Walmart has Sparky; Amazon has Rufus. These AI-powered shopping assistants have begun to take a more prominent place in the e-commerce apps of the world’s largest retailers. Although chatbots have been an early use case for artificial intelligence, they are just the beginning of how merchants can leverage this powerful technology.
As Don Apgar, Director of Merchant Payments at Javelin Strategy & Research, found in the report AI in the Payments Ecosystem merchant use cases for artificial intelligence cover such areas as transaction routing and regulatory compliance. However, merchants also must consider risks as they race to implement AI.
Fraud, Compliance, and AML
Along with customer service, one of the most frequent implementations of AI has been in fraud detection. Artificial intelligence can dig through vast amounts of data and identify patterns and red flags. This capability is especially applicable in card-not-present environments like e-commerce.
Although AI excels at parsing data, fine-tuning can be done in how models analyze their findings and present conclusions. If AI has too much autonomy in fraud response, unintended consequences can occur.
“Sometimes a decision is very obvious, but in cases where it’s not, if you’re not restrictive enough, you’re going to take a fraudulent transaction,” Apgar said. “If you’re overly restrictive, you’re going to alienate a good customer who was trying to make a legitimate purchase.”
Despite these issues, artificial intelligence has the potential to supercharge the fraud defenses of not only merchants but also the payment processors that serve them.
Another area where AI can make an impact at the processor level is in compliance. Payment processors have been increasingly held responsible for anti-money-laundering (AML) monitoring.
In this use case, AI can ensure that processors are compliant by verifying that a merchant account is legitimate. Artificial intelligence can scour the internet and provide troves of data that help processors vet their customers.
“AML is a little trickier because of the amount of data,” Apgar said. “A lot of banks and processors are having trouble with this because just simply the volumes of data that have to be analyzed to be able to detect these patterns. In today’s compliance environment, whether or not those rules continue to be enforced as vigorously as they were in the previous administration is unclear, but that doesn’t mean that AI won’t have a role in that going forward.”
Routing the Transaction
Another operational area where AI will play a larger role is transaction routing. As more payment types have become available, organizations have increasingly explored payment orchestration efforts. Selecting the most efficient payment method can dramatically cut costs and improve the customer experience.
However, determining the right path for sending a payment can be complex, especially when cross-border elements come into play.
Today, many of these platforms are rules-based, whereby the user will program rules to define the process. Some degree of adaptive learning and machine learning still comes into play, but adaptive learning is limited because it can handle only cases that it has seen before. The model understands that when a certain event occurs, a certain result was obtained.
As more variables are introduced, adaptive learning is likely to struggle.
“Machine learning is based on experience with transactions that share similar attributes, but the first time that transaction comes in the door and a transaction with those attributes has never been seen before, how do you make that decision?” Apgar said. “That’s where AI comes in. AI is able to handle broader amounts of data beyond the task at hand, which is how do I route this transaction?”
Pushing the Envelope
Though artificial intelligence can provide efficiency gains throughout an organization, the promise of AI means that it will continue to be implemented in customer-facing situations.
“If you think of AI like a search engine on steroids, it’s extremely useful,” Apgar said. “It creates a lot of efficiencies—especially for merchants—where customers come to the site and say, ‘Hey, I need help finding this; I have a question about that.’ It can bring them right to the point in the FAQ, and some small percentage of inquiries still go to a live operator.”
Although AI has been successful in many chat use cases, some organizations will want to push the envelope.
In fact, some of the world’s most dominant financial companies have already given the technology a larger role. Visa and Mastercard have rolled out platforms built to harness agentic AI. In this model, AI agents can shop and make purchases with little customer interaction.
To some consumers, it would be a substantial boon to simply give AI a general direction—find a 25th anniversary gift for my wife, for example—and have an agent do all the legwork and make the purchase. However, many customers would be hesitant to give AI the reins due to the tech’s potential to make a mistake, spend too much, or disclose private data to the wrong party.
For these reasons, merchants still must maintain a buffer around any public-facing AI initiatives.
“You never want AI right now to be in the critical path of anything, because AI is found to make mistakes,” Apgar said. “It hallucinates, as they say—it makes up stuff that’s not there. You want to be able to leverage the efficiencies of AI, but you never want it to create a point of failure in a workflow.
“It’s easy to fall into that trap where, as in the chatbot example, ‘AI is handling 80% of the inquiries—what if we just didn’t have staff?’ True, but you’re never going to get to 100%, at least not in today’s technology. At some point in the future, you will, but not now. So you always want to have that backstop, and it’s the same thing if you look at the operational side.”
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