Whilst artificial intelligence (AI) may have been integrated into the payments sector long before its recent surge, there is still vast untapped potential in the emerging tech.
Scott Dawson – Head of Sales and Strategic Partnerships at DECTA – spoke on how AI is already helping mould the payment reconciliation process, mitigating fraud risks to and from the technology, and how AI should appropriately be regulated.
Payment Expert: Firstly Scott, could you give us any use cases of how AI has helped provide innovation in the payments space?
Scott Dawson: There has been a lot of movement with companies using AI within the payments industry. For example, we’ve seen Mastercard release an AI tool to spot real-time payment scams, whilst US Bank is using AI in business travel management. AI is rarely ever out of the press.
Overall, trends come and go and those that have merit stay the course, whereas those ‘flash in the pan’ concepts fade into the ether. But I think what’s needed is an objective view of AI in banking that explores its potential while wading through the ‘hype’ and misconceptions.
It is particularly relevant to one of the long held challenges in payments: the sometimes-considerable delays between payments (or referred to as Real Time Payments).
PE: How or does AI have the potential to revolutionise the payment reconciliation process?
SD: I think it has real potential in the case of more complex payments.
AI can identify shortcuts and efficiency savings or automate the more mundane tasks. This can be achieved through its ability to process massive datasets and compare a multitude of variables in real time is a game-changer.
It can facilitate straight-through processing of payments, with far more accurate decisioning, and smart routing and distribution of payment transactions to improve authorisation and settlement.
AI-powered payment reconciliation can automatically match incoming payments with outstanding invoices, reducing the need for human intervention and speed up reconciliation times.
This will hopefully lead to some of the £50bn or more in late payments owed to UK businesses being reduced.
PE: In the same respect, does AI handling the payment reconciliation process also open itself up to heightened amounts of fraud activity?
SD: There is an inevitability that AI will be used for fraudulent activity, however, I think it has the potential to reduce fraud significantly as well.
If we start at the beginning, the key to any payments is ensuring that the person paying or being paid is who they say they are through Know Your Customer (KYC) and Anti-Money Laundering (AML) checks.
This is a process that is time-consuming due to the sheer volume of documents to examine. Fortunately, AI and ML tools can sift through the information – along with natural language processing – speed-read documents, verify whether they’re fake or genuine, and cross-reference them with other sources to ascertain authenticity.
PE: How is AI being used to identify shortcuts and efficiency savings for automating payments during a time of economic strife?
SD: It goes without saying that in tough economic times, businesses are always trying to protect their bottom lines. They want to grow revenue and be able to pay suppliers and partners and keep the ecosystem moving.
However, research shows that 55% of businesses are still owed invoices from 2022, to May of 2023. There are a lot of reasons for this, not least among them the rising cost-of-living and electricity prices, but the sheer amount of red tape around payments is a major issue.
Although B2B payments in the UK are the fastest in Europe, the average is still 23 days from invoice to payment. Compare this to B2C payments, where money is typically transferred instantly.
Clearly, instant B2B payments could be deemed as the holy grail, but it isn’t always an option for legal and compliance reasons. AI has the power to identify when it is an option and therefore when instant payment can be offered.
PE: AI in the payments space is still largely unregulated. How do you believe the tech should be correctly regulated so that it is secure but is also fostering innovation, and should some of these practices be included in PSD3?
SD: AI is simply a tool and, just like every other tool used within our industry, needs to be used in alignment with the relevant regulatory framework and controls of the business that’s using it.
The goal from a regulatory standpoint should be to create a conceptual framework for AI to work within, as opposed to trying to regulate the AI itself; otherwise, the rules and regulations we create will never be able to keep pace with the growth and innovation that will inevitably follow.
That being considered, I think it’s important that all companies in the payments and wider financial services sector should prepare for regulation in this arena. In fact, they should focus on implanting, expanding and strengthening their responsible use of data and AI policies as a whole. This is because many governments across the world are in the process of developing and writing legislation to regulate privacy and how AI impacts this.
PE: Lastly Scott, and thank you for your time, with AI rapidly evolving, how long before we see the tech breaking through language barriers in the B2B payments industry?
SD: Overall, AI systems have been present in payments and the finance industry for years – decades in some cases.
When we see AI gaining more traction today, it is usually in reference to new innovations in the field, namely large language models (usually referred to as ChatGPT) for example.
It’s difficult to see what these systems offer that isn’t already available through ML. Needing to produce large bodies of convincing text isn’t one of the pain-points of the payment industry compared to payment facilitation, cross-border payments and fraud.
It might be the case that these technologies will lead to advances in ML that can make existing systems better able to analyse the massive data sets generated by a payments company during its day-to-day activity.
The key for the payments industry must be to have a realistic view of both the technology behind AI and what will really move the needle for them. Making the majority of payments instant, especially across borders, would go a long way to fixing one of the payment industry’s most persistent challenges.
Although the pain points that need addressing in payments are varied and always evolving, we are already seeing how AI can improve outcomes for payments companies. Long may that continue.
