Artificial Intelligence in the Fund Industry:

Date: November 28, 2017

Artificial Intelligence More often than not when I browse a trade journal or the news these days, Artificial Intelligence (AI) is front and center. AI, robots, and other technologies reminiscent of science fiction, are beginning to take shape in all aspects of our lives. From the automotive industry to Amazon, and now even to the financial services industry, corporations are looking at functions where machine learning and the analytics that come with it, can create an environment of increased automation and efficiency, coupled with the ability to continually improve on existing processes.

At its most basic level, machine learning is the ability for computers to improve through the information or data they become exposed to, rather than relying on human intervention to set up individual programmatic instructions. It provides a powerful way to predict future patterns in data and analytics that can then be used for operational growth and improvement, among other things.

For many, maybe not so surprisingly, the future of artificial intelligence is scary; something that will take away jobs or limit the element of human intuition and interaction. When I think of AI in the financial services industry, rather than being fearful, I see the potential of an environment where AI and employees coexist. One that provides greater opportunity to existing workers to become more valuable members of their organization.

Within the middle and back office, artificial Intelligence won’t invalidate employees’ usefulness, in fact, it would complement their work by allowing them to focus on important tasks that still require human intervention and intuition. AI would eliminate labor-intensive tasks such as writing macros, manual trend analysis and reporting. It could, for example, complete validations and checks across areas of the organization, whereas today they are manual and repetitive for each and every cycle.

In a recent paper, Accenture detailed how advances in technology are putting asset management middle- and back-office teams front and center to support increased automation, better service, fewer errors with full audit trails, and increased visibility and transparency.

The technologies that many administrators and asset managers have historically used, such as core accounting platforms, only provide back-office workers with information and data that needs much further analysis before further dissemination and use. The question then becomes how machines can help the back office to more efficiently analyze information, make better-informed decisions, and provide proactive notification and realization of patterns, while also providing appropriate and timely information for management and strategic decision making.

With today’s machine learning technology, organizations should look for the following tasks that have the potential to be replaced by automation.

Tasks that lend themselves to artificial intelligence or machine learning:

  • Potentially high volume
  • Very manual
  • Repetitive
  • Require constant analysis or feedback for updates

AI will allow organizations to have a constant, real-time feedback and analysis loop to identify areas of inefficiency. Then, through technology, they can improve and update their processes, allowing middle- and back-office workers to prioritize and work collectively with machines to improve upstream processes, eliminate redundant scenarios and provide an environment of constant improvement.

One key point made in an article by Deloitte is that for back-office operations, great efficiencies can be gained by focusing on legacy core platforms and adding solutions that incorporate Robotic Process Automation (RPA). RPA is an industry term for machine learning or AI, with an overarching service layer (which we see many administrators striving for today). Just changing technology isn’t enough. There is significant process re-engineering that must occur to make this work in concert with the organization’s goals and strategies. Workers can then take the tasks completed by machines, inclusive of more detailed and derivative analysis and findings, and use them to be more efficient in their current roles. Simultaneously, they can expand their existing capacity to use data in more meaningful ways to the organization, investors, or regulators.

This service layer is a key differentiator that many administrators today will provide through their existing workforce in order to provide value-added services to their clients. Elements of RPA can be added here to enhance offerings such as self-service reporting.

As technology continues to evolve and machine learning and RPA gain traction in the financial services industry, integration of this technology into the day-to-day processes of asset managers and administrators will lead to more efficient, self-improving processes, while allowing back-office employees to take on more value-add activities that increase the service level provided to clients, investors and the market.

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