Online payment fraud is constantly evolving. Rule-based systems, which once formed the backbone of fraud prevention, are struggling to keep up. Fraud attempts have become increasingly sophisticated, combining automation with social engineering and stolen credentials.
For instance, the World Economic Forum warned people of the increasing number of pig-butchering scams. One of the primary reasons behind this increasing sophistication is the use of advanced technologies like artificial intelligence (AI).
Interpol states that the use of AI and cloud computing technologies has made cyberattacks more advanced. To top it off, cybercriminals don’t need advanced technical skills or a lot of money to utilize these technologies.
However, when used correctly, AI can also be the solution to combat sophisticated payment fraud. In this article, we will examine how AI can facilitate the detection of online payment fraud.
Staying Ahead of Threats with Continuous Monitoring
Some people believe that AI can replicate the functions of a human brain. However, AI does not function like a human brain; it only mimics its operations. Put simply, it cannot perform human reasoning, but what it excels at is pattern recognition.
This is highlighted in an IBM article, which states that AI models may seem intelligent, but they are just good at recognizing patterns. However, when it comes to monitoring changes, this pattern recognition becomes the most valuable asset.
For instance, machine learning (ML) algorithms can recognize patterns from historical transaction data. It can then match these patterns with any ongoing transactions to detect abnormalities.
This can be a sudden change in location, a new device login, or an inconsistent typing speed. If something stands out, the system can temporarily block the transaction or alert a fraud analyst for review.
Continuous monitoring enables businesses to respond quickly to threats without disrupting legitimate users. It also reduces the need for manual intervention, which can slow down processes and frustrate customers.
Validation Gets Smarter
Some of the most fundamental security measures to validate online payment transactions include using a one-time password (OTP), CVV code, and email verification. AI can take validation to the next level by allowing for more layered authentication.
For instance, AI-powered solutions can facilitate selfie ID verification. As noted by AU10TIX, users can take a selfie and liveness detection tests. The solution can then compare the selfie with a government-approved ID photo for accuracy.
Selfie ID verification adds an advanced check that’s hard to forge or manipulate. It is beneficial for platforms where accounts are opened or accessed remotely, such as digital wallets, freelance marketplaces, or online banking services.
Similarly, AI can also facilitate adaptive authentication. Unlike traditional methods, it can determine the level of verification required based on the calculated risk. This way, a low-risk transaction from a familiar device can proceed smoothly. However, a high-risk transaction could trigger additional verification checks.
Improving Both Security and Customer Experience
One of the biggest advantages of AI in fraud detection is that it enhances security without complicating the process for legitimate users. Traditional verification methods often created friction, asking users to complete multiple steps even when there was no real risk.
AI allows businesses to be more selective. When everything checks out, the transaction goes through smoothly. When something’s off, the system reacts instantly, minimizing damage.
As stated by Forbes, AI can accomplish all these tasks with the help of advanced algorithms, such as Convolutional Neural Networks (CNNs). These algorithms can process data quickly, balancing security with user experience. The complete authentication process can be completed within a few seconds.
The result is a better balance between fraud prevention and user experience, something that’s hard to achieve with rule-based systems alone.
Reducing False Positives Without Sacrificing Security
One challenge in fraud detection is the risk of false positives, where legitimate users are incorrectly flagged as suspicious. This creates a frustrating experience and can lead to lost sales, abandoned checkouts, or even long-term customer churn. For businesses, this often results in increased support costs and damaged customer trust.
AI systems are better equipped to handle this problem because they rely on contextual analysis rather than rigid rules.
These systems evaluate multiple variables, such as past behavior, device reputation, transaction timing, and geographic patterns, before making a decision. This multi-layered approach enables AI to create a more accurate picture of what is normal for each user.
For instance, if a user suddenly makes a large purchase from a different location, a rule-based system might automatically flag or block it. However, an AI model that recognizes this user travels frequently or has previously used that device might allow the transaction to proceed.
Over time, as the AI system gathers more data, its accuracy improves, and the number of unnecessary blocks decreases.
Frequently Asked Questions
How is AI different from traditional fraud detection tools?
Traditional tools rely on predefined rules, such as blocking transactions over a certain amount or flagging activity from outside a specified region. AI, on the other hand, learns from patterns and adapts over time. It can detect subtle anomalies that a rules-based system might miss.
Can AI detect coordinated fraud attempts across multiple accounts?
Yes. One of the advantages of AI is its ability to spot patterns across a wide dataset. If multiple accounts exhibit similar behavior, such as shared IP addresses or synchronized transaction patterns, AI can flag them as coordinated attempts. These connections are often too complex for human reviewers to catch quickly.
Is AI useful for small businesses, or only for large enterprises?
AI-based fraud detection is becoming increasingly accessible to small and mid-sized businesses through APIs and software integrations. Enterprise-level platforms offer broader customization. However, many providers now offer scalable solutions that are affordable and effective for smaller operations, especially those handling recurring payments.
AI is no longer optional in online payment security. As fraud techniques become increasingly complex, businesses require tools that can adapt just as quickly.
From smarter verification methods, such as selfie ID verification, to ongoing behavior tracking, AI provides companies with the flexibility to detect and prevent fraud. As more businesses adopt these technologies, the focus will shift from detecting fraud after it occurs to stopping it before it happens.
