Investment analysts are critical for accurately assessing potential investment opportunities. But these professionals don’t come cheap, and they are prone to bias and erratic performance. AI bots and platforms have no such issues, and they are also inexhaustible in terms of collecting and cross-referencing data, and they don’t get bored with repetitive tasks.
Those are some of the reasons why AI is furrowing its way into so many different professions and tasks. Ahmed AlNomany is founder and CEO of Batonics, a company that has created a “universal AI analyst” that he envisions replacing junior analysts in various industries, including the investment business.
How can artificial intelligence systems improve financial and investment analysis for asset managers?
In the short term, asset managers have immediate pain points where AI offers relief. These pain points often revolve around the expense of hiring teams of analysts, limitations on deal volume due to capacity, and the depth of diligence that can lead to missed opportunities and suboptimal asset management. AI provides a scalable solution, reducing overhead and inefficiencies. The long-term benefit, especially for larger asset managers, is more foundational. AI presents transformative leverage in every function of asset management, whether scouting, pricing and valuation, underwriting, or risk and portfolio management. If these bigger entities don’t integrate AI, the looming decade could phase them out.
I’ve heard you mention the “extreme inefficiencies” in investment analysis. Elaborate, please.
Consider the traditional private market fund model. Say one analyst is needed for every $10 million in AUM. With an average analyst salary at $100,000 and management fees of 1.5 percent, around 50 percent of an asset manager’s revenue is tied up in analysis. In this setup, smaller funds grapple with viability. By replacing this $100,000 per analyst cost with a $20,000 per year AI platform, smaller funds can dodge financial collapse, while larger ones can add substantial figures to their profits. This alteration isn’t merely cost cutting; it reshapes the very dynamics of fundraising.
What are the advantages to using AI rather than humans to do this work?
To understand the value of AI, look at the daily tasks of a $80,000 to $120,000 analyst. Their responsibilities, be it data collection, underwriting or portfolio management, often anchor around software such as Excel. Data integration from various sources, including Bloomberg, proprietary platforms, or direct intel, falls under their purview. Their tools, due to inherent limitations, offer basic computations. AI can quickly and more efficiently handle these tasks. This isn’t to undermine human analysts but to highlight how AI can elevate their strategic importance by taking over the repetitive tasks.
Do you envision AI democratizing access to analytical power and expertise regardless of an organization’s size?
Yes, AI is primed to democratize the analysis process in the asset management industry. This democratization, in turn, indirectly facilitates broader data accessibility. Just as today’s retail investors can craft intricate stock market models courtesy of available tools and data, AI will ensure private markets aren’t left behind. In essence, it levels the playing field, allowing entities of all sizes to harness analytical power.
How applicable is this brand of AI technology to other fields of research, analysis and innovation?
Our AI philosophy is to be “applicable universally, optimized locally.” Whether tackling credit ratios or NBA stats, at its core, data is treated as a quantitative entity. Though the foundational tech remains consistent, its specific application is tailored per industry. Today’s analysts, across sectors, manipulate data to gain insights. AI mirrors this versatility. From predicting pharmaceutical trends to enhancing public transportation efficiency, the tentacles of this technological revolution have the potential to reshape countless sectors.