Which technology is applied to identify fraudulent credit card payments?

Anomaly detection is a crucial technology used to identify fraudulent credit card payments because it focuses on recognizing patterns in data and identifying deviations from those patterns. In the context of credit card transactions, anomaly detection algorithms analyze historical transaction data to establish a baseline of normal behavior. When a transaction significantly deviates from this established norm—such as an unusually high amount or a transaction made from a different geographical location—anomaly detection algorithms can flag it as potentially fraudulent. This technique effectively helps financial institutions minimize risk and prevent fraudulent activities by highlighting transactions that warrant further investigation.

The other technologies listed play roles in different applications, but they do not specifically address the need for identifying anomalies in payment behavior. For instance, natural language processing is primarily used to analyze and understand human language data; computer vision focuses on processing visual data; and machine learning (including regression) is typically employed for predicting continuous outcomes rather than detecting unusual patterns indicative of fraud. Thus, anomaly detection stands out as the most relevant technology for this particular use case.