What does "Benford's Law" help identify in fraud investigations?

Benford's Law is a statistical principle that predicts the frequency distribution of digits in naturally occurring datasets. In the context of fraud investigations, it helps identify data anomalies in numerical patterns by indicating which digits are expected to appear more frequently. According to this law, smaller digits (like 1 or 2) occur more often as the leading digits in datasets compared to larger ones (like 8 or 9).

When auditors and fraud examiners apply Benford's Law to numerical data, they can detect irregularities or deviations from the expected distribution, suggesting the potential presence of fraud or manipulation. If the observed data significantly diverges from the expected frequencies outlined by Benford's Law, it could indicate that the numbers have been altered or that they do not reflect a natural distribution, prompting further investigation.

Understanding this application allows fraud examiners to focus their analysis on the most likely areas of concern, making it a valuable tool in their investigative toolkit. The other options, while related to aspects of fraud investigations, do not capture the specific analytical focus that Benford's Law provides on numerical data distributions.