The Role of Artificial Intelligence in Fraud Detection and Payment Security

Artificial Intelligence goes far beyond being just a technology. It revolutionizes all sectors and plays the role of a multifunctional game-changer. According to Statista, the AI market size is expected to grow from $241.8 billion in 2023 to $738.76 billion by 2030.

As for the payment sector, AI technologies have also evolved this landscape. Integrating AI-powered solutions streamlines transactions and improves user experience, but more importantly, such innovations help providers detect fraud cases and enhance payment security.

The modern world of payments consists of a wide range of multi-level components. Merchants, purchasers, service providers, and financial institutions unstoppably interact with each other. In 2024, analysts expect the total volume of digital payment transactions to reach $11.55 trillion. Hence, the rapidly growing sector becomes among the core targets of fraudsters. 

AI-driven solutions solve payment-related problems quickly and cost-effectively. These innovations are claimed to minimize or eliminate existing fraud cases and detect possible novel fraudulent mechanisms. 

Based on the recent EDC industry-wide survey, 94% of respondents understand Artificial Intelligence as a measure to revolutionize fraud detection and improve payment security.

AI-powered systems consider the most minor abnormalities and suspicious activity that are frequently neglected or overlooked by human experts. Those systems rely upon the following innovations:

• Biometric authentication

What are the critical elements of past-gen payment security measures? They believe in passwords, secret phrases, and PIN codes. Fraudsters can easily steal or hack such codes and passwords, so next-gen AI-powered security systems bet on more effective and personal measures, including fingerprints and facial or voice recognition. Introducing biometric authentication ensures authorized users only access their accounts and make payments.

• Behavioral biometrics

Artificial Intelligence unlocks a set of brand-new opportunities for both payment security providers and fraudsters. As such, AI-driven payment security systems require additional data based on behavioral biometrics. What does it mean? Each user has its own “digital behavior” represented by the individual manner of typing, swiping, and even handling a device. AI-powered mechanisms notice and utilize those behavioral patterns to protect a user’s account from breaches. 

• Transaction behavior.

Past-gen (traditional) security systems utilize a range of predetermined rules to detect abnormal transactions. In most cases, those systems disrupt authorized users and lead to false positives. AI mechanisms based on ML (machine learning) algorithms empower a system to collect transaction behavioral patterns of an authorized user autonomously without causing any inconvenience for him. 

• Natural language processing.

Due to the comprehensive integration of chatbots and virtual assistants, money transfers frequently apply to conversational interfaces. Natural language processing (NLP) algorithms provide an accurate assessment of a user’s tone and discussion context to detect possible cases of coercion and fraudulent patterns. The integration of NLP technologies both enhances payment security and improves user experience.

AI-powered fraud prevention mechanisms comprise several components and stages:

• Data collection. Accurate and qualitative data is the core component for AI-driven systems that are claimed to prevent fraud cases. A system requires access to transactional and behavioral data collected from diverse sources.
• Understanding fraudulent behavior. The next stage is identifying the features and patterns that mark fraudulent behavior. An AI-powered system distinguishes the behavioral features of an authorized user from those of fraudsters.
• Model training. Once a system has collected enough data and memorized behavioral patterns of authorized and unauthorized users, algorithms are transferred into natural conditions. Based on historical data, models are trained to detect fraudulent behavior.
• Abnormality detection. A system relies on its algorithms and informs about abnormality patterns detected.
• Continuous learning. An ML model should be constantly updated with new data and patterns to challenge changing fraudulent mechanisms.
• Alerts and reports. Once a system detects behavior that is distinct from standard patterns of authorized users, it reports suspicious activity and sends detailed reports for deeper investigation.

Hence, an AI-driven model designed for fraud prevention requires vast amounts of data, model training, and constant learning to fulfill payment security needs.

Through harnessing AI-driven monitoring systems designed to enhance payment security, businesses obtain the following advantages:

• Real-time protection

AI-backed measures provide businesses with real-time protection. Such systems monitor vast amounts of transactional and behavioral data 24/7; this is why potential fraudulent actions are detected at the very moment they happen. Immediate detection is critical in stopping fraudsters and minimizing losses.

