Leveraging Artificial Intelligence in Payment Processing Industry

Introduction

Widespread digitalization has been one of the biggest cornerstones in technology development for almost every industry. The financial ecosystem is no exception to this trend – the need for secure, efficient, and simple transactions is at its highest. The introduction of artificial intelligence into this field managed to transform the industry in a significant fashion, and any company of any size can notice the effects of this transformation.

At the same time, the reasons for AI’s sudden rise in popularity are still unknown to many people. In reality, AI can be separated into multiple technologies that affect the payment processing industry in their own specific fashion. The primary goal of this article is to explore the overall effect of AI on the payment processing field in detail.

Past and current payment methods

Over the last few decades, the overarching commercial field went through multiple transformations, successfully introducing a brand-new payment system that is slowly overtaking the traditional cash-oriented one. This payment system relies on digital technology to perform all of its operations, using multiple technological advancements to make the payment process more accessible than ever before. 

To simplify the topic, we will go over this financial evolution process step by step, starting with traditional cash payments.

Classic

For hundreds of years, physical money (cash) has been the primary payment method for products or services. It is a simple system with a tangible representation of its elements – the money itself. There is no need for any middleman in the transaction – the buyer can deliver the payment directly to the seller.

However, this method also has a number of notable issues. Transactions that revolve around physical money are not easy to track in the long run and pose a significant security risk for both the buyer and the seller. The physical form of this currency is also one of its disadvantages, with more significant payments requiring a lot of effort carrying the money in question.

Bank transfers have their own share of popularity as a payment system – especially in the field of high-profile transactions. A combination of the widespread online banking trend and easy traceability made it into a very handy system for a subset of customers.

Credit and debit cards have the title of the first significant advancement in this field that shook the foundation of physical money. They are considered much more convenient and secure than physical money, and carrying a card (or several) is much easier than carrying stacks of money. The added advantage of easy traceability is also quite significant. One of its biggest breakthroughs in popularity was the widespread adoption of “point of sale” technology, which made payment processing for physical retail that much easier.

Recent

None of the “classic” payment methods are completely deprecated by this point, and most still hold a very sizable part of the entire sphere. At the same time, there are also other payment processing methods that can be considered relatively new.

Cryptocurrencies have been an extremely popular topic for discussion for at least a decade, offering decentralization as one of its most significant advantages. They are also considered more secure than most, and the transaction fees for an average cryptocurrency transaction are extremely low. 

However, cryptocurrencies are still considered a regulatory grey area in most countries, and the overall volatile nature of these currencies is another factor that works against possible mainstream adoption in the future.

Expanding upon the topic of cryptocurrencies, CBDCs (central bank digital currency) exist to offer a more stable form of digital money. This currency is another variation of a traditional currency for a specific nation, replacing the original process of printing physical money with tokens or digital accounts supported by the government’s credibility and trust.

The rise of digital wallets is one of many advantages attributed to smartphones' explosive growth in popularity in the last decade. This payment method is secure, fast, and convenient, allowing users to store card information in a dedicated service (Google Pay or Apple Pay, for example) and confirm their purchases with nothing but a push of a button – made possible by the technology called “tokenization”.

Another vital part of this discussion is the rise of contactless payments, which happened roughly the same time as the rise of digital wallets. Most of these payment types rely on QR codes or NFC (near-field communication) technology to make contactless transactions as simple as possible. This particular technology was especially prevalent during the COVID-19 lockdown.

Payment methods and innovations

The overall field of payment methods evolves in tandem with bigger, more significant changes in technology and society. The payment processing industry has to keep growing to keep up with the ever-rising number of transactions while ensuring that all payments are safe, secure, and user-friendly. Some of the most significant challenges for the payment processing industry in terms of evolution and innovation are: 

• Security. Cyber attacks are as widespread as ever, and new methods of cyber crimes are being developed on a regular basis. Making sure that all processed payments are safe and secure is the industry's top priority.
• UX (user experience). It has become the norm for an average user to expect a fast and easy payment experience in physical stores and online shopping. That kind of unification across different payment methods is no easy task.
• Scale. Every single digital transaction has to be processed somewhere, and the sheer number of transactions that occur on a daily basis creates a lot of demand for fast and versatile systems that can handle these kinds of loads.

AI in payment processing

The definition of AI in the context of payment systems

The introduction of artificial intelligence in this field is how the payment processing industry can handle all its challenges simultaneously. In this context, AI is a combination of technologies such as predictive analytics and machine learning algorithms that can assist significantly with securing, processing, and managing digital transactions. 

AI technologies for payment processing

There are multiple different technologies that fall under the umbrella term of “AI in payment processing”, with each technology offering its own share of advantages.

