An iCub robot learns how to play from a child. Photograph: Dr Patricia Shaw/EPSRC/PA
It’s hard to miss the fanfare – AI/Machine Learning is going to bring a huge transformation to the banking sector. But
application is slow. The fit is uncomfortable. It’s become clear that to achieve the greatest results, there needs to be a collaboration between humans and machines. That will require re-skilling and a reassessment of the future of work in banking.
Most CEOs say they are still trying to work out how to make the most of the collaborative potential. When thinking about their people strategy for the digital age, less than half (44%) say they are clear about how robotics and AI can improve customer experience, for example.
Former Deutsche Bank chief executive John Cryan suggested replacing half his 98,000 staff with robots. Well, his departure has delayed that eventuality. But just this month, Citigroup’s investment bank has cast a deeper shadow over operational roles in banking. Jamie Forese, president of Citi and chief executive of the bank’s institutional clients group, forecast that Citi would shed up to half of its 20,000 technology and operations staff in the next five years. But most financial institutions I visit in my capacity as a provider of Machine Learning (ML) services are already sold on the potential of artificial intelligence to augment their staff, not just displace. The emergence of the subsets of AI; Machine Learning, Deep Learning, RPA and automation are revolutionising the banking industry. However, what is less clear is the profile of the organisations best equipped to exploit that potential. What skills do the banking professionals of tomorrow require to harness this technology?
At Seldon, we contributed to a joint research study on AI in Finance between London School of Economics and EY Financial services. And we’re not alone in identifying the areas ripe for evolution – every consultancy has produced research on the transformative power of AI.
But these areas of the bank are not static divisions to be doused in ‘Machine Learning Fairy Dust’ to solve their business challenges. Indeed any application of algorithm-based technology will hasten the evolution of the roles and departments that serve these core areas. If ML allows the investment bank to curate hitherto invisible market signals what does that mean for the role of the e-trading/Equities teams that need to react to that insight? As exciting as these tools are they will swiftly become a commodity – commercial advantage will be in the uniqueness of your data, or your speed to market. All of which will necessitate new roles in the org. Throwing 100’s of computer science PhDs at the problem will be expensive and difficult. The ‘Citizen Data Scientist’ is a more likely reality – empowered by visualisation tools and easy to use ML frameworks, the business analyst will combine a micro-second sensitivity to the markets with a broader view of economic influencing factors. Expect to see that hyper-informed perspective create new revenue opportunities and trading relationships.
Jim Marous’ The Financial Brand‘ has referenced Accenture’s research, “Between 2018 and 2022, banks that invest in AI and human-machine collaboration at the same rate as top-performing businesses could boost their revenue by an average of 34% and their employment levels by 14%.”
“As AI becomes more nuanced, its role in banks is moving beyond automation to elevating human capabilities. To benefit from the potential of AI, banks need to implement ‘applied intelligence’ – combining technology and human ingenuity – across all areas of their core business,”
Alan McIntyre, Managing Director at Accenture Banking
Steven Van Belleghem, author and co-founder
It’s possible some of the answers lie with Google’s Deepmind and their approach to ‘tuning’ their Go playing AI, Alpha Go. The London AI start-up, in the lead up to the now famous defeat of Go world champion Lee Sodol in 2016, recruited the European Go champion, Fan Hui. In a closed doors session Fan Hui would play Alpha Go, losing 5-0 to the machine. But these defeats drove Fan Hui to play more and more games with Alpha Go. The experience would widen his perspective – Alpha Go would train him. Fan would go on to defeat other masters and see his world ranking rise. The machine had provided him with a perspective on the game that no human could offer, and improved his ability.
Banks are not Amazon warehouses, now hives of human-less activity where machines repeat manual tasks. Our modern banking professional will need to be a creative curator; managing a 24/7 feed of market signal and customer behaviour, leveraging relationships to apply this insight, and standing up the new teams and divisions required to execute. Just like Fan Hui and Alpha Go, a combination of man and machine improving one another could be where Canary Wharf’s future lies.
This post was originally published on Linkedin.
Author – Lee Baker, Commercial Director at Seldon.