Powering payments with AI

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Cloud computing has led to an explosion of possibilities in technology, making it easier than ever to build, test, and scale services in a more flexible way. Its agile nature has enabled businesses to deliver existing services while experimenting with powerful technologies without disruption.

About the author

Daniel Kornitzer is Chief Business Development Officer for Paysafe.

One of the most exciting examples of this is its enablement of artificial intelligence (AI). The availability and affordability of the boundless computing resources in the cloud compared to on-premises has spurred the development of AI applications across all industries. According to IDC, spend on AU is forecast to grow 18.8% in 2022 and reach $500 billion by 2024. Companies are set to gain from the sheer scale, speed, and affordability of data processing offered by the cloud, making the adoption of AI an unmissable opportunity for companies to take advantage of.

For instance, even in the last five to 10 years, if you wanted to develop a prototype for a self-driving car you’d be looking at spending tens of millions of dollars and need an entire data center to store the hardware Fast forward to today, and thanks to AI you could probably do the same for $100,000.

When it comes to payments, AI is proving to be evolutionary in both risk and compliance, and consumer engagement.

The deep data that unlocks insight and de-risks

The evolving payments landscape – accelerated by open banking, PSD2, and faster payments – has been driving value for merchants and customers for some time in the UK and Europe. But as expected with progress and innovation comes new challenges. The ability to run credit scores and Know Your Customer (KYC) checks, and assess risk in real-time or near real-time is more important than ever.

Payments providers are central to this process. For example, false positives are a major source of revenue loss for merchants. So running all the necessary checks not only faster but smarter is key. AI is an instrumental tool for enabling this with the application of machine learning (ML) delivering more effective fraud detection and increased authorization rates.

The advantage of AI is its ability to evaluate huge amounts of transaction data and recognize patterns that wouldn’t be possible manually. AI sees beyond a single transaction going through a digital wallet. It identifies patterns of usage for this person’s account. Whether the current transaction is typical in terms of time, frequency, and value. But more than that, it surveys the entire ecosystem around that transaction – all the interconnects and relationships it has with other accounts and merchants. It can even go a step further to make links between accounts that on the surface appear unrelated, but through deeper analysis are found to share an IP address.

A ML algorithm has many advantages over manual processing in terms of the depth of the analysis it can achieve and the connections it can uncover. In doing so, it can deliver benefits to both parties: convenience and protection for the consumer, as well as faster approval rates, fewer declined transactions, and increased revenue for the merchant.

Virtually enabling richer engagement and connections

AI can also be used to attain higher levels of engagement, for example, through communication between consumers and their merchants, banks, and digital wallet providers. While chatbot technology is still developing and improving it is becoming more sophisticated and getting better at delivering service that matches, and sometimes even exceeds, that of a human agent.

The differentiator is the technology’s ability to draw on mass data from consumers – including historic transactions and behaviors – which it then uses to inform how the chatbot should respond to the request. This more complete view of customer requirements and expectations, allows the virtual agent to respond in a more tailored way and, in some instances, even predict what the customer will need next. Applied to issue resolution, this is a huge benefit and one that can only strengthen over time as the AI model continues learning to becoming more accurate and efficient.

Delivering mass personalization and customization

Perhaps the more interesting application of AI in the realm of customer engagement is how it can empower businesses to offer mass personalization or mass customization to consumers. By applying real-time analytics and AI and ML capabilities, technology can create the type of close consumer–merchant relationship that used to exist before big, impersonal stores took over the world.

For example, you’re on holiday in Las Vegas. The brand from which you normally buy jeans in your hometown realizes you’re in the area, using your location information. The branch manager sends you a text message with an invitation to come into the store to have a coffee, and attaches a discount code for your favorite style of jeans.

In this case, the merchant knows you’re there, they know what jeans you usually buy, and they know they have your favored style and size in stock. By putting all this together, making those connections and tailoring outreach, they can offer a highly personalized interaction. And whether you make use of the offer or not, you’ll definitely remember it, probably feel quite special, and maybe even develop a greater sense of loyalty to the brand as a result.

An advanced ecosystem backed by AI

AI offers a powerful set of tools to transform the consumer experience. Its ability to quickly identify connections that wouldn’t otherwise be seen means merchants can both understand and provide for their customers better, resulting in increased revenue. They can also protect their own businesses more effectively.

The applications of AI by payments providers is vital to the continuing evolution of the payments process, as we look to combat fraud more effectively and increase approval rates through addressing pain points for merchants in the transaction process.

Of course, it is worth mentioning that this technology is still evolving – and its application comes with ethical considerations – but the right application of AI in areas where it can lend efficiency, insight, and security will result in an improved ecosystem for all.

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Daniel Kornitzer is Chief Business Development Officer for Paysafe. In this role, he is responsible for developing strategic partnerships for Paysafe designed to meet customer needs and grow new revenues.