Start learning 50% faster. Sign in now
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.
Primary tillage is completely avoided and secondary tillage is restricted to seedbed preparation in the row zone only. This is done in which type of til...
Crop failure due to prolonged dry spells during crop period and less than 75 days of crop growing season are the characteristics of which type of farming?
The critical value of soil EC for germination of seed of crop is :-
M ethod of cutting trees to ground level which leads to a strong vegetative response and the regeneration of new shoots from the base is known as: <...
Which crop has the highest absolute increase in MSP for the year 2025-26?
Cultivation of crops in areas receiving annual rainfall more than750 mm but less than 1150 mm is known as
The FRP is the minimum price that sugar mills have to pay to sugarcane farmers and is declared every year before the commencement of sugar year. Who app...
Harvest index of 19% (lowest among pulses) is observed in which crop?
Which of the following is not an indicator of sugarcane maturity?
The weeds whose seed is difficult to separate from crop seed after contamination is called ___