The robotic revolution of AI and machine learning
Artificial intelligence (AI) has progressed a long way in a very short space of time, and is set to continue down its evolutionary path just as quickly. FX-MM’s Luke Antoniou strips back some of the conjecture and speculation to find out how this technology is already being used, and how it’s set to develop in the near future…
Artificial intelligence is here, and it’s here to stay, but for now at least, you can close your copy of ‘I, Robot’ without too many concerns about the future’s robot overlords.
Understanding the impact that artificially intelligent technology is having, and will have, on financial services begins with a strong dose of realism; the machines aren’t taking over, but we must face up to the fact that they can perform certain tasks more efficiently than a human being, and just as crucially, at a lesser cost.
AI-enabled software can use flawless logic without a hint of sentiment to make decisions without risk; just look at Google DeepMind’s AlphaGo, which recently defeated five of the world’s best professional Go players, with the world champion explaining that he played the perfect game and still lost. Now, apply that same logic to trading, data analysis and asset management, and you begin to glimpse a future where machines can execute instructions compliantly, efficiently and profitably.
As financial markets become ever more unpredictable, the collection, aggregation and use of accurate data is becoming crucial, and it is that data which is really the crux of this brave new technological world.
The use of predictive analytics, or predictive AI, is helping corporates and banks alike to get ahead in risk management at a time where market movement is increasingly defined by geopolitical risk.
As Mark O’Toole, Vice President of Commodities & Treasury Solutions for OpenLink, explains: “Some great strides forward have been made in predictive AI, to the point we can ask ‘how will it impact me if markets move, or currency moves against me after certain macro events across the world?’ We should now be able to predict a likely outcome, proactively assess that from a risk management perspective, and hedge in advance of that event. Brexit is an example of this, where a lot of companies clearly didn’t anticipate the Leave vote and weren’t doing any hedging of currency.”
While the potential to improve risk management with data analytics is there, it must be remembered that these technologies are still very much in their infancy, as Henri Waelbroeck, Director of Research at Portware explains: “There are opportunities for predictive analytics to assist risk management, for example network analysis, visualisation software or Bayesian network software. However, the field has been slow taking off , not only because there aren’t yet enough compelling solutions, but also because risk groups are focused on old-fashioned methodologies that have become rooted in compliance.”
Why trust a human?
Naturally, risk management is a vital part of existing within financial services, a fact only emphasised by the global financial crisis and the lessons that banks and corporates were forced to take from it. The technology must be right if it is to take over this aspect of financial security, but once it is, a question must be asked: why trust a human?
“Predictive analytics perform market checks that could be run by humans – the only difference is that machines can run those check rules much faster, and every millisecond,” explains Stephane Leroy, Business co-Founder and Chief Revenue Officer of QuantHouse.
“As time goes on, the power to analyse data signals in real-time and to memorise patterns through AI algorithms makes this whole process even more efficient.”