Verne Global

AI / ML / DL | Finance |

29 August 2018

AI is a great opportunity, but banks must innovate faster

Written by Shane Richmond (Guest)

Shane Richmond is a freelance technology writer and former Technology Editor of The Daily Telegraph. You can follow him at @shanerichmond

The impact of AI on the lives of consumers and the operation of businesses is slowly growing. Whether it’s the increasing visibility of autonomous vehicles or the small conveniences of a voice assistant such as Amazon’s Alexa, we’re beginning to get a sense of what AI can do. However, we’re still at the beginning. The truly significant changes are yet to come.

These will include widespread automation that makes whole swathes of workers redundant and leaves societies with the question of what these people will do instead. New jobs typically emerge whenever technology replaces old ones, but it isn’t yet clear what they will be in this case. And we will see computers start to manage airports, cities, energy grids and other complex systems. Few industries will be immune.

Banking is certainly no exception. According to financial research firm Autonomous, the size of the opportunity for financial institutions will be more than $1 trillion in cost savings . In a report published in July, the firm said financial institutions will cut costs by 22 per cent by 2030. This report matches similar estimates from Bain & Company, which predicted savings of $1.1 trillion and Accenture, which predicted $1.2 trillion by 2035.

Reporting on the potential for AI and banking earlier this year, the Financial Times quoted investor Jeroen van Oerle, of Robeco, on the significance of the challenge: “In order to keep relevant for the future, you need efficient back office operations… on top of that, you need to be able to tailor make products. If you cannot provide those kinds of services in the future, a competitor will and you will lose.”

The banking industry has already begun deploying AI in a variety of ways, many of which look like simple ways to test concepts, rather than complete solutions. Wells Fargo, for example, is piloting an AI-driven chatbot with Facebook Messenger, Bank of America has a virtual assistant named Erica, and UBS is doing a similar thing with Amazon’s Alexa.

Meanwhile, Citibank is using AI-powered real-time data analysis to target fraud and JPMorgan Chase has introduced its Contract Intelligence (COiN) platform to analyse and extract data from legal documents – a process that typically takes hundreds of thousands of hours for human workers.

Banks don’t have to do all of this themselves, of course. Writing on this blog at the end of last year, Verne Global’s CFO, Dominic Ward, said that an optimised HPC strategy is “critical” for the sector, making it important to choose the right partners .

He added: “The infrastructural choices firms make can have profound impacts on performance, reliability, flexibility and scalability, to name just a few considerations. As firms juggle long lists of commercial and operational priorities – from time to market to customer requirements to balance sheets – the decisions they take early in the process become crucial.”

The wrong decisions will cause banks to fall behind. Writing for Data Science Central in June , Dr Dimitrios Geromichalos pointed out that financial services companies could fall behind in a multitude of ways. For example, competitors might start offering the “tailor-made” solutions described above by Mr van Oerle – these are the kind of products with which it can be very difficult to compete. As customers see better services on the market they will start to demand similar products from their own banks and go elsewhere if they are not available.

Dr Geromichalos adds that banks that cannot use AI to assess credit ratings will find themselves taking more risk, with worse returns, than their rivals. If they cannot use AI to target fraud, then fraudsters will begin to see them as a destination. Finally, he argues, these firms will risk their reputations because “reputation-damaging projects tend to be more likely to be carried out by banks with low AI maturity”.

The scale of the opportunity, combined with the risk of being left behind, should be enough to convince banks and other financial institutions that they need to increase the pace of innovation. That means finding the right partners and choosing the technologies that can go beyond cosmetic AI experiments and have a real impact on the bottom line.


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