In the not-so-distant future, payments are expected to become more or less invisible as our financial processes become more sophisticated and frictionless. Invisible payments take physical payment methods such as cash, debit and credit cards, and wearables
completely out of the equation – creating a convenient and speedy experience. We have already seen examples, where ‘checking out’ has become obsolete – Amazon Go stores, for example, allow customers to simply walk out of the store, leaving their mobile device
to process the payment automatically.
Payments are often a key pain point for both B2B and B2C customers, so this is certainly an improvement from that aspect, but the financial industry needs to learn from the experience of companies such as Uber and Deliveroo, who have driven the wider application
of invisible payments.
How are invisible payments becoming part of our everyday lives?
As the landscape becomes more and more AI focussed, we will continue to see smarter payment methods emerging. Innovations such as Amazon Go and China’s Bingobox are an example of this shift – technology that utilises facial recognition and sensors, making
it possible for stores to identify who the customer is and what they have purchased. While in China this innovation is more commonplace, the rest of the world has a way to go. Unsurprisingly, Alipay is at the forefront of invisible/frictionless payments and
is predicting significant growth in the sector, partly driven by the launch, and upgrade, of its “Smile-to-Pay” system.
Everyday solutions such as Direct Debits will also evolve as AI matures to learn about and utilise more information about individual customers’ financial behaviours, while WealthTech solutions will be able to trade users’ funds automatically based on increasingly
sophisticated algorithms. As these transactions become more intelligent and adaptive, secure and frictionless infrastructure for invisible payments will be required to support them.
What will it take for invisible payments to thrive?
AI & Machine Learning (ML) have been hot topics over the last few years and, as with many new trends, will need to be harnessed if invisible payments are to fulfill their potential. Many AI & ML systems already exist to analyse large data sets and to make
decisions to meet our financial needs, but to take this to the next level, hybrid systems, which combine multiple AI & ML methodologies, will be necessary to handle the extra level of complexity required.
Another buzzword of the past decade has been the Internet of Things (IoT), which came with many promises. Some of these, such as smartwatches that inform you of an incoming phone call, have been fulfilled while others, such as fridges which can detect and
place an order when you are running out of a certain item, are yet to achieve ubiquity. The network of connections only continues to grow, so companies need to take full advantage of this resource if they wish to provide a truly frictionless experience.
Meanwhile, security solutions like tokenization and biometrics will need to be high on the agenda to tackle the hesitation in trusting progressive new technologies such as these. This lack of trust is not without its foundations, as financial institutions
lose billions of dollars to fraud every year.
With security and fraud prevention high on the agenda, payment processors have looked to biometric technology and tokenization to address the matter. Biometrics can be used to process an individual’s physical traits to authenticate transactions with a high
degree of success. In fact, NIST found that only 0.2% of facial recognition searches, in a database of 26.6M photos, failed to match the correct image.
While there will always be a place for the physical payment process, it seems inevitable that invisible payments are only going to grow in the coming years. However, the forms in which we will see this growth still remain unknown. What we do know, though,
is that if companies capitalise on the potential for smart, secure and seamless systems, the future of payments could be unrecognisable.