How Bot That? Artificial Intelligence and the Future of Payments

Picture customers shopping at your business for products they need the following week — but they just don’t know it. Using artificial intelligence (AI), computers can recognize buying patterns and anticipate a customer’s needs purchasing goods and services based on those needs.  

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How "Bot” That? Artificial Intelligence and the Future of Payments

Jul 23, 2018

How Bot That? Artificial Intelligence and the Future of Payments

Picture customers shopping at your business for products they need the following week — but they just don’t know it. Using artificial intelligence (AI), computers can recognize buying patterns and anticipate a customer’s needs purchasing goods and services based on those needs.

If you're a glass half-empty person you might be worried at the thought of devices communicating with one another (M2M) and what that could mean in terms of security, data protection for your customers, as well as your business. If you’re a glass half-full person you’re probably looking forward to an increase in business as customers allow their computers not only do their purchasing, but deliver it directly to their door.

Smart technology, smart business

Machine learning and AI are already transforming lives— look at self-driving cars, smart speakers, smart thermostats, virtual assistants and computers and phones that utilize face recognition technology. In regards to the payment space, though, there are several areas where AI — defined by The American Heritage® Science Dictionary as “the ability of a computer or other machine to perform actions thought to require intelligence” — is expected to have a big impact. These areas include: 

  • Risk management and fraud prevention
  • Enhanced customer service and customer retention efforts, improvement of operational efficiency and improvement of sales and marketing decisions, all of which helps generate more revenue. 

As the Internet of Things (IOT) gets everyone connected via an increasing number of devices, there will be many more ways to make payments — or, as indicated above, have M2M payments made on your behalf. 

Have we reached peak fraud?

At the moment, though, AI and machine learning are arguably having their most significant impact in the fight against fraud. That is, AI is increasingly being used to robotize instant fraud detection, with especially promising prospects for mobile payments and other higher-risk transactions, which are more vulnerable to fraud than, say, EMV®-enabled POS devices.

As explained by Value Walk  “... this is one of the many areas where AI-based technologies show their potential. AI-powered fraud detection models can be used ... to sift through tons of transactional data, flagging suspicious transactions that match a predetermined fraud model.... Additionally, as the system collects more data, the machine learning algorithms can ‘learn’ new patterns and help mitigate future risk with little input from human operators.”

Building on that, Generative Adversarial Networks (GANs) [proposed by Ian Goodfellow and other researchers in mid-2014] 1 have the potential to make AI systems even smarter by “pitting two AI [software packages] against each other to improve each system,” notes TechRepublic. The promise of GANs has already been illustrated using images, with one system acting as the “generator” and the other the “discriminator.”

“The [generator’s] job is to take a set of images, read them, then make fake imitation copies,” explains TechRepublic contributor, Jay Garmon. “Then, the discriminator’s job is to determine which images are real ... and which images are fake.... The better the generator gets at fooling the discriminator, you can take that data and retrain the discriminator to get better at spotting fakes,” he said. “Back and forth it goes until both are really, really good at their jobs.”

At least one set of academic researchers appear to have had success using GANs to “improve classification effectiveness in credit card fraud detection,” thereby demonstrating its value as a fraud detection mechanism in the payments space.2

Meanwhile, both physiological and behavioral biometrics will continue to be combined with machine learning and AI to authenticate users and transactions. In fact, Payments Journal estimates that “biometric authentication such as fingerprint ID and facial recognition will be used in more than 18 billion transactions by 2021.”3

People who bought this also bought that

At the same time, AI is also being used in a variety of ways to improve customer service, make businesses more efficient and allow employees to focus on more meaningful work (as opposed to spending time on mundane tasks).

For example, with the help of Natural Language Processing (NLP) — “a branch of AI based on machine learning [that] allows computers to process and accurately understand human speech, learning as they go”4 — NLP seems poised to power the next generation of customer service by enabling chatbots to interact with consumers, thereby enabling a dramatic increase in “conversational payments.”

“AI technology is currently used via chatbots to converse with shoppers, providing relevant content and suggestions and collecting valuable information. These smart chatbots elevate customer service and automate things on the back end, expanding retailers’ abilities to sell to anyone, anywhere in an intelligent way,” observes Patricia Carlin in an article for Patricia Carlin in an article for Forbes.

Building on that thought, Carlin notes that “on the consumer-facing front, AI has the power to personalize experiences. Customers now have the ability to quickly ask questions, obtain information, narrow searches and complete purchases by interacting with a chatbot programmed to connect in a personal way with consumers. The implications for payments reach even further,” she concludes, apparently referencing the prospect of an increase in Real-Time Payments (RTPs).

Or, as the aforementioned Value Walk article puts it, “it’s when AI bots are coupled with traditional mobile payment platforms that the real magic happens.... [And] AI bots are also being used to make payments via text, which is hands-down the most accurate definition of mobile payments.”

Sounds as easy as 1-2-3, right? That is, a future in which machine learning will uncover patterns in data and make predictions, AI will execute on those decisions, and the IOT will feature the devices that make it all possible.

Never mind the fact that using AI to predict where and when an individual consumer will be making a purchase, it will be possible to deliver much more targeted and effective promotions.

Good for business, good for people?

Of course, the promise of this level of disruption has some people pondering the overall impact on society as a whole.

A recent article by Anant Kale in Payments Source offers a healthy perspective, noting that AI is “not something to shy away from and it doesn’t signify the beginning of the end for jobs. It’s creating new opportunities and industries in its place. AI is about maximizing technology to drive greater business insights, making brands more efficient and more connected to their customers, our jobs more fulfilling and meaningful and giving us a more personalized experience in our daily lives.”5

1. Generative Adversarial Networks, Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio,

2. Using Generative Adversarial Networks for Improving Classification Effectiveness in Credit Card Fraud Detection, Ugo Fiore, Francesca Perla, Alfredo De Santis, Francesco Palmieri,

3. 7 Trends for the Future of Payment Processing, Payments Journal,

4. AI and the Future of Payments, Ralf Ohlhausen,

5. Artificial Intelligence is Not the Future of Retail – it’s the Present,

EMV is a registered trademark or trademark of EMVCo LLC in the United States and other countries.

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