We were able to scrape a huge amount of data – things like an attorney’s hourly charge relative to another attorney. It’s resulted in us being able to offer a huge body of expense-related data to our clients to make more informed business decisions,” confirms Waldron.
The investor community is beginning to expect this added transparency as a means of enriching the investment experience.
“I think autonomous tools will begin to take hold across other core processes in our business. With the volume of data and meaningful analysis we can provide to clients, we are very excited about our future,” concludes Waldron.
