Powering Payments with AI | Tech Radar
Cloud (opens in a new tab) IT has led to an explosion of technological possibilities, making it easier than ever to build, test, and scale services in more flexible ways. Its agile nature has enabled companies to deliver existing services while experimenting with powerful technologies without disruption.
One of the most exciting examples of this is its activation of artificial intelligence (opens in a new tab) (IA). The availability and affordability of unlimited computing resources in the cloud compared to on-premises resources has spurred the development of AI applications (opens in a new tab) in all industries. According to IDC, spending on UA is expected to increase by 18.8% in 2022 and reach $500 billion by 2024. Enterprises are expected to take advantage of the scale, speed, and affordability of data. (opens in a new tab) processing offered by the cloud, making the adoption of AI a compelling opportunity for businesses.
For example, even in the last five to ten years, if you wanted to develop a prototype self-driving car, you would be looking at spending tens of millions of dollars and you would need an entire data center to store the hardware. today, and thanks to AI, you could probably do the same for $100,000.
When it comes to payments, AI is proving to be scalable in terms of risk and compliance, as well as consumer engagement.
Deep data that unlocks insights and reduces risk
The changing payments landscape – accelerated by open banking, PSD2 and faster payments – has created value for merchants and customers (opens in a new tab) for some time in the UK and Europe. But as expected, with progress and innovation come new challenges. The ability to run credit scores and Know Your Customer (KYC) checks and assess risk in real or near real-time is more important than ever.
Payment providers are at the heart of this process. For example, false positives are a major source of lost revenue for merchants. It is therefore essential to execute all the necessary checks not only faster but smarter. AI is an essential tool to enable this with the application of machine learning (opens in a new tab) (ML) offering more effective fraud detection and increased authorization rates.
The advantage of AI is its ability to assess huge amounts of transaction data and recognize patterns that would not be possible manually. AI sees beyond a single transaction going through a digital wallet. It identifies the usage patterns of that person’s account. If the current transaction is typical in terms of time, frequency and value. But more than that, it studies the whole ecosystem around this transaction – all the interconnections and relationships it has with other accounts and merchants. It can even go the extra mile to establish links between accounts that at first glance seem unrelated, but upon further analysis turn out to share an IP address.
An ML algorithm has many advantages over manual processing in terms of the depth of analysis it can perform and the connections it can discover. By doing so, it can provide 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.
Enable richer engagement and connections virtually
AI can also be used to achieve higher levels of engagement, for example, through communication between consumers and their merchants, banks and digital wallet providers. As chatbot technology continues to develop and improve, it is becoming increasingly sophisticated and improving to provide service that matches, and sometimes even exceeds, that of a human agent.
The differentiator is the technology’s ability to draw on mass consumer data – including historical transactions and behaviors – which it then uses to inform how the chatbot should respond to demand. This more complete view of customer requirements and expectations allows the virtual agent to respond in a more personalized way and, in some cases, even predict what the customer will need next. Applied to problem solving, this is a huge advantage that can only grow stronger over time as the AI model continues to learn to become more accurate and efficient.
Offer customization and mass customization
Perhaps the most interesting application of AI in the field of customer engagement is how it can enable companies to offer mass customization or mass personalization to consumers. By applying real-time analytics and AI and ML capabilities, technology can create the kind of close consumer-merchant relationship that existed before impersonal department stores took over the world.
For example, you are on vacation in Las Vegas. The brand you normally buy jeans from in your hometown realizes that you are in the area, using your location information. The branch manager sends you an SMS with an invitation to come into the store for a coffee and attaches a discount code for your favorite style of jeans.
In this case, the merchant knows you are there, he knows what jeans you usually buy, and he knows he has your preferred style and size in stock. By bringing it all together, making those connections, and tailoring the awareness, they can deliver a highly personalized interaction. And whether you use the offer or not, you’ll definitely remember it, you’ll probably feel pretty special, and maybe even develop a greater sense of brand loyalty.
An advanced ecosystem supported by AI
AI offers a powerful set of tools to transform the consumer experience. Its ability to quickly identify connections that otherwise would not be visible means that merchants can both better understand and better serve their customers, which translates into increased revenue. They can also more effectively protect their own businesses.
Applications of AI by payment providers are critical to the continued evolution of the payment process as we seek to more effectively combat fraud and increase approval rates by addressing customer pain points. traders in the transaction process.
Of course, it’s 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 bring efficiency, insight and safety will result in an improved ecosystem for all.
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