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, organisations 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 J.P. 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 per cent of its operating costs. Learn more skills from Workday Training
By definition, AI is the development of computer systems to perform tasks that normally require human intelligence, such as visual perception, speech recognition, and decision-making.
Experts view AI and automation as viable solutions for effectively dealing with compliance and risk challenges, and across much more of finance than just retail banking.
“Companies have really thrown bodies at this to deal with the demands of the regulators,” says Richard Lumb, head of Financial Services at Accenture. “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. “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,” he says.
“In this context, AI is an opportunity managers cannot ignore, offering companies the ability to process vast quantities of data at lower cost.” In addition to compliance, other applications of AI include combating fraud and anti-money laundering, Rae adds.
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.” Get from Workday HCM Online Training
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 per cent accuracy in its decision-making, as opposed to 95 per cent 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?
Matthew Cooley, president, Financial Executives International, New York City Chapter, makes a valid point. “Advances in technology will continue to provide more accurate and timely data, but the strategic decisions made based on that information will always require human involvement.”
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.
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