Why AI is not just a Buzzword for Asset Managers

Every five years, a new idea (or a new packaging of an old idea) becomes the watchword in the technology industry. If it sticks, it takes over not only technology but the entire business world. Ten years ago, the idea was “big data.” Five years ago, big data’s twin, “Artificial Intelligence (AI),” took the reins and continues its lordship over all other ideas; deservedly so.

Since the concept has been bandied about and has made an appearance on almost every business plan and messaging document over the last few years, discerning watchers and analysts have started to kick the tires and ask tough questions. Most of these lead to a very natural place: It is no longer okay just to refer to AI generically; instead, companies have to show clearly how AI applies to a particular vertical, scenario, or use-case. At Pepper, we are big believers in AI as it applies to the complex world of asset management.

A short primer on the staggering size of the industry is called for. Professional asset managers oversee $120 trillion world-wide. This number exceeds worldwide GDP. More than that, the number is growing and will hit $150 trillion by 2025.

Such large pools of capital are by definition subject to a balancing act between generating return and minimizing risk. Typically, return and risk are inversely related. Great asset managers ply those delicate waters with knowledge and wisdom driven by contextual information, data, and with the help of smart systems. But even with the best of intentions and abundant skill, the sheer complexity of the matter can produce everything from errors to catastrophes.

The ability of AI to help asset managers optimize this balance is real and clear:

1. Currently, processes in asset management are highly manual. Conducting manual work at scale is costly and error-prone. AI can provide relief for these manual processes.
2. Asset managers rely on external data imports that are not structured. The systems that they rely on ingest bad or duplicate data.  AI can solve this problem and provide huge value in the quality of data ingestion and reporting.
3. Search functionality is decontextualized and sloppy. AI-driven search can be intuitive, contextual, and provide timely information for action.
4. Identifying relationships between seemingly disparate data points unlocks a wealth of hidden patterns and opportunities; this is the province of AI.

The list goes on, but these four contributions place AI at the center of simplifying the complexities in asset management.  For asset managers, AI is not just a buzzword. It’s key to their balancing act.