Artificial intelligence is revolutionising tech–and payments with it
Ralph Schneider’s big idea came to him back in 1950. The lawyer was struck to hear his client, Frank McNamara, regale an embarrassing incident when, in a Manhattan restaurant, he had the dreaded realisation that he had forgotten his wallet. McNamara had to wait for his wife to drive in from the suburbs to pay. There should be a system where people could pay for their meals later, Schneider thought. So together the pair created Diners Club, the world’s first credit card. In exchange for $5 a year, Diners Club members received a cardboard “credit identification card” enabling them to put meals from participating restaurants on a tab, and then settle up by check at the end of the month.
Schneider’s idea anticipated a shift that would liberate the idea of money from physical cash. It was the beginning of a series of innovations that have revolutionised payments over the past 70 years, taking them digital, international and, more recently, mobile. “The payments industry evolves in waves,” says Tony Craddock, Director General of trade group The Payments Association. And now, he says, we are on the cusp of a new wave that is set to reshape our experience of payments all over again: artificial intelligence (AI). “It is going to be a bigger wave than we’ve seen before.”
AI is an umbrella term that describes machines simulating the capabilities of human intelligence. AI has been part of computer science for decades, but in the last 10 years it has made particular strides. This has been fueled by the rise of machine learning (ML). In simple terms, ML involves an algorithm ingesting vast amounts of historical data to discern patterns. These patterns then allow the algorithm to make sense of new data.
ML has been a boon for the payments world, as it helps address a number of core problems. One major use case is routing money around the planet’s patchwork system of “payment rails”, the dedicated networks that make electronic transfers possible, and automating the authorization and completion of those transactions. Another is credit scoring— crunching often disparate data points to judge risk. The ability to do this on the fly, especially with non-traditional data sources, has powered the recent wave of “buy now, pay later” credit offerings. But ML has also added value in a multitude of smaller ways. It is the force behind business tools that can analyse transaction histories to model future scenarios; it is the reason that payment errors are more readily detected and more easily resolved; it is the functionality that lets accountancy software read digits on invoices for automatic reconciliation. Moreover, ML is critical in detecting and preventing fraud.
But a new type of AI is beginning to gain a foothold. For years, ML has mostly been about “predictive” tasks, in the technical sense of predicting the correct classification of new data. This year, however, the world’s attention has been drawn to staggering advances in “generative” AI (gen-AI). These are models that can produce new content. When OpenAI’s ChatGPT burst onto the scene in November 2022, it sparked particular interest in the capabilities of “large language models” (LLMs), a class of gen-AI algorithm that can understand and generate text. Users were suddenly confronted with the range of tasks that an LLM could help them do—summarise vast amounts of information, debug code, or write emails. And it could do these things well. “I’ve been in AI for 30 years,” says Manuela Veloso, Head of AI Research at J.P. Morgan. “This is a major advancement.”
With their natural language interfaces, impressive output and ability to wrangle large, unstructured datasets, gen-AI tools have caught the imagination. They not only represent a new, conversational way to interact with machines, but a way for machines to perform tasks that were previously thought to be the preserve of humans. This has spurred the digital economy to embrace gen-AI with gusto, prompting entrepreneurs to launch new startups, and tech giants to rapidly introduce new software features. It has, in turn, put the AI field as a whole centre stage.
This new era of gen-AI is set to bring yet more changes to the world of payments. On the one hand, it will ratchet up the speed of innovation, because LLMs can function as a “copilot” to help write computer programs. “Developers will spend less time writing lines of code and more time designing new statistical models and mathematical tools for actuarial challenges,” says Daragh Morrissey, Director of AI at Microsoft Worldwide Financial Services. This should shorten prototyping and deployment cycles for pay-tech developers. It may also assist merchants in integrating those new products into their own systems. An LLM trained on the developer’s support documentation, or on a merchant’s own documentation about past implementations, could enable a chatbot to field specific technical queries.
