The growing ranks of money managers looking to harness artificial intelligence, machine learning and big data capabilities in investment operations will need focused, strong leadership to gain a competitive advantage by doing so, gatekeepers say.
Over the past two years, the growth in the number of firms “seriously dedicating time” to exploring those capabilities has been significant, said Richard Dell, a London-based partner with Mercer and head of the firm's global equity manager research team.
“We would hope the managers we speak to ... understand the way the industry's evolving, the new ways of accessing the data — and are trying to do something about it, trying to stay ahead of the game,” he said.
Those who sit out the “incremental arms race” underway now to make better sense of that data risk being “left behind,” Mr. Dell said.
Left behind or worse, some money managers argue.
Incorporating artificial intelligence and machine learning will be more than “additive” for broad-based money managers, said Anthony Lawler, co-head of GAM Holding AG's London-based quant affiliate, GAM Systematic. “If you don't have it, you may well go out of business,” he said.
GAM Systematic, which uses machine learning, was managing $4.7 billion of GAM Holding's $163 billion in assets under management as of Dec. 31.
While it's still early days for the broader asset management industry's embrace of big data, the pressure to do so should become greater over time, analysts said.
With the exponential growth of data being generated around the globe, the advantages a manager with cutting edge capabilities in analyzing big data will enjoy — in terms of “seeing reality more clearly” — should grow, said Tony Ho, Hong Kong-based co-founder and managing director of Sandalwood Advisors, an alternative data platform focused on Chinese consumer spending trends.
The likelihood that high-powered algorithms will play an ever-greater role in helping managers make sense of that data poses challenges for gatekeepers serving institutional clients — both those newly focusing on AI and machine learning as well as those with longer histories covering the sector.