THE POTENTIAL OF AI IN BACK OFFICE TRANSFORMATION



What is the potential of AI in back office transformation?

Artificial intelligence can virtually transform any activity or task performed across the whole organisation, but it is important to recognise that there is still ample room to automate processes in the back offices with more traditional technologies that do not require AI or machine learning.

Notwithstanding, AI will allow us to take a leap in the automation of complex processes or tasks, primarily those that involve decision-making or have a high cognitive component.

Among the processes, some examples where I believe there is a high potential for transformation with AI in back offices would be (i) Contact Centre, both Inbound and Outbound, (ii) Cash Management (iii) Claims and (iv) Accounting.

Anyway, I believe that the maximum potential can be achieved with a task and activity automation approach rather than with and end-to-end one, where artificial intelligence can more quickly scale e.g. Intelligent document processing and control processes automation would be some use cases.

Another working line with important opportunities is related to the optimisation of back office management. An example, the capacity planning of the operations units based on accurate demand forecasting models, dynamic scheduling and skills-based classification of resources.

How has COVID-19 accelerated banks’ cost transformation programmes?

COVID-19 has disturbed businesses at every level but if there is one thing that has been clearly showcased it is that those banks that are further into their digital transformation, like BBVA with automation and DIY capabilities, are those that have been able to better cope with the situation.

Next up, those that had solid sourcing models with proven suppliers were able to better adapt to the drastic reduction in activity, leaving the banks with a higher proportion of manual and internalised processes, which translates into high installed capacity, suffering the most from the aftermaths of the pandemic. On the same scale, companies have suffered in terms of business continuity and quality. These facts highlight the importance of adopting a variable cost structure in order to be able to react and adapt with more agility in environments of uncertainty.

Last but not least, the pandemic has forced us to work remotely and we have witnessed that it is feasible to extend this model across the organisation. Accordingly, we can now rethink our corporate premises strategy to reduce fixed cost with the reduction of on-site presence of our employees and we should also seek lower costs from our suppliers if they are to transition to a remote working environment as well. However, we should also consider the additional costs that will be incurred to effectively adopt a remote working model due to the requisite improvements in terms of operational procedures and controls to maintain adequate levels of security, operational risk and productivity.

Where do you see the operations transformation going in the post-pandemic world?

COVID-19 has put the spotlight on the resilience of operations and therefore its transformation should necessarily focus on improving it and that can be achieved by increasing the levels of automation and DIY of back office tasks and activities.

In addition to it, back offices’ main strategic transformation themes, irrespective of the impact of the pandemic, should be directed towards the creation of Centres of Operational Excellence and to get there, in particular, the greatest efforts will be made to:

  • Scale automation to be able to evolve the Catalogs of provided-services, incorporating new services with greater added value: services with direct interaction with customers. Operations Offices performing as Front Offices, services and processes of corporate functions (Finance, Compliance, …)
  • Evolve service level agreements: SLAs 2.0 (customisation of price and SLA per customer)
  • Back Offices adaptation to mixed remote/face-to-face work models (or pure remote), adapting ways of working and leveraging advanced analytical models to optimise their management (demand forecast & productivity-measurement advanced models, control models adjustments to new ways of working…)

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