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Automation and AI key to future bank profitability

15 May 2017  |  5719 views  |  0

More and more bankers are suffering sleepless nights over spiralling, often out of control, costs and declining, or even disappearing, margins... And if they aren't, they should be. If they don't do something about it soon, they are going to go out of business.

It is now widely accepted that banks' post-trade operations are clumsy, incoherent, overly-complex and very expensive to maintain. Much of this is due to legacy IT systems, compromised by the continuous addition of multiple new applications and software services, due to a combination of expansion into new geographies and asset classes, as well as mergers and acquisitions.

But by far the biggest factor has been the advent of waves of new regulations and working practices which have swamped the industry since the 2008 banking crisis. The obligation to be compliant with these new rules has forced the addition of many layers of new workflows to already congested systems, as well as thousands in compliance teams to ensure they are adhered to.

It is estimated that the biggest global banks are spending well over USD 1 billion a year on compliance and the industry as a whole close to USD 300 billion. Meaningful savings here can therefore be re-deployed to deliver serious competitive advantages.

There is little doubt that banks are now awash with data and overwhelmed with managing it, analysing it and ensuring it is delivered to the people who require it to make better-informed decisions. So they need to manage the data better, simplify the systems that run the business and automate more of the business processes.

At the heart of this sort of strategic IT and business transformation will be the adoption of new technologies such as open architectures, cloud-based platforms and the deployment of a combination of artificial intelligence (AI) and machine learning (ML) capabilities. But to achieve this will require not only vision from the very top of the organisation, but also strong leadership to ensure strategic and sustainable implementation.

AI and ML are not there just to replace people. They are mainly to complete tasks which humans are unable to do, either because of their speed or complexity. These include the interpretation and implementation of the thousands of pages of regulations that have been published (and continue to rain down in ever-heavier storms) as well as the reduction in errors that occur throughout the transaction lifecycle. At best these are costly to correct, at worst they can damage reputations much more severely than balance sheets.

More importantly, AI initiatives can also bring to the bank the higher standards of intelligent trading capabilities that will provide serious competitive differentiation. While many of these capabilities have been around for years, the advent of powerful analytic resources and mass data storage and management at significantly cheaper prices has opened the door for widespread adoption.

The end result will hopefully see leaner, more agile and smarter banks that operate on significantly lower operating cost models, delivering more sophisticated and customer-centric services and are fit for purpose in this increasingly competitive and regulated market. 

TagsRisk & regulationPost-trade & ops

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