By Erik Sherman

Technology can give almost anyone in business a leg up, including asset and portfolio managers.

Data analytics can decipher market moves, investor behavior, and economic indicators,” explains Morningstar. “By understanding market movements, managers can capitalize on opportunities as they arise.”

Canadian pensions are no exception.

How Canadian Pensions Use Tech

In its 2024 annual report, British Columbia Investment Management Corporation (BCi) wrote: “As we continue to grow, leveraging data and technology seamlessly across all business functions is paramount. We integrated new technology that improved collaboration, analysis, reporting, and decision-making.”

An ESG data platform looking at data from more than 4,000 companies = was one example, using climate data, stress tending, and physical risk assessments to better understand the potential impact of climate change on real assets. They licensed a generative AI application called Hebbia to improve research capabilities. An Enterprise Risk Management framework uses keeps the ERM Committee and BCI Board appraised of priority risks.

In the 2024 annual report of the Canadian Pension Plan (CPP), the Chief Sustainability Officer (CSO) oversees the “collection, interpretation, and reporting of sustainability-related data for CPP Investments.” During its fiscal 2024, CPP implemented a “new technology and data operating model” and piloted “new AI-enabled investment research capabilities.”

Ironically, “the skilled personnel and more sophisticated investment processes, data and systems required to operate our investment departments” can help increase the operational risk that comes from the “actions of people, or inadequate or failed internal processes or systems as a result of either internal or external factors.” The more tools and systems included by a firm, the more points of failure in the overall system, so care and quality control become far more complicated. There are also inherent limitations to what technology can do.

CPP noted that “estimating the fair value of private investments requires the application of judgment alongside data.”

Similarly, the Public Sector Pension (PSP) Investment Board recognizes some ambivalence towards technology. The organization does state in its 2024 annual report that “data-driven decision-making is critical to its ability to meet strategic objectives and deliver its mandate.”

The report also noted a goal of “advance firm-wide access to data and knowledge to support insight-driven decision-making.”

They started a multi-year project to “unify public and private financial data, streamline processes, and modernize legacy technology.” That last part is to “have the data and technology required to optimize our investment decisions and the organization’s readiness for the future.” And yet, they’re focusing on generative artificial intelligence and the need to “understand the disruptive risks and opportunities AI presents to our portfolio, and look for ways to leverage AI tools to our advantage.” Similarly to CPP, sometimes technology can lead to problems.

Building the Framework

Technology can do much, can cause problems, and can also do nothing on its own because it’s a tool, not a sentient being.

“We’re utilizing AI to help us build out initial evaluations, but in my experience the large language models don’t have the insight,” says John Nicolini, managing director and senior consultant at Verus. “Someone described like a freshman. It can regurgitate facts, but it can’t give a lot of insight. I haven’t yet come across a system that is actually providing good insights into investments, investment opportunities, or portfolio decisions.”

“I think there is a lot of potential [in the technology],” says Josh Herrenkohl, a senior managing director at FTI Consulting. “It’s not at a level of maturity where it’s immediately going to replace back offices.”

There is also a level of preparation of data that must happen for technology to work. That includes cleaning data, building data dictionaries so everyone knows what different types of information means. “That’s why I say 70% of the technology challenges that most organizations struggle with isn’t’ a technology challenge,” Herrenkohl says, “it’s a data challenge.” Sometimes the issues are that data sits in different locations or on devices where integrations don’t yet exist.

“I do think there are a lot of large pension funds who really have not wrapped their arms around this issue and struggle with it on a daily basis,” he adds. The problem could be politics in large organizations, a lack of data governance, or a lack of processes. “Having the right technology is absolutely important, but I think it’s significantly less than 50% of the battle,” says Herrenkohl.

There is also the danger of over-automation that can lead to inadequate training of people in earlier career stages. A level of financial grunt work is necessary for analysts to learn how specific portfolios work. “Analysts learn a lot from building out these manual models. Skipping this step is likely to have an impact on their learning later on,” Nicolini says.

Data analytics can offer a lot to portfolio managers, financial analysts, and others at pension funds. However, getting the benefit takes patience and work.