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Cluster analysis is a powerful technique for fraud detection as it groups similar transactions together to identify outliers or anomalies. For instance, a sudden high-value transaction from a customer with a history of low spending could indicate potential fraud. By analyzing transaction clusters, financial institutions can identify patterns and detect unusual behavior, even in large datasets. This method is highly effective in differentiating genuine transactions from fraudulent ones without relying solely on predefined rules, making it adaptable to evolving fraud techniques. Why Other Options Are Incorrect: • B: Weather patterns have no relevance to online fraud detection. • C: Sentiment analysis focuses on opinions, not transactional behavior. • D: Historical comparisons may miss new types of fraud. • E: A/B testing is better suited for optimizing user experience, not fraud detection.
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