By Joel Kranc

It’s no longer an “if” or “when” but in many cases it’s about “how” and “how much.” The “it” referenced here is the use of artificial intelligence (AI) by the institutional investment community. In recent years, the integration of AI technologies has revolutionized the way these investors make decisions, optimize portfolios, and mitigate risks. From hedge funds to pension funds, AI has become an indispensable asset in the arsenal of institutional investors worldwide.

In the report, AI Integration in Investment Management, Joanne Holden, Mercer’s global head of Investment Research & Consulting and Ursula Niederberger, strategic investment research, state that, “The integration of AI within investment strategies is not a new phenomenon; it is a future prospect. Hedge funds, quantitative and systematic strategies have been harnessing the power of ML (machine learning), natural language processing (NLP) and trading-pattern recognition for many years. However, our findings demonstrate that current use of AI across investment strategies and research stretches far beyond the traditional “quant” cohort (15%–20% of respondents). Nine out of 10 managers are currently using (54%) or planning to use (37%) AI within their investment strategies or asset-class research.”

Although generative AI has dominated headlines and been central to the surge of interest since the launch of ChatGPT in November 2022, managers’ use of gen AI capabilities lags behind reported use of ML and large-language models (LLMs), says the Mercer report.

But even with a lag, some companies are already employing newer methods. Blackrock, using technology such as transformer-based LLMs that can measure or process long sequence elements (like words in a sentence) or accounting relationships, allow investment managers to predict the next words and produce human-like content, which helps improve investment predictions.

In its report, How AI is Transforming Investing, Blackrock says, “The LLMs used in our investment process are designed to complete specific investment tasks, such as forecasting the market reaction following corporate earnings calls, for example. As a result, our models are trained on a smaller set of data inputs but are expected to deliver a high level of accuracy in performing the specific task that they’ve been trained and fine-tuned for.”

So far, according to Holden and Niederberger, “Managers’ use of AI across investment research and alpha generation is largely focused on augmenting existing capabilities through the expansion of data sets and analysis, and idea generation. A minority of managers are deploying AI in more complex aspects of portfolio management.” They say that more than half of AI-integrated investment teams report that AI analysis informs rather than determines final investment decisions. A fifth report that AI proposes investment decisions, which investment teams can override.

Going forward, larger institutional investors may find they are shifting the data sources they use as a result of AI capabilities. “New technologies and advanced analytics make the industry less dependent on historical data alone and provides huge opportunities to determine associations and correlations we were not aware of before,” says Eduward van Gelderen, CIO of the PSP Investment Board. “As such, AI application could be found, amongst others, in alpha-generation, total fund management, risk management, and trading.

But even with that shift there needs to be collaboration between technology experts and research teams, notes Benjamin Roy, Chief Technology Officer with Capital Fund Management. In an article in Pensions & Investments he says, “Our experience is that in order to really exploit those models, to find new sources of data, our engineers need to work very closely with our alpha researchers. The engineers need to have a research mindset. More and more, the lines are being blurred between technology and research.”

Mercer’s Holden and Niederberger say that managers currently using AI expect the integration of these capabilities to deliver positive economic benefits, both in terms of GDP growth and US dollar contribution. While estimates of these impacts are wide ranging, on average, managers currently using AI expect a US$14 trillion boost to the global economy by 2030.

Among managers currently using AI, data quality and availability is the most-cited barrier to unlocking the technology’s full potential, they say, followed by concerns around integration and compatibility and ethical and legal considerations.

Tools that enhance our capabilities are always a welcome addition and can enhance how we do things but it comes with a caveat. In a piece about AI and asset owners features on top1000funds.com, Jacky Chen, director of total fund completion portfolio strategies at the $C25 billion ($18.4 billion) OPTrust, says, “Strong regulatory frameworks and ethical guidelines will be crucial. That requires us, as a society, to see collaboration between industry, governments, and also the public. We are all stakeholders in this, in these discussions, and we need to make sure that there is a collaborative effort that helps us to shape the landscape going forward, and we are not just wanting to focus on the innovative side. We need to make it inclusive.”