Which scenario might require the use of anomaly detection?

Anomaly detection is a powerful technique used to identify patterns in data that do not conform to expected behavior. In the context of monitoring for fraudulent transactions, anomaly detection is particularly effective. Fraudulent activities often exhibit unusual patterns or behaviors compared to typical transaction data, such as an abrupt increase in transaction size, frequency, or changes in purchase locations that deviate from a user's normal behavior.

Implementing anomaly detection models allows organizations to flag these atypical transactions for further investigation, thereby enhancing security and reducing financial losses caused by fraud. This capability to detect outliers in transaction data is critical in real-time monitoring systems used by banks and online payment systems to quickly respond to potential fraud.

Other scenarios such as identifying duplicate records, determining customer buying habits, and grouping similar products do not inherently require anomaly detection techniques. Duplicate records are identified through data cleaning processes, customer buying habits are analyzed through traditional statistical analysis, and similar products are typically grouped through clustering algorithms that focus on similarity rather than identifying deviations from standard patterns.