AI in asset management: 3 key use cases

Creating bots to perform repeatable processes

Organizations throughout the asset management sector are finding ways to create bots that perform repeatable processes to eliminate time-consuming manual work in internal workflows.

In a simple example, hedge fund employees may regularly search for statements from a dozen or more different brokers and place them in one file for review of that information. With technology that’s now available, it’s easy to create a bot that searches for those broker statements and puts them together.

Meanwhile, chat bots are becoming as common in financial applications as they are in customer service systems. Take, for example, a CFO answering a question during a boardroom presentation that requires integrating data from multiple reports. There are chat bots now that can access all the reports at once, bring the data together and help the CFO tell the story of what happened and why — all with some simple commands on a mobile phone.

These capabilities can be predictive as well as reflective. In financial planning and analysis (FP&A), teams are using AI to analyze data and drivers to support or reform business decisions. The technology helps the staff consider vastly larger volumes of data, possible outcomes, expense trends, risks and opportunities to deliver predictions that may be greatly improved and show the way to more effective strategies and tactics.

“We can use AI to more fully understand the factors that could add value or decrease value,” Pilch said. “You’re using the technology to outperform.”

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Tech provides for fuller analysis

The same predictive principles that make AI useful for FP&A can make the technology an important tool for investing. Asset management firms can use AI for portfolio management, trading and portfolio risk management — and private equity firms are using the technology to analyze huge volumes of deals and the metrics associated with them to discover their best opportunities and reveal potentially hidden risks.

Asset management firm leaders can use AI to more closely tailor a client’s portfolio to drive improved returns while decreasing risks. AI also can be used to analyze data and execute trades when certain indicators are present, and it can provide data-driven portfolio risk management.

“Large asset managers are often looking at different algorithms or different ways to use technology to help them in their trading,” Patanella said. “And then in private equity, they’re aggregating deals that have been done — or have not been done — and then looking at how it played out in a seven- to nine-year process. And then they’re benchmarking conclusions that the deal team and the board can use to make decisions.”

Asset managers also can use AI to quickly provide information based on economic events. For example, in recent times, they might have wanted to know:

• How much exposure they had to Russia — or Ukraine — through their investments.
• Which companies in their portfolios used Silicon Valley Bank for their banking needs.

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Data availability rises dramatically

Many asset management firms are satisfying their clients’ never-ending search for more data through AI-based programs that provide continuous monitoring of market data and transactions.

“Part of the firms’ external reporting to their investors is now providing them with the ability to use AI to get customized reports as opposed to the standard reports the firms might have prepared in the past,” Patanella said.

Much of the same data that asset managers can use to make investment decisions also can be provided to investors for their consideration.

This dramatic shift in the availability of data and the ability to process it comes with a significant warning, however. It’s important to have a governance process that verifies that the data being used is high-quality. It may be useful to evaluate data on the basis of the core data quality dimensions of:

• Completeness
• Accuracy
• Timeliness
• Consistency
• Validity
• Uniqueness

If you start with data of poor quality, the results of an AI analysis almost certainly will be flawed.

Market gains provide resources for AI implementation

Recent market improvements have intensified the need for asset management firms to embrace AI. The economic downturn fueled by inflation in early 2022 compelled firms to prioritize critical spending in areas such as cybersecurity and environmental, social, and governance issues, leaving limited resources for AI initiatives.

However, Patanella advises against delaying AI implementation as economic conditions improve. If better market conditions continue, asset management firms are expected to have more resources at their disposal, providing an opportunity for investing in AI use cases. Failing to seize this opportunity could potentially leave firms lagging their competition.