Financial services are failing to implement artificial intelligence successfully, European fintech executives have claimed, even as evidence mounts that the hyped technology will boost productivity and cut costs.
Job loss fears, regulatory concerns and institutional inertia are among the factors deterring bankers from fully embracing the systems that underpin products such as ChatGPT.
“The big banks will definitely not adopt [the technology] as quickly as any of the fintech,” said Tom Blomfield, co-founder of Monzo and group partner at Silicon Valley start-up incubator Y Combinator. Generative AI will however “make banks more efficient and able to provide the same products at a cheaper cost”.
Only 6 per cent of retail banks are prepared to implement AI at scale across their business, a Capgemini study found. McKinsey estimates however that it could add up to $340bn in value every year to the global banking sector, equivalent to around 4.7 per cent of total industry revenues.
Many say the technology, with its capacity to answer questions and analyse vast amounts of text and numeric data in seconds, has the power to slash costs across the industry. Yet there are fears the disruption will lead to job losses.
“People don’t understand that it’s there as a productivity tool,” said Nasir Zubairi, chief executive of fintech accelerator Luxembourg House of Financial Technology. “They still genuinely believe it will take away their jobs.”
He added: “Traditional banks are fundamentally analogue by design, and converting analogue to digital has always been a tough thing to do.”
Zubairi, speaking at the Financial Times’ TNW tech conference this month, used the example of money laundering checks, where institutions typically hire employees to trawl through spreadsheets looking for unusual activities.
He said when he demonstrated to one institution how to improve this with a customised AI model, which he estimated could save up to “€450,000 a year in salary instantaneously”, it was rejected.
“People don’t like firing people,” he added. “They want to protect the function of their job and, if they have to fire people within their team who do these jobs, they are also potentially under threat as management or their power is also being eroded in some way.”
Central banks have recently been urged to “raise their game” with AI, according to the Bank for International Settlements, which said the technology could provide productivity gains but carried risks too, such as giving incorrect information and being vulnerable to hacking.
A common issue with large language models, the technology behind most generative AI products, is their tendency to “hallucinate”, to state 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.
“There’s not necessarily a rejection of [AI], but there is hesitancy,” said Wincie Wong, head of digital at NatWest, who called for the technology’s risks, ethics and vulnerabilities to be assessed before deployment. “In the end, we are one of the large banks and a lot of customers hold their data and their financials safe with us. We need to respect that.”
Customer service is one of the areas most disrupted by AI tools, which can converse in a human-like manner and respond to queries. For more than a decade, digital banks have used machine learning to triage online questions, often directing clients to a live customer service agent.
However, LLM-powered bots can understand a wider range of queries regardless of how they are phrased, and they can execute decisions, such as ordering a bank card, removing the need for human intervention.
“I really do think it will eliminate the vast majority of customer service jobs” over “the next 12 months to the next five years”, Monzo’s Blomfield said.
Many banks and fintechs, including Klarna and NatWest, already use AI chatbots for customer service. NatWest’s Wong said they had made huge strides with generative AI in their service AI Cora, receiving more than 11mn chats over the year, with more than half needing no human intervention. In 2017, the service received 1,000 chats a month, and needed intervention.
Swedish fintech Klarna said its AI assistant could do the job of 700 customer service workers and resolve queries in under two minutes, compared with 11 minutes previously. As a result, the company expected to save $40mn in customer service costs this year.
However, Wong said training the models to be nuanced was crucial to its success. For example, it needed to understand that a change of address could have an emotional undertone, such as a family bereavement.
“Understanding the psychology behind it was really important and, if you don’t get it right, you can, to put it bluntly, piss off a lot of customers,” she added.
Banks also had to be be careful to roll out the nascent technology while adhering to the industry’s strict compliance rules and navigating an uncharted regulatory environment.
In a landmark 2022 ruling, a Dutch court ruled in favour of neobank Bunq after it sued the Dutch central bank for banning it from using AI to conduct money-laundering checks.
Regulators last month lifted restrictions on German fintech N26 after it improved its scrutiny measures. For years the bank had a limit on new client sign-ups because of its poor money-laundering controls and faced millions of euros in fines for the persistent late filing of suspicious activity reports.
Carina Kozole, chief risk officer at N26, said it worked closely with regulators on building an AI model to evaluate whether a new customer was a criminal, which had reduced instances on the platform by 90 per cent.
“If we don’t embrace AI in the industry, then in a few years, we will no longer be here,” she added. “We need to show the advantages and how we can grow compliant if we’re using AI.”
