Looking back to go forward

Despite regulation, improved controls, and enhanced practices, the financial sector is still victim to its past failures. Although new controls and regulations have enhanced the industry, there are still challenges to overcome – especially in anticipating future trends in the market.

Failing to review the past

It’s difficult to predict the future, especially in financial markets, but not impossible. While the vast majority of professionals didn’t foresee major events such as the 2008 crash or the rise of the internet, there were still some who anticipated the change in the sector and made fortunes in the process.

However, these decisions – and indeed the majority of decisions made by financial professionals – are regularly based on human analysis of data, which is prone to error and also limited in terms of the insights that can be gained from past events.

For example, in portfolio construction, many asset managers are still reliant on the 60-year old Markovitz mean-variance framework, which does not leverage the sophistication of modern technologies: this prevents institutions from obtaining maximum value from their data.

Ultimately, the entire sector is still unable to see the far-ranging impact that historical incidents have on future investment opportunities. There are numerous hidden dynamics that influence economic shifts or black swan events and without understanding what these are, there is always a likelihood of making a poor investment decision, especially in terms of hidden risks.

The risk of not embracing the future

Effectively analysing past events is not an added bonus – it can mark the difference between success and failure. Data-driven insights, which can evaluate potential patterns far more effectively than humans, can keep firms’ leaders in the market and remain competitive with other institutions and challenger services.

The reluctance to adopt new technology may be due to the lack of understanding some financial professionals have about the use of AI in the sector. The association of AI in the sector has led many to fear that its application will do everything from causing job losses to creating problems with compliance and regulation.

Rather than taking away jobs, integrating machine learning would actually enhance the working experience for many of those in the financial sector. Back-office responsibilities, largely seen as tedious and repetitive, would be assumed by AI, leaving financiers to focus on making better informed investment decisions.

Indeed, changing the perception of fear towards automation is a huge step forward if the financial industry wishes to remain competitive.

How technology can prevent an economic crash

In reality, the financial industry is well placed to embrace machine learning, since it has vast stores of historical data that AI could use to identify patterns in the markets behaviours.

Moreover, it can process this data very quickly and accurately, and also spot more complex trends that manual processes cannot. This historical data can also be compared with current scenarios, allowing for accurate predictions of future events, ranging from just a millisecond into the future, or in several months’ time.

Different types of machine learning would be better suited to different forms of analysis, however. For example, for predicting black-swan events, Generative Adversarial Networks (GANs) would be a highly effective form.

GANs learn to simulate future market scenarios by having two artificial neural networks work against one another.

GANs can be used to model the market much more thoroughly than previous techniques, since the data being used can be far broader and more detailed. It can also include data points from many different factors that influence the market, such as economic data, sentiment, current events, and so on.

With access to all this nuanced information, GANs can generate scenarios that go beyond the surface alone and consider the modelled inner workings of the market. As a result, they can generate an almost infinite number of stress-testing scenarios that closely mimic the market dynamics, including the potential for black swan events.

With AI now able to work with higher degrees of uncertainty and digest large quantities of data, financial professionals such as asset managers are better able to estimate the expected return given a target level of risk.

As a result, AI will not only help to remove poorly informed investment decisions but will also enable those in the financial industry to improve their return-related performances by making smarter decisions based on data.

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