Technology: AI and the spectre of automation

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Sitting in his office in central Madrid, Marco Bressan, the chief data scientist at BBVA, gives a small chuckle when asked about the topic of artificial intelligence. He notices that as soon as a new article is written in Wired or the Harvard Business Review, his phone starts ringing.

by Tom Upchurch

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Marco, what can we do about AI? Marco, are we doing enough on AI?" The questions all come from senior executives, desperate to harness the potential that AI promises. 

Yet Bressan is bemused by how the technology is talked about at board level and in the media. "Currently it denotes a vision of the future; an aspect of the sci-fi imagination; something that you still can’t do. But the truth is senior financial executives have been doing AI-related work, research and deployment of products for years."

At the most rudimentary level, AI involves teaching machines to learn and to interact in order to undertake cognitive tasks that were usually performed by humans. The type of AI featured in sci-fi films in which machines possess a human-like intelligence, sometimes referred to as general artificial intelligence, remains a distant and elusive prospect. The most optimistic experts, such as Google’s director of engineering, Ray Kurzweil, predict that AI will be able to outsmart humans by 2029. Conservative predictions expect this to take at least 100 years, if at all. 

Of more immediate relevance to those working in financial services is the deployment of narrow artificial intelligence. These applications undertake specific tasks using problem solving, deduction, reasoning and natural language processing. Such programmes are being applied across financial services, from the development of customer service programmes that use natural language processing to manage and field customer queries, through to programmes that can conduct financial research and make sophisticated models of financial markets to identify trading opportunities. 

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The potential for narrow applications has led to a boom in AI investment. Technology companies are undoubtedly leading the way. In 2015 the giants of AI – Microsoft, Google and Facebook – spent $8.5 billion on AI research, acquisitions and talent. 

In comparison, financial institutions have made a cautious foray into the field. A handful are making investments by hiring high-level data scientists or acquiring AI companies. The hedge fund Bridgewater Associates hired the former chief engineer behind IBM’s Watson supercomputer. BlackRock has also been busy hiring some high-profile names and has announced a joint venture with Google to explore how to use AI to improve investment decision-making. Goldman Sachs has invested in a number of promising AI start-ups, including the financial research platform Kensho.

Yet most financial institutions have been slow to adopt AI, even though it is likely to usher in a new type of bank, with data and technology as its heart. Failure to adapt may lead to extinction for some. As Neil Dwane, global strategist at Allianz Global Investors, explains: "Technological competence is absolutely essential for at least staying in the game. You may still lose, but if you’re not in it, you have no hope of winning."

Hype

AI has been around for over 50 years. Many commentators are quick to say that there have been previous periods of hype about AI. Is it really different this time around? The director of the Laboratory for Financial Engineering at MIT, Professor Andrew Lo, believes that there has been a step-change in the technology: "All new technologies come with a certain degree of hype, but I do think there has been a material change in the technology today against where things stood even just five years ago." 

The key driving force is the rapid expansion in the amount and availability of data. Whether it is data created from interactions on social media sites, financial transactions or even mobile phone data, there has been an unprecedented growth in the amount of information available for collection and analysis. In combination with exponential increases in computer processing power (Moore’s Law), increased storage enabled by cloud computing and the refinement of techniques such as deep learning, this data is used to train and optimize learning algorithms. 

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Marco Bressan, BBVA

As Bressan explains: "The data is the key enabler. Suddenly we can work with 10 years of history of every single transaction of the millions of transactions that take place every day, and use that to train some kind of learning algorithm. We could not do that before but we can do it now."

The barriers to experimenting with AI are also coming down. Although cutting-edge research and development still require highly specialized skills, it is becoming easier for non-specialists to develop basic AI programmes. As the resident fintech expert at Oxford University, Huy Nguyen Trieu, recalls: "Ten years ago, if you wanted to develop an app for your smartphone that recognized what you said, you would need a PhD from Stanford or MIT. Today, you can be a very basic programmer, using [programming language] Python and search libraries, and you can develop this app." 

Even as the requirements for experimenting with AI are becoming less stringent, financial services remains a sector with ample specialist skills in mathematics, statistics and data science. This pool of talent is helping encourage experimentation with technology. 

As Lo notes: "Financial services now has a very large population of quants in the industry, much more than ever before, who are ideally positioned to make use of these technologies."

Uneasy tension

The promise of AI may seem more tangible than before, yet determining where this technology will have the most profound impact in financial services is less certain. In March, Euromoney Thought Leadership launched a global survey with the law firm Baker & McKenzie exploring the future of artificial intelligence in financial services. The results showed an uneasy tension between attitudes that on the one hand identify AI as a tool for enhanced risk management and efficiency and on the other see it as a source of systemic risk, volatility and industry fragmentation.