A data analyst at an insurance company is tasked with assessing the probability of fraudulent claims by analyzing customer transaction data and claim history. Which of the following techniques would be most appropriate for this task?

Anomaly detection is a technique used to identify unusual patterns or deviations in data that do not conform to expected behavior. In fraud detection, anomaly detection algorithms can highlight outlier transactions that may signal fraudulent activity, such as unusual claim amounts, abnormal claim frequency, or atypical transaction locations. These irregularities are often key indicators of fraud, making anomaly detection the most suitable technique in this case. By setting statistical thresholds and employing machine learning models to recognize patterns, an insurance company can proactively identify high-risk transactions and minimize fraud-related losses. The other options are incorrect because: • Option 1 (Descriptive Statistics) summarizes data but does not specifically highlight anomalies or outliers. • Option 2 (A/B Testing) is for comparing two scenarios and would not help detect fraudulent claims. • Option 4 (Predictive Modeling) forecasts trends but is not directly aimed at identifying anomalies. • Option 5 (Customer Segmentation) groups customers based on behavior but doesn’t identify outliers.