How AI can advance open banking

By Scott Dawson

Online banking artificial intelligence
Image source: 123RF

Open Banking has a lot of potential for growth. Although seven million people and businesses used Open Banking systems in January of this year, it is still a small percentage of the 156 million bank accounts in the UK. Moreover, Bud, Plaid and Tink have been successful companies on their own terms, but they don’t match the rapid growth of companies like Klarna and Revolut. Also, familiarity with Open Banking is still low among the general public and some people are even questioning why Open Banking still exists.

Nearly every single industry has seen a significant surge in use of artificial intelligence (AI) this year, and finance is no exception. Large Language Models (LLMs) like ChatGPT are perhaps the tools that Open Banking has always needed in order to flourish. AI incorporated into Open Banking could also lead to the ‘killer app’ that will bring Open Banking into the mainstream – just as MP3 players existed for years before the iPod became the norm and in turn spurred the creation of smartphones.

However, to unleash Open Banking’s full potential with the use of AI, some tough questions need to be addressed. 

What does the future have in store for Open Banking?

When the PSD2 directive was launched in 2018, banks were required to open their APIs for authorizing third parties, and hundreds of companies have registered to use Open Banking protocols in the EU and UK. The latter has been particularly active in adopting the new system: as of 2022, 559 third-party providers were registered in Europe, and 221 of them were registered in the UK, reflecting the country’s strong standing in banking and technology.

Many of these registrations were either Account Information Service Providers or combined AISPs and Payment Initiation Service Providers. This seems to reflect that Open Banking is proving it’s worth more in the provision of innovative financial services rather than being an alternative to card payments at checkouts and eCommerce sites. 

Compared to 17 million people who use Buy Now Pay Later (BNPL) services in the UK, Open Banking remains relatively low, as stated by the seven million figure previously mentioned. With that said, it has been increasing, and may rise from 15% of the public to 44% by 2027 if this trajectory continues. Rather than coming from household names such as Klarna, the rise could be attributed to Open Banking services being integrated into more common processes. For example, consumers signing up for mortgages or loans may use Open Banking systems for assessing their bank account. In fact, most consumers may not even know that an “Open Banking” transaction has taken place; it would just seamlessly integrate into processes that they already use (such as subscription payments).

Does AI pose the solution? 

We shouldn’t suggest that integrating machine learning and big data into financial services is a new phenomenon. AI has been a part of payments and Open Banking for quite a while. A good part of those newly-registered AISPs will be using some form of machine learning, even if it is just a sorting algorithm, to make decisions based on customer account information. Even the most basic banking systems have elements of “if X then Y” logic, such as the system that sends you a text if you are overdrawn. These could be classed as a form of AI since no humans are making that decision. 

When people talk about AI today, they generally refer to the new generation of “generative AI,” pioneered by ChatGPT, not these relatively common systems that essentially function as flow charts. This type of machine learning and decision-making algorithms are capable of creating “seemingly new, realistic content.” In practice, this means that you can ask LLMs to create 10 taglines for a new product launch and it can do so, even producing entirely new content.

For example, I asked ChatGPT to write a paragraph about the adoption of AI in Open Banking applications and it produced the following:

“The adoption of Artificial Intelligence (AI) in Open Banking applications has witnessed remarkable growth, revolutionizing the financial industry. According to a recent study by McKinsey, AI applications in banking have the potential to create $250 billion to $350 billion in value annually. One of the key areas where AI is making an impact is in fraud detection and prevention. A report by Juniper Research predicts that AI-powered systems will save banks over $40 billion in annual fraud-related expenses by 2023. Moreover, AI is transforming customer experience in banking. A survey conducted by Accenture found that 79% of customers are more likely to trust AI-powered recommendations for their banking needs. These statistics highlight the significant role AI is playing in Open Banking, driving efficiency, enhancing security, and delivering personalized experiences to customers.”

The paragraph generated above highlights AI’s current strengths and weaknesses. While it is grammatically and syntactically correct, it doesn’t actually address the question, which was specifically about Open Banking and not banking in general. There was another major issue: the three studies cited don’t exist. ChatGPT doesn’t look for evidence to back up its claims like we would when creating an article. ChatGPT looks at patterns in already existing content and concludes that McKinsey, Juniper Research and Accenture are the kinds of companies that are cited in articles about the banking industry. Even asking simple questions like “how many words are in this sentence?” will produce wrong answers because it isn’t “thinking” in the traditional sense

Therefore, this kind of technology is not suitable for the kind of tasks undertaken in Open Banking applications. Existing technology already allows people to look at bank accounts and see where savings can be made or whether a potential borrower can afford a loan. LLMs can’t be applied to this. It is highly likely that although AI will be advantageous, LLMs aren’t progressing fast enough to meet the demands of Open Banking. It may be handy for customer services, where chatbots are already in use, but generative AI cannot sort fact from language which prevents it from being pushed in the mainstream and used in Open Banking.

About the author

Scott Dawson is the Head of Sales and Strategic Partnerships at DECTA. Scott is a highly motivated and results oriented individual with 20 years of experience within the payments industry. Previously, he served as Commercial Director at Neopay, the market leader at delivering compliance solutions to eMoney and payments institutions. Scott has also held fraud management positions at PSI Holdings and Neteller, before becoming Senior Fraud Manager and then Business Development Manager at ClickandBuy, which was acquired by Desutsche Telekom.

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