From Traditional to Cutting-Edge: How AI Is Shaping the Payments Landscape

The payments industry is more than ready to adopt artificial intelligence. For some, that statement may seem a touch far-fetched, as the payments ecosystem is traditionally slow-moving. And yes, the sector is usually quite hesitant towards untested innovation. But several converging factors – advancements in tech, customer demand, the use of big data, regulatory support – have primed the industry for a rapid introduction of all things AI. Ready or not, intelligent machines are here. And they will likely usher in a new era for digital payments.

What you’ll discover in this article: 

• Factors contributing to the use of AI in the payments industry
• The benefits of adopting AI
• Barriers to AI-acceptance in the payment sector
• Current and future use cases of AI
• Tips on how you can use the benefits of AI in your own business

Despite its general hesitancy to tech, the payments industry history does paint a picture of gradual evolution. As technology advanced, different solutions offered novel updates to archaic payment processes. The following industry milestones set the foundation for the AI of today:

• The credit card revolution: First, mid-20th century (1950-1960) presented the credit card, a technology based on the bank “Charg-It” system first introduced in 1946. Credit card acceptance completely changed the original cash-based methods of payment. Customers could now expand purchasing power with a magnetic stripe and ready access to a line of credit.
• The electronic transfer revolution: Electronic advancements between 1960-1980 placed the payment process into the customer’s hands. ATMs and electronic funds transfers gave customers more immediate control of how they used their money.
• The online payment revolution: The start of the 21st century introduced internet-based payments. Customers could make payments online, which provided the origins of global eCommerce (e.g. PayPal, eBay).
• The mobile payment revolution: Next, the rise of smartphones and other tech (e.g. near field communications, peer-to-peer, Apple Pay) changed how customers made purchases. Multiple payment acceptance options introduced the consumer to new levels of convenience and speed not yet seen before.
• The fintech revolution: Finally, the recent decade saw the rise of financial service tools, such as bank APIs, real-time transfers, and mobile wallets. Banks and businesses alike embraced technical solutions to solve customer needs.

Together, each innovation helped create a complete digital system. Big data, digital services, and customer demands all converged to define a tech-based payment industry. From credit cards to present day cloud-banking, tech solutions laid the necessary groundwork for AI.

Today, the AI market is growing exponentially (an expected compound annual growth rate of 37 percent from now till 2030). With a strong data foundation and a newfound evolving mindset, organizations are rapidly moving toward AI solutions. And for good reason: AI delivers several clear advantages to the payments industry.

Intelligent systems can process transactions at a rate unmatched by general tech. That has immediate benefits for transaction scaling. Traditional tools must follow a predetermined path to route a payment, but AI systems can adapt from millions of data points (e.g. preference, reliability, speed) and choose better ways. And smart algorithms can learn, which offers dynamic resource allocation.

As a result, transaction execution can be far more efficient. IBM offers the z16 mainframe that can process 300 billion inference requests per day with just one millisecond of latency. Clearing and settlement drastically improve, all with resource energy savings of 75 percent, compared to other x86 servers. AI algorithms can also prepare for future transaction volumes from historical data—yet another way it can help you manage how and when to scale. Such payment efficiency reduces costs and maintains a seamless customer experience, regardless of volume.

Intelligent machines don’t just utilize basic pattern recognition. Instead, AI engages in adaptive modeling and deep learning. As new data becomes available (on multiple layers or “nodes”), smart tools evolve their own rules or parameters.

That type of “context-aware learning” has several applications in fraud detection. Dynamic data capture offers nuanced risk assessments. Extracted features can help decipher false positives. And unsupervised learning results in far more accurate anomaly detection. Simply put, AI can better adapt to the ever-changing tactics of the fraud economy.

A great example is the new Pay-By-Face algorithms. The intelligent payment method has an error rate of  0.08 percent, far better than the average of 4.1 percent For an industry with such strict compliance and security concerns, AI-powered fraud protection can enhance security.

Many technical solutions offer a fast and convenient service experience for customers. From social media to eCommerce apps, digital solutions have created a customer-centric industry.

But AI supercharges the customer experience. Intelligent machines are able to deliver unparalleled personalization. Not only can they process millions of customer behavior data points, but they can also respond to the consumer in a human-like manner. Natural Language Processing (NLP) replies with custom interactions, improves itself from customer feedback, and even predicts future service needs.