Natural language processing (NLP) technology is a complex system that allows human language to be understood and processed by machines.  In the context of AI, this technology is at its most valuable when handling simple customer queries (transaction history, refunds, payments).

Machine learning (ML) algorithms are the branch of AI that can operate with and learn from various data sources. In the context of payments, this is an excellent tool for optimizing the transaction process and detecting potentially fraudulent transactions. Monitoring and predicting customer behavior is also within the realm of possibility for ML algorithms.

Predictive analytics is an element of ML that uses algorithms to analyze historical data and identify the most likely future outcomes. One of the most notable examples of this element of AI is forecasting transaction volumes in different time periods – making it possible to prepare additional resources for transaction processing in advance.

Neural networks are essential for fraud detection efforts by identifying anomalies and data set patterns. The original inspiration for a neural network came from the nature of the human brain and how it functions. Neural networks are combined with machine learning algorithms to identify established patterns and locate any unusual deviation from these patterns.

Blockchain technology is an honorable mention for this category, as well – a technology unrelated to AI as a topic. What is interesting about the blockchain is its potential to become the next step in ensuring transparency and security for various transactions by providing a form of a decentralized ledger system.

Combining all of the information presented above, we can list a number of payment-oriented use cases that may be improved significantly with the introduction of Artificial Intelligence:

• Transaction analysis.
• Customer service handling for low-level requests.
• Fraud and pattern detection.
• Risk evaluation.
• Multi-faceted identity confirmation (both buyer and seller).
• Much more personalized customer experience.

Benefits of AI in payment processing

The payment processing sector is one of many different fields that AI has managed to change in a very short time frame. Here are some of the biggest reasons AI is so popular now in payment processing:

• Efficiency. Processing multiple transactions simultaneously can be very challenging for unprepared systems and infrastructures. The usage of AI in this field allows for most of the low-level, repetitive tasks to be automated, reducing the chances of human error and freeing up resources for more high-profile tasks. There is also the fact that AI can be used to create automated payment gateways with swift transaction processing, boosting customer satisfaction rates.
• Security. Transaction protection is one of the biggest concerns for the industry. The introduction of AI makes it much easier to ensure competent protection for all customers. The ability to implement AI into various authentication mechanisms (MFA and biometric recognition) makes it a lot more difficult to use stolen credentials. Using ML for fraud detection simplifies the process of finding and tracking suspicious transaction patterns.
• Competition. Similar to how most of the modern markets operate, the payment processing field can be highly competitive. In this environment, even a tiny advantage over the competition can be significant for overall success – and AI is no small advantage. The introduction of AI in the field offers a better customer experience and a potential for cutting-edge payment options, making it extremely valuable.
• Costs. The aforementioned low-level task automation also reduces business expenses for these tasks – a notable advantage of AI in this field. Another advantage is that AI usage dramatically reduces the chances of fraudulent payments being processed, which, in turn, reduces the amount of money every fraudulent incident costs for the business.

It is easy to pretend that AI in this industry is just a trend that will pass soon. However, the sheer number of advantages and potential that ML and NLP bring to the table makes it highly unlikely that AI will leave this industry in the foreseeable future. On the contrary, AI-infused payment systems are the future of the entire field, creating a combination of security, versatility, and user-friendliness that is unachievable with traditional methods.

Disadvantages of AI in payment processing

As with any other technology or methodology, introducing AI in the payments industry also brings its share of challenges and disadvantages that must be addressed sooner or later. Here are some of the more noticeable examples of that:

• Integration and scalability. AI-based processes and methods tend to differ greatly from most companies' traditional business structures. As such, implementing AI in one such business can be a challenge from a technological standpoint. In this context, it is easier for smaller businesses to implement AI-oriented practices due to the sheer size of the changes that need to be made.
• Implementation price. Another notable challenge of AI implementation is the price. Integrating AI-oriented tools and services is an expensive endeavor, and most small businesses may not be able to afford it. One of the most straightforward workarounds for this issue is to use a solution or a service that already has AI-centric features included in it.
• Privacy. Security has quickly become a vital topic in the present day. The fact that most AI-related technologies operate by analyzing large data masses brings inevitable security concerns for customers and higher-level entities such as governments. Following PCI DSS or GDPR regulations can be challenging, but paying exorbitant fines for data breaches is even more of an issue.
• Surveillance. AI tools and processes can offer plenty of advantages, but they are in no way a complete replacement for human employees. Regular surveillance is still required for ML, NLP, and other AI-related technologies. The existence of a human overseer makes it possible to solve potential backend issues, monitor operation accuracy, and ensure that the system is fair and unbiased to the end user.

