Will artificial intelligence upend the financial world?

Many now accept that artificial intelligence, robotics and other high-tech developments will upend blue-collar professions such as retail sales, truck driving, package delivery, fast food and more. Some observers now estimate that self-driving vehicles could replace 1.7 million truckers in the next decade. Drivers of delivery vehicles could see their jobs replaced by Amazon drones.

But what about finance, the epitome of white collar employment? Far from being immune, white collar occupations in general, and finance in particular, are arguably even more prone to be substantially affected. Entire categories of highly-paid workers could be rendered obsolete in a matter of a few years.

So just how fast is artificial intelligence advancing? What is the outlook for finance?

AlphaGo Zero

Go playing board

Until 18 months ago ago, the ancient Chinese game of Go had firmly resisted attempts to apply computer technology, and was held up as a prime example of human superiority — the best human players were substantially better than the best computer programs. This changed abruptly in March 2016, when a Google computer program named “AlphaGo” defeated the reigning world champion 4-1, a defeat that shocked many observers, who had not expected to see this for many years. Not resting on laurels, the DeepMind researchers further enhanced their program, and then, on 23 May 2017, defeated Ke Jie, a 19-year-old Chinese Go master thought to be the world’s best human Go player.

In developing the program that defeated Lee and Ke, DeepMind researchers fed their program 100,000 top amateur games and “taught” it to imitate what it observed. Then they had the program play itself and learn from the results, slowly increasing its skill.

In the latest development, the new program, called AlphaGo Zero, bypassed the first step. The DeepMind researchers merely programmed the rules of Go, with a simple reward function that rewarded games won, and had it play games against itself — literally teaching itself to play Go with no human input. Initially, the program merely scattered pieces seemingly at random across the board. But it quickly got better at evaluating board positions, and substantially increased its level of skill.

Interestingly, along the way the program rediscovered many basic elements of Go strategies used by human players, including anticipating its opponent’s probable next moves. But unshackled from the experience of humans, it then developed new complex strategies never before seen in human Go games.

After just three days of training and 4.9 million training games (with the program playing against itself), the AlphaGo Zero program had advanced to the point that it defeated the earlier version of the program 100 games to zero. Skill at Go (and several other games) is quantified by the Elo rating, which is based on the record of their past games. Lee’s rating is 3526, while Ke’s rating is 3661. After 40 days of training, AlphaGo Zero’s Elo rating was over 5000. Thus AlphaGo Zero was as far ahead of Ke as Ke is ahead of a good amateur player.

Outlook for the financial world

If these developments seem startling, changes in the finance world are going to leave us even more breathless. As Saijel Kishan, Hugh Son and Mira Rojanasaku wrote in a Bloomberg feature report, Wall Street is entering a new era:

The fraternity of bond jockeys, derivatives mavens and stock pickers who’ve long personified the industry are giving way to algorithms, and soon, artificial intelligence.

Banks and investment funds have been tinkering for years, prompting anxiety for employees. Now, firms are rolling out machine-learning software to suggest bets, set prices and craft hedges. The tools will relieve staff of routine tasks and offer an edge to those who stay. But one day, machines may not need much help. It’s no wonder most of the jobs Goldman Sachs Group Inc.’s securities business posted online in recent months were for tech talent. Billionaire trader Steven Cohen is experimenting with automating his top money managers. Venture capitalist Marc Andreessen has said 100,000 financial workers aren’t needed to keep money flowing.

According to the Bloomberg report, some specific financial areas that are prime for automation include:

  • Sell side credit markets: Natural-language processing, data collection and machine learning are being applied to automate subjective human decisions.
  • Sell side foreign exchange: Big data and machine learning are being used to anticipate variations in client demand and the resulting price swings.
  • Sell side commodities: Trader and salesperson conversations are being catalogued to create profiles of clients.
  • Sell side equities: Artificial intelligence is being applied to order execution.
  • Buy side equities: Predictive analytics is being applied to time stock purchases and assess risk based on market liquidity.
  • Buy side credit: Computer programs are being trained to scan and understand bond covenants, legal documents and court rulings.
  • Buy side macroeconomics: Natural-language processing is being used to analyze central bank commentary for clues on monetary policy. Other software is analyzing data such as oil-tanker shipments and satellite images (e.g., Chinese industrial sites, Walmart parking lots and more) to spot trends in the economy.

Blockchain technology is also poised to make major inroads in finance. Numerous firms are already offering blockchain services for applications such as handling the settlement of pooled corporate debt, recording and settling short-term government bond trades, and trading shares in privately held securities.

Along this line, a large number of jobs in finance involve regulatory compliance — auditing, controllers, etc., particularly in the wake of the Dodd-Frank legislation passed by the U.S. Congress after the 2008-2009 financial crash. Much of this activity is to ensure that human actions follow rules. So removing human discretion through automation will streamline regulatory compliance — computer systems will follow the rules by design.

Michael Dubno, former Chief Technology Officer for Goldman Sachs, describes the situation in these terms:

The end-state of finance is similar to the end-state of almost every business: Almost every business is going to be automated to the point where very few people are involved in the running of it. … All firms have between 5 and 20 years in terms of what happens, but I think they’ll feel the effects of that way sooner.

Other white collar and service occupations

Many other professions are also destined to be transformed by AI, big data and robotics technology.

In 2011 an IBM-developed computer system named “Watson” defeated two champion contestants on the American quiz show Jeopardy! Since then IBM has deployed its Watson AI technology in the health care field. In a recent test of its cancer-diagnosing facility, Watson recommended treatment plans that matched recommendations by oncologists in 99 percent of the cases, and offered options doctors missed 30 percent of them. Also, Watson is now considered to be on a par with professional radiologists in its ability to analyze X-rays and diagnose a condition.

In the legal field, some of the basic research work is already automated, and many legal reference works are available online. But more than 1500 startups are now pursuing intelligent software to further streamline all aspects of the legal services field.

The consumer service sector will also see major changes. Many restaurants have already adopted computer terminals and smartphone apps to handle customer orders, process payments and inform the customer when the order is ready. Robots are coming to restaurants: a robot has already been deployed that can produce 400 hamburgers per hour — slice toppings, grill patties, assemble burgers and bag the product.

What will the future hold?

So where is all this heading? A recent Time article features an interview with futurist Ray Kurzweil, who predicts an era, roughly in 2045, when machine intelligence will meet, then transcend human intelligence. Such future intelligent systems will then design even more powerful technology, resulting in a dizzying advance that we can only dimly foresee at the present time. Kurzweil outlines this vision in his book The Singularity Is Near.

Futurists such as Kurzweil certainly have their skeptics and detractors. Sun Microsystem founder Bill Joy is concerned that humans could be relegated to minor players in the future, and that out-of-control, nanotech-produced “grey goo” could destroy life on our fragile planet. But even setting aside such concerns, there is considerable concern about the societal, legal, financial and ethical challenges of such technologies, as exhibited by the increasingly strident social backlash against technology, science and “elites” that we see today.

One implication of all this is that education programs in engineering, finance, medicine, law and other fields will need to change dramatically to train students in the usage of emerging technology. And even the educational system itself will need to change, as evidenced by the rise in massive open online courses (MOOC). Along this line, the big tech firms are aggressively luring top AI talent, including university faculty, with huge salaries. But clearly the field cannot eat its seed corn in this way; some solution is needed to permit faculty to continue teaching while still participating in commercial R&D work.

But one way or the other, intelligent computers are coming. Society must find a way to accommodate this technology, and to deal respectfully with the many people whose lives will be affected.

Welcome to the Brave New World!

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