Since the dawn of the commercial banking age, the transaction-led business model which underpins most of the U.K.’s high street banks has stood relatively unchallenged.
For more than a century, banks have generated revenue from payments through imposing account service fees, transaction fees, and insufficient funds charges, along with a variety of other fees for retail and commercial clients.
However, the rise of challenger banks has meant that this revenue stream is becoming increasingly squeezed. A recent report from Aite Group found that only 18% of U.K. banks can still charge what they want for payments, and four out of five banks agree that payments are becoming less profitable.
Many leading high street banks remain bullish about the trend, predicting that the rise of artificial intelligence in coming years will transform transaction banking, and in turn boost flagging profits by offering a revamped set of customer services.
“AI can be used to improve the speed and efficiency of the payment process by reducing the extent to which humans need to be involved and facilitating the processing of payments,” said Sameer Dubey, head of transaction products at Barclays. “Developments in speech recognition technology will mean that banks can increasingly process payments initiated via voice.”
However, other industry figures are warning that to capitalize on such innovations, many banks will first need to invest heavily in updating their infrastructure. This task may prove challenging, with the banks having fallen far behind the curve over the past decade.
“Since the big crash, banks have been under huge pressures … financially, regulatory, and competitively with the advances in technology,” said Simon Wilson, a director at global payments consultancy Icon Solutions.
“The single biggest factor why they are struggling to maintain profitability through payments is legacy technology," Wilson said. "Underlying hardware and software costs lead the cost equation, and as payment volumes rise due to digitalization and other macroeconomic factors, those high costs are laid bare. Implementing change on legacy technology is also risky when dealing with complex, highly integrated and ‘spaghetti like’ legacy architectures.”
One of the reasons that challenger banks like Monzo and Starling have been able to make such inroads into the price points of transaction fees, according to Wilson, is because they can leverage new technology without having to deal with legacy costs — although their ability to manage volume, regulation, and operations is yet to be proven.
However, the increasingly data-led business models implemented by these new competitors could herald a new future for how the banking industry generates revenue from payments.
“The act of moving money from A to B is taken for granted by customers who are used to moving any information across the globe in real-time,” said Wilson. “Leveraging the data that comes with that movement is where banks can start to add more value. This can range from cross-selling opportunities by analyzing a consumer’s spending and suggesting how they could make savings, to more integrated cash and liquidity management offerings for corporates. These services provide value for customers that can be monetized and enhance the overall relationship.”
Starling has attempted to do this through schemes which take customers’ payments data and use it to offer services such as interactive budgeting.
“We have been data-led from the start and these models provide greater value to our customers,” said Anne Matchell, head of marketplace at Starling Bank. “We built the idea of financial management into our banking app by combining payments data with spending categorization. This reflects a customer’s real-time balance and places transactions into ‘buckets’ such as food, entertainment, travel and so on, to improve the banking experience.”
However, Barclays' Dubey pointed out that there are still barriers preventing banks from making full use of such data, affecting their ability to generate revenue from it.
“Organizations are producing reams and reams of data that could potentially be interrogated by technologies,” he said. “Often, various pieces of data are not connected or appropriately valued. Furthermore, one machine cannot necessarily read data produced by another, and organizations are still producing data in vertical silos rather than through to an end-to-end horizontal process.”
Experts agree that it will take time for banks to be able to make full use of data, as a means of generating alternative sources of revenue.
“No one is doing a great job with the data-led model at this point,” said Erika Baumann, senior research analyst for wholesale banking and payments at Aite Group. “There are a few reasons for this. First, it is difficult to make the transition as corporates are also used to the very traditional pricing and fee model. Second, it becomes important to be able to pull in all data from all sources, in sync. This is difficult to do within a global bank with many processing systems, let alone for clients with multiple banking relationships. However, as open banking expands customer consented access to this data, this will improve.”
