Artificial intelligence roles the ‘only safe jobs in banking’
World’s biggest banks have increased the number of employees working with artificial intelligence by 13% in the past six months
Artificial intelligence (AI)-related roles could be the only “safe jobs” in the banking sector as financial organisations “relentlessly” press on with AI-led transformation.
In its latest AI talent report, banking industry benchmarking firm Evident revealed the biggest AI recruitment push yet, with a 13% increase in AI staff at banks over the past six months. It said one in 50 employees recruited by the top 10 banks now work in AI-related roles.
It found that recruitment of AI development professionals grew by 6%, hiring of data engineers increased by 14%, while the number of AI and software implementation experts hired increased by 42%.
JPMorgan Chase, Wells Fargo and Citigroup made up the top three, and while US banks dominate the top 10 in terms of AI hiring, the UK’s HSBC and Barclays made the top 10, at numbers nine and 10, respectively. France’s BNP Paribas and Spain’s BBVA were at seven and eight, respectively.
Lloyds Banking Group was in a group of companies pushing to catch up on the top 10, according to Evident. Last month, the bank announced it is training 200 of its senior leaders to ensure the organisation can get the most out of AI. The bank is working with training provider Cambridge Spark on the programme, which will embed AI skills in the leadership ranks. It also recently announced it is using Google Cloud’s Vertex AI to build a machine learning (ML) and generative artificial intelligence (GenAI) development platform, to be used by more than 300 of its data scientists.
Alexandra Mousavizadeh, co-founder and CEO of Evident, said: “Data suggests that AI roles may be the only safe jobs in banking right now. Away from the market noise and volatility, the leading banks are quietly but relentlessly pressing forward with AI transformation. They’re adding talent with increased precision, laser-targeting their efforts on where hiring will help scale AI use cases that deliver measurable value.”
Evident revealed that the top 10 banks, by AI talent volume, had twice as many AI use cases and were 1.5 times more likely to report a return on investment against deployments.
“To date, AI has mostly been used to augment workflows. But with industry headcount down by around 3% over the past two years, and major cost savings now explicitly tied to AI, the dynamic is shifting. For banks already seeing returns, there’s a clear signal that now is the time to double down,” said Mousavizadeh.
Evident revealed that the gap between the leaders and laggards is widening. “The pressure is really on the lagging banks. If you’re still in the early stages of executing an AI strategy, trying to determine the right pathway forward, and staff,” said Mousavizadeh.
The Bank of England and the Financial Conduct Authority have been tracking how financial services firms in the UK are using AI and machine learning. The results of a recent survey, which covered 120 firms, found that three-quarters are already using some form of AI in their operations.
In an interview with Computer Weekly, ING chief technology officer Daniele Tonella described the bank’s “conservatively aggressive” approach to innovating with GenAI.
“ING has set the foundation for avoiding GenAI becoming a ‘tech toy’ conversation,” he said.
The bank is enabling development around GenAI in five areas: know your customer, call centres, wholesale banking to improve customer due diligence, retail for the hyper-personalisation of offerings, and inside tech for engineering.
“We brought in strict governance that focused all exploration on GenAI on five areas, and only under the control of the chief operating officer. This is important because AI has gained a lot of attention and traction.”
Challenger banks are more accustomed to using AI. Speaking at the Innovate Finance Global Summit in London, on a panel discussing the next chapter for fintech, Zopa Bank CEO Jaidev Janardana said GenAI is being used by software teams to enable them to write code quicker.
“It is also supporting productivity in our operations, where it helps in understanding customer sentiment and responding to them more quickly,” added Janardana.