Such experience improvements even extend to adaptive dispute resolution. For example, Justt uses AI to empower human agents during a credit card payment dispute, a high-friction customer touchpoint. Cognitive computing can determine escalations, support agents in real time with problem adaptations, or improve first contact by automating tasks. And when necessary, AI can engage in smart evidence discovery and mediation support based on customized data analysis. The seamless integration of AI technology into your chargeback mitigation strategy enhances your support teams and helps deliver a positive customer experience.

AI has features that offer far more accurate predictions. First, cognitive machines can use diverse sources (transaction records, customer profiles, market trends, and external data feeds) to develop insights. But second, they can find non-linear relationships between that data. AI combines multiple forecasting models together to define information segments.

With that sort of insight, thinking computers augment human understanding. Your decision-making and business efficiency improve as you receive nuanced perspectives. AI can go so far as to predict market changes, sales trends, customer needs, and the weather.

A known success story for AI is Alibaba and its intelligent recommendations. Not only does the eCommerce company process 87 million transactions per second, but it uses PAI-Blade to optimize inferences. Such early adoption of predictive AI contributed to massive business growth over the past ten years.

Despite its benefits, there are still several obstacles that impede the widespread adoption of AI in the payments industry:

• Data availability: Smart machines require a wealth of training data. And without clean, high-quality, AI algorithms cannot learn. Industry players will find building a “data core” a time-consuming hurdle.
• Data privacy: The collection of sensitive data also raises privacy concerns. Data sharing, user consent, and unlawful surveillance are critical security issues. Addressing such concerns across different global regulatory bodies is a complex task.
• Data integration: The payment industry’s hesitancy towards innovation resulted in technical debt. Current organizations still use legacy systems that lack modern abilities. Creating interoperability (updating old systems and removing data silos) will take effort.
• Data transparency: Payments involve sensitive financial information that demands transparency. Unfortunately, AI often works in a “black box”, where the inner workings of an algorithm are hard to interpret. That lack of accountability impacts consumer trust and represents a significant barrier to wide AI acceptance.
• Bias: If supplied data with an inherent bias, intelligent machines may amplify unfair outcomes. In turn, that can segment entire parts of your market. The payments industry must exert concentrated efforts to limit such bias, as exclusion will certainly hamper adoption rates.

Inertia: Innovation often involves a slow rate of change. Minimal talent, entrenched traditional methods, and limited awareness can contribute to inertia. Delayed change management presents a large barrier to AI adoption in the payments industry.

Surpassing the barriers preventing AI adoption requires planning and execution. Luckily, you don’t need to revamp your entire business structure. Here are some simple steps that can guide an easy AI adoption strategy:

• Identify high-impact use cases: Set clear objectives that drive desired results. You might have no need for advanced AI-powered credit score modeling. All the while, a simple chatbot could streamline customer support. Select AI tools that drive immediate, customer-specific benefits.
• Build data-centered processes: Your AI tools are only as good as the initial data you collect. Plus, AI requires vast amounts of data that take time to collect. Assess your data readiness and take steps towards a data-focused culture. Those who prepare now will gain a clear competitive advantage.
• Pilot and test: Run pilot projects on a small scale. You can then adjust according to the collected feedback. That will drive a better return on investment. Find the areas that benefit from AI, then engage in gradual, resource-efficient scaling.
• Use quick win Application Programming Interfaces (APIs): AI-based APIs offer ready-made integrations to AI services. You can then add advanced tools without any advanced software development. Find APIs customized for the payment industry, as they function well within your existing technology.

Collaborate with the experts: Numerous vendors offer tailor-made AI solutions. An expert technical partner can deliver higher-quality tools with an expedited adoption process.

Interest in generative AI has reached an all-time high. And experts expect the worldwide market size to increase twentyfold by 2030, from just under 100 billion in 2021 to nearly two trillion dollars. The AI revolution is here, and it will shape the payments industry moving forward. Innovators are constantly announcing new and future projects that will transform the industry, such as:

• Dynamic pricing: Intelligent machines that adjust prices in real-time based on sales, stock, and market data.
• Risk-based authentication: AI that considers context factors (e.g. multi-modal face, voice, and behavior biometrics) to make a more accurate and secure authentication process.
• AI-powered payment methods: AI models that can register and understand payments made with emotions and gestures (e.g. smiles).
• Edge AI: AI that embeds directly into user products to hyper-personalize customer touchpoints.

While the payments industry is usually more hesitant in the face of innovation, merchants are now eager for the benefits of intelligent machines. Visible early successes, customer demand, regulatory support, and simple integrations have encouraged rapid adoption. Artificial Intelligence will shape the future of payments.

Justt understand the importance of innovation—that’s why we are building a superior chargeback dispute evidence & representment with AI.