FLASH FRIDAY is a weekly content series looking at the past, present and future of capital markets trading and technology. FLASH FRIDAY is sponsored by Instinet, a Nomura company.
Artificial Intelligence (AI) is having a transformational impact across the asset management value chain. According to Global Market Insights, AI in asset management market size valued at $2.5bn in 2022 and is projected to grow at CAGR of 24% between 2023 and 2032.
According to Bob Moitoso, Head of Asset Management, North America at Linedata, in recent years, more traditional usage of generative AI has been around alpha generation, however, now firms are starting to use these tools to streamline and run their operations.
Asset managers are increasingly using AI in their day-to-day to not only help portfolio managers make decisions on investments, but also on the investment research side to digest data to better understand what’s going on in the marketplace, as well as to create operational efficiency for their firm at large, he said.
“AI tools can answer the question of how to streamline processes and understand data so that firms can make better operational decisions,” he said.
Moitoso said that the overarching trend is that firms are expanding their use cases for AI, now using it throughout their organization and not just in the investment process.
“AI and machine learning are being sought to augment existing resources and digitize the workflow, to create programmatic processes and automate tasks. In addition, there is a growing expertise gap in asset management with repetitive tasks that can be filled by automation,” he said.
Aman Soni, VP of Data Strategy, Canoe Intelligence, added that in the alternative investment space, increased adoption of AI/ML is largely driven by firms’ ability to process and analyze vast amounts of datasets nearly in real-time, which in turn improves risk assessments, streamlines operations and reduces costs.
According to Dan Cwenar, President of Investor Communication Solutions Data and Analytics at Broadridge, there are AI applications ranging from regulatory solutions leveraging generative AI to ensure consistency between the conversional shareholder report and the new Tailored Shareholder Report, AI to support fixed income trading decisions, all the way to deep learning models trained over very large alternative structured and unstructured datasets to identify signals and prescribe investment actions.
Cwenar said that AI powered solutions require very large datasets and significant processing power, adding that “both ingredients are readily available today”.
“We see asset managers leveraging the power of AI in the form of machine learning (ML), deep learning, and generative AI to optimize operations and decision making, ultimately resulting in better cost adjusted outcomes for investors,” he said.
He further said that there are benefits across the full asset management value chain, both driving efficiency in regulatory and operations functions, and optimizing the performance of front office investment and distribution functions.
The benefits of incorporating AI and ML into asset management are multifaceted, commented Moitoso.
“Operationally, it introduces efficiency into processes, making them smoother, faster and more robust,” he said.
Given the challenge of finding experienced personnel in asset management, AI/ML presents the opportunity to automate repetitive tasks that require less expertise, reducing dependence on time from valuable resources, he added.
Opportunities and Risks
New technology like AI holds much promise, but also requires a significant investment in design, validation of results, and analysis of cyber vulnerabilities, according to Cwenar.
Moitoso added that AI/ML technology offers substantial opportunities in terms of operational efficiency and task automation, however, it also comes with inherent security and privacy risks.
“As data is being shared into public clouds, asset managers need procedures and protocols in place to prevent the sharing of sensitive information,” he said.
He added that there also remains a knowledge gap regarding the extent to which AI/ML tools can be trusted.
Despite these risks, automation offers the advantage of allowing asset managers to focus their time and energy on the projects that require their individualized expertise, instead of repetitive tasks, he commented.
Soni believes that artificial Intelligence is only as good as the data being provided to train it.
“Ultimately, quality data input results in quality data output,” he said.
While the benefits of AI are clear, it is important to note that the right governance framework is required to deploy AI tools and derive maximum benefit,” he said.
Regulation and Compliance
Concerns around regulation and compliance could pose significant hindrances to the widespread adoption of AI in asset management, said Moitoso.
Firms may be hesitant to subject their proprietary investment strategies, or ‘special sauce,’ to these potentially public venues, as that could compromise both their competitive advantage and security, he said.
Right now, new AI governance discussions are happening globally, addressing how these tools can be used, as well as what can be disclosed regarding investment processes.
“The outcome of these discussions could play a crucial role in further increasing the adoption of AI in asset management,” he said.
Cwenar added that there is always a chance that compliance will be applied to new technology, and the SEC has been vocal about AI.
However, it is the responsibility of technology companies to ensure risks are mitigated and new technologies are successfully applied in the market, he stressed.
Meanwhile, Soni thinks that the changing regulatory landscape acts as an advocate for artificial intelligence, rather than a hinderance.
He gave an example of the recent SEC Form PF amendment that promotes further transparency across hedge funds and private equity funds.
This increased transparency, he said, will support the standardization of data collection and reporting from an asset manager running their own funds, as well as enable the easy extraction of relevant information for downstream reporting for a fund of funds asset manager – paving the way for the industry to intelligently leverage artificial intelligence with the objective of improving operational efficiencies and unlocking large data sets.
The expectation is that regulatory changes will continue to drive more granular data requirements, enriching this relatively opaque asset class, Soni said.
The role of artificial intelligence will therefore grow over time – both in the extraction of relevant data from unstructured sources, and the standardization of data points across different funds and investment types, he added.
From a compliance perspective, Soni said, it’s important to understand how artificial intelligence is being applied within an asset manager’s infrastructure.
“There is an increasing willingness to embrace cloud technologies to increase time-to-value, alongside the ongoing enhancements driven by AI,” he said.
“Again, compliance policies are adapting to account for changes in artificial intelligence and how it is best implemented and maintained within an end-to-end operating model,” he added.
Bright Future
Industry experts agree that AI holds the potential to unlock a new generation of efficiency and optimization across the asset management value chain.
“While initial expectations are very high and may be over inflated, we do believe AI can power significant transformation that stands to ultimately benefit investors by delivering better cost adjusted outcomes,” commented Cwenar.
According to Moitoso, the future of AI and ML in asset management is promising: “I believe we’re just scratching the surface of its use and its potential applications. The half-life of technology is only accelerating, meaning that innovations and advancements occur at an ever-increasing pace.”
“If we have 20 AI use cases today, we can anticipate there will be 50 or more tomorrow. This democratization of data-driven decision-making will likely reshape the landscape of asset management in the years to come,” he said.
Soni thinks that the use of AI will grow in importance within the asset management industry.
“With the continued fee pressure, drive for workflow efficiencies, and growing reporting requirements, asset managers need to better utilize technology as an enabler,” he said.
Coupled with this is the drive for data standardization across public and private market portfolios. Investors want a single view across their entire portfolio, he added.
“To achieve this, technology will play a pivotal role by enabling firms to improve time logs, broaden coverage of data points, and increase accuracy,” he said.
