AI in payments : the way is paved

The history of Artificial Intelligence (AI) started centuries ago with myths, stories and rumours of artificial beings endowed with intelligence or consciousness. The vision and statement of AI kept evolving over the years, finally reaching its current definition: a branch of computer science dealing with the simulation of intelligent behaviour in computers. It is the capability of a machine to imitate intelligent human behaviour.

Artificial intelligence stands out as the transformational technology of our digital age, quickly establishing itself as the next big wave of digital disruption.

With the constant improvement of the technology, the use of AI in our everyday life is growing steadily. This can be seen in the fast growth of AI investment, mainly led by digital giants (Google, Apple, Amazon, Baidu…), with a total investment amounting between $26B and $39B recorded in 2016.

As an example of the headway made by the technology, the ability of AI systems to recognise objects (including biometric face recognition) has now been enhanced enough to exceed human performance.

Source: ImageNet Large Scale Visual Recognition Challenge

However, we are far away from a generic intelligent machine: AI machines currently need to be task-specific in order to deliver satisfactory results.

AI adoption by sector

According to McKinsey Global Institute, AI adoption is greatest in sectors already established as strong digital adopters. They are also the most determined in AI investment.

Source: McKinsey Global Institute – AI the next digital frontier?

A noteworthy observation is that, amidst the sectors with the highest AI adoption rates, we can find Financial Services standing neck-and-neck with the more commonly expected sectors (Automotive/Assembly & High tech/Telecom).

AI in payments

The Payment industry is the perfect candidate to adopt AI capabilities. With AI, the payment industry has joined efficiency and effectiveness to prevent risk, create value, reduce costs, drive efficiency and foster innovation.

Today, AI in payments is mainly used for customer on-boarding (identification), fraud prevention and detection. Other common use cases in payment include:

  • Pricing and promotion: analysing consumers’ data, including usage patterns, payment history, purchasing behaviour and generating automated targeted promotions.
  • Behavioural coaching: educating customers according to their behaviour.
  • Analytics driven accounting: automated analysis of data.
  • Predictive maintenance: the power of machine learning to detect anomalies (smart monitoring).
  • Real-time forecast: automating the management and processing of the data.

AI addresses inefficiencies in payments processing and operations by bringing machine learning capabilities to optimise and secure payments. The technology also creates tailor-made customer experiences, which not only applies to payments but also to a wide array of other industries.

Whether at a business level, by optimising processes, or at a customer level, by providing the best purchasing experience, AI very clearly opens up a wide scope of possibilities to enhance overall day-to-day life.

As an example, the 3D Secure 1.0 protocol was not the most convenient tool to secure online payments. Users had to deal with complicated steps, including pop-up windows, having to wait for a code by text to authenticate a transaction etc. The process was not smooth and generated many dropouts amongst the users.

EMVCo had to improve the overall user experience process and satisfy through this new version both current and future market requirements.

The new 3D Secure 2.0 protocol focuses on a seamless and convenient payment experience to customers and enhanced fraud protection for merchants. Indeed, the former secure code is no longer requested for each and every authentication. The new protocol added intelligence on the authentication process to be more relevant and user friendly: Card Issuers can integrate a module to enhance their decision algorithm engine so they do not only rely on static data and rules but leverage on all the power of AI.

HPS is working on numerous AI applications and developing new enticing use cases.

To know more about HPS’ engagement in payments-centred AI, please contact