• Scalability

Machine learning systems are scalable and fulfill the needs of growing businesses. Despite the growing transaction volumes, AI-powered mechanisms can still provide the highest level of security. Such systems effectively work with large amounts of data and don’t require additional staffing.

• Cost-efficiency

Harnessing Artificial Intelligence is cost-effective. First, businesses do not need to hire teams for manual fraud detection. Business owners can reorient their teams to other essential tasks. Furthermore, AI-driven solutions are much more effective, so businesses lose less funds due to fraud. 

• High accuracy over time

Fraudulent techniques change over time, and new ways appear to bypass the existing protective measures. AI-driven systems can analyze as much data as necessary and constantly train, improve, and stay effective.

Besides fraud mitigation, AI-backed solutions affect customer reputation. The feeling of safety and protection from fraudulent cases works even better than ads and promotions.

The opportunities and advantages of Artificial Intelligence in fraud detection are apparent. Meanwhile, are there some practical benefits from the integration of AI-based systems?

Card fraud detection

In 2021, the overall losses due to card fraud reached $32.34 billion, among the most widespread cases. Fraudsters utilize unique bots to attack cards. AI-driven solutions are used to prevent suspicious activity. Those systems rely on both IPs and behavioral patterns and effectively distinguish bots from real people. 

Fake account detection

Accounts created by bots allow fraudsters to affect your product’s reviews, share fake information, ruin analytics, and more. AI-backed systems may increase security standards on the authorization stage without causing any inconveniences for newcomers.

Businesses understand the importance and perspectives of AI-driven payment protection; therefore, companies throughout the world effectively integrated diverse solutions that help detect fraud immediately and enhance the overall level of payment security:

• Rakuten France. The corporation suffered from constant attacks and needed to hire more and more staff to prevent those threats. The integration of an AI-based solution solved the problem completely.
• VISA. One of the world’s largest payment providers integrated VISA Advanced Authorization and had its data 100% analyzed through AI algorithms. As a result, the corporation saved $25 billion that could have been stolen due to fraud.
• US Bank, RBC, and Santander utilize the AI-driven solution called Personetics. The platform prevents fraud through behavioral pattern recognition systems and guarantees financial data protection. 

Today, the challenges payment services face have reached another level, and traditional protective systems cannot handle the situation anymore. Integrating AI-powered fraud detection solutions and enough attention to cybersecurity is not a competitive advantage but a “must-have” measure. Otherwise, payments-related businesses will become easy targets for fraudsters of the new generation.

On the other hand, the future of Artificial Intelligence in payment security is related to some challenges:

• Lack of qualitative data and availability problems. The efficiency of AI-driven fraud detection solutions directly depends on the quality and amounts of data received. At the same time, such systems need more accurate, relevant, and complete data that leads to better results.
• Incompatibility with existing tools. AI-powered solutions frequently are not compatible with existing outdated systems, and the integration is quite a complicated task. Business owners need to upgrade other components in order to get innovative solutions that function properly.
• Regulatory norms and standards. No matter how effective a solution is, it should correspond primarily to the regulatory norms. As for AI-driven systems, there appear to be some problems because many countries are uncertain of their attitude toward Artificial Intelligence.

While talking about the future of AI solutions in payment security in general, the dominance of Artificial Intelligence is inevitable. Since fraudsters use AI to invent new tactics, businesses have to do the same to protect their customers. 

Artificial Intelligence in payment security is a two-edged sword. On the one hand, fraudsters obtain innovations and advanced technologies to breach biometric authentication systems and upgrade the level of their attacks. On the other hand, AI-driven protective solutions based on data analytics, behavioral patterns, and ML models provide the highest level of security for both business owners and their customers.

Harnessing AI in payment security is not something good or bad. Companies should regard fraud detection technology as the only way to protect their customers.