Normalization of anomalies is a technique used to detect fraud by evaluating:

• A. Assessing whether unusual transactions deviate from typical patterns and are consistent with legitimate activity
• B. Random sampling
• C. Guesswork
• D. Auditing only high-value transactions

Answer: (A)

Normalizing anomalies means putting unusual events into the context of normal behavior so you can judge whether they are just rare quirks within legitimate activity or genuinely outside what’s expected and potentially fraudulent. By building a baseline of typical patterns—such as common transaction amounts, frequencies, channels, times, and locations—you can compare an unusual transaction to that baseline. If it fits the established pattern and can be explained by legitimate activity, it’s less suspect; if it deviates in a way that doesn’t align with how the user normally behaves, it’s a red flag.

This is why the option describing assessing whether unusual transactions deviate from typical patterns and are consistent with legitimate activity is the best fit. It captures both the comparison to normal behavior and the judgment about whether the anomaly can be explained by legitimate activity, which is exactly what normalization of anomalies aims to do.

Other approaches miss the point: random sampling doesn’t use the baseline of normal behavior to interpret anomalies; guessing offers no systematic method; auditing only high-value transactions restricts the scope and doesn’t involve normalizing anomalies against patterns.