The media discussion about artificial intelligence (AI) has gravitated heavily around the idea that machines will soon replace humans, leaving them with nothing to do, or at best forcing humankind to become data scientists. Almost certainly there is some truth in the rhetoric that AI will take over from humans in certain jobs eventually, but certainly in the financial services that time is not now. The truth is that AI is not about to automate most professions but rather complement human intelligence.

One question that changed the world

It was in 1950 that the mathematician and grandfather of computer science, Alan Turing, posed the question ‘Can machines think?’.

Turing lived in a time when answering such questions helped nations win wars but who knows if his brilliant mind could have foreseen that this particular question would still be being pondered decades later. Pondered perhaps does not do justice to what is now a multi million-dollar industry.

For example, Google recently invested $400 million in artificial intelligence (AI) start-up DeepMind – an investment which is merely the tip of the iceberg. Recently, Google founded the People + AI Research Initiative (PAIR) to better explore these interactions with the support of researchers from MIT. Similarly, a group of international researchers from different organisations support the idea that AI is more a myth to debunk than a field of study and, therefore, its content should be better disseminated to the wider public.

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A member of this collective, Barbara J. Grosz from Harvard University, notes that science is increasingly interested in developing systems that work with people instead of machines that replace them, imagining ‘…a future in which computer systems make us feel smarter, not dumber, and work seamlessly with us, like a good human partner.’ Such approaches go almost completely unnoticed in most media debates, although they envisage a business world where machines make our lives easier and businesses more profitable.

Natural language processing, a sub-field of AI is a good example of augmented intelligence. NLP is the development of systems capable of reading and understanding the languages that humans speak. At the heart of this technology is the effort of interpreting a large amount of ‘unstructured data’ (i.e. data that cannot be read by machines yet, such as PDF files, images and audio material). This level of automation is already having significant practical implications.

FinTech is revolutionising banking

5,000 FinTech start-ups were identified by a 2016 report by Ernst & Young, and the vast majority of them have a mission statement to innovate the banking and financial services sector in some way.

Notably, AI is able to vastly improve upon client servicing, trading, post-trade operations such as reconciliations, transaction reporting, tax operation and enterprise risk management, just to name a few.

>See also: There’s more than one side to AI in banking

It is usually after such a list of currently human led roles that alarm bells start ringing and people predict the downfall of a working mankind. Indeed, a study released in 2017 by PwC points toward the fact that: ‘AI will gradually replace humans in some functions like personal assistants, digital labor, and machine learning.

But challenges will persist due to bias, issues of privacy, trust, a lack of technically trained staff, and regulatory concerns. These challenges are in terms of the evolution of the workforce gargantuan which is why certainly in the near future a human led approach will undoubtedly prevail. Augmented Intelligence, in which machines assist humans, is the answer.’

In the banking world adoption of this mix of human and artificial intelligence could lead to a process split of 34% informed by machine algorithms and 66% by human judgment.

AI is not technology for technology’s sake

It is important to remember that AI is designed to improve people’s lives and businesses. FinTech is leveraging this technology because it significantly improves customer interactions, and not only by implementing peripheral gimmicks like chatbots.

Key tasks in the financial industry require the reading and understanding of large amounts of information that are only partly structured. NLP is making processes much easier, faster and more precise with less effort than ever for humans.

>See also: What does digital transformation mean for finance teams?

Start-ups such as MonkeyLearn are already leveraging NLP to automate business intelligence information, whereas leading Japanese asset manager Nomura Asset Management (NAM) is investing to understand whether this technology can improve the decision-making processes of portfolio management’s investors.

Even providers of legal services are turning towards NLP, automated document analytics and law case reporting within due diligence processes. A good example of this is found in virtual data rooms such as Drooms NXG that utilises NLP to sort, process and allow data to be interrogated by multiple users all in the cloud.

These solutions were not developed with the goal of replacing manpower with automated data analysis. On the contrary, the goal is to empower professionals by developing intuitive tools and harnessing AI to make processes faster, easier and more accurate and their business lives more successful.

Sourced by Jan Hoffmeister, managing partner, Drooms