Artificial Intelligence in Investment Management Part 2: The Service Providers

Artificial Intelligence in Investment Management Part 2: The Service Providers

Unlike the SEC, the investment management industry has already moved on from big-data primary school and is now studying in AI secondary school. And these students are already reaping some early harvests. In a recent Ignites article, Trust, but Verify: Shops Looking for Better Ways to Check Admins’ NAV Work, Ms. Jackie Noblett noted that asset managers are increasingly looking to automated tools to monitor the work of their service providers. This desire for automatic monitoring will open the door for AI machines to gain employment at asset managers around the world. As these machines become more sophisticated, they will do the work currently done by many service providers, such as fund administrators and fund accountants, and this work will become more efficient, automated, and fast.

But the rise of AI systems will be challenging to service providers that did not cleanly manage the big-data phase of technology development. Many service providers use a combination of internal and external systems to manage operating processes, many of which have multiple data feeds coming from various third-party providers. This complex web of systems and data feeds can create operational risk if service providers did not adequately integrate the systems; therefore, service providers need to understand their technology infrastructure and update or replace systems that don’t easily integrate with other systems before adopting AI systems. Once this data-cleaning and integration step is completed, service providers can successfully implement AI systems.

But successful migration from big data management to AI implementation is occurring throughout the investment management industry, even at rating agencies. For example, Morningstar is now using AI systems for reporting on fund performance, costs, and risk. On March 5, 2018, Morningstar began using its Morningstar Quantitative Rating system to rate algorithmically U.S. mutual funds and ETFs. According to a Fund Operations report on Incorporating Fund Technology (published in March 2018), this new system can “grade six times more funds than the 3,700 rated by [Morningstar’s] more than 100 analysts….”

The rise-of-the-machines is driving many service providers to develop their own in-house AI expertise. For many businesses, AI will first be deployed in areas such as process automation, customer service, and human resources, but it's only a matter of time before other areas such as accounting and legal are integrated with AI systems. This integration will drive operating costs lower because the systems are more scalable than humans and provide more consistent output than a team of people.

Although the displacement of people may sound threatening to workers, the biggest benefit of AI will be the ways it compliments humans. As AI systems take over routine tasks and simplify complex tasks, the firm’s people can focus on client engagement, product development, and solving problems, areas that are more satisfying to people than mere routinized tasks.

The improved efficiencies and lower costs that service providers will achieve from AI systems will eventually be passed on to asset managers. And this is good news for smaller managers who are increasingly struggling to keep up with increasing regulatory costs. Further, larger service providers may become more willing to take on smaller asset managers as clients. Today, the largest service providers ignore smaller managers because the service provider’s revenue and profitability from these managers are inconsequential to the mega-providers. But the increased scalability of AI systems may cause these service providers to reconsider. Together, the lower cost services and increased scalability should act as a counter-force to the increasing consolidation in the asset management industry.