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The amount of data that artificial intelligence can handle dwarfs the capacity of the human brainCC

The next wave of artificial intelligence (AI) will likely be used by hedge funds making long-term macro plays around things like oil prices, involving more data than an analyst could crunch in a lifetime. Although habitually secretive, the use of machine learning and AI among the hedge fund community has been well publicised.

Bridgewater Associates, the world's largest hedge fund with about $154 billion (£109bn) under management, has been vocal about its use of AI. And it's not uncommon to hear about investment firms hiring data scientists with PhDs in neural networks, or physicists and astronomers who can remove the noise from data signals.

Publicis.Sapient AI leader Josh Sutton, who has worked in financial services for 15 years and has some hedge funds among his clients, expects to see a confluence of causal analytics and long-term macro strategies.

Using Bridgewater as an example, Sutton told IBTimes UK: "If you look at their historic trading strategies, it's been very much long-term bets around what's happening at a macro level. They have built their entire business on having some of the best research and analytics in the industry and some of the smartest minds thinking on that.

"When you combine those two things, I would definitely expect artificial intelligence to be applied to identify large-scale trades that might not be evident to an individual researcher.

"Look at how a lot of funds are making large bets around what is going to happen to the price of oil, for instance, how is that going to impact different industries, what does that mean from a portfolio investment?

"Based on this set of five or 10 things that I believe to be true about what's going to happen over the next five to 10 years, how does that translate into different bets that I can make across various industry performance?"

Sutton said he expects machine learning to be overlaid with more common sense AI technologies on top to mimic the role of an analyst.

"That's an area where I think you are starting to see a lot of very private interest, but interest none the less, from a number of the leading financial firms. How you can deploy AI tech to mimic the role of analysts at scale. That's to do everything from putting together valuation models around companies that you would normally hire an analyst to do, to doing entry-level macro analysis of what's happening in various industries, based on digesting large amounts of data and coming up with hypotheses."

A report out today from Citigroup predicts some 30% of banking jobs could be lost to digitalisation over the next 10 years. So are analysts' jobs going to be under threat in the future?

"I think they absolutely are going to be," said Sutton, who has seen variety of firms pushing to automate a lot of the entry level analyst work that's being performed, and building AI platforms accordingly.

"And that's going to present a little bit of a quandary because this is going to do two things, I believe.

"Firstly, it's going to dramatically increase the coverage that companies can have, where historically they have been constrained by the number of analysts that they pay and the amount of research that an analyst can perform during a normal work week. That constraint goes away as you look at leveraging AI platforms.

"But a separate, longer-term challenge is that the analyst role has typically been a grooming role for talent. It enables people to come in, learn the business and progress through the organisation to things that require a greater degree of insider trading and portfolio management capability.

"So you are taking away some of that training ground. It's a little bit of a Catch 22, in that companies are having greater capability to cover more and generate more insights from wider coverage area. But at the same time will suffer a little bit from a lack of grooming for future talent."

Sutton agreed we will see an evolutionary shift in the role of the human workforce. In terms of how this relates to financial services, he said it would ultimately be a good thing.

"I think we are going to be deploying our talents and intellect at more challenging and intriguing problems."

This data analytics headspace includes things like using AI to analyse the data signals of individuals that work for various companies – everything from social media to shopping habits – to generate interesting insights about how those companies are performing.

"We are working with a couple of hedge funds on this area right now; using AI platforms to read all of their various social media and public tweets and other types of information that they make available. To actually get a true sentiment analysis track of the company.

He said regulators in Europe have been considering such methods with a view to how AI might be used to self-police companies.

"Being able to read and understand lots of data about people that work at different organisations can help identify indicators for potential illegal or at least borderline activities."