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    Question

    Which approach is most effective in leveraging data for fraud detection in financial transactions?

    A Using only random sampling techniques to identify unusual patterns. Correct Answer Incorrect Answer
    B Applying historical transaction data to detect anomalies and flag potential fraud. Correct Answer Incorrect Answer
    C Focusing exclusively on demographic factors of customers. Correct Answer Incorrect Answer
    D Developing a static model that doesn’t require real-time updates. Correct Answer Incorrect Answer
    E Avoiding machine learning methods to reduce complexity in analysis. Correct Answer Incorrect Answer

    Solution

    In fraud detection, historical transaction data is vital for identifying anomalies that suggest fraudulent behavior. Data analysts employ machine learning algorithms and statistical models to detect unusual patterns in transaction data, such as atypical spending or high-frequency transactions. Techniques like supervised learning (for known fraud cases) and unsupervised learning (for anomaly detection) enhance fraud prevention by adapting to evolving fraud tactics, making this approach crucial for risk management in finance. Option A is incorrect as random sampling is insufficient for effective fraud detection. Option C is incorrect because demographic data alone doesn’t highlight transaction irregularities. Option D is incorrect as static models fail to capture dynamic fraud patterns. Option E is incorrect since machine learning enhances fraud detection capabilities significantly.

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