With the speed and volume of financial transactions running through modern card processors, simple if/then/else rules aren't up to the task in the fight against fraud. A rules-based system needs constant updates as fraud tactics are always changing. To combat these changes, processors have turned to machine learning. Biometrics are used to prevent fraud from getting into the system in the first place. We'll examine how both benefit payment processing.
You've likely heard of AI (artificial intelligence), but what about machine learning? Some people use the terms AI and machine learning interchangeably. However, they are different. Machine learning is a subset of AI. AI is the field of creating computers and robotics that imitate human behavior and capabilities. Machine learning is a set of algorithms computer scientists use to train software to detect fraud.
The benefit of machine learning over rules-based systems is that it can process very large data sets and learn from them as well. Basically, it learns and improves on the job. The more data, the more it learns and improves.
Machine learning can detect card processing fraud in real-time. This is especially helpful in avoiding chargebacks. The system will flag fraudulent transactions before they ever have a chance to process. For users, systems based on machine learning provide a better user experience. In contrast, rule-based systems can require multiple verifications from the user, which is not the case with machine learning.
Biometrics are used at the point of entry — before anything is submitted for processing. This means biometric authentication has the chance to thwart fraud before it ever begins. Biometric authentication includes facial recognition, retina scanning, and finger and hand print scanning. For most users, biometric authentication uses their mobile phones as a proxy. Biometric authentication is built into mobile phones and passed on to card processors, avoiding the need to build first-hand biometric authentication systems in many cases.
Machine learning and biometrics have reduced financial fraud, even in an age when fraudsters continue to come up with deceptive tactics every day. Machine learning and biometrics remain at the edge of fraud detection and prevention technology.
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