The machine learning specialists Goldman Sachs especially needs in 2019

It may be a whole new year, but some things haven't changed. In January 2019 there's still just as much buzz around machine learning as there was in December 2018. This may even be the year in which banks have something to show for all their investment in sentient algorithms.

As we start the year, Goldman Sachs is currently advertising around 100 jobs specifying that candidates must have machine learning skills globally. Machine learning skills are an increasingly important component of various jobs at the firm, from engineering through to trading and structuring.

However, one subset of machine learning stands to be increasingly important at GS (and by extension other banks) in 2019 - natural language processing (NLP). In a recent interview with Yale School of Management, Charles Elkan, whom Goldman hired from Amazon in April 2018 to help lead its machine learning and AI strategies, explained why.

Large banks are "party to thousands or possibly hundreds of thousands of contracts," said Elkan. He added that it's not realistic for banks to know in real time what the content of those contracts, or which clauses might be activated by events in future. However, with natural language processing, Elkan said banks will be able to automate not only their understanding of what each contract contains, but their understanding of the news flow that might impact their innumerable contracts.

In this way, Elkan said banks will be able to use NLP to develop a, "close to real time understanding of what their full portfolio of positions is." In turn, he said this will greatly improve risk management and will be "good for society:" by reducing surprises, NLP will reduce the risk of future financial crises.

Elkan doesn't say so explicitly, but the implication of his Yale interview is that NLP might turn out to be the most important application of machine learning in investment banks. While other industries can make use of data going back decades, Elkan notes that this is less relevant in banking. Data on lung cancer from the 1990s is still relevant now, he notes - but financial markets have changed a lot in the past 20 years.

Goldman is currently advertising 23 jobs for natural language processing experts, while over 200 current staff specify it as a specialism on their CVs. Elkan himself is a specialist in machine learning and data science who previously worked on Amazon's recommendation engine. One of Goldman's top NLP specialists is Ramanathan Narayanan, a new York-based managing director and technology fellow with a PhD in text mining from Northwestern University.

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