The Case for AI in Asset Management Distribution

ChatGPT, have you heard of it? How about artificial intelligence (AI) or machine learning (ML)? Of course, you have. Unless you've been under the proverbial rock for several months, talk of AI has inundated our airwaves, inboxes and feeds. If you’re an asset manager, specifically someone focused on sales and marketing, you’ve probably wondered how this new technology can be leveraged to grow your AUM.

A cursory Google search for “artificial intelligence” yields headlines we've all become familiar with: Jobs at risk from AI revolution. AI will drive mass extinction. AI will create more wealth in the next five years than ever before.

Histrionics aside, theoretically and practically, AI has been around and ingrained in our lives for years. Recent events have only amplified awareness of its existence and the implications of its proliferation. We interact with AI daily through recommendation engines used in apps like Spotify or Netflix, search engines like Google, and ad targeting that leaves us wondering whether our phones are listening to us. AI is making significant inroads to changing the way sectors like Healthcare, Transportation and others have operated for decades.

However, understanding AI architecture, associated jargon, and its growing application to the asset management distribution ecosystem could be clearer. It doesn’t help that there is a dearth of information that addresses the complexities of AI or educates asset managers on its real-world applications when it comes to growing AUM. As AI turns its eye toward asset management, sifting through the noise to understand the more practical realities of this evolving technology and how you interact with it has become increasingly crucial for myriad reasons.

AI already addresses challenges faced by asset managers by helping organizations stay competitive from a performance perspective through alpha generation; content creation; identifying areas of operational efficiency and resource optimization; and enhancing scalability. More recently, AI is being applied to distribution by leveraging investor data and ML to match products to leads for higher conversion in a personalized, precise fashion.

That’s not to say that AI is the elixir to all asset manager ills, as the solutions available to address headwinds have yet to be readily understood by many in the industry. Leaders in these organizations will benefit from understanding AI's practical nature, even at a basic level, and how it can be leveraged, particularly to enhance AUM growth as well as the availability and efficacy of platforms in the marketplace.

An obvious question to ask before digging into AI for sales and marketing enablement is whether you even need to adopt this new technology. It's a fair question. Organizations, in many cases, are saddled with tech debt, already purchased several data sets, are overburdened with underutilized applications, and have some less advanced technology that already addresses one or more of these challenges. Additionally, the ubiquitousness of data means that asset managers are purchasing more than they can reasonably ingest and from which they can derive actionable insights to enhance their businesses.

These concerns cannot and should not impede the adoption of newer technologies, especially those that can address them holistically and efficaciously. Even better if they integrate into your existing CRM or other apps in your tech stack.

If this were 1999 and you asked yourself whether your business should have a website simply because you already publish your phone number in the yellow pages, what would the answer be? Looking back, it seems unthinkable that any organization would go without. Asset managers need AI today the way they've needed a digital presence since the 1990s and early 2000s. You cannot expect to boost your brand, bolster AUM growth, and stay competitive without quickly implementing what is arguably the most significant piece of technology our generation has seen or will ever see.

We are all familiar with the phrase, "Investment products are no longer bought; they are sold." Asset managers often lament that the industry's commodification is driving down management fees, share of wallet and brand recognition. The average asset-weighted equity index ETF expense ratio has dropped from 30bps to half that since 2005. Mutual fund expense ratios have experienced the same phenomenon, falling from 1% to 40bps. There are more products and more new issuer entrants to the market every quarter.

From an AUM perspective, between Q4 2021 and Q3 2022, the world's 40 largest asset managers experienced, on aggregate, a 14.9% fall in AUM, a revenue decline of 22.9%. Furthermore, increasing market volatility leads to less predictability, and asset managers need new strategies to cushion themselves.

The next pertinent question is whether new technologies can save us. There are certainly good arguments that AI, especially as it evolves, will help asset managers usher in a new era of growth.

A recent study by McKinsey highlights the significance of AI adoption in the asset management industry. Asset managers who have invested in AI-enabled distribution analytics have experienced incredible results. AI implementation has led to a 20% growth in subscriptions and reduced redemptions between 5% and 8%.

These numbers are not insignificant and make it abundantly clear that any asset manager looking to insulate themselves from the trends squeezing revenues must assess new pathways to growth. Furthermore, they must do it now or risk falling behind their peers who are making investments in technology.

Organizations that have not embraced AI due to uncertainty about its value, budget constraints, or are questioning whether to build this in-house, should first explore turnkey solutions with existing infrastructure and expertise to reduce time to market and implementation costs.

It’s increasingly apparent that AI is not merely a trendy buzzword; it is a transformative force reshaping distribution in the asset management industry by creating more precise matches for products and influencing conversion rates. And it’s happening now. Embracing AI is not optional but a strategic imperative for asset managers seeking to thrive in increasingly competitive market and industry environments. Furthermore, adopting outsourced platforms, like TIFIN AMP, can help asset managers of all sizes harness vast industry data and leverage teams with significant AI and ML expertise to produce better distribution outcomes.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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