How Payment Systems Implement AI in their Operations

Artificial intelligence has made waves not only in music, art, and services but also in payment industry. Fintech professionals are actively integrating AI into their processes, seeking ways to minimize manual labor in daily operations. This allows for cost savings, increased efficiency in routine tasks, faster information processing, and ensures nothing is overlooked.

The AI approach is straightforward and precise, which is convenient for tasks that don't require creativity. However, human involvement remains essential, as even in payment industry, new ideas and solutions always come from human mind.

So, what does AI do in today's payment processes, and why is it good (or not)? Let's delve into this in a new article by Monoup.

Fraud Detection and Transaction Monitoring

Let's start with the most fundamental area where AI has excelled: fraud detection technology and transaction monitoring has become initial way to integrate AI in payments. Adyen has already implemented this functionality and shows great results to merchants.

Payment fraud analysis systems today can use machine learning to assess the risk of each individual payment and assign scores to each transaction based on a wide range of factors. This helps payment managers decide on risk thresholds and approve or decline payments based on the likelihood of fraud. All this happens in real-time and very quickly, which is practically impossible with human intervention alone, as people cannot process large datasets quickly and efficiently as computer can.

Automatic monitoring systems have existed for a long time for this very reason. The main improvement AI brings is its ability to “learn independently” and remember past events to apply new knowledge immediately in the present time. In contrast, traditional automated systems are fully configured by humans and require manual adjustments to change their set approach. These systems operate based on predefined factors that remain static until changed. Adjusting risk factors and monitoring typically takes days or even weeks, as managers analyze time periods and decide to change current settings because they didn't work well previously.

Both systems have their place in today's payment processing, but AI-based systems can create a more adaptive fraud detection solution that works more efficiently. However, it's crucial to monitor and configure AI correctly to ensure it performs the necessary tasks, as errors and misinterpretations can still occur. For example, in the AML field, human intervention is often required because cases can be unique, and making decisions solely based on automation can be extremely challenging. Sometimes you just can’t asses the case based on numbers, you need to see it deeply into connections and logic.

Chatbots and Customer Support

One of the most popular use cases of AI, not just in payments but in other industries, is automation of customer support in chats. This technology enables adaptive communication via chatbots, reducing the workload on live support teams, especially for routine inquiries like transaction searches that involve repetitive actions.

AI-based chatbots can learn from an expanding knowledge base, improving the quality of their responses and engaging in dialogue with customers. However, for complex issues, customers still need to communicate with a live support operator, which remains an option in most businesses. The quality transition to automating mass, repetitive tasks with AI enhances customer satisfaction and work-life balance for employees.

The key is not to overdo it and ensure customers don't leave your website dissatisfied with unresolved problems. Additionally, many people dislike communicating with bots due to the lack of real-world interaction. Therefore, a fully automated support team can evoke negativity. It's important to strike the right balance between automated and manual responses to avoid overloading the team while maintaining a positive customer experience. By the way, Klarna already have gained some valuable experience on this.

Payment Routing

AI also makes significant strides in payment routing: real-time payment analytics and route changing help improve approval rates and save on payment fees. The focus here is on AI's ability to learn and analyze large volumes of diverse information simultaneously. The human brain cannot process certain payment errors without additional data from the acquirer or analysis of recurring cases. With AI, you can obtain different types of information simultaneously and make informed decisions based on technical data.

AI can adapt to changes in payment rules and regulations, system stability levels, and performance indicators of all payment gateways at once. This makes AI a valuable tool for quality payment routing, but achieving high performance requires training. It's also important to note that not all methods work for every business. Therefore, combining advantages of automated AI systems and manual settings can help mitigate the drawbacks of each and focus on the strengths.

Data Analytics and Forecasting

In data analytics, automation systems are indispensable. Businesses constantly accumulate vast amounts of data and numbers. Manually sorting through this data would take time and money as manual labor is always cost more.

Using various databases and Business Intelligence systems is no longer an innovation but a necessity. How does AI help here? It can identify patterns and learn from existing data. AI models don't just collect and present information; they can draw conclusions and establish dependencies. This is crucial for modern businesses to understand which indicators affect target metrics, why this happens, and at what stages of the product lifecycle. All this provides direction for business development and identifies strengths and weaknesses. While AI-based forecasting models are not perfect and should not be taken at true all the time, they can offer valuable insights.

Overall, payment industry has leveraged the best of AI: ability to significantly enhance data management and replace manual labor where it doesn't yield high returns. Payments are an integral part of our lives, and directing AI efforts towards this area is undoubtedly the right decision. Many systems suffer from operational limitations, lose funds due to fraud, or spend exorbitant amounts on data processing. AI can simplify and accelerate these processes, which is a welcome development. However, like any technology, AI has its pros and cons. Among the downsides of artificial intelligence are errors and inaccuracies it can make, which means it still requires serious monitoring. Worth to note that we are in the early stages of AI development, and it will likely evolve in the future, along with its applications in our lives.

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