Put your finance team on the road to AI and automation (1/2)
- Your finance team should become an early adopter of AI and automation in order to reduce the burden of manual processes, recommends Workday's Tim Wakeford
The journey of continuous improvements in efficiency, alongside technological progression, is driving unparalleled business change. This much is clear from an EY study, where 65% of finance leaders say having standardized and automated processes — with agility and quality built into those processes — is a significant priority. In the same survey, 67% of finance leaders say improving the partnership between finance and the business is also a major priority.
Automation represents an opportunity to reduce the burden on finance professionals, particularly around the cornerstones of traditional activities, such as transaction processing and audit and compliance. These goals are effectively dependent on freeing people from repetitive tasks so they have time to work on higher-value tasks. Research from McKinsey Global Institute estimated in 2014 that activities comprising 34% of a financial manager’s time could be automated by adapting current technologies, freeing up finance professionals for more strategic activities.
Automation reduces the burden on finance
So, what does this bright future look like, with finance taking more of a strategic business advisory role? At Workday, we’re seeing forward-thinking financial executives shift to automating their finance function’s repetitive, manual roles and using those investment dollars for the creation of centers of excellence. These centers shift the emphasis from number crunching to financial analytics and forecasting, strategic risk and resilience, compliance and control, and better overall data-driven financial management.
Automation represents an opportunity to reduce the burden on finance professionals, particularly around the cornerstones of traditional activities, such as transaction processing and audit and compliance. These activities in their current form prevent finance from being more strategic business partners.
Finance as an early adopter of AI
Contrary to the popular perception of finance being risk-averse, it is actually the poster-child industry for the early adoption of many new technologies, particularly AI. In the retail banking sector, organizations have started to harness AI systems to meet ever-growing regulatory demands that are getting too costly to handle with just people. Citigroup estimates that the biggest banks, including JP Morgan and HSBC, have doubled the number of people they employ to handle compliance and regulation, costing the banking industry $270 billion a year and accounting for 10% of its operating costs. Richard Lumb, head of Financial Services at Accenture, remarks:
Companies have really thrown bodies at this to deal with the demands of the regulators. They have had no option. But now we are shifting from a revolution of labour arbitrage and offshore to a revolution of automation.
Shamus Rae, head of Artificial Intelligence at KMPG, concurs, adding that, in addition to compliance, other applications of AI include combating fraud and anti-money laundering:
There’s never been so much data at our fingertips — and arguably there’s never been greater internal and external pressure to analyse that data to manage compliance and risk.
In this context, AI is an opportunity managers cannot ignore, offering companies the ability to process vast quantities of data at lower cost.
Human error and trusting the machine
While the use of AI systems can help eliminate risks associated with human error, it does raise questions around how much trust the traditionally risk-averse finance function will place in ‘the machine’. Risk and audit functions require evidence that processes are effective, but the fact that AI handles large data volumes – and also self-learns – raises questions about complete accuracy.
If a cognitive system delivers, for example, 97% accuracy in its decision-making, as opposed to 95% with humans, is this enough for the organisation? Who should make that call? And how do you know whether accuracy goals are achieved? Where does the human intervention end and the machine begin?
We are beginning to see a familiar pattern emerge, particularly from a finance perspective. Resource-intensive, repetitive tasks, such as data entry and transaction processing, are well suited to automation and AI. Yet far from the idea of the culling of the workforce mentioned earlier, a picture of a much more strategic, more efficient finance function is emerging, powered by these new technologies, yet still highly dependent on a skilled workforce.
In the second of this two-parter, to be published in two weeks' time, I want to look at some of the first steps finance professionals should take on the road to AI and automation.