Despite recent advances, many applications of artificial intelligence are still in their infancy, which is particularly true in asset management, writes Mark Ainsworth, head of Data Insights and Analytics at Schroders.
So is AI-driven asset management that far off? Despite the advances in this area, Schroders believes that some reservation is warranted before giving our fiduciary responsibilities (and capital) over to machines.
One type of AI that has potential for the investment industry is machine learning: the use of statistical algorithms and techniques to learn, and systematically improve, outcomes for a given task without any explicit programming. One of the most common commercial applications of machine learning is predictive analytics, i.e. using existing data to help forecast what future results might be.
AI is a heterogeneous computer science and there are a number of different types of predictive analytics systems within it. It is worth comparing how the use of intelligence augmentation (IA) and AI could be applied to investing. When it comes to obtaining optimal AI outputs, there are five key computation parameters that in our view are necessary elements for AI to succeed:
1. A constant environment where the rules are fixed and don’t change
2. The data is digital and quantifiable
3. Data is abundant (this could vary by industry)
4. There is low uncertainty
5. Objectives are clear
For investors, IA is a much more relevant area of science than AI. It enables firms to extract insights few others can discern – even with the data being in plain sight. This has tremendous advantages when it comes to fundamental investing.