We have seen the development of AI systems transform many industries; from healthcare to human resources, the use of artificial intelligence has helped reach new levels of accuracy and success, whether it be in identifying a scan correctly or hiring the right person. One industry that is yet to fully embrace the potential of AI in the financial sector, namely investment management.
Investing is all about making predictions about the future, whether it be the value of a company, index or currency. Like all predictions, investment predictions are based on combining data with a model or method, and due to human behavior, we assume that we always have an advantage, a higher probability of being successful. While the ‘edge’ is the result of either superior information or superior decision-making methods, the failure of managers to consistently generate the promised returns reveals that managers seem to lose the ‘edge’ very quickly and easily. Active managers generally use the same information and methods to decide what is the best investment option, yet the simplicity of the process has led even the best active managers to struggle.
Fortunately, there is AI, which could provide the ‘edge’ by applying powerful computational methods to large and complex data sets. Artificial intelligence such as deep reinforcement learning has been able to make predictions with astonishing accuracy. The advanced AI uses artificial neural networks with a reinforcement learning architecture, that enables software-defined agents to learn the best actions possible in a virtual environment in order to attain their goals. Despite all this development, these AI systems have had minor uptake in the finance industry, as only a handful have realized the potential and incorporated AI into their investment processes, while their competitors seem to make false claims that they have adopted AI. These claims are not just duplicitous but often come in one of three forms: by introducing a new data set into their traditional AI processes, creating the illusion that they are in fact developing AI by bringing computer scientists to client meetings, or simply misappropriating the term machine learning to include traditional forms of statistical analysis such as linear regression or cluster analysis.
These companies perpetuate the idea that AI can only, and will only ever be a secondary to human knowledge in the investment process. This is not only providing misinformation but excluding themselves from potential success that could be achieved by integrating deep reinforcement learning into their asset management. Deep learning and reinforcement learning are much more powerful than classical machine learning and, in more and more cases, human intelligence. Given that AI could help managers achieve the edge they need to succeed, why is the rate of uptake so low? Because managers recognize that adoption requires a huge change in operations. AI not only alters the investment process and investment worldview, but it radically transforms the whole business – requiring new talent, data, different research and development processes, and most importantly funds to finance this multi-million-pound endeavor – all without the certainty of a net benefit.
Considering the human tendency to ‘stick to what we know’, it is justified that many cautious managers have desperately clung to old management processes, and despite having mediocre investment performance, many active managers still enjoy profit margins greater than 30%. The indirect intention of putting their personal interests ahead of their clients by sticking to the traditional methods can only last so long. Many clients are now aware and consider it critically important that managers are using AI in their investment processes. Japan’s government pension investment fund has recently partnered with Sony Computer Science Laboratories to develop and deploy a deep learning-based asset management application. As clients become more aware of the power of artificial intelligence in asset management, the threat only increases to those who choose not to harness the dynamic technology, a decision which could prove to be fatal in the future.
