Harnessing the power of AI to augment human investment decision-making

We helped a leading global asset manager reap operational efficiencies from its digital transformation journey, and it then turned to us for help with the next step: the transformation of its investment processes through new technologies including artificial intelligence (AI) and robotic process automation (RPA).

The company wanted to accelerate and improve the decision-making process by using AI to interpret larger and more complex data sets and, in some instances, automate decision-making altogether. The aim was to support portfolio managers and research analysts to deliver more alpha (the active return on an investment, compared with a benchmark or market index), as well as design innovative new investment products.

  “Analyzing data from 10 years of financial statements for dozens of companies in a typical fund manager’s portfolio is the stuff of nightmares for research analysts — yet it’s the perfect learning opportunity for AI,” says Thierry Grouès, Associate Partner with Ernst & Young’s Strategy, Customer and Innovation Team.

Our task was to collaboratively design the new foundations of the asset manager’s investment processes. Working closely with the company’s executive team, we identified portfolio management and research as the areas most likely to benefit from an investment in AI and RPA, underpinned by a new operating model and partnerships with external suppliers.

We worked closely with the client’s research analysts, portfolio managers and IT specialists to identify pain points and co-create solutions. “The best way to introduce AI is simply to get on and do it,” explains Grouès. “We focused on what could be done as quickly as possible. You can’t use AI without data, so we also brought in new sources of data and used this to develop algorithms.”

The digital transformation was carried out as swiftly as possible, with sprints to help deliver minimum viable solutions. These solutions, which were introduced within as little as three months, included:

• A common screen that aggregates all the different data used in the decision-making process
• A facility for portfolio managers to call up a summary of sentiment analysis on a company or asset using a simple name search, rather than having to trawl through a dozen internal databases and multiple external sources

Regarding portfolio management, the company now uses AI-assisted or fully automated investment decision-making, together with alerts for portfolio managers to manage stock-specific and market risks. Over time, machine-learning capabilities mean that the algorithms will develop an increasingly refined understanding of users’ requirements, further improving the decision-making process. Meanwhile, RPA is being used to reduce the burden of manual, repetitive tasks on employees, releasing them to spend more time on value-adding activities, while enhanced controls manage and minimize risk of human error.