Financial services are failing to successfully implement artificial intelligence, European fintech executives have argued, despite mounting evidence that the hyped technology will boost productivity and cut costs..
Fears of job loss, regulatory concerns and institutional inertia are among the factors discouraging bankers from fully adopting the systems that underpin products like ChatGPT.
“The big banks definitely won’t accept [the technology] as fast as any of the fintechs,” said Tom Blomfield, Monzo co-founder and group partner at Silicon Valley incubator Y Combinator. However, generative AI will “make banks more efficient and able to provide the same products at a lower cost.”
A Capgemini study found that only 6 percent of retail banks are ready to implement artificial intelligence at scale in their business. However, McKinsey estimates that it could add up to $340 billion a year to the global banking sector, equivalent to around 4.7 percent of the industry’s total revenue.
Many say this technology, with its ability to answer questions and analyze vast amounts of text and numerical data in seconds, has the power to reduce costs across the industry. Still, there are fears that the disruption will lead to job losses.
“People don’t understand that it exists as a productivity tool,” said Nasir Zubairi, chief executive of fintech accelerator Luxembourg House of Financial Technology. “They still really believe it’s going to take their jobs.”
He added: “Traditional banks are fundamentally analogue by design and converting analogue to digital has always been a difficult task.
Speaking at the TNW Financial Times technology conference this month, Zubairi used the example of money-laundering checks, where institutions typically hire staff to go through spreadsheets and look for unusual activity.
He said when he showed one institution how to improve this with a customized AI model that he estimated could immediately save up to “€450,000 a year in salary”, it was rejected.
“People don’t like to fire people,” he added. “They want to protect the function of their jobs, and if they have to lay off people on their team who do those jobs, they’re also potentially at risk because leadership or their power is also being eroded in some way.”
Central banks have recently been urged to “up their game” with AI, according to the Bank for International Settlements, which said the technology could bring productivity gains but also carries risks such as providing incorrect information and being vulnerable to hackers.
A common problem with large language models, the technology behind most generative AI products, is their tendency to “hallucinate” and present inaccuracies as fact. They are also known to generate information based on the data they have been trained on, leading to concerns about sensitive or secure information.
“It’s not necessarily a rejection. [AI], but there is hesitancy,” said Wincie Wong, head of digital at NatWest, who called for an assessment of the risks, ethics and vulnerabilities of the technology before deployment. “After all, we are one of the big banks and many customers keep their data and their finances safe with us. We have to respect that.”
Customer service is one of the areas that AI tools are disrupting the most because they can converse like humans and respond to questions. For more than a decade, digital banks have used machine learning to triage online questions and often direct clients to a live customer service agent.
However, bots with LLM technology can understand a wider range of queries, regardless of how they are phrased, and can make decisions such as ordering a bank card, eliminating the need for human intervention.
“I really think it’s going to eliminate the vast majority of customer service jobs” over the “next 12 months to five years,” Monzo’s Blomfield said.
Many banks and fintechs, including Klarna and NatWest, are already using AI chatbots for customer service. NatWest’s Wong said they have made huge progress with generative AI in their Cora AI service, receiving more than 11 million chats in a year, with more than half requiring no human intervention. In 2017, the service received 1,000 chats per month and needed an intervention.
Swedish fintech Klarna said its AI assistant could handle the work of 700 customer service workers and resolve queries in less than two minutes, compared to 11 minutes previously. As a result, the company expected to save $40 million in customer service costs this year.
However, Wong said training models to be nuanced was critical to his success. For example, she needed to understand that a change of address could have an emotional undertone, such as a bereavement in the family.
“Understanding the psychology behind it was really important, and if you don’t get it right, you can, to put it mildly, piss off a lot of customers,” she added.
Banks also had to be careful to adopt the nascent technology while adhering to the industry’s strict rules and navigating an uncharted regulatory environment.
In a landmark ruling in 2022, a Dutch court ruled in favor of neobank Bunq after it sued the Dutch central bank for banning it from using AI to carry out money laundering controls.
Regulators lifted restrictions on German fintech N26 last month after it improved its control measures. For years, the bank had a limit on new client registrations due to poor money-laundering controls and faced millions of euros in fines for persistently late reporting of suspicious activity.
Carina Kozole, director of risk at N26, said she worked closely with regulators to create an artificial intelligence model to assess whether a new customer was a criminal, which reduced instances on the platform by 90 percent.
“If we don’t embrace AI in this industry, we won’t be here in a few years,” she added. “We have to show the benefits and how we can achieve compliance if we use AI.”