Artificial intelligence (AI) continues to build its presence across global industries, so its progression into the asset management sector is only natural. But AI can be understood differently by different people, so definitions are important.

One should think of AI as a field in computer science that builds “intelligence” into electronic systems. These AI systems are able to perceive data environments, learn, and take actions to achieve objectives. This definition presents a compelling value proposition for AI versus traditional portfolio management systems with respect to today’s growing data environment.

The key technological advantage of AI investment platforms is their overall flexibility to appropriately process massive amounts of dynamic market data and identify investing opportunities. AI can help us better understand what to trade, and how to trade it.

Important AI capabilities include: consuming, learning, and aggregating growing volumes of data across mediums, continually adjusting portfolio risk based on observed market signals (and the concurrent removal of rigid factor-based criteria), and the ability to connect new market signals and derive an optimized portfolio free of human bias. High-performing systems should be able to assimilate and manage both structured and unstructured data in a timely manner, and appropriately process erroneous or blatantly fake financial news.

Many early stories of AI investment systems described the brute force design of jamming massive data sets into the conceptual “black boxes” and allowing the machine to produce a series of recommendations. Better solutions exist and are more suitable for the explosion of available investment data and the demand for system operational observability. Read more from…

thumbnail courtesy of