Just a few years ago, taking out cash before an event or dinner was not a strange sight — but as digital transactions become widespread, paying for your expenses with coins and bills isn’t as common anymore. The world of electronic payment processing is growing fast, and the industry has begun using artificial intelligence (AI) tools, including machine learning, to keep up with the challenges and opportunities. 

Most financial institutions are now using artificial intelligence, including machine learning, to tackle difficult issues such as cybersecurity, document digitization, and faster payment processing. And as companies go on to handle larger and large volumes of data, new use cases are continually in development. Read on to find out how machine learning is driving efficiency and innovation in the payments industry.

A brief introduction to AI

When American computer scientists John McCarthy, Marvin Minsky, and a group of accomplished academics organized the world’s first conference on artificial intelligence in 1956, their idea was to explore different ways a machine could think like a human. Of course, “thinking” is a broad idea. Their main idea was to create technology capable of abstracting thought, solving problems, and improving on its own.

“Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it,” McCarthy said. (Forbes) This, however, is not that simple. Sixty years after that first meeting, the area of artificial intelligence has branched into a vast number of areas — machine learning, generative AI, natural language processing, speech, expert systems, robotics, and computer vision, to name a few. 

Despite such growth, the ability to emulate the way humans think and learn (general AI) has been largely left untouched. Yes, programs have managed to beat people in both Go (AlphaGo, 2016) and chess (Deep Blue, 1998). Yet those are two very specific tasks with a high level of complexity. If you asked AlphaGo to make your bed or play with your dog, the program would have no idea what you are asking.

Machine learning and deep learning in finance

Most advancements in AI focus on specific tasks (also known as narrow AI), such as playing chess or recognizing a human face. In the past two decades, however, many industries have begun using a specific subset of artificial intelligence to provide better and more efficient services: machine learning. 

As computer scientist Tom Mitchell puts it, machine learning is when a computer program “learns from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” (Efficient Learning Machines) In simpler terms, an algorithm can measure performance in certain tasks, then learn and improve upon the experience.

Currently, one of the most used techniques in finance is deep learning, which is a subset of machine learning. It has gathered attention in recent years for performing unsupervised learning and having a strong capability of generalization and processing big data. These models use neural networks, which are described by cognitive scientist Melanie Mitchel as elements that simulate neurons in the human brain.

Deep learning has developed a large number of different models, most of them based on neural networks, to tackle different issues within finance and banking.

Innovating the industry with fraud detection and beyond

The use of machine learning algorithms (specifically deep learning) can be divided into two major areas in payments: banking and credit risk and financial investment. Stock market prediction is a primary area of focus, according to a study from the United International College in China.

Machine learning algorithms can detect highly-probable fraud and prevent transactions from being approved, while still reducing the amount of false positives and further reducing the costs. It can also help reduce complexity and make sense out of emerging fraud patterns and their correlations. 

An artificial intelligence system, such as the one used by payment processors like VISA or Mastercard, have the ability to learn user behavior and understand patterns. Such a system uses rule-based logic to derive insights into which variables may lead to fraud. When there is a sign of irregular activity, the payment processing service contacts the bank to let them know what’s going on. 

Historical data — the information gathered from hundreds of thousands of transactions — can also help the algorithm make quick adjustments to its logic without any human interference.

AI/ML is optimizing the customer experience and operations

In 2017, one of the largest banks in Singapore launched a virtual assistant that guided users through the digital experiences of finance. At that time, the use of chatbots in service platforms was not new, yet their application to payments was a game-changer. By providing a human-like experience for people who needed support or details on their accounts, the system reduced human error and sped up information. (PaymentsJournal)

Today, chatbots and other forms of NLP or natural language processing (such as voice processing services) are common practice. You can create an entire shopping experience within a voice channel, just as if you were speaking to a sales representative.

And the innovative use cases for AI and machine learning technologies are constantly evolving, especially after the explosion of generative AI. Smart chatbots can now leverage company and customer data to make personalized product offerings or recommendations, for example. Indeed, generative AI and ChatGPT are just the next transformative step for technologies that have long been in development.

Machine learning has also been incredibly helpful in supporting data-driven decisions, as quantitative methods are now used by key industry players. The more data you have, the greater opportunities machine learning models have to achieve significant operational and strategic efficiencies. These can help individuals, small businesses, and even large corporations.

Build the digital solutions you need to move forward

At Blankfactor, we’re a digital partner native to payments. From machine learning and artificial intelligence to data engineering, our experts are versed in the latest technologies moving financial services forward. We can design and deliver innovative solutions from end-to-end — fast.

Contact us today for a 60-minute solution session.