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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.
Which one of the following is the data distribution when mean and median values are same?
Which concept holds that consumers will not buy enough of organizations product unless it takes large scale selling and promotion effort?
The proportion of area under different crops at a point of time in a region is called:
Kranz anatomy type photosynthesis is shown by
A region with black soil experiences warm climate, while a neighboring region with red soil has comparatively cooler conditions. The reason th...
The maximum error degrees of freedom for same number of treatments and replication of an experiment can be achieved in
The pressure system is characterized by circular or elliptical isobars with the lowest pressure at the center is called
The Rhizobium species suitable for soybean crop is
Maximum CEC (cation exchange capacity) is of
_______is a fast-growing crop that is grown between successive plantings of a main crop.