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Artificial Intelligence Increasingly Important for Private Fund Managers’ Middle and Back Office
06/11/2024
Artificial intelligence (AI) has long been deployed by asset managers in their investment strategy, particularly those that invest in and trade the public equity markets. The sheer volume of price history alone, for example, lends itself well to a technology that can analyse and determine patterns in minutes, as opposed to the weeks it would take a human to perform the same task.
Until the past few years, spending money on AI technology in the middle and back office has been less of a focus for asset managers. After all, it’s the less ‘sexy’ part of the business and has historically played second fiddle to the ‘rockstars’ of the front office.
That’s changing, however. And for a few reasons.
First is the continued fee compression seen in the private markets. As more firms and funds launch, competition increases, driving down costs - classic demand and supply forces. Asset managers need to find ways to lower their own costs so that they can deploy more capital (or maintain the spend) into the investment function. AI is a perfect use case for this; automating tasks previously done manually and speeding up other processes, both of which can save money.
Second is the ‘FOMO’ (fear of missing out) created by the recent explosion in the use of generative content tools like ChatGPT. These tools – which are improving seemingly by the week – can support an asset management firm with producing investor letters and notifying the appropriate people about any potential cybersecurity issues, are just two of many use cases for this technology.
Third is the increasing regulatory burden placed on asset managers in the market. Costs in the compliance function continue to increase due to the ever longer reach of the regulators. These costs aren’t purely hard currency costs; the time costs of compliance impact investment firms here as well. AI can save time and money supporting the compliance function, whether internal or outsourced, with both analysis and reporting.
Fourth is in trade reconciliation. While this is more specific to hedge funds than illiquid private fund categories, the recent move to T+1 settlement in the United States, Canada and Mexico has placed even more emphasis on the need for swift trade matching, and AI’s ability to analyse extensive data sets in order to match trades accurately is a clear use case here.
These themes are all medium to long term, structural trends, which are not going away. A quote often attributed to Bill Gates goes something like this: “If your business is not on the internet, then your business will be out of business.” The asset management version of this would seemingly be,
“If your asset management business is not using AI in the middle and back office, your asset management business will be out of business.”
AI is already an established tool for the portfolio manager to effectively implement their investment strategy. Now, it’s an essential tool for the middle and back office, too.
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