How AI is Transforming Fraud Detection in Payment Processing
Artificial intelligence (AI) has become a buzzword in many sectors, but it holds real value in some applications. One of its most promising uses in customer experience management is its ability to detect and prevent fraud.
Customers expect electronic payment processing to be fast and seamless. At the same time, businesses must ensure they have enough oversight to stop fraud in these transactions. AI can uphold both needs through a few necessary improvements.
Individualized Behavioral Analytics
Older anti-fraud measures typically rely on rules-based anomaly detection. As a result, they tend to flag anything that might seem suspicious in a general context, even when specific situations may vary. AI takes a more granular approach, analyzing individual users’ behaviors to understand each transaction in context.
Because AI tailors things to the individual and considers a broader range of data points, it’s more reliable. MasterCard claims its anti-fraud AI has increased detection rates by 20% while reducing false positives by more than 85% as a result.
Real-Time Detection
In addition to delivering higher accuracy and precision than conventional means, AI fraud detection is faster. The same speed that’s driving companies to handle 85% of customer interactions with chatbots makes AI an ideal tool for catching potential fraud in the moment.
Unlike manual methods, AI can monitor all transactions around the clock. It can also identify anything that doesn’t fit a specific user’s previous behavior in real time. This means more effective damage control in the event of actual fraud and minimal processing times for customers making legitimate purchases.
Adaptive Insights
AI can also transform fraud detection through its ability to adapt to new data. One of the biggest pitfalls of conventional alternatives is that they can’t account for changes in customers’ shopping habits. Sometimes, breaking the norm is not a sign of theft but a signal of shifting preferences, and AI can recognize such changes to minimize false positives.
Far too many customers experience falsely declined purchases, and such incidents often lead to lost business. Some brands have experienced order declines of 19% per month after introducing anti-fraud solutions with high false positive rates. Because AI adapts to prevent these outcomes, it ensures transactions remain seamless, reducing customer churn.
Tips for Implementing Anti-Fraud AI Effectively
CX designers can’t afford to overlook these advantages. Still, like all tools, anti-fraud AI requires proper usage to reach its full potential.
Ensuring responsibility and accuracy in training is one of the most crucial steps. Generally speaking, the more training data a model has, the more accurate and less prone to overfitting, which causes false positives, it becomes. However, gathering vast amounts of real-world financial records can introduce privacy concerns.
Synthetic data — which mimics real-world information but contains no true details on real people — is the solution. Studies show that machine learning models trained this way can be even more accurate than conventional approaches. Consequently, these generated datasets are ideal for increasing data volumes without raising risks. Doing so will also minimize the risk of creating a biased model.
Relatedly, organizations must secure their AI anti-fraud models and any related databases. Hackers can alter an algorithm’s efficacy by changing just 0.001% of its data, and AI malfunctions may expose sensitive payment details. Encryption, both at rest and in transit, access restrictions and real-time security monitoring can assuage these concerns.
Teams must pay close attention to anti-fraud models after deploying them, as they may need adjustment early on. Regularly reviewing the system’s performance is key to ensuring it remains as efficient and reliable as possible.
AI Fraud Detection Can Revolutionize CX
Fraud detection must be instantaneous and accurate to uphold CX needs. AI is the optimal way to achieve both goals.
While AI carries risks and complexities of its own, its benefits are far beyond what traditional alternatives can achieve. By learning how to capitalize on this potential today, businesses can ensure better customer service and security in the future.
