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Putting AI to Work in the Finance Industry

Putting AI to Work in the Finance Industry
Technology has a long history of disrupting the financial services sector, including the advent of the ATM machine or, more recently, algorithmic trading. As significant as these changes were however, both examples are dwarfed by the prospect of what advanced AI is already doing — and will continue to do — to revolutionize the industry.
Many hear the term "AI" and immediately worry about displaced jobs, and it’s true that AI and machine learning can take over jobs that could previously only be done by people. So the question becomes: Will AI dramatically cull the workforce in financial services or will its productivity gains create new opportunities that are still highly dependent on a skilled workforce?
A look at some recent survey data, a case study and even those historical precedents involving ATMs and algorithmic trading paint a much more complex and optimistic picture of AI’s role in the finance industry — one in which technology innovations spur productivity gains that create plenty of new opportunities for the human workforce.

Perceptions and Planning Around AI

AI derives its power from the ability to self-improve and adapt with minimal human intervention. The powerful algorithms at the heart of AI learn from experience and — in doing so — are able to take on more complex problems and act with autonomy to make decisions in environments where there is more ambiguity and changing circumstances.
While it’s true that AI and machine learning can disrupt the nature of the jobs people do in finance, it’s not a foregone conclusion that this disruption will reduce the number of people working in the industry.
For instance, in a November 2017 survey on enterprise AI of 260 executives in nine countries by the independent research firm Vanson Bourne, 100-percent of financial services executives surveyed believe that by the year 2030 the adoption of AI will have an impact on human tasks. But only a quarter of those respondents believed AI would actually replace humans in the workforce for most enterprise tasks.
The rest believed that AI and humans will strategically co-exist in the workforce, with people elevated to enhanced roles and responsibilities that play to their strengths. Further, when it comes to the motivation for AI investment, the focus is much more on growth and ROI (62-percent) than mere cost-cutting (38-percent).
Renowned economist, Avi Goldfarb, Professor at The University of Toronto and co-author of “Prediction Machines:  The Simple Economics of Artificial Intelligence” has a perspective rooted in economic theory. According to Goldfarb, “A job is augmented when machines take over some, but not all, tasks. This is likely to become quite common as a natural consequence of the implementation of AI tools.  In high-paid careers like finance, prediction is a core skill that will become more and more automated by machines, and consequently result in valuing complementary human skills like judgement.”

A History of Technology Augmenting the Workforce

This outcome of technology — in this case, AI — augmenting rather than reducing the role of humans in financial services is welcome. But it shouldn’t be unexpected. Previous innovations in the sector also defied predictions about massive job losses — leading to actual workforce benefits instead.
When you consider the arrival of ATMs in the 1980s — not to mention subsequent advances in online and mobile banking — you might conclude that bank teller jobs would go the way of the horse carriage driver.  Yet, the opposite happened.  Since 2000, bank teller jobs have increased, outpacing the overall labor force growth rate.

As James Bessen explained in a book about innovation and workforce issues, the reason for this was that ATMs allowed banks to employ fewer tellers at a branch, which made it cheaper to run a branch.  Since it was cheaper to run a branch, banks opened more branches to better serve customers and grow revenue. Also, the role of the teller began to emphasize more relationship and sales skills.
In a similar sense, even though Goldman Sachs has embraced automated trading and related innovations, the number of employees at Goldman Sachs has remained relatively constant over the last decade.  Part of this can be explained by shifting required skills of traders from executing trades to salesmanship and relationship building — as well as new engineering jobs to build and operate the technology infrastructure. 
This is all food for thought as we see the current wave of innovation sweep through the financial services field. Especially as the customer experience gets elevated to mission-critical status for most banks, AI is positioning people to better support that experience. 
This optimism is shared by early adopters of AI.  “Our artificial Intelligence initiative is focused on improving the customer experience without compromising privacy,” says Dominic Venturo, executive vice president and chief innovation officer for U.S. Bank.  “Emerging AI capabilities are helping us create highly contextual and personalized actions based on data, and dramatically reducing the fraud rate.”  
AI is certainly a game changer for the industry, but the human workforce is still very much part of the game — and everyone stands to benefit. All signs point to AI’s role in continuing to create value in many ways, but cost savings through job elimination is not one of the primary drivers of AI ROI, now or in the foreseeable future.
Portrait of Chad Meley

Chad Meley

Chad Meley is Vice President of Solutions Marketing at Teradata, responsible for Teradata’s Artificial Intelligence, IoT, and CX solutions.

Chad understands trends in machine & deep learning, and leads a team of technology specialists who interpret the needs and expectations of customers while also working with Teradata engineers, consulting teams and technology partners.
Prior to joining Teradata, he led Electronic Arts’ Data Platform organization. Chad has held a variety of other leadership roles centered around data and analytics while at Dell and FedEx.
Chad holds a BA in economics from The University of Texas, an MBA from Texas Tech University, and performed post graduate work at The University of Texas.
Professional awards include Best Practice Award for Driving Business Results in Data Warehousing from The Data Warehouse Institute and the Marketing Excellence Award from the Direct Marketing Association. He is a regular speaker at conferences, including O’Reilly’s AI Conference, Strata, DataWorks, and Analytics Universe. Chad is the coauthor of the book Achieving Real Business Outcomes From Artificial Intelligence published by O'Reilly Media, and a frequent contributor to publications such as Forbes, CIO Magazine, and Datanami.

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