Potential use cases for AI in payment processing

Payment processing is a sizable industry, and there can be many different examples of how the introduction of Artificial Intelligence can prove beneficial in some way. There are four main business types that would receive the biggest number of benefits from this kind of transformation.

Software-as-a-Service

In this case, AI can automate processes, manage data flows, save costs, and boost efficiency. It can help achieve an incredible level of personalization, boosting total conversion rates for marketing campaigns. It can automate mundane, repetitive tasks such as report generation or data entry – freeing up more time for high-priority tasks. There is also the fact that AI can perform predictive analytics, improving customer retention and noting down various sales opportunities in the process. The same process can detect unusual patterns and mitigate cybersecurity threats.

E-commerce

One of the biggest advantages of AI for E-commerce is a simplified checkout process with automatic customer information filling and multiple tailored payment methods to choose from. There is also the ability to offer additional personalization beforehand by providing complementary products and noteworthy discounts based on customer purchase/browsing history.

Subscription-based businesses

Similar advantages are applicable to subscription-based companies, leveraging additional methods of managing recurring payments. AI can be used to perform churn predictions while also automating customer communication and implementing retention strategies with a high degree of personalization. Other use cases for AI in payments include usage data analysis, pricing insights, and streamlined processing for recurrent payments.

Business-to-Business

Document management automation is beneficial for B2B companies, with tasks such as invoice processing being greatly simplified with the introduction of AI. It is also possible for artificial intelligence to be used to work through various disputes and other payment-related issues, significantly improving the overall efficiency of business tasks.

How AI in payment processing is going to evolve

Deeper and more thorough AI implementation in the payment processing industry is the inevitable future for this field, affecting all of the associated markets, as well – including fintech, e-commerce, and retail. It is logical to expect all of the most vital elements of AI in the payment industry to change and evolve over time. You can find some of the most prominent examples of how the industry would change in the context of AI implementation below.

• Accessibility. More businesses are going to adopt AI practices for payment processing in some way or another as technology gets cheaper and more accessible.
• Better fraud detection. The ongoing evolution of AI-centric fraud detection systems is a matter of time since fraud methods also evolve on a regular basis. The key to mitigating this issue at its core is a constantly evolving system that improves over time while ensuring that all security and regulatory requirements are always met.
• Better customer experience. The level of personalization and user-friendliness will improve over time, bringing in more loyal customers, boosting total sales, and so on.
• Faster analytics. The overall decision-making will improve even more as real-time data processing measures improve, allowing for more informed decisions to be made within the appropriate time frame.
• New payment processing approaches. Plenty of different payment methods emerged in the last few decades, and even more methods can be introduced in the near future. It is highly likely that these payment alternatives would be faster, more secure, and more accessible for end users.

Lazy AI and payment processing

Artificial intelligence took the entire world by storm, changing multiple industries in a matter of months. The payment processing industry is still modifying its practices and standards to stay current with AI implementation. However, there are also other examples of AI implementation in this field – examples that are not directly associated with modifying payment processing methods.

Lazy AI is an excellent example of such a business – it is a company that offers the ability to create applications and write code with little to no technical background. The core of Lazy AI is its artificial intelligence that works with pre-existing templates that customers can modify to suit their needs and preferences.

As for the actual examples of how Lazy AI can be used in the payment processing industry – a good example would be the well-known payment processing service, Stripe. We can offer three examples of how an average user can create an entire application that works with Stripe API in a matter of minutes.

• A template for Customer Subscription retrieval via Stripe API and a Flask web service – a convenient way to receive all active customer subscriptions in a single list.
• A template for setting a Default Payment Method using Stripe API and Flask web service – a helpful app that makes it much easier to set up a default payment method for every user.
• A template for creating and modifying Stripe Payouts using Stripe API and Flask web service – can be used to create and modify payouts for Connect Stripe Accounts.

This list is, of course, far from conclusive – there are hundreds of different templates that Lazy AI can offer for all kinds of use cases. These templates can be customized and modified to any customer’s specific preferences, creating a personalized experience for every single customer.

Conclusion

Artificial intelligence is an incredible technology that exceeds the bounds of a single industry. It can be an essential cornerstone for the inevitable future of the payment processing industry, providing safer and faster transactions than ever before. This evolution would affect every customer in the industry, including small startups, large-scale corporations, and everything in between. 

It can be challenging to implement AI elements in a payment processing system right now, but the possibility of being at the forefront of the next technological advancement is worth far more than any implementation cost. Most businesses would be unable to survive without adapting and evolving to meet the standards of the new reality – and AI in payment processing is the new reality for this industry.